E.coli, the metabolic response to an external perturbation passes through a transient phase in which the activation of a number of latent pathways can guarantee survival at the expenses of growth. Growth is gradually recovered as the organism adapts to the new condition. This adaptation can be modeled as a process of repeated metabolic adjustments obtained through the resilencings of the non-essential metabolic reactions, using growth rate as selection probability for the phenotypes obtained. The resulting metabolic adaptation process tends naturally to steer the metabolic fluxes towards high growth phenotypes. Quite remarkably, when applied to the central carbon metabolism of E.coli, it follows that nearly all flux distributions converge to the flux vector representing optimal growth, i.e., the solution of the biomass optimization problem turns out to be the dominant attractor of the metabolic adaptation process.In simple organisms like  E.coli. Although they often give good results in practice, they normally rely on heuristic considerations like “evolution has tuned metabolic fluxes to optimize growth, hence optimizing growth gives reasonable fluxes”. The main result of this paper is to show that metabolic adaptation naturally leads to optimal growth, in the sense that the flux distribution associated to optimal growth is the dominant attractor of the fitness landscape of the metabolic adaptation process.In modeling metabolic networks, concepts like biomass optimization are often used to determine flux distributions of simple organisms such as  Constraint-based computational methods such as Flux Balance Analysis (FBA) are nowadays widely used when investigating metabolism of bacteria and other simple unicellular organisms , 2. WithE.coli, the short-term metabolic response to genetic and environmental perturbations is characterized by a growth arrest and by the activation of a number of latent pathways, a strategy which can favor the survival of the organism at the expenses of efficient biomass production  and u = [u1 … un], and S is the stoichiometric matrix. The FBA optimal flux vector is given byg = ξTv is the growth rate, i.e., the linear combination of fluxes that constitutes the biomass reaction. When the optimum is degenerate, a secondary optimization criterion can be used to discriminate among equivalent optimal solutions. For example, overall enzyme investment is minimized by the pFBA solution vpFBA, which corresponds to minimization of the sum of the  fluxes [In FBA , the pol) fluxes .k = {vk, Γk}k = 0,1,2,…, where v0 is a randomly chosen initial condition in Γ0 = Γ. The stochastic process can be summarized as follows. At step k, assume the population of bacteria has an homogeneous metabolism, i.e., all cells have the same nk active reactions with the same fluxes vk−1 (this corresponds to a specific sampling of our Markov chains). From vk−1, it is possible to obtain nk + 1 different phenotypes, corresponding to the resilencing of one of the enzymes (nk possibilities) or to the current phenotype remaining unchanged for another step. The nk possible silencings of a reaction yield the nk reduced polytopes Γk,i = Γk−1 ∩ {ℓi = ui = 0}, i = 1, …, nk. The corresponding fluxes vk,i are computed via a MOMA projection on these reduced polytopes:Following , we assuvk,i leads to a possible growth rate: gk,i = ξTvk,i, i = 1, …, nk. Viable phenotypes have gk,i > 0 while non-viable phenotypes  have gk,i = 0. In what follows these growth rates will be placed on the diagonal of a fitness matrixgk,0 represents the current growth rate.Each choice of nk + 1 possible choices gk,i, it is possible to associate selection probabilities through a basic replicator equation which uses the gk,i as fitness function. Denote pk,i, i = 0, 1, …, nk, the probabilities (or frequencies) associated to the gk,i. If Δt is the time duration of each step, then the replicator equation isuniform priors: at the begin of the time interval the selection probability is pk(0) = 1/(nk + 1), where non-uniform priors: at the begin of the time interval the selection frequencies are not uniform but are themselves expressible as a Boltzmann distributionβk has the interpretation of an inverse temperature. In this case, at the end of the time interval we obtainA through derivation of these selection probabilities is available in the To the pk(Δt) has the meaning of transition probability between the current state 𝓢k−1 = {vk−1, Γk−1} and the possible states achievable at the k-th step 𝓢k,i = {vk,i, Γk,i}, i.e., i = 0, 1, …, nk. Since the fluxes vk,i can take any value between lower and upper bound, the corresponding transition matrix is infinite dimensional. However, in order to understand the properties of the stochastic process we are considering, it is useful to look at its projection over the subspace of active reactions . In terms of this projection, the possible states of the Markov chain are the 2r possible combinations of the r reactions of the network, see r = 4. Denote 𝓩1, …, 𝓩r2 these discrete states and Pij = ℙ(Xk = 𝓩i ∣ Xk−1 = 𝓩j) the corresponding transition probabilities. As in our model the resilencings are irreversible, P is triangular, i.e., it is completely reducible, see P is the result of a projection, it is P = P(v), i.e., the exact transition probabilities pk depend on the values of the fluxes and hence on v0. However, even in the complete model the fully reducible structure is preserved. In particular it follows that a certain number of states 𝓩i must correspond to ergodic classes, i.e., “absorbing” states in which the Markov chain stabilizes. Full reducibility implies that each ergodic class is composed of a single state. Periodic chains of states are impossible.In both cases described above E.coli [E.coli central metabolism , hence numerical computations are necessarily limited to a fraction of all possible trajectories. For this study, some 105 trajectories have been generated, starting from randomly chosen initial conditions in the polytope of admissible fluxes Γ and using various forms for the priors.The metabolic adaptation process described in Methods and in E.coli . In ordegFBA, and for nearly 50% of all trajectories the growth at the end-point is ≥ 0.85 gFBA, see g/gFBA < 0.85. Much more remarkable is the correlation between the end-point flux distribution v and the flux distribution given by the pFBA criterion vpFBA: the mean of the correlation is 0.96 and the median is 0.98, with 88% of all trajectories achieving a correlation of at least 0.9, see vpFBA, regardless of the biomass they can produce, see Fig. A of Given the irreversibility of the resilencing, the number of steps required to reach an end-point state can be computed from the trajectories. A trajectory is considered absorbed into an end-point state  when no further silencing happens for 5 consecutive steps. With this stopping condition, the expected time until absorption of our trajectories is 24.6 ± 4.1 steps. Except for pathological initial conditions , all trajectories stabilize to flux vectors of positive growth. Nearly 20% of all trajectories reach the maximal growth computed by the FBA criterion, The time evolution of the 3D histogram of vpFBA, although through slightly different paths. None of the trajectories of the second row of Fig. D of gFBA. However, all of the end-points flux vectors become aligned with vpFBA (correlation higher than 0.97). In this case the two values of g reached by the trajectories correspond to two different ergodic states, as can be seen by the grouping of the number of active reactions eventually reached. It should be observed how for this phenotype of non-optimal growth the number of reactions is much less than for vpFBA. This is indeed a constant pattern in our metabolic adaptation strategy. As can be seen in Fig. G of R of active reactions of v and g are positively correlated: for strains that have sub-optimal growth more resilencings are possible i.e., more directions with slow but positive Δg exist and are explored. In fact, Fig. G of L of a trajectory is inversely correlated with the growth g of v.Examples of the resulting trajectories are shown in Figs. D-F of vpFBA, i.e., to alternative FBA optima. An example of such trajectory is shown in Fig. E of vpFBA, a few do not (one is shown in green), and stabilize in an alternative FBA flux vector of correlation 0.75 with vpFBA. Cases like this lead to a correlation corr which decreases when vk falls into the basin of attraction of a local maximum other than vpFBA. In the ensemble of the trajectories, however, these situations are unfrequent: if we look at the average of all trajectories, corr is always monotonically increasing, regardless of the final g achieved, see g is monotonically growing on the vast majority of the trajectories , then the convergence rate to v becomes aligned with vpFBA, the growth rate it can produce depends on the norm of v. In fact, v‖ at ergodicity and g: when the recursive process of silencings and adjustments leads to a v which is smaller in norm than vpFBA, also the corresponding g will be smaller than gFBA.It follows directly from the linearity of the expression for the biomass that when a trajectory v of norm comparable to vpFBA, one can look at how many of the bounds that delimit the polytope of admissible fluxes at step k, Γk, become active during the adaptation . Fig. N of v‖ < ‖vpFBA‖) tend to saturate less than those achieving higher growth. Hence fluxes that tend to stay in the interior of the polytope rarely will reach a high ‖v‖. From Fig. O of In order to understand when an initial condition can lead to an end-point 3 genes differentially expressed [Mathematically, the dynamical model used in this paper to describe metabolic adaptation has many aspects in common with standard evolutionary models based on natural selection . The onlxpressed . A similxpressed . After av0, is chosen randomly in the polytope Γ, which implies that at the begin of the Markov chain most reactions are already active.As shown in , differeE.coli mutants can be reproduced closely by the metabolic adaptation process described in the Methods section. Proceeding in this way corresponds to fixing specific initial conditions on the Markov chains, and following the specific family of trajectories that results from them (activatory phase included). It becomes then interesting to see what happens when these “nominal” trajectories are compared to more general trajectories in which the initial fluxes v0 are randomly chosen in Γ. For glucose as single carbon source, the two types of trajectories are compared in Fig. M of vpFBA. Notice how the pgi mutant has a secondary peak at low correlation: this corresponds to a less frequent second phenotype of lower growth, described in [When instead the effect of a specific perturbation can be explicitly included in the FBA model, then the Markov chains can be used to investigate also the early stages of the transient, with the activation of the latent pathways. This is the point of view taken in , where tribed in . Such a ribed in  and the A number of possible optimality criteria alternative to biomass optimization have been investigated in the literature , 28–30. vpFBA, as Figs. J-L of vpFBA). On the contrary, in the case of all reversible reactions the ratio g/gFBA achieved by the trajectories is even better than in g of 0.91gFBA and a median value of 0.997gFBA, see Fig. L of It is shown in  that in a priori guarantee that a greedy fitness landscape constructed in this way i) may be regular; ii) may achieve maximal growth, and iii) may lead to flux distributions that resemble those of the pFBA. In our trajectories, in fact, what we observe is that the fitness landscape is rugged, but the plethora of local maxima have all a very small basin of attraction, as opposed to the global maximum which attracts around 50% of all trajectories when we count based on growth. If instead we look at normalized flux distributions then the basin of attraction of vk on vpFBA. The robustness of the convergence is also reflected in the low sensitivity to the randomness of the Markov chains, see It is worth remarking that the method used in this paper is fundamentally different from a dynamical FBA . In the In conclusion, one can say that simple flux reorganization rules based on local fitness are sufficient to drive the cell toward a more efficient use of the metabolic resources. It is quite remarkable that most of the trajectories end up in the pFBA optimum, without knowing it, and without ever using growth rate to update metabolic fluxes . Clearly this fact provides a further evidence in favor of the FBA criterion, and one could even speculate that it provides a more fundamental principle, from which FBA follows as a corollary.S1 textDetails of the population dynamics model and of the selection probabilities. Further considerations of the method and on its applicability.(PDF)Click here for additional data file.


