A metabolism can evolve through changes in its biochemical reactions that are caused by processes such as horizontal gene transfer and gene deletion. While such changes need to preserve an organism’s viability in its environment, they can modify other important properties, such as a metabolism’s maximal biomass synthesis rate and its robustness to genetic and environmental change. Whether such properties can be modulated in evolution depends on whether all or most viable metabolisms – those that can synthesize all essential biomass precursors – are connected in a space of all possible metabolisms. Connectedness means that any two viable metabolisms can be converted into one another through a sequence of single reaction changes that leave viability intact. If the set of viable metabolisms is disconnected and highly fragmented, then historical contingency becomes important and restricts the alteration of metabolic properties, as well as the number of novel metabolic phenotypes accessible in evolution.We here computationally explore two vast spaces of possible metabolisms to ask whether viable metabolisms are connected. We find that for all but the simplest metabolisms, most viable metabolisms can be transformed into one another by single viability-preserving reaction changes. Where this is not the case, alternative essential metabolic pathways consisting of multiple reactions are responsible, but such pathways are not common.Metabolism is thus highly evolvable, in the sense that its properties could be fine-tuned by successively altering individual reactions. Historical contingency does not strongly restrict the origin of novel metabolic phenotypes. We did this for 500 potential metabolisms of size 1400 (similar to that of E. coli), 1000, 500, and 400 . In this way, we were able to show that all 500 potential metabolisms are connected at each of these sizes. Thus, down to a size of n = 400 reactions, the genotype network of metabolisms viable on glucose is not highly fragmented, and one component comprises the vast majority or all metabolisms.If genotype networks were highly fragmented at a given size Gi and Gi+1 through viability-preserving reaction swaps. In this way, we were able to show that all 500 potential metabolisms are connected at each of these sizes. Thus, down to a size of n = 425 reactions, the genotype network of most metabolisms viable on all 10 carbon sources consists of one connected component.Because many free-living microorganisms are viable on multiple carbon sources, we generated 500 additional potential metabolisms through the reaction deletion process just described, but with the additional constraint that they remain viable on ten sole carbon sources . Specifically, we created again potential metabolisms of size 1400, 1000, 500, 450 and 425 . We then repeated the procedure that attempts to connect genotypes V(n). Specifically, let us consider the null hypothesis that more than one percent of V(n) resides outside this largest component. If this null hypothesis is correct, then the probability p that a randomly drawn viable genotype is not on this largest component is greater then p = 0.01. Moreover, the probability that some number M of genotypes drawn at random from V(n) all fall on the largest connected component would be smaller than (1-p)M. In our case, M = 500 and (1-p)M < 0.99500 = 0.0066. In other words, the results of our sampling allow us to reject the above null hypothesis at a significance level smaller than 1 percent.It is possible to make this point more quantitatively and establish a statistical bound on the fraction of potential metabolisms contained in the largest connected component of n that is small compared to the total number of possible reactions. To examine their size for larger metabolic system, we studied the 500 minimal metabolisms that we derived from the sequential random deletion strategy described in the previous section , or whether they might themselves form large components. To this end, we simply asked whether these networks have any viable neighbors in Ω(n), metabolisms that differ by a single reaction swap, which are also viable. The result . Taken together, this means that minimal metabolisms themselves must form large components and are certainly not isolated. It mirrors the situation in central carbon metabolism, where newly emerging minimal metabolisms at a given size n also form connected components, albeit small ones , all (n = 29–51) or the vast majority of metabolisms of size n form a single connected component (network) in the space of metabolisms.To our knowledge, our analysis of all possible ≈ 10nt goals -50. Our n = 400–5906) the vast majority of potential metabolisms form a single connected component of a genotype network. More specifically, with a probability of greater than 0.99, more than 99 percent of all viable metabolisms exceeding 400 reactions are part of the same component. We note that it would have been sufficient to perform the sequential reaction deletion procedure needed to arrive at this conclusion for metabolisms of size n = 400, and not also for metabolisms of size n = 400–1400, as we did. The reason is an elementary observation we made about metabolic genotype space: If a set of viable metabolisms V(n) is connected for some number of reactions n, then V(m) must be connected for all m > n, provided that no new minimal metabolisms appear at any value of m. The largest minimal metabolism we found has n = 391 reactions, and while we cannot exclude the existence of minimal metabolisms above n = 400 with certainty, such potential metabolisms would be increasingly rare at large n. They would create new genotype network components that would comprise a vanishing fraction of the rest of the connected genotype network .In genome-scale metabolisms, where exhaustive enumeration is no longer possible, and where we required the synthesis of 63 common biomass molecules for viability, we found viable potential metabolisms with as few as 324 reactions, and for 93.23 percent of the size range of viable metabolisms . That is, the hypothetical landscape of Figure Figure  While we study only viability on a carbon source, other metabolic properties such as mutational robustness and access to novel phenotypes are also important ,11 and mn = 400 are not usually found in free-living organisms. They occur in (endo)symbionts [With possible exceptions in some marine bacteria ,52 metabymbionts ,54 and  At least one of these pathways must comprise more than one reaction, otherwise the two metabolisms could be converted into one another through a single reaction swap. We have provided two examples, one involving the biosynthesis of erythrose-4-phosphate and ribose-5-phosphate through variants of the pentose phosphate pathway, the other concerning the biosynthesis of phosphoenolpyruvate.essential pathways with more than one reaction do not exist. They can usually be converted into each other by single reaction changes that do not cause a loss of viability. Second, our analysis required that we impose change through reaction swaps – a reaction addition paired with a deletion – that leave reaction numbers constant. However, this is not usually how evolutionary change in a metabolism’s reactions occurs. For example, horizontal gene transfer frequently adds more than one gene and thus more than one reaction to a metabolism [For two reasons, such alternative essential pathways are not likely to hamper the evolution of most metabolic systems. First, we observed fragmentation only for relatively small metabolisms, which means that in larger metabolisms, alternative tabolism -63. In atabolism . In sum,de novo with increasing metabolic complexity n is closely linked to the metabolism size at which fragmentation occurs, viable genotype networks V(n) would still remain connected over the vast majority of the range of n. Indeed, we found that genome-scale metabolisms viable on 10 carbon sources and comprising 425 reactions are connected in genotype space and belong to the same component. Possible exceptions might involve metabolisms viable on hundreds of different carbon sources, but even environmental generalists are typically not viable on that many.  might have led to greater genotype network fragmentation. This possibility is suggested by our analysis of central carbon metabolism, where metabolisms viable on 10 carbon sources must have at least 34 reactions, and fragmentation of genotype networks stops at 37 reactions. However, for genome-scale metabolisms, the metabolism sizes at which fragmentation would cease would increase only modestly with each additional carbon source on which viability is required. This is because previous work has shown that viability on every additional carbon source requires on average the addition of only two reactions to a metabolism . For exa sources ).Another limitation of our work is that we only considered viability on carbon sources. We cannot exclude the possibility that viability on sources of different chemical elements may lead to different fragmentation patterns. However, it is unlikely that carbon sources are exceptional in this regard. For example, the minimal size of metabolisms viable on different sulfur sources comprises only 90 reactions, and is thus even smaller than that of metabolisms viable on carbon . The reaA further limitation is that we focus on evolutionary constraints caused by the presence or absence of biochemical reactions, rather than on differences in the regulation of existing enzymes or their encoding genes. Such regulatory constraints can influence important metabolic properties such as biomass growth rate . HoweverFinally, we do not consider one potential cause of genotype network fragmentation: If one required for viability that biomass precursors need to be synthesized at a high rate, then genotype networks may fragment more often than we observe. However, fast biomass synthesis and its main consequence, rapid cell division, are not universally important outside the laboratory environment. For example, a survey of microbial growth rates shows that many microbes have very long generation times in the wild . Rapid gIn sum, our analysis has shown that over a broad range of metabolic complexity, historical contingency is not likely to strongly constrain the modulation of metabolic properties, or the accessibility of novel metabolic phenotypes. Only the smallest metabolisms, which typically do not occur in