However, advanced design strategies for acetogens are currently hampered by incomplete knowledge about their physiology and our inability to accurately predict phenotypes. Here we describe the reconstruction of a novel genome-scale model of metabolism and macromolecular synthesis (ME-model) to gain new insights into the biology of the model acetogen Clostridium ljungdahlii. The model represents the first ME-model of a Gram-positive bacterium and captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions, thus significantly broadens the scope and predictability. Use of the model revealed how protein allocation and media composition influence metabolic pathways and energy conservation in acetogens and accurately predicted secretion of multiple fermentation products. Predicting overflow metabolism is of particular interest since it enables new design strategies, e.g. the formation of glycerol, a novel product for C. ljungdahlii, thus broadening the metabolic capability for this model microbe. Furthermore, prediction and experimental validation of changing secretion rates based on different metal availability opens the window into fermentation optimization and provides new knowledge about the proteome utilization and carbon flux in acetogens.The unique capability of acetogens to ferment a broad range of substrates renders them ideal candidates for the biotechnological production of commodity chemicals. In particular the ability to grow with H Clostridium ljungdahlii has been studied intensively for its ability to produce biofuels from sustainable resources, like syngas. We describe a novel genome-scale model of metabolism and gene expression (ME-model) to gain insights into this model acetogen. This first ME-model for a Gram-positive bacterium contains all major metabolic and biosynthetic pathways and calculates accurate proteome allocations under diverse growth conditions, thereby significantly broadening the scope of predictability of metabolic models. Furthermore, the ME-model enables rational medium design for improved production. Our experimental validation implies wide applicability to others strains for rapid improvement of yield and titer in biotechnology-relevant applications.Acetogens are renowned for their potential biotechnological applications. The model acetogen  The Wervation . Energet complex . Models rgy flux –8. WhileSo-called metabolic and gene expression models (ME-models) contain not only metabolic reactions, but represent all major cellular processes like macromolecular synthesis and basic transcriptional regulation, significantly broadening the scope and predictability of microbial systems biology ,10. In MTrace metals, fundamental for all living organisms, are required for catalytic processes essential to energy conservation, metabolism, replication, and maintenance. Yet metals pose a unique challenge for standard computational models as they are neither produced nor consumed biochemically in the model and are generally treated as a lumped sum in the biomass objective function , which pClostridium ljungdahlii ME-model, named iJL965-ME, captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions. Furthermore, the reconstruction includes WLP, with updated cofactors, and its associated mechanisms for energy conservation. The model accurately predicted secretion of acetate, ethanol, and glycerol during changing carbon and metal availability and revealed how protein allocation and media composition influence metabolic pathways and energy conservation in this model acetogen.Here, we reconstructed and deployed the first ME-model of a Gram-positive bacterium. The completed C. ljungdahlii (iHN637) [We first updated and created an existing genome-scale M-model of (iHN637) . By usin(iHN637) –22, 28 ri.e., E-matrix) was reconstructed [C. ljungdahlii do not exist, this rate  was set to 25 s-1, the average turnover rate of all enzymes in acetogens listed in Schiel-Bengelsdor and Dürre [Next, a gene expression network . iJL965-ME accounts for all of the major central metabolic pathways and biomass synthesis pathways as well as transcription, translation, macromolecule modifications, and translocation reactions . Becausei.e., maximum nutrient uptake) and nutrient-limited growth conditions. Due to internal constraints on protein production and catalysis, referred to as proteomic limitations [2+H2, or fructose , iJL965-ME correctly predicted the start of ethanol secretion after acetate secretion due to trade-offs in protein production . Furthermore, 2,3-butanediol is produced towards the stationary phase after acetate and ethanol [Although 