NADPH regeneration capacity is attracting growing research attention due to its important role in resisting oxidative stress. Besides, NADPH availability has been regarded as a limiting factor in production of industrially valuable compounds. The central carbon metabolism carries the carbon skeleton flux supporting the operation of NADPH-regenerating enzyme and offers flexibility in coping with NADPH demand for varied intracellular environment. To acquire an insightful understanding of its NADPH regeneration capacity, the elementary mode method was employed to compute all elementary flux modes (EFMs) of a network representative of central carbon metabolism. Based on the metabolic flux distributions of these modes, a cluster analysis of EFMs with high NADPH regeneration rate was conducted using the self-organizing map clustering algorithm. The clustering results were used to study the relationship between the flux of total NADPH regeneration and the flux in each NADPH producing enzyme. The results identified several reaction combinations supporting high NADPH regeneration, which are proven to be feasible in cells via thermodynamic analysis and coincident with a great deal of previous experimental report. Meanwhile, the reaction combinations showed some common characteristics: there were one or two decarboxylation oxidation reactions in the combinations that produced NADPH and the combination constitution included certain gluconeogenesis pathways. These findings suggested cyclization pathways as a powerful way for NADPH regeneration capacity of bacterial central carbon metabolism. When oral (OH•) . The cenal (OH•) ,5. Certaal (OH•) . TBrookeal (OH•) ,8. The ral (OH•) .+ [Pseudomonas fluorescens, which results in more NADPH generation and an inhibition of NADH generation[Escherichia coli, we also found that oxidative stress could induce a transition in the central carbon metabolism system from its normal condition to a sub-optimal condition, where more NADPH could be generated[In these anti-oxidative metabolites, it is reduced nicotinamide adenine dinucleotide phosphate (NADPH) that has the central role in the cells' anti-oxidative defense strategies in most organisms. It is the shared carrier for all the cellular reducing power and the primary participant in anti-oxidation. Through+ . Therefo+ ,5. For eeneration. In Eschgenerated.Escherichia coli[Escherichia coli NAD-dependent glyceraldehyde 3-phosphate dehydrogenase (GAPDH) with a NADP-dependent enzyme from Clostridium acetobutylicum [Clostridium acetobutylicum in Corynebacterium glutamicum and almost trebled the NADPH availability [Besides, NADPH provides basic reducing equivalence for reductive whole-cell biotransformation and NADPH-dependent enzymes play important roles in manufacturing industrially valuable compounds. As a result, the bioavailability of NADPH within the cell is often the rate-limiting factor for the over-production of desired compounds and improving NADPH regeneration capacity becomes one of the objects of current metabolic engineering. Siedlerchia coli. Martı´nutylicum . Jiang elability .Although the central carbon metabolism system is not very complicated, combinations of its various sub-pathways result in substantial flexibility and diveThe theoretical analysis presented in this work uses the concept of “elementary flux modes/extreme pathways” (EFMs) as its base ,20,21. AElementary flux modes have been applied successfully in metabolic network analysis for several cases. For example, Figueiredo et al. established all potential NAD synthesis and degradation pathways by use of EFMs, and found that substrate specificity differed significantly in species like yeasts and humans. ThroughIn this study, using a central carbon metabolism networks modified from that one reconstructed by Palsson et al., all EFME.coli’s biomass composition was taken to represent the biomass synthesis. In total, 72 reactants and 97 reactions are involved, of which 20 reactions are responsible for exchange of intracellular and extracellular materials. The network captures a full representation of the central carbon metabolism of bacterium In contrast, this network does not contain the compartment information. However, it would also be applicable to eukaryotic organisms, since the transportations between compartments would not affect the elementary flux mode enumeration. The reversibility of the reactions is defined mainly based on original articles and the BRENDA database[+ dependent and NADP+ dependent) were considered in this study. No other restriction was applied in the computation.The metabolic networks for EFM computing and subsequent analyses are detailed in  database. For exa2) and to simultaneously generate D–Ribulose-5–phosphate and NADPH. “ICDHyr” catalyzes isocitrate oxidation to generate 2-Oxoglutarate and NADPH. “ME2” produces PYR and NADPH via malate oxidative decarboxylation. Thus, NADPH regeneration flux of the central carbon metabolism system could be defined as:The NADPH fluxes are as follows: “G6PDH2r” catalyzes G-6-PDH to simultaneously generate 6-phospho-D-glucono-1,5-lactone and NADPH. “GLUDy” catalyzes Glutamate to simultaneously generate 2-Oxoglutarate and NADPH. “GLUSy” catalyzes the transamination between 2-Oxoglutarate and Glutamine, thereby generating 2-molecular Glutamate by consuming 1 NADPH molecule. “GND” catalyzes 6-Phospho-D-gluconate to release carbon dioxide  of sample number for each category could be considered as an indicator of the degree of balance in clustering. Among the 49 clustering results, the optimal cluster number was determined by combining Q and SD.ΔfGi'° is the formation free energy of chemical compound i, m is the number of compound categories that participate in the reaction, ni is the stoichiometric coefficient of compound i in the reaction, R is the universal gas constant, T is Kelvin temperature of 37°C that is optimal for growth of most organisms, and xi is the activity or concentration of compound i.The most common criterion to evaluate the thermodynamic availability of a reaction is the change in Gibbs free energy under physiological condition. The Gibbs free energy for a given reaction (or reaction group) can be calculated as ,32:ΔrG'E. coli from Chassagnole et al. and Bennett et al. was used as the representative value under normal physiological conditions [-5M and a maximum of 0.02M, and the intracellular H ion concentration was set to pH = 7 as previously described[fGi'° could not be measured for most materials, the group contribution method was used to calculate the value [The concentration value of corresponding metabolites in nditions ,34. Sincnditions , the randescribed. As ΔfGihe value ,37.fG' value from Eq  was taken as the primary testing result, and the response surface chart is shown in The optimal clustering results are shown in Mean flux values of the cluster center are shown in E. coli’s metabolic enzymes could form an NADPH generation network, during which ME flux demonstrated significant up-regulation. Moreover, due to the poor correlation between ME and G6PDH, the fluxes of both reactions could be increased at the same time, through certain biological operations.Briefly, either G6DPH or GND could bear a large flux themselves, so they are major contributors to total NADPH generation. Specifically, the maximal total NADPH flux can be obtained if both G6PDH and GND fluxes are high while other reactions have nearly zero flux. Meanwhile, ME plays an important role in total metabolism flux. One certain suboptimal value of total NADPH flux is achieved by increasing ME flux and decreasing other fluxes. Singh et al. reported that serial operation of Contrarily, ICDH contributes less to NADPH flux, which might be attributed to the small ICDH flux. Only when the whole tricarboxylic acid cycle is at a high level does the ICDH contribution to NADPH increase to 50%. That indicates that although ICDH is an important NADPH-generating enzyme during general physiological functioning, it does not have the potential to increase. In addition, GLUDy and GLUSy always have similar fluxes, so the generated and consumed NADPH cancels each other out. Therefore, this branch does not make an obvious contribution to NADPH generation.Based on the above distributions, it could be observed that it is difficult to amplify the fluxes of these reactions simultaneously without changing the topology structure of the metabolism network, because of the trade-off between different reactions. To activate the NADPH generation capacity of the central carbon metabolism pathway, emphasis should be put on G6PDH, GND, and ME rather than on the other two pathways that have little space for improvement.Several different cyclized flux modes with high NADPH availability were observed in the clustering results, namely the PP-EMP cycle, PP-ED-EMP cycle 1, PP-ED-EMP cycle 2, and ME-PPC cycle, ME-GLX cycle and ME-TCA cycle . The cheE. coli’s NADPH generation. As seen in Afterwards, the structural constitutions of the elementary modes with high NADPH generation capacity were analyzed. The mode corresponding to The two NADPH generation modes in top of 2 for 2 NADH and 2 NADPH regeneration.The other three modes are related to malic enzyme. In bottom of In 2. This process generates 1 mol NADH and NADPH.In The above six modes have some shared characteristics. Firstly, to enable high NADPH productivity, all six modes form an open loop based on a combination of various metabolism pathways. The loop is necessarily open otherwise it is not allowed in thermodynamics. Secondly, these open loops are conservative in structure. There is always a carbon skeleton input reaction for each loop as shown in black in Chemical thermodynamic analysis is required to verify whether the elementary modes obtained from mathematical analysis can operate under normal physiological conditions. For chemical reactions/combinations, the framework for thermodynamic analysis is already relatively mature ,32,36, bTo be representative, the values measured in the steady logarithmic growth phase were considered to be the stress-free under natural physiological conditions ,34. BaserGm’, highest possible ⊿rGm’, and lowest possible ⊿rGm’ were computed, with the results shown in rGm’, the top end of the bar chart is the highest possible ⊿rGm’, and the bottom end is the lowest possible ⊿rGm’. For the PP-EMP cycle, the values were -40.9, -10.3, and -71.5 kCal/mol, respectively. For the PP-ED-EMP cycle 1, the values were 2.34, 15.6, and -53.0 kCal/mol. For the PP-ED-EMP cycle 2, they were -22.1, 0.144, and -46.9kCal/mol. For the ME-PPC cycle, they were -2.32, 14.5, and -27.8kCal/mol. The physiological⊿rGm’s of ME-GLX and ME-TCA cycle are both less than zero. From the thermodynamic values, it can be seen that the PP-EMP cycle, PP-ED-EMP cycle 2 and ME related cycles are physiologically preferred and can happen under natural physiological