free-living organisms, are likely to be subject to such constraints, which stem from genotype network fragmentation. Additional factors that we did not consider explicitly are likely to further reduce such fragmentation. They include pervasive promiscuous enzymes, which are capable of catalyzing more than one biochemical reaction ,69, and S, of dimensions m × n, where m denotes the number of metabolites, and n denotes the number of reactions in a metabolism [Sv = 0, where v denotes a vector of metabolic fluxes through each reaction in a metabolism. The above equation has a large space of possible solutions, but not all of these solutions may be of biological interest. To restrict this space to fluxes of interest, FBA uses linear programming to maximize a biologically relevant quantity in the form of a linear objective function Z[Flux balance analysis (FBA) is a constraint-based computational method ,70 that tabolism ,70. FBA c contains the set of scalar coefficients that represent the maximization criterion. The individual entries of vectors a and b, respectively, contain the minimal and maximally possible fluxes for each reaction in v. Irreversible reactions can only have fluxes with positive signs, whereas irreversible reactions can have fluxes of both signs.The vector E. coli, because most of them would be required in any free-living organism. They include 20 proteinaceous amino acids, DNA and RNA nucleotide precursors, lipids and cofactors. We use these biomass precursors to define the objective function and the vector c. We employed the package CLP  to solve linear programming problems. The computer program required for FBA is available in Additional file We are here interested in predicting whether a metabolism can sustain life in a given spectrum of environments, that is, whether it can synthesize all necessary small biomass molecules (biomass precursors) required for survival and growth. For our analysis of central carbon metabolism, there are 13 such essential precursors , protons, water, molybdate, copper, calcium, chloride, magnesium, manganese and zinc . For ourE. coli central carbon metabolism [N = 51 intracellular reactions, and we analyzed the viability of metabolisms comprising all possible 251 subsets of this set. The reconstruction in [We use a global set of reactions in central carbon metabolism, which is based on a published reconstruction of tabolism . From thtabolism , we delection in  also invE. coli metabolic model (iAF1260), which comprises 1,397 non-transport reactions [E. coli transport reactions in this set [E. coli from any of the carbon sources we consider here. We note that this metabolism may not be biologically realizable, for example, because it may contain thermodynamically infeasible pathways. However, we merely use it as a starting point to create smaller and ultimately minimal metabolisms through the sequential reaction deletion process described below.We refer to the known universe of biochemical reactions as the set of reactions known to occur in some organism based on currently available biochemical knowledge. To arrive at this set, we curated data from the LIGAND database ,32 of theactions . At the this set . Unsurpri-th entry corresponds to the i-th reaction in some global set or universe of biochemical reactions. This entry will be equal to one if an organism’s genome encodes an enzyme capable of catalyzing this reaction, and zero otherwise . Any one organism's metabolic genotype can be thought of as a point in this space. Genotypes (metabolisms) viable in a given chemical environment are those that can synthesize all biomass precursors from nutrients in this environment. Specifically, a metabolism is considered viable on a carbon source if its biomass synthesis rate is more than one percent of the biomass synthesis rate of the central carbon metabolism (N = 51) on that carbon source. We note that many of the metabolisms we study here, may not be realized in extant organisms. We thus refer to these metabolisms as potential metabolisms.The genes encoding the enzymes that catalyze a metabolism’s reactions constitute the metabolic genotype of an organism. For our purpose, a more compact representation of a metabolic genotype is useful, which represents this genotype as a binary vector whose at least one of the 10 carbon sources. The computer program required for computing essential and non-essential reactions is available in Additional file We define a reaction as essential for viability if its elimination abolishes viability in a given chemical environment. To identify all such essential reactions in a given metabolism, we eliminated each reaction and used FBA to assess whether non-zero biomass growth flux was still achievable. For our analysis of viability on 10 different (sole) carbon sources, we defined a reaction as essential if its elimination abolishes viability on 51 (1015) possible metabolisms in central carbon metabolism would be infeasible. Fortunately, such brute-force enumeration is also not necessary, for two reasons. The first originates from the notion of “environment-general superessential reactions” [51 to 2(51–6) = 245(≈1013).To identify all viable metabolisms by exhaustive enumeration of viability for all 2actions” . These an-1 reactions, we need to evaluate only the viability of those that are derived from viable potential metabolisms with n reactions through removal of one reaction. We incorporated this idea into an algorithm that allowed us to enumerate all viable genotypes [The second reason derives from a simple observation that reduces the number of genotypes whose viability needs to be determined even more dramatically: Removal of a reaction from an unviable metabolism cannot result in a viable metabolism. This means that among all metabolisms with enotypes .To sample large  metabolisms, we started from the global metabolism of 5,906 reactions and deleted (eliminated) from it a sequence of randomly chosen reactions, while requiring that each such deletion preserves viability. Specifically, we chose a metabolic reaction at random and equiprobably among all reactions, deleted it, and used FBA to determine viability of the resulting metabolism. If the metabolism was viable, we accepted the deletion. Otherwise we randomly choose another reaction for deletion, and so on, until we found one whose deletion left the resulting metabolism viable. We also kept a count of the number of successive attempted deletions that resulted in a non-viable metabolism. This count was reset to zero if the deletion of a randomly chosen reaction was successful. Once that count reached 1000, that is 1000 successive attempts at reaction deletion abolished viability, we considered the metabolism a good candidate for a minimal metabolism. To confirm minimality, we deleted each reaction in this metabolism, and if every such deletion resulted in non-viability, we declared the metabolism to be minimal. The computer program required for generating viable potential metabolisms by random reaction deletion is available in Additional file G1 and G2 can be connected to one another through viability-preserving reaction swaps, we used the following heuristic approach. It does not rely on reaction swaps of arbitrary reactions, which we found to be too inefficient, but takes advantage of existing reactions in the two genotypes to accelerate the process. It defines a “walker” genotype G1 and alters it through multiple random steps (reaction swaps) that approach the other, “target” genotype G2. Before starting this walk, we established two lists of reactions L1 and L2. L1 contained all reactions in G1 that were not contained in G2. In this list we placed reactions non-essential in G1 first (and in random order), followed by reactions essential in G1 . Conversely, L2 consisted of arbitrarily ordered essential reactions in G2, followed by arbitrarily ordered reactions nonessential in G2.To find out whether two genotypes G1 a reaction from L2 , and (ii) deleting from G1 a reaction listed in L1. Subsequent steps used subsequent reactions in each list for addition and deletion.Each step in the random walk consisted of two parts, i.e., (i) adding to G1 until all essential reactions from G2 in list L2 had been added to G1, and continued from there on to adding nonessential reactions from L2. During part (ii) of any given step, if none of the remaining reactions in the list could be deleted from walker G1 without losing viability, we reverted the last reaction addition, and chose instead a reaction at random from the universe as a candidate for addition. Before adding it, we ensured that the chosen reaction shared all of its substrates and products with other reactions in the random walker. If the product of the reaction was not shared with another reaction, we checked if it could be secreted by a transport reaction. A candidate reaction that did not fulfill both criteria would be disconnected from the rest of metabolism, could therefore not possibly contribute to viability [As this walk through genotype space progressed, we continued adding reactions to iability , and we G1, perhaps from one of several alternative metabolic pathways. Second, they reduce the chances of yielding an unviable genotype after a reaction deletion. However, the probability that the deletion of a reaction from walker G1 can render it unviable increases with the number of reaction swaps, because past steps may have rendered previously nonessential reactions essential. We therefore also needed to use FBA after each deletion to ensure that G1 retained viability after a reaction deletion.The two parts of each step ensure, first, that essential reactions from the target are preferentially added to the walker, thus increasing the likelihood of adding “useful” reactions to G2, or until we had performed 5000 attempted swaps. In the latter case, we declared G1 and G2 disconnected. We note that this is no proof of disconnectedness, as some path may exist that this procedure cannot find. However, in practice, all our attempts to connect genotype pairs in this way were successful.We continued this guided random walk for as many swaps as needed to reach the target The computer program required for checking connectedness between a pair of metabolic genotypes using the above procedure is available as Additional file n reactions, then there are N-n reactions that are not part of the focal metabolism, where N is the total number of reactions a