Lemna minor) was assessed. The efficacy of such eco-friendlier a.s. ratios was then assessed using a target weed, Portulaca oleracea. Single and mixture toxicity testing with L. minor revealed an antagonistic joint action of the a.s., suggesting an environmentally protective effect of the combination compared to single dosing of a.s. The efficacy testing against the target weed of single and combined treatments of the a.s. showed that (i) the a.s. behave antagonistically throughout the whole P. oleracea response surface; (ii) there were no environmentally safe a.s. combinations ensuring target-efficacy; (iii) terbuthylazine alone was effective in controlling P. oleracea with no environmental hazardous potential, dosed at concentrations 10-fold lower than those involved in commercially recommended application doses. Overall, this case-study suggests that modelling tools widely used in the field of environmental risk assessment of PPPs may also have application in PPP design stages for a more efficient meeting of efficacy and environmental friendliness requirements.The development of environmentally friendly plant protection products (PPPs), including pesticides, is a challenge nowadays. A commercial herbicide combining terbuthylazine and nicosulfuron as active substances (a.s.) was selected as a model PPP. The suitability of manipulating the ratio between a.s. towards alternative formulations with reduced impacts in a non-target indicator ( However, the use of PPPs may involve risks to human and environment health, reflecting into significant costs4. Regulatory agencies worldwide have already recognized this problem and have been developing tight screening protocols before the marketing of PPPs can be authorized, as well as supporting tools. Amongst these latter, modelling tools have been developed addressing PPP transport and inputs in aquatic systems. In the European Union, for example, the FOCUS platform is used to assess Predicted Environmental Concentrations  in surface and groundwater, depending on PPP application doses and their physicochemical properties. PEC values can be compared with ecotoxicological benchmarks retrieved following ecotoxicity tests, allowing conclusions on PPP environmental hazard potential6. Still, PPPs have been reaching surface water through different transport pathways, e.g. runoff and leaching, often causing hazardous contamination scenarios10, with exposure to pesticide residues being often clearly linked to significant ecosystem risks13.Agriculture relies on plant protection products (PPPs) to ensure improvements in quality and yield of crops14. Common strategies to develop eco-friendlier PPP formulations include (i) the use of natural products or greener equivalents in PPP formulations16; (ii) the improvement of PPP application techniques and target delivery20; (iii) and the combination of already licensed active substances (a.s.)21, based on a putative synergic behavior that intends the use of lower a.s. quantities with the same levels of efficacy against the target. This latter solution is often used also to improve the control of a broader range of weeds22 but the success of such an approach is controversial. Whether some argued that synergic behavior can be reached, experimental evidence exists that pesticide mixtures rarely result in synergic effects23. Additionally, the interactive effects between formulants should be considered while assessing the toxic potential of each formulated PPP, rather than focusing only each single formulant,to predict joint action27. This is feasibly achievable using well-developed and established mixture toxicity assessment tools currently available (e.g.29). This rationale motivated the present study, where we focused on the manipulation of the ratio between two a.s. of a commercial herbicide to preliminarily evaluate the suitability of this strategy as an additional approach to formulate eco-friendlier products.In order to counteract these environmentally hazardous scenarios, the agrochemicals industry has been impelled to innovate in the formulation of its products30, applied in crops to control weeds such as Portulaca oleracea and Amaranthus spp. Its action is systemic and residual following a single application per year, the effects in target weeds being perceptible 7–10 days after the application through a visible weeds control31. Nicosulfuron and terbuthylazine belong to sulfonylureas and 1,3,5-triazines chemical groups, respectively. Nicosulfuron prevents the growth of susceptible plants by blocking the synthesis of branched-chain amino acid through the inhibition of acetohydroxyacid synthase, and terbuthylazine inhibits photosynthesis by acting as a photosystem II blocker32.This commercial herbicide is the 2-way formulation Winner Top® , with nicosulfuron and terbuthylazine as a.s. plus undisclosed formulantsPortulaca oleracea, commonly known as purslane, was selected as a representative target weed within the study since this species is a major target of the focused commercial herbicide as defined in the product documentation31. This is a major worldwide weed affecting several crops, including maize, rice, wheat, cotton and sugarcane33, which contributes to its representativeness as a test species. Adding to its small size, rapid growth and tolerance to a relatively wide range of culturing conditions33, purslane is easy to find in seeds given that there is some tendency to cultivate the species for its medicinal properties and traditional use as salad and soup vegetable34, all contributing for the species suitability for handling and testing in the laboratory. Taking into account the susceptibility of freshwater ecosystems to PPP residues contamination (see above), the macrophyte Lemna minor was selected as the non-target test species to address the possibility of establishing eco-friendlier alternatives to the commercial a.s. combination. This option was ruled by (i) the established status of Lemna sp. as standard ecotoxicological test species35; (ii) the herbicidal and systemic nature of the PPP, which a priori suggests that macrophytes should be more sensitive  than non-plant indicators and equivalent indicators lacking a vascular system such as microalgae; (iii) the available database on the ecotoxicity of each a.s. confirming Lemna sp. as a very sensitive species37.L. minor was assessed following single exposure to each a.s. to feed reference mixtures toxicity models of Concentration Addition (CA38) and Independent Action (IA39), and further prediction of mixture toxicity response surfaces. CA assumes that mixture components act as dilutions of each other since they have a similar toxicological mode of action, while IA assumes that the components of a mixture act independently through a dissimilar mode of action, thus the effect of one of the components in the mixture should remain unchanged in the presence of another component (see details on the mixture toxicity theory in e.g.40). Responses of L. minor to mixtures as predicted by these reference models allowed the definition of the mixture treatments for further testing, triggering the second tier of the study. Here we aimed specifically at defining the actual response surface of L. minor following exposure to the mixture of terbuthylazine and nicosulfuron towards spotting deviations  from the reference models of mixture toxicity. The least hazardous mixtures between the a.s. (e.g. antagonistic combinations) would represent eco-friendlier alternatives to the ratio between a.s. used in the commercial formulation. The third tier of the study then aimed at testing the efficacy, towards the target weed P. oleracea, of these eco-friendlier mixtures of nicosulfuron and terbuthylazine, thus validating their suitability to control a model weed.Given that only the a.s. terbuthylazine and nicosulfuron are disclosed as active components of the model commercial formulation, these were the components considered for testing within the present study, which was structured following a tiered approach through the accomplishment of sequential specific aims. In a first tier, the response of the non-target L. minor to single exposures allowed to define the mixture scheme for the second tier of the study, and provided an overview on the relative toxicity of the mixture components. Single-chemical concentration-response curves were well covered by experimental data . Maximal inhibitory effects of 76% and 56% were reached at 69 and 257 µg/L nicosulfuron and terbuthylazine, respectively, and the higher toxicity of nicosulfuron to L. minor was confirmed by an estimated 7 d-EC50 value for frond yield inhibition (3.31 µg/L) lower by one order of magnitude compared to the corresponding benchmark of 74.5 µg/L found for terbuthylazine , meaning that the former is much more lipid-soluble thus of easier internalization through cell membranes41. However, L. minor is a rooted macrophyte, and the translocation of substances absorbed through the roots should compensate for a lower direct intake of nicosulfuron via inner frond surface contact compared to terbuthylazine . Thus, it is conceivable that the