2,3-butanediol has been described previously as potential secretion product, the model did not predict production of 2,3-butanediol because it promotes production of the most energy efficient metabolites , which predicted that glucose and fructose would have identical growth rates, iJL965-ME correctly predicted slower growth on glucose than for fructose. Furthermore, iJL965-ME highly improved predictions of the ratio of maximum acetate secretion rate to substrate uptake rate compared to the M-models iHN637 and iJL680  following acetate and ethanol production during growth on xylose or glucose but not on arginine, pyruvate, or CO, which may be due to where the substrate enters the metabolic network so that glycerol is produced through byproducts of glycolysis (xylose and glucose) or by reverse glycolysis . Like ethanol, glycerol secretion occurred due to trade-offs in proteome limitations resulting in overflow metabolism, as the cell no longer invested resources to recycle glycerol, a byproduct of cardiolipin production  [in silico results strongly correlated to RNA-seq data for C. ljungdahlii grown on CO, CO2+H2, or fructose  ,26. To t2+H2, and 224 for fructose). Growth-correlated genes that were shared between conditions involved genes related to translation  were correlated with growth rate  to H2, while non-WLP redox reactions were correlated with growth rate  was removed by producing ethanol   could only be imported or exported, and only Co was predicted to participate in flux-carrying reactions that were not a transport reaction or biomass production [Metal availability and growth rate are linearly correlated in M-models, even though there is contrary experimental evidence . For exaoduction . Thus, min silico and in vivo. Due to C. ljungdahlii’s reliance on WLP for autotrophic growth, nickel was predicted to be essential for CO-growth, which was experimentally confirmed in the related acetogen C. ragsdalei [in vivo growth rate in a quadratic fashion as predicted  [Cofactor integration in iJL965-ME, however, allows systematic interrogation of the effects of metal availability. Particularly, iJL965-ME’s nickel-containing proteins, CODH4 and carbon monoxide dehydrogenase:Acetyl-CoA synthase (CODH_ACS), are part of the WLP, while a third nickel-containing protein  does not carry flux on CO. This network configuration afforded the possibility of controlling this pathway through changes in media composition both agsdalei . Althougredicted  and prevagsdalei . Experime (CODH) . Accordigr = 98%, Δfructose = 99%, C. ljungdahlii was grown either without added nickel (0x) or with high nickel concentrations (10x). Both cultures consumed the same amount of fructose (p = 0.26) and produced identical amounts of ethanol (p = 0.95), but exhibited different growth rates (p = 0.062) and final concentrations of acetate (p = 2.2e-4)  . Increas 2.2e-4)  and finaC. ljungdahlii. The reconstructed C. ljungdahlii ME-model (iJL965-ME) was not only more accurate than the M-model at predicting growth rates and acetate secretion rates, but was also capable of predicting secretion of ethanol and until now unknown secretion of glycerol , leading to ethanol production as the main fermentation end product  was grown under anaerobic conditions containing PETC medium (ATCC medium 1754 without fructose) at 37°C. Fructose cultures were grown in 125 mL serum bottles containing 100 mL of medium plus 28 mM fructose, CO in 125 mL serum bottles containing 25 mL of media and bottles were pressurized once with CO to 1.25 bar. Pyruvate, xylose, glucose, and arginine experiments were performed in test tubes containing 10 mL of medium and equimolar concentrations of carbon atoms (30) per carbon source, such as 5mM fructose and 10mM pyruvate. Medium contained 0.10 mM of NiCl2*6 H2O (defined as 1x). For testing the effect of nickel, final concentrations of 0 mM (0x), 0.50 mM (5x) and 1.0 mM (10x) of nickel were added to the media before autoclaving. Growth was routinely determined by measurement of OD600. Concentrations of fructose, acetate, ethanol, and glycerol were determined by high-performance liquid chromatography (Waters) as previously described [escribed . DetectiThe presence of glycerol in the cell cultures samples was investigated with GC-MS. An Agilent 7890B GC with a 7200 Accurate Mass QTOF MS  with an Electron Ionization source (EI) instrument was used. GC separation was carried out on a HP-5ms (5%-Phenyl)-methylpolysiloxane GC column  with ID of 0.25 mm, 30 m length and 0.25 μm film thickness. Prior to analysis, the 0.2 ml sample aliquot was lyophilized at room temperature and reconstituted