metabolism could possibly have. One can thus obtain a neighbor of the focal metabolism by deleting one of its n reactions and simultaneously adding one of the N-n reaction from the universe of reactions. Any metabolism therefore has n × (N-n) possible neighbors. To identify the viable neighbors of a minimal metabolism, we generated all possible n × (N-n) neighbors, and used FBA to determine their viability on glucose. .Two metabolisms are adjacent or neighbors of each other with respect to a reaction swap if they differ by one such swap. If the focal metabolism contains The computer program required for computing the viable neighbors of a metabolic genotype is available as Additional file http://www.oaslab.com/Drawing_funnels.html).We used MATLAB (Mathworks Inc.) for all numerical analysis. Genotype space visualization was generated using the script available at  can be represented as a binary vector of length N, with n entries equal to one and all others equal to zero. The reactions that are present in the above hypothetical genotype are shown in black and the reactions that are absent are shown in grey.Click here for fileNumber of potential metabolisms viable on glucose with respect to metabolism size.Click here for fileNumber of potential metabolisms viable on all ten carbon sources as a function of metabolism size.Click here for fileAn example of a minimal metabolism viable on glucose. The figure shows an example of a minimal metabolism of size 23, which is also one of the smallest metabolisms viable on glucose. All 13 biomass precursors are framed with solid rectangles. Only important transport reactions and cofactors are shown. Enzymes catalyzing each of the reactions are shown in uppercase italic typeface. Abbreviations are spelled out in Additional file Click here for fileNumber of metabolisms viable on all ten carbon sources as a function of metabolism size.Click here for fileSupplementary results.Click here for fileConnectedness of genotype networks containing viable genotypes of n = 28, 29, and 30 reactions. The figure shows the connectivity of genotype networks for reactions in central carbon metabolisms, as a function of size n. Each circle corresponds to a connected component, and the number in each circle corresponds to the number of genotypes in this component. The components at size 28 were obtained by full enumeration, but for larger sizes such an approach is not feasible. Instead one has to use a form of recursive evaluation that we illustrate here for two larger sizes. Panel (A) shows the two disconnected components in the network corresponding to size 28, one containing 434234 genotypes, and the other component containing just 4 genotypes. The addition of one reaction to these 4 genotypes results in 88 genotypes of size 29, which must be connected (see main text). (B) Aside from these 88 connected genotypes, there are also 28 minimal genotypes of size 29 (red circles). We verified computationally that both groups of genotypes (88 and 29) were connected using breadth-first search and found that they form a single component of 116 genotypes. We were also able to demonstrate that this component is connected to the 1773853 connected genotypes that are parents of the large component at size n = 28 (panel A). The two thus form a connected genotype network of 1773969 metabolisms of size 29. Panel (C) shows that adding one reaction to these genotypes results in 5900563 connected genotypes at size 30. In addition, 15 new minimal metabolisms (red circles) come into being at size 30. We found that they were connected to the remaining 5900563 genotypes, thus forming a single connected network comprising 5900578 genotypes.Click here for fileAn example of essential pathways that result in fragmentation of genotype space. The figure shows the two essential pathways (green and blue) in a pair of genotypes belonging to the two disconnected components in the genotype network of potential metabolisms with n = 25 reactions  in Figure C in Figure s Figure C. The reClick here for fileThe essential computer programs used in this analysis.Click here for file


Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis (13C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape ( Arabidopsis  of cultured developing embryos and derive two genotype specific CB-models. Using 13C-MFA derived flux constraints and the COBRA loopless algorithm . Developing embryos were dissected aseptically about 20 days after flowering and grown in a liquid medium at 20°C under continuous light (50 μmol m−2 s−1). Cultures were kept in tissue culture flasks with vented seal cap , containing 13 ml of liquid medium and four embryos per flask. The liquid growth medium contained 20% (w/v) polyethylene glycol 4000, sucrose (80 mM), glucose (40 mM), Gln (35 mM), and Ala (10 mM). In labeling experiments, sucrose and glucose were partly substituted with 13C-labeled analogs so that [1-13C]- and [U-13C6]- mono and disaccharide hexose moieties are overall at 8.125 and 10 mol% of total hexose moieties, respectively . 13C-labeled substrates were purchased from Omicron Biochemicals (http://www.omicronbio.com/). Inorganic nutrients were as described in Schwender and Ohlrogge . After 10 days of culture, embryos were harvested, rapidly rinsed with NaCl solution (0.33 M), and after determination of fresh weight embryos were frozen in liquid nitrogen and stored at −80°C.Plants of oilseed rape accessions 3170 and 3231 (both maintained by the IPK Genebank) were grown in 30 cm plastic pots in a general purpose soil mix  under constant conditions , methanol/water soluble (polar), and insoluble cell polymer fraction as reported earlier  using the instrument Ultimate 3000  coupled to triple quadrupole mass spectrometer . The amino propyl column  was used with varying eluent gradients, adjusted to either negative or positive scan mode of the MS. Some metabolites were detected via ion chromatography  coupled to the same MS in negative ion mode. All chromatographic conditions, MS settings and ion traces used for multiple reaction monitoring in the various detection modes were exactly as listed in Rolletschek et al. . CompounAmong the 57 analyzed free metabolites quantified by LC/MS, sucrose, Gln, malate and citrate are the most abundant ones, accounting for 80% (w/dw) of the free metabolites. In order to account for fluxes into these four major free metabolites, the weight of the methanol/water soluble fraction was divided among those metabolites.13C-labeling signatures were measured by mass spectrometric analysis as outlined before . Particular amino acids (derived from hydrolysis of cell pellet fraction), glycerol and fatty acids (derived from the lipid fraction), and glucose (derived from starch in cell pellet fraction) were analyzed . With a 90% confidence level, χ2-value should be less than 108.65  to pass the statistical test. From the 500 random start optimizations, 28 and 43 solutions had a sum of squared residuals smaller than the χ2-critical value for genotype 3231 and 3170, respectively. For each genotype, the final flux estimates were derived by averaging the 10 best fit flux sets. Then statistical uncertainty in the best fit flux values . Two of them  were relatively close to zero and were therefore defined as knock-out constraints in bna572+ (Table 2).The resulting flux distributions for genotypes 3231 and 3170 were used to constrain the flux space in the large scale stoichiometric metabolic model (bna572+) by transferring 9 out of 13 possible degrees of freedom. In the following we shortly summarize the necessary considerations about the definition of flux states, precision of flux estimates and the treatment of possible incompatibilities between the models based on network stoichiometries. Table es Table . Twenty 13C-model to bna572+ can cause unbalanced flux states in bna572+, in particular if absolute flux values are to be transferred. For example, in the 13C-model the uptakes of Ala and Gln are not derived based on tracing nitrogen, but based on unlabeled amino acid substrates isotopically diluting intermediates derived from 13C-labeled sugar substrates. While nitrogen is not balanced in the 13C-model, it is comprehensively balanced in bna572+. By transfer of Ala and Gln uptake rates from the 13C-model even very small numerical deviations from the nitrogen balance would render the bna572+ stochiometry non-solvable. As shown by this example, transfer of absolute net fluxes was avoided by formulation of flux states in the 13C-model by using flux ratios  are detailed in Table In Table Arabidopsis genes encoding for central metabolism enzymes to B. napus orthologs, we obtained predicted protein sequences of the diploid progenitor species of B. napus, Brassica rapa, and Brassica oleracea from genomic databases  and a true/false presence/absence call for gene expressed based on expression profiling in developing B. napus genotypes 3170 and 3231 were generated from the Systems Biology Markup Language (SBML) encoding of bna572+ . The COBRA toolbox 2.0.5 , libSBML (version libSBML-5.8.0-libxml2-x86) was used for metabolic modeling. We include a test script (File S3) for simulation in MATLAB using COBRA. The reader is referred to a protocol  and DNA (dna[u]) contents (0.1% w/w each), nucleic acid composition, and amino acid composition were modeled after bna572  were used for metabolic simulation. Flux variability analysis (FVA), a two level optimization, was used to quantify minimum and maximum values for all reactions. Minimization of the molar sum of all substrate uptakes and light flux has been introduced before as the primary linear objective applicable to various simulated substrate combinations . In secondary optimizations solutions that gave 100% of the optimal value were obtained. The COBRA setting for tolerance of optimal value accuracy (the COBRA variable CBT_LP_PARAMS.objTol) was applied globally at 10−10 μmol/h. FVA was implemented by setting the “allowLoops” option of the “fluxVariability” function to “false.” To improve the performance of loopless FVA, it was performed only on loop reactions. Loopless FVA sometimes took longer than usual , and the solver logged the message “Consider using CPLEX node files to reduce memory usage.” Minimum and maximum fluxes returned by COBRA's loopless FVA algorithm had to be adjusted because “fluxVariability” underestimates them by a factor of 1000 for some unknown reason . For some reason the maximum flux of reactions #552–553 for flux ratio-constrained 3170 was sensitive to CBT_LP_PARAMS.objTol; for these optimizations the tolerance was increased to 10−9 μmol/h.The COBRA toolbox 2.0.5 , respectively.The variability type . Loops were identified by computing type III extreme pathways using the Expa toolbox  structured knowledgebases , resulting in 762 bidirectional best hits. Ninety five percentage of these alignments had >80% identity in amino acid residues. Using TargetP (version 1.1), localization prediction was identical between tentative orthologs in 88% of all cases and for about 5% the difference in prediction was between mitochondrial and plastidic localization. We conclude that proteins encoding for central metabolism are highly conserved between B. napus and A. thaliana not only on a protein sequence level but also with regards to subcellular localization.The use of A. thaliana genome only plastid isoforms of transketolase and transaldolase are known  has been localized to the chloroplast based on in vivo protein tagging (YFP) . All of them include conserved transit peptide sequences that are predicted to localize to the chloroplast (TargetP).Revision of gene associations resulted in 100 of the original reactions in bna572 receiving gene associations, while for four reactions existing gene associations were removed based on compartment specific information. An example of revisions made is the compartmentation of the Pentose Phosphate Pathway (PPP). Bna572 features a complete cytosolic PPP , alpha tocopherol, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. The new subsystems are presented as a COBRA reaction map (File S5).Taking a pathway-by-pathway approach to the reconstruction process, the model was expanded to 669 reactions, 458 metabolites (672 compartmented metabolite pools), and 962 genes Table . Bna572+To assess whether the revision could impact any of our formerly reported simulations, stoichiometry and reaction directionality of bna572 were compared against bna572+. Disregarding the balances of waters and protons (except for proton motive force) and the new biomass equations, the first 572 reactions of bna572+ are identical to bna572. In other words, no changes were made to the stoichiometric and thermodynamic directionality constraints of the first 572 reactions of bna572+.Arabidopsis and Brassica do not synthesize 3′5′-hydroxylated anthocyanins  by reactions 635, 637, 638, 639, 641 Table . LiteratConfidence scores were derived to quantify the level of biological evidence for reactions in the network Tables . The scoBna572+ is under continuous development and the version presented here and being used in simulations is dated February 18, 2014 (see model ID in File S1). Bna572+ is available in COBRA-compatible SBML . Analysis of alternative biosynthetic pathways indicated which routes are predicted to carry flux with respect to the optimal flux space. For the vitamin E subsystem, tyrosine or phenylalanine contribute to the formation of the polar chromanol ring of alpha-tocopherol. For the isoprenoid pathways, the 5-C isoprenoid precursor isopentenyl diphosphate is produced by the non-mevalonate pathway, while the mevalonate pathway is predicted to not be used. Lastly, sinapate is predicted to be synthesized via conventional free phenylpropanoid acid biosynthesis, not the alternative route via shikimate O-hydroxycinnamoyltransferase (HCT) (#661). Since we showed that none of these reactions are blocked Table , our anaArabidopsis gene orthologs reported to be expressed in developing B. napus embryos . Table 13C-MFA network, reactions are derived mainly by lumping successive or parallel reactions that can be represented by a common transition of carbon atoms and by lumping of metabolite pools . From the results of flux parameter fitting for the two genotypes, flux differed significantly between the genotypes for 13 reactions  Table . Two freB. napus accessions 3231 and 3170 differed principally in two components. Accession 3170 is 10% (w/dw) higher in lipid content and 3231 is by 6% higher in starch content , “non-variable”  and “never used” (both bounds are zero) .The biomass composition of the two nt Table . Essentins Table  are simins Table . In addiFA Table . Flux stts Table . Insteadly Table . We alsoly Table . In parted Table . This ves Figure . This agty Table . The 15 B. napus accessions 3231 and 3170 predicted with FVA. The change in predicted flux between the high oil genotype 3170 and 3231 was qualitatively characterized by assigning each reaction a responsiveness type (Table 13C-MFA derived constraints, 351 oil- and 41 starch-responsive reactions were identified (Table To better understand the genotype-specific differences in carbon allocation to embryo biomass during development, we compared the metabolic phenotypes of pe Table , the freed Table .We further characterized the metabolic phenotypes by subsystems Table . Lipid b13C-MFA derived fluxes, together with elimination of unboundedness based on the loopless-COBRA algorithm. However, interpretation in particular of qualitative flux variability types and of flux responsiveness has to be made carefully, bearing in mind that small differences might not be statistically significant. While the 13C-MFA model results in flux estimates with statistical uncertainty measures, the constraint-based approach produces flux estimates without statistics. We address this problem in part by not including in the CB model any flux estimates from MFA that are statistically poorly defined. Yet, the biomass composition data for the two compared B. napus accessions were defined in the CB-model using the commonly used biomass equation - without statistical measures. Between the two accessions 3170 and 3231 there is a very small difference in protein content (Table 13C-Flux analysis, this difference does translate into statistically insignificant differences in amino acid and protein biosynthesis fluxes, while in the CB-model, the same small differences are judged “TAG responsive” by the criteria used to determine quantitative responsiveness. Furthermore, some pattern of pathway usage being observed in response to imposition of 13C-constraints onto genotype specific bna572+ cannot easily be explained. Imposing genotype 3170 derived 13C-constraints resulted in a small scale energetically wasteful futile cycle of fatty acid synthesis and degradation. This is unexpected since the 13C-MFA model does not contain beta-oxidation. Altogether this means that interpretation of the small flux responses should be done with caution and a comprehensive statistical treatment of CB-predictions is warranted.We have demonstrated here how the solution space in the large scale model bna572+ can be further constrained based on experimental data, namely B. napus seed development, bna572 (Hay and Schwender, B. napus genome, our model is referenced to the Arabidopsis genome. Such hybrid models that are indirectly derived by orthology to Arabidopsis are not uncommon, including models of C4 and CAM plants (De Oliveira Dal'molin et al., Arabidopsis genome as a proxy is highly justified given the close relationship between B. napus and Arabidopsis within the Brassicaceae (Al-Shehbaz et al., Arabidopsis genome as a proxy, B. napus hiseq transcriptome sequencing data can be integrated with bna572+.Bna572+ extends the previous predictive model of Arabidopsis which were derived in a top-down manner starting with a set of genome predicted metabolic genes (Table Arabidopsis genome is largely underexplored in terms of transport proteins in primary and secondary metabolism. It appears that in genome-scale Arabidopsis reconstructions a high percentage of reactions is associated with the cytosol (Table Brassicacea community and foreseeable community-driven efforts for Arabidopsis consensus models.Bna572+ is relatively small compared to reconstructions of es Table . Given tol Table . This inol Table . Howeverol Table , which mArabidopsis, leaving the arduous task of model refinement to the reconstructionist. Other platforms have been designed for model building and curation. For example, the rBioNet extension for COBRA offers a convenient graphical user interface for quality control and assurance (Thorleifsson and Thiele, According to published guidelines (Thiele and Palsson, With the COBRA standard, a wealth of methods is now applicable to bna572+ for systems-level analysis and metabolic engineering. For example, gene deletions in the reaction network could be characterized as is commonly done with microbes (Monk and Palsson, Arabidopsis genome scale model (Mintz-Oron et al., Genome-scale models of higher organisms typically represent global metabolic reconstructions. To simulate cell-type or tissue specific metabolism, models are being integrated with information on tissue specific gene expression (Machado and Herrgard, 13C-MFA and CB-modeling. 13C-MFA results in empirical flux data based on the use of 13C-tracers, while CB-modeling is a rather predictive modeling approach. A principal problem commonly encountered for CB-modeling is that of the missing of constraints (Reed, 13C-MFA-derived empirical flux data. In order to improve the predictive power of CB-modeling a variety of approaches have been developed in trying to further narrow down the most likely biologically relevant flux solutions (Park et al., 13C-MFA data. Flux ratio constraints have been used to better predict experimentally determined fluxes (Hay and Schwender, 13C-MFA has been transferred to the large scale model via seven flux ratios and two flux constraints (Table Here we integrated ts Reed,  and the ts Reed,  or by rats Reed, . If objets Table . As a rets Table .in silico biomass component tradeoffs (Schwender and Hay, Another major problem with FVA is the prediction of flux states involving net flux around a closed cycle of internal reactions. In this study, the predictive power of bna572+ was severely challenged by such thermodynamically infeasible loop flux Figure  proved tB. napus accessions which differed mainly in lipid and starch content of in-vitro cultivated embryos. By using constraints derived from the flux estimation by 13C-MFA for two genotype specific FVA models, we expanded on the concept of in silico biomass component tradeoffs (Schwender and Hay, In this study we compared two The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Paenibacillus polymyxa is a facultative anaerobe known for production of hydrolytic enzymes