impairment of aminoacid synthesis by nicosulfuron can more prominently constrain L. minor growth than photosystem II inhibition by terbuthylazine at equivalent doses, hence the lower sensitivity of L. minor biomass to the latter.This difference in toxicity - as read from an integrative endpoint such as biomass yield - between substances designed to have an efficient herbicidal action should result from the interplay between intake suitability, the metabolic pathway affected and the meaning of the impairment for the overall physiological performance of the organism. Terbuthylazine has a higher Kow than nicosulfuron  occurred consistently at lower nicosulfuron doses , a good description of the mixture toxicity by an IA model was somewhat expected taking into account the basics on mixtures toxicity theory (e.g.40). Also, although no previous studies were found regarding the toxicity of mixtures between sulfonylureas (such as nicosulfuron) and triazines (such as terbuthylazine) for a more direct discussion, antagonistic effects of pesticide mixtures are not rare. For example, they were already found following exposure of fish to glyphosate and cypermethrin43, of midges to pyrethroids and neonicotinoids44, and of bacteria to quaternary mixtures between ionic liquids structurally much resembling new-generation pesticide molecules with long carbon-based alkyl chains45. Despite these examples and the fact that pesticide mixtures are rarely synergic against biological targets23, the finding of an antagonistic behavior in the present study was somewhat surprising since we worked with the components of a PPP formulation (Winner Top®) and such formulations are, in principle, designed towards the best efficacy against the target. Under this ruling principle, it seems logical that antagonistic interaction between a.s. should be avoided. Given that L. minor shares with the target weed the metabolic pathways involved in their toxicity, antagonistic effects of the a.s. combination within the commercial formulation in P. oleracea should reasonably be expected, unless there is a susceptibility modulation by a non-identified mechanism. Although nothing could be found in the literature regarding L. minor or P. oleracea, some maize hybrid crops are tolerant to nicosulfuron46, which is related to different rates of absorption/translocation and/or better detoxification metabolism48.Nicosulfuron and terbuthylazine are herbicides with dissimilar modes of actionP. oleracea involved additional single-chemical treatments, thus allowing direct interpretation of the weed sensitivity to each mixture component  both on the basis of dry weight . Given that there were some difficulties while recording the number of leafs at the end of the tests, and provided that the allosteric decay model used to estimate concentration-response curves fitted better to the experimental dry weight data from single-chemical testing , further interpretation will be based on dry weight records only. As applied singly, nicosulfuron induced significant decrease in P. oleracea dry weight only at 1.42 and 2.84 Toxic Units (TU)  benchmark and is inconsistent with regulatory environmental safety demands6. Terbuthylazine significantly impaired P. oleracea dry weight at application doses corresponding to values lower than EC20 to Lemna minor  compared to 58.22 (5.77) g/ha. This was in line to the relative sensitivity order found for the non-target indicator L. minor, further supporting the possibility of the occurrence of similar tendencies between species in the response to the mixture challenge.The mixture design applied to TU) Fig. , correspTU) Fig. , which t10, 8 = 13.65 with P = 0.001; Fig. L. minor EC20 values (70% inhibition at 1.37 TU), which was the only mixture tested that was able to significantly impair P. oleracea biomass yield. This suggests that there is no advantage of dosing a mixture rather than one of the a.s. singly when the intent is the control of the weed. The apparent decrease of the mixture efficacy compared to terbuthylazine dosed singly was confirmed throughout the whole response surface. The mixtures toxicity model best fitting the experimental data based on several indicators was the dose-ratio dependent IA (Table 129). Although the significant fitting of the mixture toxicity models  than the concentration involved in the recommended application dose of Winner Top® , which is inherently environmentally unsafe considering that 72 g/ha delivers the L. minor EC20 in surface waters. Such overdosing recommendation in the commercial product is likely linked to the recognition that significant off-target losses may occur during application even with the great efforts to improve formulations in this sense, but this paradoxically implies a raised hazardous potential towards non-target species and edge-of-field ecosystems receiving the residues that do not reach the target51. On the other hand, nicosulfuron alone exerted significant toxicity towards P. oleracea only at 60 g/ha  with recommended application rates corresponding to 40–60 g/ha. Based on our results , no relevant effects in P. oleracea should be expected at these doses and they can represent significant environmental hazard.At this point it is reasonable to assume that a terbuthylazine alone would exert better and environmentally safer herbicidal toxicity than a two-way formulation with nicosulfuron as used in the model commercial compound Winner Top®. Terbuthylazine alone, applied at much lower concentration (at 72 g/L), would be more effective in reducing /ha Fig. , corresp/ha Fig.  which is53. Besides these application and effectiveness features, improved handling, storage and safety (of the operator and the environment) is also taken into account55. Under this context, emulsifying concentrates were for long a primary option because they constitute chemical solutions and the formulation tends a priori to be stable. However, most emulsifying concentrates contain volatile aromatic solvents which are intrinsically hazardous57, and their replacement with new formulation types including suspension concentrates has been favoured56. Suspensions generally perform worst per se because the larger suspension particles are less likely to penetrate cuticles, and adjuvant dispersants and/or surfactants are commonly added to the formulations since they can facilitate this part of the job60 although the stabilization of such a system is often a challenge61.A relevant argument counteracting the above interpretation could be given on the putative role of the formulants other than the a.s. present in the commercial formulation in reversing the antagonistic effects between the a.s. However, we tested on this and remarkably observed a much lower inhibition caused by Winner Top® compared to the equivalent mixture combination of its a.s, both combining 72.74 g/ha of terbuthylazine and 4.86 g/ha of nicosulfuron , namely those involving laboratory and greenhouse tests, eventually field trials, for confirming the biological activity of the renewed product should not be disregarded. This applies also to the formulation solutions applied following the establishment of the a.s. combination. In fact, formulation technology is seen as an enabling arena within agrochemicals development, adding value to the products as to their stability, convenience, human and environmental safety, but primarily by enhancing the a.s. efficacy61. On the contrary, our results suggest that the formulants used in Winner Top® rather spoil the a.s. efficacy. They indicate further that assessment methods merely based in predictive approaches (e.g. in silico tools and QSAR modelling), as those that have been for long used to assist formulation design69, may be short in capturing the actual efficacy of the designed product and the most favourable formulation for further development, both from an economic (important savings would be made by producing a single-way terbuthylazine formulation) and an environmental (PEC references can apparently be met with formulation changes) point of view. It is worth remarking in this context that our workflow considered simple and affordable (time- and cost-effective) laboratory testing, as well as modelling tools (FOCUS and mixture toxicity assessment tools) of free web access with familiar interfaces.The huge costs of research and development to place new a.s. in the market - which have been increasing due to a major contribution of environmental chemistry and toxicology regulatory demandsL. minor and P. oleracea, respectively. No solvent carriers were used.The chemicals used in the toxicity assays were the commercial formulation Winner Top® , terbuthylazine  and nicosulfuron . Winner-Top is a two-way herbicide formulating terbuthylazine (250 g/L) and nicosulfuron (16.75 g/L). Stock solutions were prepared immediately before each assay by dissolving the a.s. or diluting the commercial formulation in culture medium or in tap water for testing with L. minor were maintained in 500-mL Erlenmeyer vessels filled with ca. 200 mL of Steinberg medium35, at 20 °C with a photoperiod of 16 hL:8 hD (light intensity: ≈2000 Lux). Cultures were renewed once a week. Growth inhibition tests with L. minor followed the OECD guideline 22135 adapted to 6-well microplate use71, at 23 °C, under continuous illumination (intensity: ≈1700 Lux), for 7 days. In brief, tests started by inoculating each well with 3 healthy colonies with 3 fronds each, which were then allowed to grow under defined treatment ranges  were purchased from a local supplier. Vegetative vigour tests with the target weed P. oleracea were run by adapting from the OECD guideline 22772, using three replicates per treatment (see Table 2 circular area) holed at the bottom for the placement of a cotton rope allowing bottom watering throughout the test. Although LUFA is a natural soil widely accepted as a standard matrix for soil toxicity tests, three samples of the test soil were briefly characterized (n = 3): pH in H2O = 5.32 ± 0.02 SD, conductivity = 309 µS/cm ± 17.8 SD, water content = 6.18% ± 0.08 SD, loss-on-ignition organic matter content = 2.32% ± 0.22 SD, determined according to standard protocols73. LUFA was hence found adequate for testing with P. oleracea, after pH adjustment using CaCO3 (500 mg/Kg dry-weight soil) to reach the plant optimal range33. Ten seeds were evenly distributed within each replicated pot with moistened soil and the pots were then placed at 20 ± 3 °C under a 16 hL:8 hD photoperiod . Nutrients  were added to the bottom watering supply vessel once after seedling (14 mL/L).Seeds of the weed 31). The plants were then treated by spraying over the leaves  with the test solutions  and residual distribution74; the significance of regressions was validated through by the F-test of overall significance75. Prediction of the joint action of chemicals was carried out by integrating these experimental data into the reference mixture models of CA and IA assuming no interaction between the components of the mixture. CA is mathematically represented in equation i represents the individual concentrations of each component present in the mixture with a total effect of x% and ECxi are those concentrations of the components that would alone cause the same effect xi as observed for the mixture. IA was calculated by multiplying the probability of non-response of each ith component of the mixture, following equation i represents the individual concentrations of each component in the mixture and E(Ci) is the effect of Ci when the ith component is dosed singly. The effective mixture concentrations are commonly presented using the dimensionless TU scaling allowing a measure of the toxic strength. The sum of the quotients Ci/EC50i was applied for the purpose following the CA principles for ith mixture components29.Frond yield records following single exposure of L. minor (Table 40). The experimental responses (L. minor frond yield) were compared to the reference CA and IA models to assess the most suitable modelling approach. Deviation functions to the reference models adapted from Jonker et al.29 were fitted to the experimental data to identify if synergistic/antagonistic (S/A) effects, dose-level (DL) or dose-ratio (DR) dependent effects were more suitable to describe the experimental data. These effects can be denoted by the values of two additional model parameters, ‘a’ and ‘b’, as detailed in Jonker et al.29. Comparisons between different models fitting significantly to the dataset were carried out using the F-test under the assumption that a simpler model (i.e. with less parameters) represents a better fitting solution than a more complex one; the rank from the simpler to the most complex is as follows: Baseline > S/A > DL > DR. The Akaike’s Information Criterion (AIC) was also calculated as a complementary approach to express model fit in a quickly comparable way, with a lower AIC value denoting higher likelihood associated with the model76. Mixture toxicity modelling and analysis was run in a customized MS®Excel® spreadsheet .Based on the expected response curves resulting from the combination of responses to single chemical exposures, a 5 × 5 incomplete factorial (Ray) design (nicosulfuron x terbuthylazine) was used to test mixtures with or Table . The conP. oleracea was tested against mixtures eliciting up to 20% L. minor frond inhibition (mixture EC20) estimated based on the best fit of mixture models to the experimental data (see above); note that the EC20 is a standard protective benchmark5. Namely, application doses of nicosulfuron and terbuthylazine were combined, corresponding to concentrations composing L. minor mixture EC1, EC5 and EC20; additional single chemical treatments were added to the treatments range to better complete the exposure design by covering more widely single chemical concentration-response curves (see Table http://eusoils.jrc.ec.europa.eu/projects/stepsonetwo) was used to calculate application doses for testing with P. oleracea corresponding to L. minor ECx, assuming that these later correspond to surface water PEC values. FOCUS predicts pesticide residue concentrations in edge-of-field waterbodies following given application doses and taking into account pesticide properties constraining their fate through the soils. Since FOCUS runs backwards compared to our needs , a previous calibration step was necessary by simulating several application doses for each a.s. and retrieving corresponding PEC values at day 1 (simulation parameters were retrieved from dedicated EU reports and are exposed in Table S237). Obtained regressions were as follows for nicosulfuron (equation r2 = 1.00) and terbuthylazine (equation r2 = 1.00).In order to assess the efficacy of eco-friendly formulations between nicosulfuron and terbuthylazine, the target weed P. oleracea dry weight) were compared with CA and IA and then added the deviation functions as described above for L. minor mixture toxicity results.The experimental responses  was down-ranged allowing direct comparison with one of the combinations between a.s. . Specific data generated during and/or analysed during the current study are available from the corresponding author on reasonable request.Supplementary information


E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of  Metabolic network models are among the best-studied biological networks1345710So far, several gap analysis algorithms have been developed to be applied in metabolic network reconstruction. 13C labeling data or metabolomics data which are required for OMNI161718As it is shown in i and j in a metabolic network, how their fluxes (vi and vj) depend on each otheri and j are “fully coupled” if they always have proportional flux values, vi = cvj, where c is a constant indicating the ratio of vi and vj. If zero flux through one reaction, vi = 0, always implies zero flux through the other reaction, vj = 0 (but not vice versa), j is said to be “directionally coupled” to i. If two reactions are not flux-coupled, they are defined to be “uncoupled”Flux coupling analysis (FCA) is a computational method to determine, for each pair of reactions In this work, we present a novel gap analysis method, GAUGE, which uses FCA of metabolic networks together with the publicly available gene expression data, in order to propose a strategy for gap finding and gap filling. It has been previously suggested that existence of a pair of fully coupled reactions with (nearly) uncorrelated gene expression suggests a gap in the networkE. coli. The goal of our method is to improve a metabolic network such that there is maximum consistency between experimental gene expression data and theoretical flux coupling relationships. For this purpose, we first find gaps by identifying pairs of fully coupled reactions with “low” gene co-expression. Then, in a two-step algorithm based on mixed integer linear programming (MILP) formulation, we try to minimize the inconsistencies by adding a minimum number of reactions from a dataset of all known reactions to the network.As a test case, we try to analyze the gaps in the genome-scale metabolic model of Suppose that we have an incomplete metabolic network model with known gene-protein-reaction relationships. The goal of GAUGE is to suggest extra reactions whose addition to the network makes gene co-expression relations become more consistent with flux coupling relations. Here we present a brief description of this method and the data we used.E. coli, iJR904, which was reconstructed in 2003E. coli metabolism.The metabolic network model of E. coli were obtained from a previous studyE. coli gene expression data which had been available from Stanford databaseE. coli K12 growing under different conditions, such as different nutrient sources, mutations or environmental perturbations. Then, for each pair of genes in the dataset the Pearson correlation coefficient of gene expressions was computed.Dataset of gene co-expressions of The universal dataset of reactions was obtained from KEGGg1, g2) are found such that deletion of g1 inactivates all the reactions associated with g2, and vice versa. We require to find such gene pairs since our experimental data is for the co-expression of genes, not reactions. For this purpose, the following procedure is performed for every pair of metabolic genes in the model. First, g1 is removed from the model. Then, those reactions that cannot carry nonzero flux after the removal of this gene are identified. If all of the reactions that are associated with g2 are inactivated, then g2 is said to be coupled to g1. If g1 is coupled to g2 and vice versa, then we say that g1 and g2 are fully coupled. This procedure ensures that two genes entirely depend on the function of each other, and that they do not exhibit multiple functions which would justify independent gene expression. 