condition. The lowest possible ⊿rGm’ of the PP-ED-EMP cycle 1 was significantly less than zero, while the activity of metabolites changed while super-oxidative stress occurred[Then, the normal ⊿ occurred. This coEscherichia coli and confirmed that it was due to a partial cyclization of the pentose phosphate pathway by 13C metabolic flux analysis [Corynebacterium glutamicum. TheΔpfkA mutant strain reached a yield of 4.8 mol NADPH mol-1 glucose while theΔgapA mutant reached a yield of 7.9 mol NADPH mol-1 glucose[E. coli are subject to oxidative stress, the PP-EMP cycle reduces the glycolysis pathway and increases the pentose phosphate pathway [The physiologically feasibility proposed by the thermodynamic analysis has placed our calculation and analysis on solidated and safe position. However, the prediction capacity and confidence of the framework should be confirmed in context of real experiments. After a thorough mining of current literature of related topics, most of these cyclized pathways calculated has indeed been reproduced in other experimental reports. Siedler et al. doubled the NADPH regeneration rate by deleting the phosphofructokinase gene pfkA in analysis . Immedia1 glucose. Both si pathway ,40. ThisPseudomonas under oxidative stress from clinical infection, and found that carbon metabolism flux completely transferred from the EMP pathway to G6PDH. Moreover, most 6-phospho-D-glucono-1,5-lactone entered the ED pathway after G6PDH, rather than through the non-oxidative branch[Pseudomonas fluorescens SBW2 wild type underwent a significantly high flux in gluconeogenic pathway, which together with a vigorous ED pathway formed a PP-ED-EMP cycle 1 as in this study. This flux distribution had contributed to an obviously higher NADPH production than that of a mutant strain [Gluconobacter oxydans 621H degrade the glucose via partially cyclic Pentose Phosphate Pathway and Entner-Doudoroff pathway, which are no other than the EMP-PP cycle and PP-ED-EMP cycle 1[Secondly, PP-ED-EMP cycles are also observed. Berger et al. investigated the flux distribution of ve branch. This sit strain . MoreoveP cycle 1.Saccharomyces cerevisiae, the ME-PPC cycle was activated by malic enzyme, malate dehydrogenase and pyruvate carboxylase. This metabolic shunt achieved a transhydrogenase-like shunt [3) treatment. In one genotype, the increase in PPC activity was quite well correlated to the increase in NADP-ICDH activity and NADP-ME activity. This phenomenon resembled the activation of ME-TCA cycle [Additionally, the ME related cycles are also common in physiological experiment. In a xylose-fermenting recombinant ke shunt . Dghim eCA cycle .NADPH regeneration capacity of central carbon metabolism system was better understood through computation of its EFMs. An SOM clustering algorithm was applied to EFMs with high NADPH productivity, and 16 categories were obtained. The relationship between each NADPH generating enzyme and total NADPH flux was discussed. Several cyclic pathways that supported high NADPH generation were found, and proved to be totally feasible in cells via thermodynamic analysis. Comparison with previous studies suggested that these pathways were indeed NADPH generation strategies used by various organisms. Meanwhile, the pathways showed some shared characteristics: an open loop was formed based on the combination of various metabolism pathways, and one or two decarboxylation oxidation reactions in the loop generate NADPH; there are also certain gluconeogenesis pathways in some loop’s constitution. Our results suggested that analyzing the relationships between various metabolism pathways at an overall systematic level facilitates the understanding of metabolic-related changes—this not only indirectly validates the flexibility of the bacteria’s operation on metabolism branches, but provides a new perspective for designing combinations of different metabolic reactions to achieve desired cofactor availability and internal environment.S1 Appendix(DOC)Click here for additional data file.S2 Appendix(DOC)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


Nucleotide degradation is a universal metabolic capability. Here we combine metabolomics, genetics and biochemistry to characterize the yeast pathway. Nutrient starvation, via PKA, AMPK/SNF1, and TOR, triggers autophagic breakdown of ribosomes into nucleotides. A protein not previously associated with nucleotide degradation, Phm8, converts nucleotide monophosphates into nucleosides. Downstream steps, which involve the purine nucleoside phosphorylase, Pnp1, and pyrimidine nucleoside hydrolase, Urh1, funnel ribose into the nonoxidative pentose phosphate pathway. During carbon starvation, the ribose-derived carbon accumulates as sedoheptulose-7-phosphate, whose consumption by transaldolase is impaired due to depletion of transaldolase’s other substrate, glyceraldehyde-3-phosphate. Oxidative stress increases glyceraldehyde-3-phosphate, resulting in rapid consumption of sedoheptulose-7-phosphate to make NADPH for antioxidant defense. Ablation of Phm8 or double deletion of Pnp1 and Urh1 prevent effective nucleotide salvage, resulting in metabolite depletion and impaired survival of starving yeast. Thus, ribose salvage provides means of surviving nutrient starvation and oxidative stress. In natural environments, microbes face myriad environmental stressors including starvation for essential nutrients, such as carbon, nitrogen and phosphorous. Yeast has evolved metabolic responses to these stressors, which enable their survival and effective resumption of growth when conditions improve. Recent evidence suggests that these metabolic responses are substantially hardwired in metabolic enzymes themselves . In paraA particularly important function of these kinases is to control levels of ribosomes, which constitute about 10% of yeast dry weight. This is achieved through regulation both of ribosome production and of ribosome degradation via autophagy (ribophagy) . RibosomThe carbonaceous product of nucleotide degradation is ribose-5-phosphate, which must be converted into glycolytic intermediates in order to extract energy and generate anabolic precursors. Such conversion is carried out by the nonoxidative pentose phosphate pathway, a series of five reversible reactions whose net stoichiometry is 3 ribose-5-phosphate ←→2 fructose-6-phosphate (F6P)+1 glyceraldehyde-3-phosphate . Both F6E. coli could also utilize the glycolytic enzyme phosphofructokinase (Pfk1) to phosphorylate S7P into SBP, which is further converted to erythrose-4-phosphate (E4P) and glycolytic intermediate dihydroxyacetone phosphate , the direct product of RNA degradation, increased only slightly. Nucleosides and bases, however, rapidly accumulated to greater than 10-fold basal levels  to measure the changes in the metabolome profile of l levels . Moreove13C-glucose for 70 min followed by carbon starvation , AMP-activated protein kinase (SNF1), and target of rapamycin complex 1 (TORC1). PKA is an inhibitor of macroautophagy but is inactivated upon glucose removal . SNF1 is species . Thus, b species . To test species . Thus, Tin vitro  . In contlower Km ; thus, PP levels . Thus, wPNP1 or URH1 or both. Following carbon starvation, the pnp1 strain accumulated more of the nucleosides, inosine and guanosine and less of the bases hypoxanthine and guanine than did wild-type cells; similarly urh1 accumulated more uridine and cytidine and less uracil  and pyrimidine uridine hydrolase (Urh1) have been shown to catalyze these reactions . To inves uracil . Adenosiectively . StrainsPGM3, but not PGM1 or PGM2, led to accumulation of ribose-1-phosphate and upstream purines in the ribose salvage pathway (PRM15.Further metabolism of ribose-1-phosphate requires its conversion to ribose-5-phosphate. Phosphoglucomutase 3 (Pgm3) has been proposed to be a phosphoribomutase capable of catalyzing this conversion . Consist pathway . Thus, PS. cerevisiae cannot utilize ribose as the sole carbon source, in the absence of glucose, it did assimilate extracellular ribose, albeiet at a slow rate  has been annotated as a ribokinase due to its similarity to ribokinases in other organisms. While low rate . Rbk1 delow rate . Thus, aTKL1/TKL2), preventing conversion of pentose phosphates into sedoheptulose7-phosphate. Unlike wild-type, the tkl1/tkl2 strain did not accumulate S7P and SBP upon carbon starvation  led to chronic S7P accumulation  into F6P and E4P. Indeed, genetic inactivation of both transaldolase isozymes  as sole carbon source. DHA can be transported into yeast cells and phosphorylated into DHAP, which then forms GAP. These reactions can support yeast growth, but only after several days of adaptation . In the The results from the above experiments present a conundrum regarding the source of SBP. Previous studies have shown that SBP is produced in yeast via a reversible reaction catalyzed by FBP aldolase, whose substrates are DHAP+E4P . HoweverE. coli phosphofructokinase can phosphorylate S7P to SBP . Moreover, many degradation enzymes have promiscuous activity against a diversity of substrates, rendering their functions difficult to determine from biochemistry alone . For exaotidases  and nuclotidases , but whiotidases .PHM8 deletion occurred in S7P and SBP levels, which are more sensitive to flux through the ribose salvage pathway than are Phm8’s direct substrates and products. Measurement of these more distal metabolites was a key benefit of metabolomics over a more targeted analytical approach.To analyze the physiological function of nucleotide degradation enzymes in yeast, we employed metabolomic analysis of targeted deletion strains , and exabcy1 and snf1) exhibited a distinct pattern of PPP metabolites , an essential activator of the PEP-consuming enzyme pyruvate kinase. The elevated PEP provides a readily accessible reserve of high-energy phosphate bonds, which facilitate fast uptake of glucose if it becomes re-available , 2012b. phm8 deletion strain, however, show that nucleotide salvage plays an indispensable role. This can be rationalized based on the abundance of ribosomes, which are needed in large amounts only during rapid growth . In organisms where Phm8 orthologs are present, our results may directly facilitate mapping of their nucleotide degradation pathways. In other organisms, the approach laid out here may translate even though the gene product does not. For example, although mammalian cells are not normally subject to acute starvation, autophagy is essential for survival of the neonatal period  deletion set . Double Saccharomyces cerevisiae was characterized as described previously  coupled by electrospray ionization (ESI) (negative mode) to a high-resolution, high-accuracy mass spectrometer  operated in full scan mode at 1 s scan time, 10standard . Isomerseviously . Metaboleviously . Energy eviously .S. cerevisiae genomic DNA. The amplified fragments were cloned into a modified pET15b vector  and overexpressed in the E. coli BL21(DE3) Gold strain  as previously described (p-nitrophenyl phosphate (pNPP) and 90 phosphorylated metabolites as described previously (Km and kcat), phosphatase activity was determined in the presence of 5 mM MgCl2 over the range of substrate concentrations (0.001–9.5 mM), and the kinetic parameters were calculated by nonlinear regression analysis of raw data to fit to the Michaelis–Menten function using the GraphPad Prism Software .The genes encoding Phm8 (Yer037w) and Sdt1 (Ygl224c) were amplified by PCR using escribed . The receviously . For theSupplementary Figures S1–4, Supplementary Tables S1–3