and various important biofuel molecules. Despite its wide industrial use and the availability of its genome sequence, very little is known about metabolic pathways operative in the Paenibacillus system. Here, we report metabolic insights of an insect gut symbiont, Paenibacillus polymyxa ICGEB2008, and reveal pathways playing an important role in the production of 2,3-butanediol and ethanol.P. polymyxa ICGEB2008 with 133 metabolites and 158 reactions. Flux balance analysis was employed to investigate the importance of redox balance in ICGEB2008. This led to the detection of the Bifid shunt, a pathway previously not described in Paenibacillus, which can uncouple the production of ATP from the generation of reducing equivalents. Using a combined experimental and modeling approach, we further studied pathways involved in 2,3-butanediol and ethanol production and also demonstrated the production of hydrogen by the organism. We could further show that the nitrogen source is critical for metabolite production by Paenibacillus, and correctly quantify the influence on the by-product metabolite profile of ICGEB2008. Both simulations and experiments showed that metabolic flux is diverted from ethanol to acetate production when an oxidized nitrogen source is utilized.We developed a metabolic network model of P. polymyxa ICGEB2008 and could show the presence of the Bifid shunt and explain its role in ICGEB2008. An in-depth study has been performed to understand the metabolic pathways involved in ethanol, 2,3-butanediol and hydrogen production, which can be utilized as a basis for further metabolic engineering efforts to improve the efficiency of biofuel production by this P. polymyxa strain.We have created a predictive model of the central carbon metabolism of The online version of this article (doi:10.1186/s13068-015-0338-4) contains supplementary material, which is available to authorized users. Paenibacillus polymyxa are highly diverse and have been described in the literature for various important symbiotic relationships. It was found to be associated with plants where it helps in N2 fixation [P. polymyxa is a non-pathogenic strain for the production of industrially valuable 2,3-butanediol [P. polymyxa strains have been deposited at NCBI. However, the genome annotation is far from complete and, to our knowledge, metabolic reconstructions have not been performed for any Paenibacillus sp.The ecological roles of fixation  and secrfixation . P. polytanediol , which iEscherichia coli [Saccharomyces cerevisiae [P. polymyxa, very little is known about its metabolism [The investigation of metabolic pathways allows in-depth insight into the molecular mechanisms of a particular organism. The improved understanding of the architecture of cellular metabolism and the enormous amount of genomic data available today can help draw the entire metabolic map of a cell and redesign it by rational and directed metabolic engineering. The detailed biosynthetic pathways have been established for several model microorganisms such as hia coli , 6 and Srevisiae , 8. But,tabolism , 10.Paenibacillus sp. ICGEB2008 (referred to as ICGEB2008) [In this report, the metabolic capabilities of GEB2008)  have beeGEB2008) , 13. TheGEB2008) . In combGEB2008) , 16, we P. polymyxa as a non-pathogenic, non-obligatory host for 2,3-butanediol production [als, aldB, bdh), ethanol  and acetic acid  production , generates acetyl phosphate and erythrose 4-phosphate. The final products of the fermentation route are formed by the sequential action of the enzymes: transaldolase (EC 2.2.1.2), transketolase (EC 2.2.1.1.) and xylulose 5-phosphate phosphoketolase (EC 4.1.2.9) which generates glyceraldehyde 3-phosphate, that enters the Embden–Meyerhof–Parnas pathway , and ace.2.22, geThe simultaneous presence of these two pathways introduces a degree of flexibility into metabolism by allowing uncoupling of the production of energy and redox equivalents. As discussed below, this regulatory potential is critically important for anaerobic growth, where electron sinks are not abundant.Due to its capability of producing a number of enzymes hydrolyzing diverse polymeric, oligomeric or dimeric carbohydrates , 13, ICGMathematical models are useful to understand the biochemical capabilities of an organism and to make predictions about the impact of genetic and/or environmental perturbations on metabolic fluxes and growth. Clearly, such understanding supports the development of targeted strategies to improve the yield of the valuable product 2,3-butanediol in biotechnological applications. Therefore, we constructed a stoichiometric model of ICGEB2008 focussing on carbon metabolism. From the genome sequence and the MetaCyc database [The production of 2,3-butanediol and ethanol was observed majorly under anaerobic conditions  reaction, which mimics the additional ATP maintenance demand, and predict growth and by-product formation and comparing the predicted values with experimentally determined quantities. In Fig.  strains , which w2, a byproduct with extreme oxidation state, which was not experimentally measured. The predicted value of CO2 formation under the assumption of biomass maximization was 1.96 mM/mM glucose. To study whether this value was potentially overestimated by the model, we systematically fixed the CO2 formation to values between 1.5 and 2 mM/mM glucose  to explain and predict the by-product formation under different growth conditions. Our model successfully predicted the influence of the redox balance in ICGEB2008. Using a combinatorial experimental and modeling approach, we have shown the influence of the redox state of the nitrogen source on by-product yields and composition. We predicted a diversion of metabolic flux from ethanol to acetate in more oxidizing environments, which we experimentally confirmed by growing ICGEB2008 on nitrate. The model further supported the existence of a formate hydrogen lyase as redox valve. By the detection of hydrogen in the overhead, we could further support this finding experimentally. This finding could serve as a starting point for further development of the organism as a potential hydrogen producer as an additional useable by-product. We have also elucidated the presence of the Bifid shunt in the bacterium as a bypass for the glycolytic pathway, which is active under anaerobic growth without the presence of an alternative electron acceptor such as nitrate.The diverse metabolic capabilities of Paenibacillus polymyxa ICGEB2008 and investigated the metabolic pathways operative in this strain. We have demonstrated the potential of metabolic modeling to simulate the capabilities of ICGEB2008 and elucidated the Bifid shunt in P. polymyxa ICGEB2008. In addition, we have shown that the redox state of the nitrogen source is critical for the by-product profile. The present study can serve as a basis for further metabolic engineering efforts to improve the efficiency of biofuel production by this P. polymyxa strain.In summary, we have developed a metabolic model for the industrially relevant strain, 600 reached 0.8 and the culture was grown further for 48 h. The products secreted were quantified using HPLC. Substrate specificity was established by growing ICGEB2008 anaerobically in 125 ml serum bottles containing media with 2.5 g/l of different carbon sources. The carbon source utilization was estimated using HPLC. The values obtained for cell biomass, substrate, utilization and product synthesis were used for calculation of biomass and product yields (mmol/mmol substrate). For calculating biomass yield, a molecular formula CH1.9O0.5N0.2 of cells was used with an average molecular weight of 24.7 [For biomass composition and product analysis, ICGEB2008 was grown in minimal medium  containiDifferent components of cells were analyzed for their composition , 27. Celhttp://rast.nmpdr.org/) [−05 and their annotations have been provided in the Additional file https://img.jgi.doe.gov).The initial annotation of coding sequences of ICGEB2008 was achieved using the automated server RAST (dr.org/) , which i3COO)2·H20 in 4 % ammonia, 0.1 ml of 1 % MnSO4·4H20 in 5 % acetic acid, and about 0.1 g of finely powdered zinc were added. This mixture was shaken vigorously for 1 min and filtered; 2 ml of the filtrate was placed on ice and 0.5 ml of 1 % sulfanilamide in 5 N HCl was added. The sample was incubated on ice for 15 min followed by the addition of 0.5 ml of 0.02 % N-(1-naphthyl)-ethylenediamine solution and incubation at room temperature for 30 min. After incubation, 2 ml of water was added and absorbance was measured at 540 nm in spectrophotometer.Ammonium ion production was estimated as follows. To 1.5 ml culture supernatant, 50 μl manganous salt solution, 1 ml alkaline phenol reagent and 0.5 ml hypochlorite solution were added. The reaction mix was boiled for 5 min and color development was monitored at 625 nm . ResiduaFructose-6-phosphate phosphoketolase was assayed based on Tannock’s protocol  as follocells anaerobically in minimal media in the sealed serum bottle for 36 h and headspace gases were analyzed by GC (Carboxen-1010 Plot column in Perkin Elmer’s Clarus 500GC) for hydrogen estimation.Hydrogen was estimated by growing Paenibacillus and were only present in single reactions.The metabolic model of ICGEB2008  was recoWe introduced batch reactions for biosynthesis of the 20 proteinogenic amino acids and all nucleotides (four ribonucleic acids and four deoxyribonucleic acids). Finally, we added two batch reactions for lipid biosynthesis—one forming palmitic acid and another to convert three palmitic acids and glycerol-phosphate into one lipid. We chose this simplification over the actual lipid metabolism because the general requirements for energy and reductant are similar enough for our study between the different lipids. To create these batch reactions, the biosynthesis pathways from MetaCyc were combined into one reaction by adding up all substrates and products.+, water, nitrite and ATP.The current model is restricted to anaerobic conditions and, therefore, works under a strict redox balance. However, we have included a batch electron transfer chain using nitrate as substrate. This reaction mainly converts ADP, Pi, nitrate and NADH to NADMetabolic models allow us to investigate the metabolism of the modelled organisms. A robust method often used in this kind of studies is flux balance analysis (FBA) . The hugN is the stoichiometric matrix of all reactions and v is the flux vector representing the reaction fluxes. aj and bj are the lower and upper bounds for each flux vj in v. For irreversible reactions, aj is set to 0, while as default no other bounds are applied. We performed several scans to investigate maintenance energy requirements and redox state. These scans are based on the common FBA formulation. Scanning is then performed by adding a constraint (aj = bj) in Eq. , the biomass flux was fixed at the optimal value during minimization. All modeling was performed using ScrumPy. CPLEX was used as solver for FBA problems.The general formulation of the linear problem solved in FBA is:https://github.com/QTB-HHU/Paenibacillus. A list of all the reactions and accession numbers for all the enzymes carrying out these reactions  is provided as Additional file The model and the source code for model analysis is available at