4 and R5 are fully coupled reactions. However, according to the above procedure their corresponding genes, G1 and G2, are not fully coupled. This is because G1 is also associated to R7 and its function is independent of function of G2. Furthermore, R7 and R10 are also fully coupled reactions. However, since there is an “or” relationship between R10 genes, G1 in not dependent to any of them. We computed gene coupling relations for all of the gene pairs in the model.In the first step, gene pairs like  the reaction pairs identified as discrepancy candidates; and (ii) the universal dataset of metabolic reactions. The goal is to add the smallest possible number of reactions from the universal dataset to the network, such that the highest possible number of inconsistencies are resolved.We assume two consecutive steps for fixing model gaps. First, we assess whether addition of new reactions from the KEGG dataset or changing the reversibility type of irreversible reactions can change the coupling type of the candidate reaction pairs. If some of the discrepancies cannot be resolved by this way, we then check whether the addition of exchange reactions can fix the model gaps.S, U and Ue matrices, and L, H, R, D, De and IRR sets as inputs which are defined as follows. S, U and Ue contain the stoichiometric coefficients for the reactions in the original model, the “universal” dataset of KEGG reactions and the universal dataset of exchange reactions, respectively. L is the set of fully coupled reaction pairs whose corresponding genes are uncorrelated, i.e., their Pearson correlation coefficient values are below a certain threshold. Similarly, H, is the set of fully coupled reaction pairs whose corresponding genes are highly correlated, i.e., their Pearson correlation coefficient values are above a certain threshold. In this study 0.2 and 0.8 are chosen as thresholds for reactions in L and H, respectively. R, D and De are set of the reactions of the original model, the universal dataset of KEGG reactions and the universal dataset of exchange reactions, respectively. Finally, IRR is the set of irreversible reactions in the original model.In the first step, the algorithm takes U matrix to select reactions for addition to the model from KEGG dataset. If some of the inconsistencies could not be resolved this way, we run the MILP again with Ue and De as inputs, to select the candidate reactions from the dataset of exchange reactions.By imposing the following constraints, the output of the algorithm would be the maximum number of the inconsistencies that can be resolved between gene co-expression and flux coupling relations. Note that first we use the v and y are vectors that contain the fluxes through reactions of the original model and the universal dataset, respectively.Constraint (1) imposes a stoichiometric mass balance on all of the metabolites, where To count the number of resolved inconsistencies, we need another constraint:i represents a pair of fully coupled reactions with fluxes ui and wi, whose corresponding genes are uncorrelated, and λi = ui/wi is a constant. Vector d is a binary vector such that di = 1 if the two reactions of the reaction pair i are not fully coupled anymore after the network completion, and di = 0 otherwise. Here ɛ is used to avoid numerical errors of the MILP solver. In this work, we assume that ɛ = 10–6.In constraint (2), every Note that Constraint (2) is not presented in the form of a linear constraint. However, it can be shown that application of the following four linear constraints is equivalent to Constraint (2):e, f and g are binary vectors and M is a sufficiently large value.where H remain fully coupled after addition of reactions from the dataset. Constraint (7) is used for this purpose:It is also necessary to ensure that all of the reaction pairs in j represents a fully coupled reaction pair with fluxes pj and qj. Similar to Constraint (2), μj = pj/qj is a constant.In Constraint (7), every Whenever the capacity constraints are known, it is important to include them in the MILP:L are not fully coupled anymore. To obtain the best solution, the following objective function is used:By applying the above mentioned constraints, it is possible to find solutions in which inconsistent reaction pairs of Using this objective function one can determine the maximum number of inconsistencies that are resolved using the MILP.Z*, will be determined. Now, in the second step of GAUGE, the goal is to minimize the number of reactions that should be added to the network or made reversible to resolve the inconsistencies. The inputs and constraints of the MILP of the second step is the same as the first step, with four additional constraints (11 to 14):When the above-mentioned MILP is solved, the maximum number of inconsistencies which can be resolved, say bl are used for counting the number of added reactions from the universal dataset. Here, bl = 1 if the corresponding reactions in the dataset carries nonzero flux. In other words, by imposing this constraint, bl = 1 if yl > 0.In Constraint (11) binary variables We also consider the possibility of making some reactions reversible, for resolving model inconsistencies. To count the number of reactions which are made reversible, the following constraints are added:hm, is set to 1. Again M is a large value and ɛ = 10−6.Constraints 12 and 13 ensure that if an originally irreversible reaction takes a negative flux after network completion, the value of binary variable, Now, to ensure that maximum possible number of inconsistencies are resolved in the second step, we apply the following constraint:d to its maximum value obtained by solving the first MILP.Constraint (14) fixes the sum of elements in Finally, to ensure that minimum number of reactions are added to the network or made reversible to resolve the inconsistencies, we use the following objective function for the second MILP:Altogether, by solving this MILP, we will find the minimum number of modifications which should be made to the network to maximally resolve the network inconsistencies.For calculating alternative solutions, additional constraints were iteratively added to the second MILP and the problem is solved again. This procedure is repeated until all of the optimal solutions are found. The additional constraints are as follows:Q is the number of solutions that are already identified. This constraint ensures that every solution vector differs with the previously found solutions by at least one element.where U and D, Ue and De are used as inputs; Constraints 12 and 13 are not considered anymore; and all of the irreversible reactions of the original model will take the lower bounds of zero.We should emphasize that if some of the network inconsistencies are not resolved by adding reactions from KEGG or making some reactions reversible, addition of exchange reactions are considered. For this purpose, the following changes are made to the above-mentioned MILPs: Instead of https://github.com/zhalehhosseini/GAUGEThe code is implemented for COBRA toolbox and is available here GapFind/Gapfillin silico flux coupling relationships of the iJR904 metabolic network of E. coliL as the set of reaction pairs with absolute correlation coefficients of less than 0.2 and H as a set of reaction pairs with absolute correlation coefficients of greater than 0.8. The goal of GAUGE is to change the coupling type of the reaction pairs in L while keeping the coupling type of the reaction pairs in H unchanged. For the iJR904 metabolic model, L and H include 134 and 41 reaction pairs, respectively. The existence of each inconsistent reaction pair in L implies that there are some missing reaction(s) in the network. Addition of such reactions to the model will change the coupling type of the reaction pair in L from full coupling to other types of flux (un) coupling relations. We used GAUGE to resolve the inconsistencies by adding reactions from KEGG and changing the reversibility type of reactions or by adding exchange reactions to the model.Here, we describe the use of GAUGE to resolve the inconsistencies between experimental gene co-expression data and L. Out of the 132 inconsistency cases, 54 cases were resolved by adding reactions from KEGG, 2 cases were resolved by forcing irreversible reactions to have flux in the backward direction, and the others by allowing the exchange of metabolites between extracellular space and cytoplasm. At minimum, addition of 31 KEGG reactions and 18 exchange reactions and changing the reversibility type of 1 