in 50 μL of HPLC-grade methanol with 5 sonication. A 20 μL aliquot of supernatant was then carefully transferred into a 2 mL vial with a spring insert and capped with a septum cap.m/z range of TOF MS analyzer was set to 35–400 amu with acquisition rate of 20 spectra/second. For the first 1.65 min of the analysis the detector was turned off (solvent delay). The methanol solvent blanks and empty vial blanks were interspersed with the samples; a solvent blank was run prior to each sample to ensure absence of carryover. In order to eliminate potential systematic bias, the samples were randomized.For analysis, a 1 μL aliquot of sample was injected by the auto-sampling robot. The GC inlet was maintained at 250°C and set for 10:1 split. The GC separation was as follows: start at 40°C and hold for 1 min; 20°C/min oven ramp to 45°C, hold of 0.1 min; 20°C/min oven ramp to 300°C, hold for 0.1 min; 50°C/min oven ramp to 320°C for a complete run time of 14.6 min. The helium carrier gas was set to constant 1.2 mL/min flow. The scanned th Edition). The library matching was performed using the NISM MS Search software v. 2.3.The chromatograms were analyzed using Agilent’s MassHunter software v. B.08.00. Prior to analysis, an authentic standard of glycerol at ~1 mM concentration processed in the same fashion as the samples, was injected to determine the retention time of the compound for the analysis conditions and establish that the EI fragmentation pattern obtained on the instrument is identical to that in the search library  at 4°C. To stabilize RNA, 500 μl of Trizole reagent (Thermo Fisher Scientific) was added to 50–100 μl of cleared cell lysates, vortex mixed and stored at -80°C. The samples were brought to room temperature and 140 μl of chloroform was added to each tube, vortex mixed and centrifuged at maximum speed at 4°C for 10 mins. The aqueous fraction was isolated and total RNA was extracted using the RNeasy mini kit (Qiagen), the volume was brought to 900 μl using RLT buffer, 600 μl of 95% ethanol was added and mixed in order to bind the RNA. The RNeasy protocol was then followed as recommended by the manufacturer to isolate pure RNA. The ribosomal RNA (rRNA) was depleted using the Ribo-Zero rRNA Removal kit (Illumina). Strand-specific RNA-seq libraries were prepared using the Stranded RNA-seq Kit (Kapa Biosystems). The libraries were paired-end sequenced with Illumina HiSeq 4000. The sequencing reads were mapped to the C. ljungdahlii genome was reannotated using RAST and PROKKA to account for the most recent information and methods in functional annotations [A previously published M-model, iHN637, was updated to remove obsolete metabolic reactions and add new reactions to reflect current literature ,19,24. Totations ,22. If botations  were carotations . All M-motations .Escherichia coli, Bacilllus subtilis, and C. ljungdahlii, as well as manual curation of the published annotation, and genome annotations obtained by RAST and PROKKA were used to identify potential E-matrix proteins [E. coli [B. subtilis [et al. to fill in missing knowledge, template reactions [E. coli and B. subtilis proteins. The modeled protein complexes contained updated stoichiometry and modifications. COBRAme was used to comprise this information into a cohesive model [Bidirectional hits and functional overlaps (using RAST annotations) between proteins –22. Usin[E. coli ,10,25 aneactions  and rho eactions . The genve model .2+H2 and fructose RNA-seq data [C. ljungdahlii. For example, a membrane version of the protein was used to transport metabolites without an assigned transporter. Demands for glycerol, DNA, murein, ATP maintenance were added based on the iHN637 biomass objective function [All parameters from COBRAme were carried over except for the following: The (non) growth associated maintenance (instead set to iHN637’s); the unmodeled protein proportion of proteome (set to 0.35 based on relative protein weight of unmodeled proteins using RNA-seq data as a 1-to-1 proxy for protein levels); and median enzyme efficiency . COBRAme-compatible versions are available as json files. A version containing all of the reactions and metabolites in iJL695-ME is available as iJL965_ME_reduced.json, and a fully functioning json version can be created by loading iJL965_ME_full.json and running load_iJL965_me.S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(XML)Click here for additional data file.S4 File(PICKLE)Click here for additional data file.S5 File(JSON)Click here for additional data file.S6 File(IPYNB)Click here for additional data file.S7 File(ZIP)Click here for additional data file.