Salmonella enterica serovar Typhimurium and identified pairs of reactions (cut sets) predicted to be required for growth in vivo. We termed such cut sets synthetic auxotrophic pairs. We tested whether these would reveal possible combined targets for new antibiotics by analyzing the performance of selected single and double mutants in systemic mouse infections. One hundred and two cut sets were identified. Sixty-three of these included only pathways encoded by fully annotated genes, and from this sub-set we selected five cut sets involved in amino acid or polyamine biosynthesis. One cut set (asnA/asnB) demonstrated redundancy in vitro and in vivo and showed that asparagine is essential for S. Typhimurium during infection. trpB/trpA as well as single mutants were attenuated for growth in vitro, while only the double mutant was a cut set in vivo, underlining previous observations that tryptophan is essential for successful outcome of infection. speB/speF,speC was not affected in vitro but was attenuated during infection showing that polyamines are essential for virulence apparently in a growth independent manner. The serA/glyA cut-set was found to be growth attenuated as predicted by the model. However, not only the double mutant, but also the glyA mutant, were found to be attenuated for virulence. This adds glycine production or conversion of glycine to THF to the list of essential reactions during infection. One pair (thrC/kbl) showed true redundancy in vitro but not in vivo demonstrating that threonine is available to the bacterium during infection. These data add to the existing knowledge of available nutrients in the intra-host environment, and have identified possible new targets for antibiotics.Bacterial infections remain a threat to human and animal health worldwide, and there is an urgent need to find novel targets for intervention. In the current study we used a computer model of the metabolic network of  Despite substantial progress in infection biology research over the last decades, bacterial infections remain a major threat to human and animal health worldwide. With the rapid evolution and spread of antimicrobial resistance Bacterial metabolism constitutes a recent area of interest with regard to host-pathogen interactions because of the potential of identifying novel anti-infective targets that inhibits pathogen survival in the host System level approaches describing the metabolic capacities of bacterial pathogens has revealed that bacterial metabolism can be highly robust with extensive redundancies, not only within the pathogen but also between the pathogen and the host Salmonella enterica serovar Typhimurium (S. Typhimurium) using a published genome-scale metabolic model (GSM) et al.Exhaustive identification of synthetically auxotrophic pairs using purely experimental approaches would involve constructing and characterizing many thousands of mutant strains, which is, if not infeasible, cumbersome. Also, since this approach would be limited to combinations of two genes, possible combinations of pairs of enzymes encoded by redundant genes would be missed. An alternative, explored here, is to use a computer model of the metabolic network and identify pairs of reactions that when removed from the network renders it incapable of synthesizing biomass, i.e. cut sets. Here we investigate the metabolic network of in silico metabolic modeling to identify predicted redundancies in pathways in the metabolism with a targeted validation by infection experiments with selected synthetic auxotrophic pairs involved in amino acid and polyamine biosynthesis. Specifically, we have used S. Typhimurium as a model to investigate the role of 5 cut sets, suggested by computational analysis. Results have disclosed specific information about the available nutrients in the host environment, and we have identified potential novel combined targets for antimicrobial therapy.In the current study we have combined S. Typhimurium 4/74 was used as wild-type strain in all experiments. This strain has been described previously and its virulence is well defined S. Typhimurium KP1274 was used as primary recipient for plasmids S. Typhimurium strains were maintained in LB media (Oxoid). For solid medium, 1.5% agar was added to give LB agar plates. Chloramphenicol (10 µg ml−1), kanamycin (50 µg ml−1) or carbenicillin (75 µg ml−1) was added as required. Growth phenotypes were investigated in LB (Difco), M9 minimal media  or M9 minimal media supplemented with 50 mg/l alanine, thiamine, glycine, glutamine, proline, arginine, aspartate, glutamate (M9+) as suggested Escherichia coli Top10 competent cells were used for DNA cloning and were grown in LB media or on LB agar plates at 37°C with appropriate selection.asnA/asnB was achieved by cloning asnA into pSLD64, while genetic complementation of other genes was achieved by PCR amplification and subsequent cloning of genes into pACYC177, essentially as described XhoI and BamHI. Enzymes were used according to the manufacturer’s recommendentation (Thermo Scientific). All constructs were verified by restriction analysis and sequencing.Gene deletions and concomitant insertions of an antibiotic resistance cassette were constructed using Lambda Red mediated recombination as described elsewhere in vivo fitness of bacterial strains as previously described 3 bacteria of each strain. The exact c.f.u. and ratio between wild type and mutated strains were enumerated by plating as described below. Mice were killed at 6 days post-inoculation by cervical dislocation. Severely affected animals were sacrificed early to this time point for animal welfare reasons, but otherwise treated as the rest of the group. The spleens were removed aseptically and bacteria recovered and enumerated after plating a dilution series on to LB agar. One hundred colonies were randomly picked and tested for resistance to the relevant antibiotic to determine the proportion of mutant strains. The competitive index was calculated as the mutant/wild type ratio of the output versus the mutant/wild type ratio of the inoculum.Female C57/BL6 mice (Nramp-) (20–25 g) were used to assess Mice experiments were conducted with permission from the Danish Animal Experiments Inspectorate, license number: 2009/561–1675.Statistical significance of the differences between wild type and mutant strains was determined using GraphPad Prism version 5.0 (GraphPad software) with one-sample t-test analysis. Grubb’s outlier test was performed to exclude outliers with a significance of 0.05.The model used, which contains 1099 reactions, has been described elsewhere The model was analyzed using Flux Balance Analysis i) all cut sets of size one were identified by attempting to solve t); (ii) all two-reaction combinations which were not supersets of reactions identified in (i) were sequentially set to zero and combinations which rendered the problem infeasible were recorded. For each cut set the cause of the lethality was analyzed by identifying which biomass components were not produced when the pair was removed from the network. This was done by attempting to solve the FBA (with a given pair fixed to zero flux) for each component of vector t fixed to a non-zero value, whilst fixing all other components to zero.All model analysis was carried out using the ScrumPy package et al.The gene associations of the selected reaction pairs were obtained from the BioCyc database S. Typhimurium is an important intracellular pathogen of both humans and animals causing self-limiting enteritis and life threatening systemic infections. Replication and dissemination of S. Typhimurium inside the host is essential for establishing a systemic infection and is dependent on the ability to acquire and utilize the nutrients available in this niche in addition to the ability to synthesize essential nutrients that are only present in limited concentrations S. Typhimurium causes a systemic disease in susceptible mice, thus serving as a suitable model for studying bacterial systemic infections S. Typhimurium S. Typhimurium at different points during the systemic stage of infection is largely unknown, but contains glucose and certain amino acids S. Typhimurium during infection S. Typhimurium exhibits carbon catabolite repression A metabolic model of The damage analysis resulted in a list of 102 reaction pairs hampering the production of essential biomass components . The numS. Typhimurium during systemic mouse infection prsA, argD, fbp, adk and nadE.A number of genes were predicted by the analysis to be essential  transporter reaction of a molecule synthesized by the reaction see . Five ofprsA) has been reported to be conditional essential in E. coliE. coli null mutants require additional mutations and several additional metabolites to be viable Salmonella infection, prsA was not reported, suggesting that prsA mutants are not viable S. Typhimurium under the conditions used for construction of mutants.The homolog of STM1780 , minimal media with glucose (M9) and M9 additionally supplemented with specific amino acids (M9+). The composition of the latter medium corresponded to the input provided in the computational cut set analysis. According to the model analysis, single mutants should grow like the wild type strain, whereas double-mutants were expected to be unable to grow in M9+ as they would be auxotrophic for at least one biomass component. Genes in the selected reactions were subjected to mutational analysis. Initially we tested growth of the mutants asnA/asnB; serA/glyA and thrC/kbl) confirmed the cut set analysis since single mutants grew well in M9+, while the double mutant did not grow. Contrary to this, even the double mutant in the pair speB/speF,speC was not attenuated for growth, while both single and double mutants of trpA/trpB were affected for growth  was calculated for each infection pair  indicating that these reaction pairs contributed significantly to the fitness of the bacterium during infection, while the double mutant thrC/kbl was unaffected. Interestingly the single glyA mutant also proved attenuated, despite its ability to grow in vitro  requires either threonine or isoleucine for growth. The fact that isoleucine can support growth of the thrC mutant without being