Drug side effects cause a significant clinical and economic burden. However, mechanisms of drug action underlying side effect pathogenesis remain largely unknown. Here, we integrate pharmacogenomic and clinical data with a human metabolic network and find that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects. We show such dysregulated metabolic pathways contain genes with sequence variants affecting side effect incidence, play established roles in pathophysiology, have significantly altered activity in corresponding diseases, are susceptible to metabolic inhibitors and are effective targets for therapeutic nutrient supplementation. Our results indicate that metabolic dysregulation represents a common mechanism underlying side effect pathogenesis that is distinct from the role of metabolism in drug clearance. We suggest that elucidating the relationships between the cellular response to drugs, genetic variation of patients and cell metabolism may help managing side effects by personalizing drug prescriptions and nutritional intervention strategies. Adverse drug reactions are an important clinical problem. Here the authors combine information about drug-induced gene expression changes and genetic variability of patients with a genome-scale metabolic model to identify drug-induced changes in cellular metabolism that may be linked to drug side effects. Adverse drug reactions, commonly known as side effects, are thought to be responsible for as much as 11% of hospital admissions1in vitro drug-treated gene expression profiles to predict clinical drug effectiveness17in vitro data may contain features that are clinically conserved. However, demonstrating the relevance of in vitro drug response features to clinical side effect pathogenesis presents a significant challenge, due largely to the lack of suitable validating data sets and difficulty of clinical experimentation.Recent literature suggests that altered gene expression induced by drug treatment may be one mechanism by which drugs induce systemic off-target effects121314in vitro drug treatment data to identify candidate side effect-linked features and a large collection of historical clinical and disease model data as a source of validation . MetChange is a constraint-based modellingWe first identified drug-induced metabolic gene expression changes within 6,040 gene expression profiles in the Connectivity Map (CMap) data set, representing three human cell lines exposed to 1,221 drug compoundsS. cerevisiae was analysed25S. cerevisiae, iMM904 . Second, it was found that known metabolic drug targets as found in the DrugBank database28P<1.65 × 10−10 for the highest scoring bin). Third, MetChange scores were found to be able to predict drug–protein literature co-associations within the PubMed database in a statistically significant manner (non-parametric perturbation P<0.01).To further validate the metabolic perturbations predicted by the MetChange method using the CMap data set, we performed a number of high-throughput computational analyses comparing MetChange perturbations with drug response properties . First, in vitro studies using these drugs.Finally, we validated perturbation predictions in targeted experimental measurements in MCF-7 cell culture under treatment by the drugs metformin, haloperidol and genistein. These drugs were chosen because of previous evidence suggesting significant metabolic perturbation by these drugs. Measured metabolites were chosen to have broad coverage of pathways and target highly perturbed pathways as predicted by MetChange. Drug concentrations were chosen based on previous First, treatment with the antipsychotic haloperidol revealed decreased uptake of vitamin B6, consistent with the calculated decrease in the B6 processing pathway . Second,disease-linked drug-changed (termed DISLoDGED) metabolic pathways were identified with this analysis. These pathways are MetChange calculated metabolite production pathways that are over-represented in perturbations by drugs with particular side effects. DISLoDGED pathways are potential side effect to pathway relationships, hypothesizing that drug-induced perturbations away from metabolic homeostasis are involved in side effect pathogenesis. The calculated DISLoDGED pathways thus comprise a large set of omics-driven hypotheses of side effect pathogenic mechanisms (P=0.055), whereas upregulated pathways were not predictive (permutation P=0.39). We then sought to determine whether the metabolic expression changes induced by drug treatment were involved in the pathogenesis underlying drug side effects.Using MetChange scores generated from the CMap data set, we then identified the drug-induced metabolic gene expression changes that are most conserved among drugs with particular clinically described side effects. We used a machine learning approach to select discriminating metabolite production pathway perturbations from the set of MetChange-calculated pathway changes based upon side effect frequency data reported in the Side Effect Resource (SIDER) databasechanisms . These achanisms . Downregin vivo links found within the literature between metabolic function and disease, called the Metabolism Disease Database (MDDB), that consists of curated primary literature and existing databases (hosted at sbrg.ucsd.edu/mddb). Data collected through manual curation of the literature included disease-linked: (i) metabolic gene variants, (ii) physiological system-specific metabolism, (iii) metabolic pathways, (iv) chemical inhibitors of metabolism and (v) nutrient deficiencies and supplements. Data aggregated from existing databases included metabolic gene variants affecting disease incidences from a large genome-wide association study (GWAS) database28We assessed the relevance of DISLoDGED metabolic pathways to side effect pathogenesis through the use of a large body of clinical, biochemical and genetic literature on metabolism–disease relationships. We constructed a database of P-value 2.2 × 10−11, see We first examined whether DISLoDGED metabolic pathways contain genes with variants that alter clinical side effect susceptibility, according to the data in MDDB . Causal in vivo, demonstrating that the in vitro-derived DISLoDGED pathways are similarly perturbed in vivo  and inositol metabolism (one-tailed hypergeometric P=0.02), as well as a depletion of variants in central carbon metabolism (one-tailed hypergeometric P=0.035). We found that DISLoDGED pathways showed similar results, including an enrichment of downregulated DISLoDGED pathways in inositol metabolism (P=0.018) mirroring the enrichment found in disease-linked genetic variants, as well as enrichment of DISLoDGED pathways in non-central metabolism , whereas upregulated DISLoDGED pathways were depleted in disease-associated nutrient deficiencies  inhibitors targeted at downregulated DISLoDGED pathways reproducing the clinical side effect and (ii) inhibitors targeted at upregulated DISLoDGED pathways treating the clinical disease. We found that metabolic inhibitors targeting downregulated DISLoDGED pathways specifically were significantly more likely to cause corresponding side effects , although the relationships did not reach statistical significance for given sample sizes available.Finally, we compared calculated DISLoDGED pathways grouped by nearest nutrient with clinical therapeutic nutrient supplementation data . We sougThe systems biology-based workflow developed in this study predicts side effect-linked dysregulated metabolic pathways (termed DISLoDGED pathways) from drug treatment of human cells in culture. We contextualize numerous independent data types that together support a key role of drug-induced non-pharmacokinetic metabolic dysregulation in side effect pathogenesis. Through the construction of a comprehensive resource on metabolic involvement in disease, we corroborate the predictions made using five independent clinical, genetic and biochemical bodies of the literature. These results provide understanding of the mechanisms underlying side effect pathogenesis, which have remained largely opaque despite recent progress identifying causal protein-binding events and genetic susceptibility factors.in vivo data. The use of systems biology approaches to study drug side effects has become an active field in recent years. Previous studies have predicted drug–protein-binding events responsible for side effects912The work presented relied upon the development of a systems biology workflow to identify side effect-linked features and validate the relevance of these features using historical in vivo gene expression data, may be limited. Owing to the use of in vitro data, corroboration against in vivo data appears critical. Further deployment of the presented workflow may hinge upon expansion of curated disease data necessary to corroborate the clinical relevance of in vitro drug treatment features. Furthermore, the definition of pathways used to integrate disparate data types has yet to be concretely established and may be an area for further workflow optimization.The workflow presented is highly dependent upon large amounts of gene expression data obtained under treatment by diverse drugs to filter out perturbations due to factors other than common side effects. Normalization of data across multiple studies and platforms is a substantial issue. Thus, data generated within a single study is ideal, but such large studies are rare. For this reason, the potential to extend the workflow to new data types, for example, P-values of a Kolmogorov–Smirnov test for the likelihood