reaction are needed to resolve the inconsistencies of these 132 cases. The detailed information about these results and the procedure of computing alternative solutions are described in the Resolving the inconsistencies can be done by two different approaches: all inconsistencies can be resolved at once or they can be resolved once at a time. Clearly, these two approaches may result in different set of predicted gap-filling reactions. E. coli.Here we discuss a few examples of resolved inconsistencies by GAUGE predictions that evidence from databases or literature exist for their presence in iJR904 with Pearson correlation coefficients of less than 0.2. GAUGE predicts 5 separate reactions to resolve the inconsistency in this case. Interestingly, for two of these reactions (R02260 and R09796) evidence can be found for their presence in E. coli3233E. coli K12 genome. More precisely, R00203 is catalyzed by lactaldehyde dehydrogenase (E.C. number 1.2.1.22). This enzyme is encoded in E. coli genome and catalyzes the conversion of l-lactaldehyde to l-lactate. It is also shown that this enzyme catalyzes the conversion of methylglyoxal to pyruvate (reaction R00203) in E. colim for this conversion is higher compared to the conversion of l-lactaldehyde to l-lactate. It should be noted that R00203 and R00205 in the Figure, differ in the cofactor used by their catalyzing enzymes.E. coli genome (b3177). Additionally, based on the KEGG database this reaction is present in folate metabolism pathway of E. coli K12.d-malate oxidase is an enzyme with the same E.C. number which is encoded by b1800 gene in E. coli model, and interestingly, this enzyme is also annotated as “putative tartrate dehydrogenase” in E. coli.TARTD and TARTRt7, as another inconsistent pair found by GAUGE, are shown in s-dihydroorotate. The gene for transporting orotate to the cell is also known to be present in E. coliDHORTS and ORPT are inconsistent reaction pairs in We have run GapFind/GapFillin silico no growth vs. in vivo growth) and GNG (in silico growth vs. in vivo no growth) cases respectively. Since GAUGE only predicts reactions for being added to the model, only the MILP for resolving NGG cases was run to obtain comparable results. Altogether, 37 NGG cases were identified. Every NGG case was used separately and all of the alternative optimal solutions were calculated for each case. From these cases, 18 cases could be resolved using one of the three possible strategies, namely, addition of reactions from KEGG, changing irreversible reactions to reversible ones, and addition of exchange reactions. The total of 69 reactions were predicted for being added to the model or changing their reversibility type.GrowMatch solves two MILPs to add and remove reactions for resolving the NGG .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Clostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship.icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium’s metabolism, such as changes in the bacterium’s growth in response to different environmental conditions.We present icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens.After an extensive validation process, we used The online version of this article (doi:10.1186/s12918-017-0395-3) contains supplementary material, which is available to authorized users. Clostridium difficile is a gram-positive, spore-forming, anaerobic bacterium, which infects or colonizes various animal species. Clinical manifestations in humans range from asymptomatic colonization to mild diarrhea, pseudomembranous colitis, and death [C. difficile infection among hospitalized patients is antibiotic use, which promotes toxicogenic C. difficile strains to proliferate, produce toxins, and induce disease [C. difficile infection (CDI) call for other antibiotics, such as metronidazole for mild infection cases and vancomycin for more severe cases [nd death . Infectind death . The pri disease . Infecti disease . Currentre cases . The emere cases .The quest to better understand this bacterium and identify novel drug targets against it can benefit vastly from a model of the genotype-phenotype relationship of its metabolism. Methods to model the genotype-phenotype relationship range from stochastic kinetic models  to statiDespite the success of metabolic modeling in capturing large-scale biochemical networks, the approach is limited as it describes cellular phenotype simply in terms of biochemical reaction rates and is thereby disconnected from other biological processes that impact phenotype. Moreover, metabolic models cannot account for changes in the metabolism of the bacterium in response to different environmental conditions. Recent advances in the omic technologies, such as genomics (genes), transcriptomics (mRNA), and proteomics (proteins), have enabled quantitative monitoring of the abundance of biological molecules at various levels in a high-throughput manner. Integration of transcriptomic data has been shown to be effective in improving metabolic model predictions of cellular behavior in different environmental conditions .C.difficile strain 630. We expanded the network iMLTC806cdf [Bacillus subtilis [Here we present an integrated model of the metabolism of TC806cdf  with regTC806cdf . The metsubtilis , 16.synonymous codons. These codons, which code for the same amino acid, are surprisingly used differentially in protein-encoding sequences [To bridge the gap between gene expression data and protein abundance, we accounted for the codon usage bias of the bacterium. During translation of a mRNA to a protein, the information contained in the form of nucleotide triplets (codons) in the RNA is decoded to derive the amino acid sequence of the resulting protein. Most amino acids are coded by two to six equences . The codequences , 19. To We used the modified network and flux balance analysis  to simuliMLTC806cdf network [C. difficile, which was released on March 20, 2015. During curation, we manually considered the directionality and gene-reaction associations of each reaction in the existing network. We also manually expanded the existing network according to the procedure specified by Thiele et al. in [Clostridium difficile is mostly dominated by C16:0, C16:1, C18:1, and C18:0 [icdf834 modifies and expands pathways concerning lipid metabolism in the existing network, such as those where compounds and reactions involved had been grouped together. By expanding the metabolism of the bacterium, we can also account for the wide array of fatty acids C. difficile can metabolize from its environment. This can provide important insights as many Gram-positive bacteria have been found to be able to incorporate and metabolize extracellular fatty acids [iMLTC806cdf [In modifying the  network , we cons network  and inco network  databaset al. in . We suppnd C18:0 . The majnd C18:0 . Our modty acids . When deTC806cdf .iMLTC806cdf had been obtained from the metabolic network of Staphylococcus aureus [C. difficile’s biomass. Therefore, upon increasing the granularity of the network, we assumed coefficients from the biomass equation of the iBsu1103 metabolic network developed for Bacillus subtilis, where these lumped lipid and teichoic acid species have been replaced by explicit species.The lipid component of the biomass equation of s aureus , where lOne constraint-based method for simulating the metabolic steady-state of a cell is flux-balance analysis (FBA), which can be used to analyze the metabolic network solely on the basis of systemic mass-balance and reaction capacity constraints. FBA simulations have been able to capture microorganism growth, nutritional resource consumption, and waste-product secretion rates of various cell types .S of size (m×n). This matrix contains the stoichiometric coefficients of each of the m metabolites in the n different reactions. In the matrix, each row represents one unique metabolite and each column represents one reaction. The stoichiometric matrix helps enforce a mass balance constraint on the system. The mass balance on the cell for i=1,…,m metabolites and j=1,…,n reactions constrains the metabolite concentrations xi, as shown in Eq. vj is the flux through reaction j. The first step of FBA involves representing the metabolic network in the form of a numerical matrix Under the steady state assumption n>m), any v that satisfies Eq. S.In most metabolic models, there are more reactions than there are compounds . BecauseFBA can be used to find and determine points within the solution space that are most representative of the biological system using linear programming methods. Studies have revealed that metabolic fluxes in microorganisms are best predicted by maximizing the cellular objectives of growth . To detec is a vector of weights and indicates how much each reaction flux contributes to the biomass objective function. The maximum growth rate can be achieved by determining the flux distribution v that results in maximal biomass flux. Additional constraints can be added through the upper bound vj. These bounds mandate the minimum and maximum fluxes allowed for a certain reaction and further decrease the