converted to threonine is an indication that the threonine flux from glycine is enough to supply threonine-charged tRNAs for protein synthesis. Since the flux of threonine from glycine was not sufficient to support growth of the thrC mutant in the absence of Isoleucine, we expected that either the LtaA reaction or the Kbl/Thr path was nonfunctional. We considered the thrC/kbl pair suggested by the model analysis as a cut set. In our strain we found that a thrC mutant grew somewhat slower than the wild type, while a kbl strain had no growth related phenotypes. The thrC/kbl double mutant however was unable to grow in minimal medium without threonine supplements, showing that the mutant pair is truly auxotrophic. Interestingly, however, we did not observe any clear infection defect as shown by the C.I. values of either single or double mutants. This confirms that the host must supplies sufficient threonine during all phases of the infection, as recently reported Threonine can be synthesized in two ways aside from the threonine biosynthesis pathways. Both of these auxiliary reactions use glycine as precursor. Threonine may be formed in a single step from glycine and acetaldehyde catalyzed by the speA and speB gene products or directly from ornithine by either of the speC or speF gene products. Putrescine can subsequently be converted into spermidine by the co-action of the speE and speD gene products  or the two M9 media  when complemented by the wild type allele in trans. As expected, complementation of the double mutant with single genes was unsuccessful. Single gene knock-outs were without impact on fitness in vivo, while the double mutant could not compete with the wild type strains. The most obvious explanation for this observation is that tryptophan is not provided by the host during S. Typhimurium infection. However, the observation contradicts a previous observation by Steeb et al.S. Typhimurium during infection. In support of our conclusion, Henry et al.S. Typhimurium in cell cultures, observed that lack of TrpC, which performs the step above the one by TrpB/TrpA in the biosynthesis of trypophane, caused formation of filamentous cells, and thus this enzyme appeared essential for in vivo growth. However, further studies are indicated to clarify this matter.Finally,  cut set . They fo+ medium , suggestS. Typhimurium required for intra host survival. Our results revealed that asparagine, tryptophan and most likely glycine are not present in sufficient amounts to support growth while threonine and serine are. These data add to the existing knowledge of available nutrients in the intra-host environment et al.glyA mutant and the trpA;trpB double mutant had reduced competitive fitness compared to the wild type. The conclusion regarding the glyA mutant should be further substantiated by studies comparing GlyA and GCV performance. Furthermore, we additionally found that asparagine and putrescine are only present in limiting amounts during infection. This is particularly interesting since the SpeB, SpeC, and SpeF enzymes, facilitating putrescine biosynthesis, were not detected in Salmonella bacteria isolated from spleens S. Typhimurium in vitro, indicating that they are more specifically related to virulence than to central metabolism. The putrescine biosynthetic genes constitute a novel target for anti-infective therapy with potential of broad effect against several intracellular pathogens.In the present study we have used genome scale metabolic modeling to identify pairs of reactions in Figure S1Growth phenotypes of five selected cut sets in M9 media with supplements (see text). The media corresponds to the one used as input in the cut set analysis using the S. Typhimurium genome scale model.(PDF)Click here for additional data file.Figure S2The transformation of L-asparatate to L-asparagine catalyzed by the reactions of AsnA and AsnB. AsnA uses ammonia as a reactant where AsnB predominantly uses glutamine.(PDF)Click here for additional data file.Figure S3The biosynthesis of serine. Serine can be produced from glycine through the action of GlyA and also from 3-phospo-glycecerate (3PG) by the action of SerA/B/C.(PDF)Click here for additional data file.Figure S4The polyamine biosynthesis pathways in Salmonella. Putrescine can be formed from arginine through the reactions catalyzed by SpeA and SpeB and from ornithine through the reaction catalyzed by SpeC/SpeF. Spermidine is formed from putrescine through the reactions of SpeE and SpeD.(PDF)Click here for additional data file.Table S1Primers used for construction of mutants and for cloning purposes.(PDF)Click here for additional data file.Table S2A list of 102 reaction pairs identified as unfeasible for production of essential biomass in the damage analysis.(PDF)Click here for additional data file.


Karenia brevis, responsible for early harmful algal blooms in the Gulf of Mexico, produces many secondary metabolites, including potent neurotoxins called brevetoxins (PbTx). These compounds have been identified as toxic agents for humans, and they are also responsible for the deaths of several marine organisms. The overall biosynthesis of these highly complex metabolites has not been fully ascertained, even if there is little doubt on a polyketide origin. In addition to gaining some insights into the metabolic events involved in the biosynthesis of these compounds, feeding studies with labeled precursors helps to discriminate between the de novo biosynthesis of toxins and conversion of stored intermediates into final toxic products in the response to environmental stresses. In this context, the use of radiolabeled precursors is well suited as it allows working with the highest sensitive techniques and consequently with a minor amount of cultured dinoflagellates. We were then able to incorporate [U-14C]-acetate, the renowned precursor of the polyketide pathway, in several PbTx produced by K. brevis. The specific activities of PbTx-1, -2, -3, and -7, identified by High-Resolution Electrospray Ionization Mass Spectrometer (HRESIMS), were assessed by HPLC-UV and highly sensitive Radio-TLC counting. We demonstrated that working at close to natural concentrations of acetate is a requirement for biosynthetic studies, highlighting the importance of highly sensitive radiolabeling feeding experiments. Quantification of the specific activity of the four, targeted toxins led us to propose that PbTx-1 and PbTx-2 aldehydes originate from oxidation of the primary alcohols of PbTx-7 and PbTx-3, respectively. This approach will open the way for a better comprehension of the metabolic pathways leading to PbTx but also to a better understanding of their regulation by environmental factors.The toxic dinoflagellate  Karenia brevis (K. brevis), a dinoflagellate responsible for recurrent harmful algal blooms in the Gulf of Mexico. PbTxs have been found to affect marine ecosystems, being associated to massive fish kills, bird deaths or marine mammal mortalities, and may induce human food or respiratory intoxication through the bioaccumulation of the toxins by comestible shellfishes or aerosols -acetate as a radiolabeled precursor and this toxin as our main target. We performed five feeding experiments. A first set of experiments  inoculated together with increasing concentrations of [U-14C]-acetate without antibacterial agent. The second set of experiments was performed with addition of antibacterial agents just after the exponential stage followed by feeding of two different concentrations of the same acetate precursor the next day (A and B). These experiments allowed us to assess the effect of the concentration of acetate on the metabolomic profile. The decision to add some antibacterial agents in the medium was made because cell death was observed with higher concentration of acetate. Additionally, quantification of the specific activity of the labeled compounds in experiment B afforded some information on the controversial metabolic pathway of these toxins.PbTx-2 is the most abundant metabolite usually produced by K. brevis NOAA1 strain were measured in each experimental condition.The growth rates of 10k medium, the control culture exhibited a growth rate of 0.29 div·day−1. With an initial concentration of [U-14C]-acetate of 100 kBq·L−1 , the rate was 0.31 div·day−1. This rate decreased to 0.07 div·day−1 when the concentration of acetate was raised to 500 kBq·L−1  and a rapid death of the culture (within three days after inoculation) was observed with a concentration of 1000 kBq·L−1 . As previously reported, the increase in acetate concentration induced a bacterial proliferation and consequently a decrease in the pH from 8.1 to 7.6, which is unsuitable for the algal culture [In the first set of experiments performed in 1 L f culture . Because14C]-acetate, 200 kBq·L−1,  and [U-14C]-acetate, 500 kBq·L−1  respectively, the growth rates were 0.17 ± 0.02; 0.16 ± 0.01 div·day−1. In this case, neither significant change in the pH nor cell death was observed in the culture media, confirming our hypothesis on the bacterial proliferation inducing cell death. The repeatability of the growth curve was rather satisfactory for each experiment, the lag phase ended 12 days after inoculation and the subsequent exponential phase lasted 15 days.In the second set of experiments performed in 2 L volume media where antibiotics penicillin G and streptomycin were added at day 26  and the cultures fed with + Δ + 0.33 ppm). A second purification step on a C18 column was necessary to isolate pure PbTx-2 from the t6 fraction, the first coeluting peak being inactive in the RBA. PbTx-3 was identified in t4 without any further purification (m/z 897.4999 [M + H]+ Δ + 0.49 ppm). On the basis of HRESIMS data, we were also able to identify PbTx-1 (m/z 867.4941 [M + H]+ Δ + 5.9 ppm) and PbTx-7 (m/z 869.5050 [M + H]+ Δ + 0.54 ppm) in t8 and t5’ respectively.PbTx-2 was easily located in fraction t6 by coelution with a commercial standard and additional HRESIMS analysis , other compounds highly incorporated radiolabeled acetate. HRESIMS did not allow the identification of these highly radiolabeled compounds because the detected mass did not correspond to already known metabolites from this species but several explanations can be found for this high incorporation. First, highly radiolabeled compounds, such as t9, t10, and t12, may be intermediates in the biosynthesis of PbTxs. Alternatively, these rather non polar non identified metabolites may originate from other metabolic pathways involving acetate as a precursor like usual fatty acid pathway. Indeed, recent studies evidenced the presence of fatty acid synthases in K. brevis -CH3COONa to have respectively in media A0, B0 and C0, a total radioactivity of 100 (0.026 µm), 500 (0.13 µm) and 1000 (0.26 µm) kBq·L−1 . Cultures were harvested 18 days after inoculation. Growth rates were measured for the exponential stage in div·day−1 between days 5 and 15. Between these two dates the formula for growth rate is:N1 and N2 are the number of cells at day 5 and day 15 after inoculation.. 