that the cumulative distribution of expressions of genes in each pathway have not changed between conditions. MetChange defines a different production pathway for each metabolite in the network, based on calculation of functional states of the metabolic network, and scores for each metabolite how gene expression along this production pathway has changed between conditions. As a result, MetChange has the potential to give finer resolution results, as its results are defined at the level of individual metabolite scores rather than pathway scores. However, the overall performance of the methods are difficult compared due to the fundamental difference in resolution of outputs.Owing to the ubiquity of gene set enrichment analysis (GSEA)In the comparison between MetChange and comparable methods , it is aP-values from the CMap data used in this study. Briefly, between cell-specific Recon 2 models, error was between 10 and 25% across replicates when given the same metabolite uptake constraints. The largest difference observed was by enforcing measured metabolite uptake constraints. Differences in MetChange scores between models constrained and unconstrained by metabolite uptakes were around 70%, indicating that significantly constraining the flux state of the model alters MetChange scores more so than topological differences. Thus, further improvements in prediction accuracy might be seen by accounting for the baseline metabolic differences between cell lines.There are at least two obvious potential improvements that could be made to the workflow used in this work. First, as the cell lines within the CMap are within the NCI60 cell line panel, and expression, growth and most recently exometabolomicOne interesting outcome of the work was that, although previously identified genetic susceptibility factors in the literature are dominated by genes involved in pharmacokinetics, we observe overlap of drug-induced metabolic changes with nine genes that affect pharmacodynamics. This may suggest that such non-pharmacokinetic genes may play a larger role in side effect pathogenesis than currently appreciated. We did, however, see some alteration of pharmacokinetic genes in a few cases as well . In addiA natural question that may arise is whether certain pathways or side effect disease classes are more successfully predicted by the method used in this work than others. A sensitivity analysis shows that the method appears to be fairly robust in being able to predict DISLoDGED pathways in various areas of the metabolic network and across disease classes. Restricting to at least five pairs investigated for both the down-regulated DISLoDGED pathways and random pairs, deficiencies of nine nutrients  had disease associations better predicted by down-regulated DISLoDGED pathways compared with random, whereas one (choline) did not. Similarly, in side effects with at least two pairs investigated in both random and real, seven side effect/nutrient deficiency relationships were better predicted by down-regulated DISLoDGED pathways , whereas only two were better predicted by random nutrients (ecchymosis and tendonitis). These types of sensitivity analyses on predictive capability for particular nutrients and side effects are vulnerable to error because of small sample size but corroborate overall results that dysregulated DISLoDGED pathways are predictive of metabolic pathways associated with corresponding pathologies.in vivo side effect susceptibility within in vitro data suggests that early-stage drug screening to identify and manage side effect risk may become possible, analogous to other ‘disease in a dish' effortsin vitro data, it may become easier to identify susceptibility factors for more rare disease classes as well. This workflow is currently limited to cases in which gene expression alteration underlies side effect pathogenesis, which is an undefined subset of all side effects. The results presented show the utility of integrating large, standardized data sets, such as the CMap, with clinical data types such as side effect incidence, genetic studies and disease–nutrient associations, in the context of a highly annotated network with clear functional connections. Such integration of disparate data sources is a key challenge in many areas of the life sciences today.Although challenges remain, the ability to observe perturbations important to As described schematically in P-values to their corresponding reactions based on the Boolean gene–protein–reaction associations as was previously describedP-value of 0. Metabolic exchange was set to an exchange value of −1 for DMEM (MCF-7) or RPMI (PC-3 and HL-60) media metabolites. In cases where no metabolite production was possible with open constraints, the metabolite was removed from further analysis. For the cases of in vitro experimental validation under genistein treatment, for which data was generated for the fraction of metabolites generated from glucose, the MetChange algorithm was run using glucose as the sole carbon source to enable direct comparison with the data.Gene expression data for the AffyMetrix HT Human Genome U133A platform were obtained from the CMap databaseThe Metabolite-Centered Hotspots of Altered Network Gene Expression (MetChange) algorithm was used to analyse differential quantitative gene expression profiles in the context of a genome-scale metabolic network59The MetChange algorithm defines a consistency metric of an expression profile with optimally producing each metabolite in the metabolic network. For each metabolite, a sink reaction is created and flux through the sink reaction is maximized using flux balance analysis:S is the stoichiometric matrix, v is the reaction flux vector, α and β are vectors for the lower and upper bounds of the reactions and cv,i corresponds to the imposed reaction objective for each ith sink reaction. This results in a set of optimal production fluxes for each network metabolite.where P-value from mapped expression data to solve a second optimization problem. To obtain metabolite production ‘consistency' scores, xi, for each ith metabolite, scores were generated by setting the lower bound of the metabolite sink reaction to its maximal production flux, v,imax, and minimizing the inner product of the reaction detection P-values:Each reaction is then weighted by a detection P-values p were stored in a weighting vector cp, and each vector component cp,i=pi mapped to each ith reaction. This optimization is the GIMME algorithm-like step of the MetChange algorithm. Each ith component in cp serve as a cue for whether a reaction is detected or absent . Hence, this second optimization determines: (i) a flux distribution which maximizes the production of a metabolite and (ii) generates a weighted flux distribution score defining consistency of the production of that metabolite with expression data. These scores cannot be directly compared across metabolites because of the fact that each metabolite has a different network flux state at maximal production. Thus, we compared metabolite production consistency scores between treated and control samples with a standardized score:where mapped detection A MATLAB implementation of the MetChange algorithm is provided in the Supplementary Software File.S. cerevisiae were mapped to a genome-scale yeast metabolic reconstruction, iMM904 . The scores were mapped to their respective reactions according to the gene–reaction association in the metabolic network. We then assessed whether absolute changes in expression levels are predictive of the magnitude of metabolite change by adding the standardized reaction scores, weighted by the absolute stoichiometric coefficients. We then assessed whether higher gene expression for a reaction was indicative of a higher product concentration and lower reactant concentration. The mapped reaction score was added to the score for all metabolites in the reaction, multiplied by the signed stoichiometric coefficient for each metabolite.P-values from the CMap database build 02 . Metabolite common names were obtained from the Recon 1 network reconstructionText querying on side effect–metabolite, protein–metabolite and drug–protein co-occurrences were performed using the Entrez Programming Utilities (NCBI) using a simple ‘AND' specification. Side effects were obtained from the SIDER databaseTo calculate network distance from metabolites, the DrugBank database28MetChange scores were compared against expression data, randomized data and reporter metabolite analysis results. Data and scores were pooled into bins based on their standard scores. For each bin, the average of the median network distance of metabolites to metabolites of known drug-targeted enzymes was calculated for comparison. Metabolites predicted from the expression data set were determined by taking the highest expression change among reactions involving the metabolite and binning accordingly. The random data set was generated by permuting the expression data set 1,000 times and calculating the average bin value across all 1,000 sets. The reporter metabolite data set was generated as described in the original publicationDrug–protein and protein–metabolite literature co-associations were found as described. Associations were binary based on the presence or absence of known literature association. A receiver operating characteristic (ROC) curve was generated for the ability of MetChange scores to indirectly predict drug–protein association. MetChange scores for all three cell lines (not averaged) were used together. At increasing thresholds from 0 to effectively infinity, metabolites with absolute MetChange scores greater than the threshold were scored as significantly changed for each drug. Proteins associated with perturbed metabolites were then determined using the protein–metabolite literature co-associations. These proteins were used as guesses for true drug–protein literature associations for the drug corresponding to the sample, and the true positive rate and false positive rate were calculated. Varying the threshold from 0 to effectively infinity then generates the ROC curve. To assess statistical significance of the resulting AUC, 1,000 permutations of MetChange scores were analysed in the same way and a non-parametric rank test was conducted on the resulting AUCs.Drug side effects were taken from SIDER databaseA genetic algorithm was then implemented, termed SiderFinder . The matGenetic algorithm creation, mutation