space of allowable flux distributions for the relevant system. The mathematical representation of the metabolic reactions, the objective function, and the capacity constraints define a linear system as shown in Eq. where mmol/gDW·h, where gDW is the dry weight of cell mass in grams and h is the reaction time in hours. The bounds enforce thermodynamic constraints by dictating whether reactions are reversible or irreversible. The lower and upper flux bounds were arbitrarily chosen to be -10 mmol/gDW·h and 10 mmol/gDW·h for reversible reactions. For irreversible reactions, mmol/gDW·h and mmol/gDW·h. For our analysis, we used the COBRA toolbox 2.0 [The model fluxes are usually given units of lbox 2.0  in Matlaz objectives, as shown in Eq. One limitation of using only biomass as the objective is that goals in metabolism are often different and simultaneously competing so the scalar notion of “optimality” does not hold; examples of such trade-offs include maximizing energy production while minimizing protein costs . Moreovef(v) is equivalent to maximizing −f(v).Note that, without loss of generality, we assumed that all the functions have to be maximized since minimizing a function Various works have attempted to systematically evaluate the ability of different objectives functions to reliably predict intracellular flux , 32. AccIn a maximization multi-objective problem, a vector that is part of the feasible space is considered to be Pareto-optimal if all other vectors have the same or a lower value for at least one of the objective functions. Therefore, a Pareto-optimal solution is found when there exists no other feasible solution which would increase one objective without decreasing another objective. The set of Pareto-optimal solutions constitutes the Pareto-optimal front . In the individuals, representing potential solutions to the problem. These arrays are then ranked based on the values of their objective functions. Potential optimal solutions are generated by retaining the best individuals and by generating new individuals through the use of variation. This process is continued until no further improvements are detected on the Pareto front. The population size and the number of populations used with this algorithm were 140 and 1400, respectively. To solve the linear programs, we used the Gurobi solver  [As proposed by Costanza et al. , to solvbi Inc.) .To validate our choice of objectives, we conducted a genetic analysis using multi-objective optimization. In this analysis, binary “knockout” vectors were created, with each containing a 1 in the location of a gene set to be off . This anA facet of living organisms is their homeostasis, otherwise known as their ability to remain robust to external and internal perturbations within a certain range. External perturbations include changes in temperature or food supply while internal perturbations include spontaneous mutations. The robustness of biological systems is partly due to the presence of parallel metabolic pathways. Robustness represents the insensitivity of a system to changes in system parameters.γ where γ applies noise σ, assumed to be Gaussian, to the system ψ for the trial τ. As proposed in [ρ of 1: Global Robustness (GR) analysis can be used to survey the parameter space to determine the region where the cell exhibits specific features. More specifically, we perturbed the flux bounds of the metabolic model and observed the resulting effects on biomass production. The perturbation function posed in , a robusε is the robustness threshold. The GR was defined as the percentage of trials determined to be robust. We arbitrarily defined ε to be 1% of the metric ϕ(ψ) and we arbitrarily limited the noise to 1%.where To increase the reliability of the model, gene expression data was added to the FBA framework Fig. . To relaC. difficile was obtained from the Kazusa Codon Usage Database [k and the frequency of the most preferred synonymous codon for that amino acid: The codon usage table for Database , which lC. difficile from UniProt [We obtained the mRNA sequence associated with the 834 genes of  UniProt . The cou UniProt . The CAIwk(l) is the weight associated with codon type k for lth codon along the length L of the gene. Because a large codon bias has been shown to result in higher protein expression levels, the gene expression data gt for each gene t was scaled by CAI such that genes with the low codon bias had lower expression where L is the number of codons in the genes and GSEj for gene set j of reaction j from the expression of individual genes gt′, which in our case has been scaled by their respective codon usage. When two genes are connected through an AND operator, the gene set expression for reaction i, gi, is the minimum of the scaled expression of the individual genes t making up the gene set. The gene set expression for two genes connected by an OR operator is the sum of the scaled expression of the individual genes. In each reaction of the model, to map the gene set expression into a specific condition of the model, we used the piecewise muliplicative function h and the associated hj was adopted as a multiplicative factor for the flux bounds [Each of the reactions in the metabolic model depends on a gene set, which is represented through the use of AND/OR operators. In this formulation, if a gene set is composed of two genes and an AND operator, both genes are required to carry out the corresponding reaction. On the other hand, if two genes connected by OR, one gene is sufficient in carrying out the reaction. This formulation can be transformed to derive the gene set expression x bounds  : \docuh was chosen because at high mRNA abundance, an increase in mRNA abundance has been found to produce a relatively small increase in the protein synthesis rate. On the other hand, at low mRNA abundance, an increase in mRNA abundance has been found to produce a large increase in the protein synthesis rate [The function sis rate .Finally, we validated our context-specific metabolic models by incorporating codon usage and differential gene expression data into our model. We then compared trends in our models’ biomass predictions to literature on the bacterium.C. difficile R20291 (ribotype 027), for which gene essentiality data was available for comparison with our in silico results, had been grown on Tryptone-Glucose-Yeast Extract (TGY) broth. To approximate this medium, we used the complex medium defined by Larocque et al. during essential gene analysis of iMLTC806cdf [For each gene in the model, essential gene analysis involved removing reactions catalyzed by the gene or by a complex involving that gene and then using FBA  to prediTC806cdf .The growing research attention on metabolic pathways, rather than on specific reactions, is motivated by novel methods that allow for a better understanding of the functionality of complex webs of metabolic reactions. To date, much of the study of metabolic pathways, their crosstalks, and their role in the overall metabotype has been carried out with statistical and model-based approaches , 46.iMLTC806cdf and icdf834, we used Pathway-oriented Sensitivity Analysis (PoSA) [sensitive pathways, which make a large impact on model outputs. In other words, we perturbed pathways by mutating the genes that govern their biochemical reactions and analyzed the result on the outputs. In the knockout vector y={b1,b2,…,bs,…,bp}, bs represents the perturbations on the genes governing the metabolic pathway s, where |bs|=Ws (number of genes partaking in the sth pathway). Because the gene knockouts are represented through the use of binary variables, we perform combinatorial perturbations, namely the bits in bs are switched from 0 to 1 or from 1 to 0; note that if a gene in bs is set to 1, this gene is knocked-out in the model.Sensitivity analysis is used to identify model inputs that have a large influence on the model outputs. To find the metabolic pathways that have the largest effect on the outputs of s (PoSA) . PoSA inbs can be defined as follows: According to , the Patbs; y; F(y) is the vector v of fluxes as produced by the model; finally, Δs is a scale factor defined as: where μ and σ are determined by calculating the mean and the standard deviation of the distribution of the PEE for each input. Pathways with a large μ have a large influence on the output. A large σ indicates an input whose influence highly depends on the value of other inputs. By perturbing the genes through the use of knockouts and comparing the outputs of the model with and without the genetic manipulations, we detected the most sensitive pathways of the metabolic models.Next, the sensitivity indices flux control coefficient [vydh through a particular reaction, catalyzed by enzyme ydh, of the metabolic pathway with respect to the concentration xxase of an enzyme xase: PoSA provides valuable information on sensitive pathways that can be targeted by therapies, but often more specific drug target predictions are desired. To understand how a metabolic pathway is controlled and can be altered, its control structure has to be determined. The fficient  is the