26 days after inoculation, penicillin-G (53.3 mg) and streptomycin sulfate (55.5 mg) were added in media A and B. The following day, the culture was enriched with [U-14C]-CH3COONa to have respectively, for media A and B, a total radioactivity of 200 (0.052 µm) and 500 (0.13 µm) kBq·L−1. After 72 h, the culture was harvested. Growth rates were measured for the exponential stage in div·day−1 between days 12 and 27.Cultures were grown in 2 L volumes following the same conditions as described in Section 3.1N1 and N2 are the number of cells at day 12 and day 27 after inoculation.We used the same formula as above where in this case, The whole culture was filtered using gravity or mild vacuum pressure <5 kPa) on 1 µm polycarbonate filters . Filters were pooled and extracted with 20 mL methanol into a 50 mL conical Falcon tube  and then stored overnight at –20 °C -acetate into the toxins was monitored by Radio-Thin Layer Chromatography . In a typical experiment, the fractions were deposited on a 20 cm × 20 cm silica plate and radio-analyzed during one hour. For each fraction, 20 µL of the methanolic solution described above were spotted on the plate for the radio-counting. The analysis was followed in real time by Gina Star software and stooped after 1 h of counting. To assess the radioactivity in Bq of the fractions collected after HPLC, a standard curve of [U-14C]-proline was used. The specific activity (SA) was then assessed dividing the radioactivity by the corresponding quantity obtained by HPLC-UV.Incorporation of [U-14C-acetate appears as a powerful method to address the metabolic activity but also some metabolic pathways of toxic dinoflagellates producing polyether derivatives. It allows working at close to natural conditions avoiding changes in the metabolism of these microalgae induced by the presence of large quantity of precursors or antibacterial compounds. We were able to demonstrate that the metabolic activity of brevetoxins started early during the cell growth of Karenia brevis and this result should be taken into account for further feeding experiments. The measurement of the specific activity of the major PbTxs gave important results on the chronology of a key metabolic reaction. The specific activities of both major toxins, PbTx-1 and -2, featuring a key terminal aldehyde and representing both types of brevetoxins, were found to be twice lower than the corresponding alcohols PxTx-7 and PbTx-3. This result implies that PbTx-1 and PbTx-2 should be produced by oxidation of the allylic alcohol of PbTx-7 and PbTx-3 respectively, a result in contradiction with the accepted hypothesis that several brevetoxin congeners are derived from the aldehydes analogues. This result underlines the importance of feeding experiments with radioisotopes to assess the metabolic pathways of these complex toxins but also the chronology of the involved biochemical steps.Several results should be highlighted from our study. First, radiotracing with


Genome-wide sensitivity screens in yeast have been immensely popular following the construction of a collection of deletion mutants of non-essential genes. However, the auxotrophic markers in this collection preclude experiments on minimal growth medium, one of the most informative metabolic environments. Here we present quantitative growth analysis for mutants in all 4,772 non-essential genes from our prototrophic deletion collection across a large set of metabolic conditions.in silico predictions and hitherto unavailable experimental data.The complete collection was grown in environments consisting of one of four possible carbon sources paired with one of seven nitrogen sources, for a total of 28 different well-defined metabolic environments. The relative contributions to mutants' fitness of each carbon and nitrogen source were determined using multivariate statistical methods. The mutant profiling recovered known and novel genes specific to the processing of nutrients and accurately predicted functional relationships, especially for metabolic functions. A benchmark of genome-scale metabolic network modeling is also given to demonstrate the level of agreement between current Saccharomyces cerevisiae and its response to the most basic of environments. While choice of carbon source has the greatest impact on cell growth, specific effects due to nitrogen source and interactions between the nutrients are frequent. We demonstrate utility in characterizing genes of unknown function and illustrate how these data can be integrated with other whole-genome screens to interpret similarities between seemingly diverse perturbation types.These data address a fundamental deficiency in our understanding of the model eukaryote  Saccharomyces cerevisiae due to efforts in the yeast community to assemble a near complete collection of non-essential single-mutant strains .S. cerevisiae deletion collection is composed of uncharacterized ORFs [Approximately 13% of the zed ORFs , 692 of FMP32. The fmp32Δ0 strain displays dramatically decreased fitness under arginine and proline conditions. While the protein product of FMP32 has been detected in highly purified mitochondria [fmp32Δ0 strain was included in our liquid confirmation assay and these sensitivities were confirmed in this independent, small-scale assay  is responsible for the conversion of proline into glutamate for further use as a nitrogen source. Our analysis suggests that FMP32 is similarly involved in the respiratory response under proline, though the reason for its additional sensitivity under arginine remains unclear. These examples show the utility of interactions between genes and simple environments in uncovering the function of both individual uncharacterized genes and genes without a previously observed fitness defect in more complete media.The genes with the highest profile similarity to UT1, PUT3) and mitPUT3[RRF1). PUT3 iJust as gene-gene correlation predicts functional similarities, we expect a high correlation between condition pairs to reflect a substantial overlap in the cellular machinery required to utilize the provided carbon and nitrogen sources. When our matrix of z-scores is hierarchically clustered in both the gene and condition dimensions, a structure clearly driven by carbon sources emerges Figure , as is oFMP32) exhibited an effect in multiple instances of a particular nitrogen source , but without a specific pattern with regard to carbon source (or vice versa). This is expected behavior for genes required for the utilization of a particular carbon/nitrogen source regardless of the context. In order to more formally extract a list of sensitivities for each source of carbon or nitrogen regardless of its partner, we employed a method known as non-negative matrix factorization (NMF) [Further examination of gene and environmental profiles after clustering revealed cases where a gene  ,35 to deGAP1 is transcribed at equal levels in cells grown on urea and glutamate, permease activity in urea grown cells is 100 times higher than glutamate-grown cells [One example of a decomposed signature involves genes that are sensitive when glutamate is chosen as a nitrogen source. These genes are enriched for annotations relating to endocytosis, endosome and vacuole related transport, and retrograde transport  and genes involved in acetyl-CoA biosynthesis that appear to be specifically sensitive under ribose (P < 1.4 × 10−6). As more complex environments are mapped, multivariate statistical techniques will become increasingly important in determining which environmental constituents are actually relevant to which experimental observations, and care should be taken when designing experiments to ensure their successful application .Many mutants (92) appear in both the galactose and ribose signatures, and overlapping GO enrichments in these conditions reveal many of these genes to have known involvement in various aspects of respiration. For example, enrichment for GO terms relating to mitochondrial organization and translation, as well as 'aerobic respiration', appear highly significant in both of these signatures . The strength and specificity of this similarity is not driven by a handful of mutants in the collection, but instead by trends across a much larger set of genes. We speculate that the profile similarity in this case may be due to accumulation of aspartate, which is upstream of homoserine and threonine biosynthesis, and is excreted in part through urea production. Growth on urea in the setting of the respiratory growth of galactose may result in the accumulation of aspartate.Beginning to test the immense space of possible environmental and chemical conditions combined with experiments that have queried the space of genetic perturbations  allows uet al. first apet al., we founet al. P < 10−18[et al. P < 10−9[The idea of comparing environmental and genetic perturbations can be generalized to other genome-wide perturbation data as well. For example, we observe significant correlations between our glutamate signature and a rapamycin sensitivity profile as measured by two different chemical genomic screens . The en P < 10−9. Thus, tHIS3, URA3, LEU2, and MET15. The resource used in this study differs in that URA3, LEU2, and MET15 are in their native genomic locations, with the exception of HIS3, which is provided by Schizosaccharomyces pombe HIS5 under the SGA reporter [The creation of the original yeast deletion collection has had a profound impact on the way in which reverse genetic experiments are performed. Yet despite a staggering number of successful studies, the inherent auxotrophies create a major blind-spot in a fundamental area of cellular function, and previous reviews of the topic have called for the creation and use of standardized prototrophic strains for metabolic experiments . Recentlreporter . WithoutThe use of a genome-wide prototrophic strain collection enables truly informative sensitivity screening in metabolically controlled conditions. This represents a first step in probing how nutrients in the environment jointly affect cellular response with or without additional genetic perturbation. This study demonstrates that much work is yet to be done to understand growth in even simple environments. A solid grasp of the surprisingly complex responses to simple environments will add much needed context to studies done in more complex environments.S. cerevisiae. Central to that goal is a version of the deletion collection that is unhindered by historical auxotrophic requirements.This study has demonstrated the potential of this collection, when screened against simple environments, to uncover phenotypes for hundreds of mutants that are phenotypically normal in standard lab conditions. We believe that the stock of simple