and crossover parameters were used as implemented in the OptGene function of the COBRA Toolbox 2.0 ref. . The genStatistical analysis of literature co-associations of side effects with side effect metabolite signatures was performed with (i) a non-parametric permutation test on the drug side effect metabolite signatures against 1,000 permutations of the signatures for the AUC of predicting presence/absence of literature side effect/metabolite co-association in PubMed abstracts and (ii) a hypergeometric test for the enrichment of literature association among side effect/metabolite pairs in predicted signatures.To enable statistical analysis of predicted DISLoDGED pathways, disease–nutrient pairs were randomly selected from observed downregulations, observed upregulations and random associations chosen by resampling the former two lists through bootstrapping. These disease–nutrient pairs were then evaluated for existence of established relationships using manual literature searches while blind to the origin of the pair to prevent investigator bias. The list of collected metabolism–disease relationships is not yet comprehensive because of the scope of the effort, but instead relationships were investigated in a targeted manner.Data collection for the database covered literature up to and including May 2013.We curated a database of 1,394 distinct GWAS publicationsα=0.1. Enrichment where there was only a single disease within the class was discarded, as were in conflicting cases (in which a nutrient was downregulated in certain diseases in the class and upregulated in others).To assess whether common metabolic perturbations were observed among related diseases, we manually grouped side effects by pathophysiological disease class. Enrichment was assessed with the hypergeometric test, at a significance level of We attempted to identify all cases of genetic basis for side effect incidence reported in the literature, including GWAS and targeted genetic studies. To be eligible for comparison, we required that the side effect be a near or exact match and the metabolic pathway of the susceptibility gene be within the scope of our model. For example, immune-related genes were excluded due to nonspecific metabolic association, whereas G protein–coupled receptors known be regulated by particular metabolic pathways were included. We generally excluded genes related to drug pharmacokinetics, including drug metabolism and transport, as the effects of polymorphisms on susceptibility are generally nonspecific to the pathology. We also required that the pathology be manifested in nucleated cells , as gene expression changes are assumed to be irrelevant to pathologies of enucleated cells. Based on these criteria, well over 20 studies were excluded, whereas 9 genetic susceptibilities spanning 6 side effects were valid for comparison with predictions. We also mention two cases in which pharmacokinetic genes do overlap with conserved side effect-linked gene perturbations, suggesting possible interactions between pharmacokinetic and gene expression effects of drug perturbation. Lists of included and excluded studies are found in To populate MDDB, we searched the literature for associations between the pathology of the side effect and deficiency relationships between the closest nutrient to the metabolic perturbation and the occurrence of the pathology of the side effect. In an automated search, PubMed abstracts were searched for a number of side effect and nutrient synonyms along with a list of deficiency synonyms . StatistIn the manual alteration search, a number of possible nutrient–disease relationships were identified, such as an inhibitor of the metabolic pathway causing the side effect, an inhibitor of the metabolic pathway curing the side effect and so on . Then, sIn the manual deficiency search, deficiency relationships were defined such that the DISLoDGED nutrient pathway could meet any of three possible criteria to be considered a ‘hit.' First, the deficiency of the nutrient could be known to be associated with the occurrence of the side effect pathology. Second, supplementation with the nutrient is known to alleviate the side effect pathology. Third, a physiological dysregulation of the pathway is known to be associated with the side effect pathology. PubMed and Google Scholar were both used for this study. Patents were not accepted as valid references, unless associated with a peer-reviewed publication.To determine whether DISLoDGED pathways are significantly predictive of side effect/nutrient deficiency relationships, we generated a list of ‘random' side effect/nutrient pairs for comparison with DISLoDGED pathway–disease pairs through resampling of the pairs. Kolmogorov–Smirnov tests were used to ensure the distributions were not significantly different between DISLoDGED pathways and random side effect–nutrient pairs in terms of the frequency of occurrence of nutrients, as resampling should guarantee.We then performed the nutrient deficiency literature search in two phases. The first was blinded and the second was an expansion and additional curation of the blinded study. The initial study was blinded to ensure that there was no selection bias in the search process. Random pairs were mixed with DISLoDGED pathways, both upregulations and downregulations, and information on their origin was removed. The literature searches for deficiency relationships were then conducted by investigators not involved in the generation of the distribution and thus were unaware of the treatments or treatment distributions. Statistical tests were only performed on the blinded literature results. The initial blinded list was then expanded to examine additional DISLoDGED pathways and curated to ensure consistent criteria between investigators. Although the curated list is unblinded, we have a greater confidence in this list for research purposes due to the consistent criteria and expanded list of investigated relationships.in vitro experiments in the MCF-7 cell line for three drugs: metformin, genistein and haloperidol.Drug-induced metabolic perturbations calculated using the MetChange algorithm were validated using L-glutamine . 4 × 106 cells were seeded in supplemented MEM medium  into 150 mm dishes. For labelling with [U-13C] glucose , the medium was replaced with supplemented MEM with 2 g l−1 glucose total of which 50% was [U-13C] glucose. Approximately 2.0 × 109 cells were harvested by incubation with trypsin for 5 min at 37 °C  and adjusted for total protein amount . Intracellular polar metabolites were extracted by rapid quenching with 50% methanol at −40 °C; total lipids were extracted using chloroform . The cell extracts were dried by vacuum evaporation. Organic acids were derivatized to form the corresponding oximes and trimethylsilyl derivatives. Acyl-carnitines were derivatized to their corresponding methyl esters. Polar metabolites were dissolved in 99.9% 2H2O with 0.75% 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid . Lipophilic metabolites were dissolved in 2H-chloroform with 1% tetramethylsilane .Human MCF-7 breast carcinoma cells  were maintained in supplemented DMEM media  with 10% (v/v) fetal bovine serum , 1% (v/v) antibiotic/antimycotic solution , 1% (v/v) non-essential amino acids , 1% (v/v) MEM vitamins , 1 mM L-carnitine . Each experiment was performed in three biological repeats.Cells were exposed for a 24 h period to varying drug concentrations (as indicated in their respective citations below) while control cells were exposed to the corresponding DMSO  dilution: metformin , genistein , haloperidol . For acyl-carnitine measurements, the medium was supplemented with 1 mM NMR experiments were performed on a 500-MHz Bruker Avance spectrometer with a 5-mm TXI z-gradient probe  at 298 K. 13C enrichments were determined by one-dimensional 1H NMR difference spectroscopy (13C-coupled spectrum minus the 13C-decoupled spectrum). Two-dimensional indirect-detected 13C, 1H J-resolved HSQC spectra were recorded over an experimental time of 1.5 d per spectrum with 5,120 indirect 13C data points, at 80 p.p.m. 13C sweep width, 40 p.p.m. carrier position, 4,096 direct 1H increments, 3 s recycling delay and 8 scans. Before Fourier transformation, the data were multiplied with a squared sine-bell window function, phase corrected and zero-filled to 8,192 data points, for indirect 13C sampling.m/z=99, formed by collision-induced dissociation.Quantification of organic acids was accomplished by gas chromatography-mass spectrometry (ThermoFisher Trace GC Ultra/DSQ II single quadrupole mass spectrometer). Calibration standards were prepared in water and spiked with stable isotope-labelled internal standards. Detecting the derivatized organic acids was achieved by single ion monitoring of each derivative after gas chromatography separation. Acyl-carnitines were quantificated by LC/MS/MS (Waters Ultra Performance LC/triple quadrupole mass spectrometer) using flow injection analysis with electrospray ionization. Calibration standards of the acyl-carnitines were prepared in bovine serum spiked with stable isotope-labelled internal standards. Parent ion scanning was used to detect parent, acyl-carnitine molecular ions that produced a characteristic acyl-carnitine fragment ion, Pyridoxal 5′-phosphate or vitamin B6 was determined in an enzymatic plasma assay , which we adapted to quantify tissue culture supernatant or intracellular Vitamin B6 content by using PBST buffer with 0.1% Tween-20 .−1 protein  and were compared with acyl-carnitine (C12-C16) levels in pmol mg−1 protein. Organic acids were determined in nmol mg−1 protein.Acyl-carnitine species from 2 to 16 carbons and organic acids of the tricarboxylic acid cycle were analysed by liquid chromatography mass spectrometry and show dose-dependent associations upon metformin treatment in MCF-7 cells. Acetyl-carnitine (C2) values were determined in nmol mgHow to cite this article: Zielinski, D. C. et al. Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis. Nat. Commun. 6:7101 doi: 10.1038/ncomms8101 (2015).Supplementary Table 1, Supplementary Notes 1-4 and Supplementary ReferencesResults of a number of computational analyses conducted in the manuscript, as well as experimental data. First worksheet of the file is a table of contentsTables of the Metabolism Disease Database (MDDB) used for validation of computational predictions in the manuscript. First worksheet of the file is a table of contents.