fxxase. Flux control coefficients provide a quantitative measure of the degree of control an enzyme exerts on a metabolite flux and can quantitatively substitute for the qualitative concept of essential gene [In our calculations, the enzyme concentration was assumed to be equal to the gene expression level adjusted by CAI. When calculating the flux control coefficients, we considered a 1% perturbation in the enzyme concentration ial gene . Thus, tLimma [We used microarray analysis to determine the combination of genes which were up-regulated or down-regulated in different environmental conditions. We used Limma , a packaC. difficile strain 630 is composed of a circular chromosome of 4,290,252 bp coding for 3968 open reading frames (ORFs), along with a plasmid containing 7881 bp coding for 11 ORFs [iMLTC806cdf, which contains 806 ORFs, as shown in Table iMLTC806cdf. The Excel and SBML file, along with the the justification for keeping the dead-end metabolites in the model, have been uploaded to http://github.com/ssahebkashaf/Peptoclostridiumdifficile630. The code for all of the analyses employed in our work is also freely available on this repository.The genome of  11 ORFs . The modiMLTC806cdf [see Additional file icdf834 and iMLTC806cdf to identify essential amino acids and metabolizable carbon sources. The removal of amino acids that were not found to be essential or to affect growth, did not affect model-predicted biomass production in both models. Moreover, no biomass was produced in the absence of essential amino acids  [C.difficile.We repeated analyses previously conducted by Larocque et al. to validate  valine)  in both C. difficile is not restricted to metabolizing sugars and can ferment other compounds, even amino acids, to obtain both its carbon and energy [With regards to carbon sources, both models were able to correctly predict a range of carbon sources that are utilized by the bacterium. Moreover, the bacterium was able to generate biomass in the absence of other carbon substrates, such as fructose, mannose, mannitol, and sorbitol. This finding is consistent with literature, which maintains that d energy .C. difficile’s metabolism.Our modeling approach is intended to simulate the conflicting objectives faced by the bacterium, where optimal performance in one objective coincides with sub-optimal performance in another objective. We used a knockout parameter space to find the genetic designs that would optimize the two objectives. In Fig. σ) and a tolerance (ε) of 1% [see Additional file σ is increased or ε is decreased. This facet of the bacterium’s metabolism was biologically relevant as bacteria such as C. difficile are able to grow despite small fluctuations in their physical environment. Robustness analysis illustrated that the global behavior of our metabolic model matches our expectations from biological rationale and supported the use of our models to predict the behavior of the bacterium in different environments.We gauged the robustness of our model by determining the change in the maximal biomass flux in response to different perturbations. Global Robustness (GR) analysis revealed that the biomass production was fully robust to perturbations for a flux bound perturbation  database  under thC. difficile as compared with the controls [C. difficile in response to sub-MIC levels of different antibiotics into our model. As compared with the C. difficile grown on BHI broth, toxigenic strains of C. difficile grown on sub-inhibitory concentrations of antibiotics exhibited reductions in their biomass, with those grown on amoxicillin showing the smallest growth  [iMLTC806cdf predicted 48 essential genes and had a 86.5% accuracy in predicting gene essentiality, icdf834 predicted 46 essential genes and had a 92.3% accuracy [see Additional file Essential genes have been cited as promising targets for development of new antimicrobials due to their importance for bacterial survival . Using Fvailable . We perfype 027) . While iμ and the standard deviation σ were computed as a result. We performed the pathway-based sensitivity analysis and identified sensitive pathways before and after modifying the metabolic model as shown in Fig. μ, and thereby the greatest control on biomass production or growth in both iMLTC806cdf and icdf834 is the valine, leucine, and isoleucine metabolism pathway. These three amino acids are essential to the bacterium and their metabolism was also expected to be essential. The second most sensitive pathway is alanine, aspartate, and glutmate metabolism in iMLTC806cdf and glycolysis/gluconeogenesis in icdf834. Additional sensitive pathways in icdf834 include pyrimidine metabolism and pyruvate metabolism. Model findings suggest that therapies against infection may likely be more effective if they target key enzymes in these sensitive pathways.For our PoSA analysis, we chose the gene expression profile of the bacterium when grown on BHI broth. Each pathway was assessed through random perturbations of its reactions, and the average perturbation C. difficile can be used to predict better drug targets for patients. Therapies aimed at reducing growth of C. difficile should target enzymes with high flux coefficients as, according to our model, their activity is most closely tied to biomass production.To find more specific therapeutic targets, flux control coefficients for enzymes on biomass production in the metabolic model were determined and compared for BHI broth (E-BUGS-56), cysteine supplementation GSE22423), and heat shock (E-GEOD-37442) gene expression data. The four enzymes with largest flux control coefficients in each condition are shown in Fig. 23, and hC. difficile and used it to create context-specific metabolic models of its metabolism that allow us to understand how the bacterium alters its metabolism depending on its environment. To predict the bacterium’s behavior in different environmental conditions, the model was integrated with transcriptomic and codon usage data to generate reliable and context-specific metabolic flux distributions. We validated the model by conducting robustness and sensitivity analyses. We further assessed its predictive potential by comparing model predictions with published experimental data to gauge the consistency of model findings with the current knowledge of C. difficile’s metabolism. Through this literature-based validation, we found that the model is a valuable tool for qualitatively understanding the behavior of the bacterium in different settings. The model can also be used to find potential therapeutic targets by allowing for determination of essential genes and context-specific sensitive pathways and flux control coefficients.In this study, we expanded the existing metabolic network for C. difficile’s metabolism. Our model can be used by biomedical researchers to study the bacterium and devise targeted treatments. Our approach can also be scaled-up to simulate the interactions between the gut microbiota and the host using a bottom-up modeling approach. By accounting for the gut microbiota-host interactions, we can construct a whole gut model response to infections and other inflammatory events, paving the path towards more informed and effective treatments.Context-specific metabolic models can allow for a better understanding different medically-relevant conditions  and can be continuously refined by integrating novel information regarding Project name:icdf834Project home page:http://github.com/ssahebkashaf/Pep-toclostridiumdifficile630Operating system: platform independentProgramming language: MATLABOther requirements: GurobiLicense: University of CambridgeAny restrictions to use by non-academics: license needed


Grain chalkiness is a highly undesirable trait deleterious to rice appearance and milling quality. The physiological and molecular foundation of chalkiness formation is still partially understood, because of the complex interactions between multiple genes and growing environments.We report the untargeted metabolomic analysis of grains from a notched-belly mutant (DY1102) with high percentage of white-belly, which predominantly occurs in the bottom part proximal to the embryo. Metabolites in developing grains were profiled on the composite platforms of UPLC/MS/MS and GC/MS. Sampling times were 5, 10, 15, and 20 days after anthesis, the critical time points for chalkiness formation. A total of 214 metabolites were identified, covering most of the central metabolic pathways and partial secondary pathways including amino acids, carbohydrates, lipids, cofactors, peptides, nucleotides, phytohormones, and secondary metabolites. A comparison of the bottom chalky part and the upper translucent part of developing grains of DY1102 resulted in 180 metabolites related to chalkiness formation.Generally, in comparison to the translucent upper part, the chalky endosperm had lower levels of metabolites regarding carbon and nitrogen metabolism for synthesis of storage starch and protein, which was accompanied by perturbation of pathways participating in scavenging of reactive oxygen species, osmorugulation, cell wall synthesis, and mineral