experiments that might reveal a phenotype for these mutants has not yet been exhausted and expect that this whole-genome prototrophic collection will be an invaluable resource to the community. The rising number of metabolomics studies, fueled in part by the increasing accuracy of experimental mass-spectrometry, as well as the growing interest in metabolism as central to many common ailments in humans, make it more important than ever to properly design metabolically relevant experiments in the model eukaryote in silico predictions from constraint-based metabolic models, it is far from a fully understood system. Our results show a generally weak ability to predict condition-specific sensitivities, though performance is clearly above a random baseline. The prediction of condition-specific sensitivities is admittedly more difficult than the prediction of sensitivities in general, but it was our estimation that FBA and MoMA would be well suited to approximate our observations given our simple experimental setup. Their only moderate success in doing so demonstrates the current limitations of constraint-based modeling and the difficulty of relating models built from biomass predictions to quantitative growth rate data. There might be several possible reasons for the discrepancy between in silico and in vivo results. First, the success of predicting growth defects hinges on the proper formulation of biomass composition. While a single biomass composition is used for all our simulations, it likely changes across environmental conditions. Future studies could address this issue by measuring the composition of yeast cells under different nutrient settings. A second limitation of purely flux-based models is their inability to make predictions about components that have an indirect effect on metabolism. Consider, for example, the enrichment for transport-related genes whose deletion confers glutamate-specific sensitivities. Their putative role in nutrient sensing and signaling reflects the fact that, despite its constrained nature, the metabolic network operates as part of a much larger and more dynamic network. More generally, the basic constraint-based modeling approaches ignore regulatory mechanisms. Several attempts have been made to bridge this gap and they rely either on 'omic' data to constrain the activity of specific reactions [For example, while central metabolism is unrivaled among cellular processes with respect to our ability to make eactions -44 or oneactions -47. We fP < 6.2 × 10−5). We believe this shift may be explained by fundamental cellular rate limitations. Failure to segregate chromosomes in the midst of even moderate growth  can have very severe consequences, ultimately limiting growth rate, whereas comparatively slow growth  affords additional time for slowly segregating mutants to complete segregation. These mutants grow faster than we expect despite no apparent link between carbon metabolism and chromosome segregation. Thus, growth rates under one condition disclose information about the interplay between a wide variety of cellular subsystems, giving us a readout of the internal cellular state. Similarly, a mutant profile across many environments gives us information about how essential that gene may be in any of those various cellular states, in addition to elucidating any direct role that gene may have in direct utilization of the provided nutrients. Analysis of our growth data recapitulated the role of vesicle trafficking in the regulation of the amino acid permease Gap1, relating growth on glutamate to the drug rapamycin. This broader view of whole-genome screen information then allows for integration of profiles across different perturbation types , and should ultimately aid us in applying knowledge gained in one arena to observations made in another.Central to the understanding of the network as a whole is the idea that a whole-genome screen reveals indirect as well as direct consequences of the perturbation tested. Positive gene-environment interactions under ribose conditions may well illustrate this point. The median z-score for the 166 genes annotated to 'chromosome segregation' in GO is negative for all seven galactose conditions, yet positive for all seven ribose conditions  [MATα can1Δ::STE2pr-SpHIS5 his3Δ1 lyp1Δ0 strain, creating diploids . These were sporulated and successive pinnings on selective media were used to select prototrophic MATa strains carrying each deletion allele. These prototrophic strains were organized into an array of 16 plates including one entire plate of the wild-type strain (hoΔ::KanMX), with additional wild-type replicates in each row and column of every plate . The entire prototrophic collection is available upon request, as is the individual SGA-ready prototroph strain for crossing into other collections.As recently described , the strMinimal growth media were prepared using yeast nitrogen base  with the specified carbon and nitrogen sources. Carbon sources included glucose, galactose, ribose, and glycerol. Nitrogen sources included ammonium, allantoin, arginine, glutamate, glutamine, proline, and urea. Carbon sources were provided at a concentration of 2%; nitrogen sources were 3.8 mM with respect to nitrogen.Sixteen 16 × 24 well plates were grown in 28 chemical conditions for 24 to 48 hours. Plates were scanned on a flatbed transparency scanner at 0, 5, 10, 24 and, in the case of glycerol, 48 hours. Each condition is composed of one carbon source and one nitrogen source. In total, 4,772 mutants were grown, and colony areas were extracted from tiff images by CellProfiler  and precA) and the remaining five replicates were LOWESS smoothed (window size = 50% of available data) and normalized by:Six replicates of the glucose:ammonium combination were merged to form a reference condition, establishing a baseline score for each deletion. The six replicates were first normalized to each other to control for differences in the overall scale of growth rates, then averaged together according to the following procedure. For each array plate (p) the glucose:ammonium replicate with the fewest missing data points was held out , each plate was LOWESS smoothed (window size = 50% of available data) against the constructed glucose:ammonium reference plate, then normalized:In certain cases, a growth rate of NaN was assigned to a colony due to insufficient data being collected by CellProfiler. In an effort to recover any good data, these cases were visually inspected by five researchers operating independently and a vote was taken to determine whether to leave it as missing data (NaN) or assign it a growth rate of 0, indicating that the colony appeared to be correctly plated but non-viable. In total, 1,362 of 2,601 colonies were recovered this way.For each array plate, at each position, a strain-wise standard deviation is calculated across the residuals of the six glucose:ammonium (GA) replicates.Similarly, a plate-wise standard deviation is calculated that accounts for the general growth variation on the plate, separately for each condition. These are then combined, and a z-score measure is calculated for each strain on each experimental plate:These z-scores are an expression of the difference in magnitude and direction between the growth observed at each position of a plate under a given condition from the same position (and hence deletion) under the reference GA model.The plate level spatial smoothing filter is similar to that found in . First, P-values for the experimental z-scores could be calculated. This information allowed the use of Benjamini-Hochberg procedure to establish condition-specific effect thresholds as a function of a desired FDR  divided by the time to saturation. A simple model was favored in order to robustly accommodate drastic differences in curve characteristics between fast growth and slow growth conditions .We adjusted the liquid growth scores by dividing the mean of mutant growth slopes by the mean of wild-type growth slopes in the relevant condition. We further normalized these scores by dividing them by the corresponding adjusted mutant score in glucose:ammonium so they would reflect condition-specific effects, similar to our modified z-score derived from the agar experiment.GO and KEGG annotations were downloaded in January 2011 ,50.S. cerevisiae metabolic models were used for mutant biomass prediction. The Yeast Consensus Reconstruction version 5.35 (Yeast5) [Two (Yeast5)  and iMM9(Yeast5) . Yeast5 Wild-type biomass production flux for each condition was obtained using FBA  in MATLAFBA and MoMA biomass fluxes were correlated with both raw and normalized (z-score) experimental growth rates using the Spearman rank correlation. Predicted biomass fluxes were also normalized for comparison to experimental growth rate z-scores (separately in each condition):Prediction of positive z-scores was also carried out, though performance was generally below random expectation  were removed, as were the columns involving growth on glycerol. We then defined a Coefficient matrix that related Condition rows in the data to their component Sources. This matrix then had C columns and S rows. For example, the glucose-urea column has a 1 in the glucose row and a 1 in the urea row. Our task is then to find a Signatures matrix (Genes × Sources) such that the difference between the Data matrix and the Signatures-Coefficients product is minimized:Growth data were decomposed using a variant of NMF . FollowiCoefficient matrix, we removed all but one glucose:ammonium (glucose:ammonium01) column, removing the same columns in the Data matrix. Traditional NMF would use a multiplicative update algorithm applied to both the Signature and the Coefficient matrix to find the best fit to the data; however, we chose to fix the Coefficient matrix at the initial defined values (0 or 1). This gives each Signature column equal weight and prevents over-fitting caused by the sparsity of the Data matrix and the dramatically different number of non-zero elements from one column to the next. The multiplicative update was applied for 20 iterations, though in practice the results converged in fewer than 10, and repeated trials from different random initializations of the Signature matrix showed the results to be quite stable. Genes were added to the signature list in Additional file To ensure linear independence among the columns of the P-values were Bonferroni corrected to account for the number of terms/pathways tested against.For the comparison to auxotrophic SGA data represented in Figure Figure FBA: flux balance analysis; FDR: false discovery rate; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; MoMA: minimization of metabolic adjustment; NMF: non-negative matrix factorization; ORF: open reading frame; SGA: synthetic genetic array.The authors declare that they have no competing interests.DCH, OGT, CLM, and AAC conceived the study. DCH and AAC performed the laboratory experiments. CN provided experimental resources for the liquid growth assay. BJV, CP, and TS analyzed the raw data. BJV, CP, and EK designed and performed further computational analysis and experiments. BJV, DCH, BS, BP, CLM, and AAC wrote the manuscript. All authors read and approved the final manuscript.Raw growth rates for all 28 conditions and all 4,772 strains.Click here for fileRaw growth rates for 701 wild-type replicates in all conditions.Click here for filez-score data for 21 conditions.Click here for fileFDR 10% and 20% thresholds for z-score data.Click here for fileA list of 565 galactose-sensitive genes as well as overlap details between this set and three other full genome studies using the auxotrophic collection on galactose.Click here for fileSummary of fast/slow prediction accuracy of FBA models (sheets 1 and 2). Also contains KEGG pathway enrichments for sets of genes predicted to be sensitive but not observed and vice versa (sheets 3 and 4).Click here for fileList of genes in each signature and GO enrichments.Click here for file