ion homeostasis. Based on these results, metabolic mechanism of chalkiness formation is discussed, with the role of embryo highlighted.The online version of this article (doi:10.1186/s12870-017-0985-7) contains supplementary material, which is available to authorized users. Chalkiness is the opaque portion in rice endosperm due to loose packing of starch granules and protein bodies , 2. Depepyruvate orthophosphate dikinase, starch synthase IIIa, UDP-glucose pyrophosphorylase, cell wall invertase, and H+-translocating pyrophosphatase (V-PPase), as reviewed by Sreenivasulu et al. [Chalk5, which encodes V-PPase with inorganic pyrophosphate hydrolysis and H+-translocation activity. They found that Chalk5 could disturb pH homeostasis in the endomembrane trafficking system, resulting in an abnormal decrease in protein body number and size, causing aberrant shape and spatial arrangements of starch granules and protein bodies.Chalkiness is a complex quantitative trait, governed by polygenes and subjected to large variations in environmental factors like temperature and humidity and cultural practices such as fertilization and irrigation . By moleu et al. . Based ou et al.  studied Rice quality is essentially determined by chemical composition of the storages of starch and protein that derive from carbon (C) and nitrogen (N) metabolism during grain filling. Metabolomics is one of the “omics” studies for integrative analysis of metabolites in organisms . TogetheUsing chemical mutagen of EMS, we indentified a notched-belly mutant with high ratio of white-belly grains . The notWe obtained a notched-belly mutant with a high ratio of while-belly grains. The notched line can be seed after 5 DAA, possible due to the restriction of the hulls on caryopsis elongation Fig. . The conThe notched-belly mutant has high percentage of notched-belly grain, being 66.58% under greenhouse condition in 2014  upper part and chalky (Ch) bottom part of developing grains produced in the greenhouse to three analytical platforms , we identified 214 rice grain metabolites . Note that in the time course comparisons there were many more compounds which decreased over time, as opposed to increasing over time . Cys, Met, and SMM were reduced in the chalky endosperm, while MTA showed an increasing trend irrespective of a decrease on 5 DAA , S-methylmethionine (SMM), and DAA Fig. . MethionOne important metabolite pool in plants is the hexose phosphates, and another is composed of pentose phosphate pathway intermediates and the triose phosphates glyceraldehydes 3-phosphate and dihydroxyacetone phosphate . The hexF6P and intermediates in glycolysis pathway and tricarboxylic acid (TCA) cycle were altered in the chalky endosperm. F6P and 3-PGA in the chalky endosperm showed a similar trend, being not significantly different on 5 DAA and 10 DAA, but higher on 15 DAA and 20 DAA in comparison with that in the translucent endosperm , and then kept at a comparable level after that  and its precursor I5P over the time course, with the trend accelerated on 20 DAA  was slightly decreased in the chalky part on 5 DAA, while nicotinate and trigonelline (N’-methylnicotinate) were elevated during seed development. Phosphate decreased for both parts, and it was lower on 5 DAA and 10 DAA whereas slightly higher on 20 DAA in the chalky endosperm.In general, CPGECs increased as seed development progressed , as also discussed in our previous works on matured grains , 22. BecChalk5 [Starch and protein, the products of C and N metabolism, are major factors affecting rice grain quality. Grain chalkiness is the result of poor filling of starch granules or imbalances in the subtle adjustment to starch degradation pathway triggered under stress . In addiChalk5  and the Chalk5 . C and NIn the present study, we detected several key metabolites involved in sensing and regulating carbon and nitrogen status. The chalky part contained lower sucrose content, as was accompanied by decrease in Glu and Gln, while Asp and Asn, carrying more N atoms per C, were increased on 15 DAA and 5 DAA, respectively. The inverse changing patterns of Glu/Gln and Asp/Asn indicate that the C skeletons for biosynthesis of amino acids may be limited in the chalky part so that it uses a more economical compound to transport N. Supporting the argument above, Arg, with a high N:C ratio (4:6) and serving as a main N storage compound, was up-regulated in the chalky endosperm. This was partially due to higher levels of its precursors, N-acetylglutamate and citulline. In addition, Arg plays a major metabolic role in seed maturation and germination, phloem and xylem transport, and accumulates under stress conditions .It has been suggested that GABA, a four-C nonprotein amino acid, constitutes a readily accessible nontoxic reserve of C and N for amino acid metabolism and tricarboxylic acid (TCA) cycle activity, which is of particular relevance during stress events. In Arabidopsis, GABA shunt metabolic pathway that converts Glu to GABA has been shown to have a profound effect on the C-N balance of the seed . Our resSulfur is essential for synthesize Cys, Met, and other essential and secondary metabolites like MTA, SAM, and SMM . Cys hasThe overproduction of ROS is likely to result in damage to proteins, lipids, carbohydrates and DNA in plant cells, in particular under abiotic stresses including drought, high temperature, and chemical toxicity . ROS canThe accumulation of osmolytes is the common strategy adopted by plants to combat the environmental stresses. Among them, betaine has strong osmoprotectant properties and confers tolerance to salinity and drought . TrehaloPlant cells are surrounded by a cell wall that plays a key role in plant growth, structural integrity, and defense. Liu et al.  showed tmyo-inositol-1-phosphate and possibly free myo-inositol as a common intermediate [Plants acquire minerals for sustaining growth and development. We identified several metabolites related to translocation and storage of minerals such as phytic acid, nicotianamine, and phytosphingolipids, which changed significantly between the chalky and translucent endosperms. Phytic acid is a nearly ubiquitous component of plant seeds and is usually the most abundant form of phosphate . It has rmediate . In addiIn summary, the notched-belly mutant is applicable to explore the physiological and molecular foundation of grain chalkiness, for it provides a high efficient comparison under the same genetic background that can minimize the influence of genotype and environment. Using this mutant, we observed a reduction in activity of C and N metabolism used for starch, protein, and cell wall synthesis in the chalky endosperm, which coincided with the disturbance of other pathways like osmoregulation, ROS scavenging, and ion homeostasis, implying a stressful condition (oxidative or osmostic) in the chalky part. It is still uncertain what triggers this metabolic shift. In addition, we cannot rule out the possibility that the differences in metabolite levels between the two sections are caused by some constriction between them due to the notched line; however, there is currently no evidence that this is the case. Clearly the development of chalkiness is a complex trait and this is corroborated by the metabolomic analysis in this study. An integrated approach using a combination of omics platforms and an examination of the physiology of the grain will be needed to understand this phenomenon fully.Wuyujing3 had become a popular japonica rice cultivar in southern China with high eating quality since it was released in 1992 . It is a2O5, and 0.9 g K2O/pot, and those for topdressing at the panicle initiation stage were 1.0 g N, 0.6 g P2O5, and 0.9 g K2O/pot. All the plants were grown under natural environment condition at seedling stage. Approximately 2–3 days before anthesis, 45 pots were transferred to a 28/23 °C plant chamber  until maturity. This plant chamber has the light intensity above 75% of ambient light condition, and the relative humidity was maintained 70 ± 5% by a humidity regulator [In 2014, five plants of DY1102 were hand transplanted in a plastic pot filled with 15 kg clay soil, 30 cm in height and 34 cm in diameter. The soil contained 0.83 g/kg total N, 10.72 mg/kg available P, and 69.15 mg/kg exchangeable K. The basal fertilizers before transplanting were 1.0 g N, 1.2 g Pegulator . Flowerihttp://www.metabolon.com/) by the composite platforms: UPLC/MS/MS positive ion mode, UPLC/MS/MS negative ion mode, and GC/MS [Metabolite profiling was performed by SJTU-Metabolon Joint Metabolomics Laboratory  and Kyoto Encyclopedia of Genes and Genomes pathway  to construct the metabolic atlas.Raw data of three analytical platforms were extracted, peak-identified and quality control processed using Metabolon Laboratory Information Management System. Metabolites were identified by automated comparison to reference library that was created using approximately 1500 authentic standards in Metabolon. Metabolites quantification, data normalization, and significant changed metabolites analysis were performed according to Lawton et al.  and Rao