Arabidopsis thaliana. Applying a method for kinetic parameter normalization, several millions of probable enzyme kinetic parameter constellations were simulated and evaluated with regard to the stability information of the metabolic homeostasis. Information about the stability of the metabolic steady state was derived from real parts of eigenvalues of Jacobian matrices. Our results provide evidence for a differential stabilizing contribution of different subcellular compartments. We could identify stabilizing and destabilizing network components which we could classify according to their subcellular localization. The findings prove that a highly dynamic interplay between intracellular compartments is preliminary for an efficient stabilization of a metabolic homeostasis after environmental perturbation. Further, our results provide evidence that feedback-inhibition originating from the cytosol and plastid seem to stabilize the sucrose homeostasis more efficiently than vacuolar control. In summary, our results indicate stabilizing and destabilizing network components in context of their subcellular organization.Biochemical regulation in compartmentalized metabolic networks is highly complex and non-intuitive. This is particularly true for cells of higher plants showing one of the most compartmentalized cellular structures across all kingdoms of life. The interpretation and testable hypothesis generation from experimental data on such complex systems is a challenging step in biological research and biotechnological applications. While it is known that subcellular compartments provide defined reaction spaces within a cell allowing for the tight coordination of complex biochemical reaction sequences, its role in the coordination of metabolic signals during metabolic reprogramming due to environmental fluctuations is less clear. In the present study, we numerically analysed the effects of environmental fluctuations in a subcellular metabolic network with regard to the stability of an experimentally observed steady state in the genetic model plant The online version of this article (doi:10.1007/s12064-016-0225-y) contains supplementary material, which is available to authorized users. A characteristic feature of eukaryotic cells is a compartmentalized subcellular structure resulting in numerous cellular reaction environments. Due to this compartmentalization, various biochemical reaction conditions are established in a cell enabling highly coordinated and tightly regulated reactions and reaction sequences. Particularly, such highly compartmentalized subcellular structures are a characteristic feature of plant cells Lunn . The firAs sessile organisms, plants have to cope with fast and fluctuating changes in environmental conditions, e.g. a sudden increase/decrease in light intensity or temperature. To prevent a disbalance of primary and secondary photosynthetic reactions, which could easily cause the generation of reactive oxygen species (ROS) and the irreversible damage of membrane systems and cells, a new and stable metabolic homeostasis has to be established within a relatively short time period. In biochemical networks, the metabolic homeostasis results from a concerted orchestration of enzymatic activities which determine metabolic fluxes, i.e. rates of metabolic interconversion and membrane transport. The coordinate reprogramming of a metabolic homeostasis, which finally results in a new acclimated stable homeostasis, is a complex process comprising numerous enzymatic reactions and regulatory molecular circuits. Decades ago it was outlined that by linear approximation of enzymatic reaction chains, i.e. the assumption that enzyme velocities linearly depend on substrate concentrations, analytical solutions can be obtained for metabolite concentrations and metabolic fluxes at steady state conditions  were interpreted as a pool of all involved membrane transporters, even though some of these transporters might still have to be characterized . Environmental fluctuations α, e.g. occurring due to sudden changes in light intensity or temperature, directly affected the flux F of v1 as a constant scalar (v1 = αF). The pool of plastidial sugar phosphates was substrate for two reactions: v2, the export to the cytosol (P-SugCyt), catalysed by membrane transporters, and v10, representing the intraplastidial conversion to starch (StaPla). Cytosolic sucrose, SucCyt, was the substrate for the export reaction via T4 supplying the pool of extracellular sucrose, SucExt. Reaction v5 indicated the transport of sucrose from the cytosol (SucCyt) to the vacuole (SucVac). In both compartments, hydrolytic cleavage of sucrose via invertase yielded free hexoses [v6 (HexVac) and v7 (HexCyt)]. Vacuolar hexose transport to the cytosol was described via transport process T8 resulting in a circular structure. Degradation of plastidial starch (StaPla) to hexose equivalents and their transport to the cytosol was described by transport T11 and reaction v11. Reaction v9 described the phosphorylation of cytosolic hexoses via hexokinase activity . Finally, reaction v12 which was coupled to transport T12, described the transport of apoplastic sucrose, SucExt, to sink organs (SinkExt).Based on approaches of mathematical modelling in previous publications , steady state concentrations of different metabolites  and their fluxes νj. Experimentally determined steady state concentrations of metabolites, which are provided in the supplements (Supplementary Table S1), were derived from previous studies (Nägele and Heyer Ext), which were not experimentally determined in these studies, were set to a low (0.01 mM), medium (15 mM) and a high (45 mM) concentration when compared to the cytosolic sucrose concentration (15 mM). With these settings, three possible scenarios were simulated: (1) a sucrose concentration gradient from the intracellular to the extracellular space (SucExt low), (2) no sucrose gradient (SucExt medium), and (3) a sucrose concentration gradient from the extracellular to the intracellular space (SucExt high). To define a steady state equilibrium, the input flux v1 and output flux v4 were set to an equal value (αF). The flux F was set to be constant (F = 1). For simulation of random environmental fluctuations and the response of the system, alpha (α) and the proportion characters beta (β) and gamma (γ), were randomly varied in the interval . While β quantified the relative proportion of carbon flux from chloroplast to cytosol, γ quantified the proportion of carbon flux from cytosol to vacuole. Based on the SKM approach  of the considered metabolic system, normalized to steady-state fluxes ν(c0) and metabolite concentrations ci0, . A detailed explanation of these entries in context of enzyme kinetics, such as Michaelis–Menten kinetics, was provided previously (Reznik and Segrè A non-zero element Λ and θ matrices derived in the present study is provided in the Supplements (Supplementary files S2 and S3) while a short and very fundamental explanation of eigenvalues in context of a differential equation is provided in the following paragraph.A detailed and explicit formulation of the J characterize a metabolic steady state with regard to its stability. The steady state concentration c0,i of a metabolite ci, is related to the time-dependent concentration ci(t) by a fluctuation term Eigenvalues of the Jacobian i(t) Eq. :4\documeThe first element of a Taylor expansion reveals the linearization around the considered steady state Eq. :5\documeC represents constants which depend on the initial fluctuation conditions. The complex number λi represents the eigenvalues of the Jacobian matrix J  < 0, the fluctuation term λi) > 0 results in an exponential increase (instability). While the imaginary part of λi, im(λi), is necessary to exactly classify the local dynamics and to differentiate a node from a focus or a saddle . To simulate different activation and inhibition scenarios, the θ matrix was adapted accordingly. In general, for an activation, the corresponding 6 iterations were calculated to reduce the probability of numerical coincidences. For reactions v2 to v12, the enzyme-substrate interaction was described by entries in the elasticity matrix θ indicated by the subscript index . Elasticities were chosen randomly in the interval of  for every iteration process to reveal the system behaviour over all 106 iterations. The source code which was applied for calculations is provided in the supplements (Supplementary file S5).Stability characteristics of steady states were evaluated by the maximum real part of the eigenvalue  or doubled .In the next step, instances of metabolic activation and feedback-inhibition were incorporated into the model, belonging to the central cytosolic carbohydrate metabolism as described previously in a simplified model structure  by extracellular sucrose , the combination of cytosolic and plastidial feedback-inhibition was found to yield a stable system Λ derived for the metabolic structure provided in Fig. Matrix θ derived for the presented metabolic structures (PDF 195 kb)Matrix Detailed Information about eigenvalue real part distributions (XLSX 35 kb)® script for calculation of eigenvalues (ZIP 2 kb)Matlaba) were performed 106 times for low concentration of extracellular/apoplastic sucrose concentrations and for F = 0.5 (b-e) and F = 2 (f-i). Steps of metabolic activation are indicated by grey filled circles, steps of inhibition are indicated by grey filled diamonds and dashed lines. Particular settings in the θ matrix are indicated within the single histograms (TIFF 2445 kb)Histograms of maximum eigenvalue real parts with cytosolic activation and inhibition under varying steady state fluxes. Calculations for the shown model configuration (Below is the link to the electronic supplementary material.