Here, we perform a detailed comparison of yeast experimental epistasis data to predictions generated with different constraint-based metabolic modeling algorithms. The tested methods comprise standard FBA; a variant of MOMA, which was specifically designed to predict fitness effects of non-essential gene knockouts; and two alternative implementations of FBA with macro-molecular crowding, which account approximately for enzyme kinetics. The number of interactions uniquely predicted by one method is typically larger than its overlap with any alternative method. Only 20% of negative and 10% of positive interactions jointly predicted by all methods are confirmed by the experimental data; almost all unique predictions appear to be false. More than two thirds of epistatic interactions are undetectable by any of the tested methods. The low prediction accuracies indicate that the physiology of yeast double metabolic gene knockouts is dominated by processes not captured by current constraint-based analysis methods.Computational predictions of double gene knockout effects by flux balance analysis (FBA) have been used to characterized genome-wide patterns of epistasis in microorganisms. However, it is unclear how  Epistasis is said to be negative (aggravating) if the double mutant has lower fitness than expected, i.e., if its fitness is lower than the product of the single-mutant fitnesses; epistasis is called positive  if the double mutant has higher fitness. Understanding the distribution of epistasis is fundamental to our understanding of gene function and interaction4. Epistasis is important for a wide range of theoretical issues in biology, including the evolution of sex6, speciation7, ploidy8, mutation load9, and genetic buffering10; epistasis is also fundamental to our understanding ofhuman disease12 and drug resistance13.Epistasis measures the extent to which the consequences of a mutation in one gene depend on mutations in another gene23. However, such experiments are technically demanding, and the number of possible interactions grows quadratically with genome size. An attractive alternative to the generation of experimental knockouts for all possible gene combinations is the in silico prediction of double gene knockout effects. One approach towards the computational prediction of epistasis uses machine learning based on various experimentally observed gene and gene pair properties; Table 1 of ref.24 provides on overview over such predictions.Epistasis can be assayed experimentally through the analyis of double gene knockoutsin silico models of gene function, which are inherently more suited to generate increased biological understanding. Epistasis is a property of functional links between genes, not of individual genes. Thus, large-scale predictions of epistasis from first principles are only possible with computational models that account for functional connections between gene products. The best-studied complex biological system is metabolism. Excellent representations of metabolic networks have been compiled for several unicellular organisms such as E. coli25 and the baker’s yeast Saccharomyces cerevisiae26. So far, all attempts at genome-scale in silico epistasis prediction34 have used flux balance analysis (FBA), which maximizes the yield of biomass production in the wild-type and in the mutants36, or a variant of FBA that attempts to minimize the difference between wild-type and knockout distributions of metabolic reaction rates .Here, we will focus instead on prediction methods based on in silico. Segrè et al. first used FBA to study the spectrum of epistatic interactions between metabolic genes in S. cerevisiae27. These authors introduced a new concept of epistasis between functional modules rather than between individual genes, intended to describe functional relationships among metabolic pathways. They found that modules interact with each other ‘monochromatically’, i.e., epistatic interactions between two specific modules are either largely positive or largely negative27. Examining the metabolic networks of E. coli and S. cerevisiae, He et al.28 found negative epistatic interactions largely among nonessential reactions with overlapping functions; in contrast, positive interactions were found predominantly between reactions without overlapping functions, and these were frequently essential28.Several studies used these simulation methods to perform large-scale characterizations of epistasis et al.29 studied epistatic interactions between yeast gene deletions based on their influence on the reaction rates of individual enzymatic reactions. They found that gene pairs interact incoherently relative to different phenotypes, and that genes involved in many genetic interactions across multiple phenotypes tend to be highly expressed, to evolve slowly, and to be associated with human diseases29. Xu et al.30 compared epistatic interactions for different alleles of the same gene; alleles of different enzymatic activities were simulated by reducing the admissible flux (reaction rate) relative to the wild-type by a given percentage. They found that different alleles of the same gene typically interact with very different gene sets in silico; they argued that the distribution of the sign of epistasis in their simulations can speed up the purging of deleterious mutations in eukaryotes30. Finally, Barker et al.31 studied epistatic relationships between genes in various environments, finding that epistatic interactions can differ substantially between growth conditions and that the epistasis network structure differs fundamentally between condition-independent (stable) and condition-dependent interactions31.Snitkin in silico analyses of epistatic landscapes summarized above purport to fundamentally advance our understanding of epistasis in nature, it is not clear that in silico and in vivo epistasis are correlated sufficiently on the genome-scale to allow such conclusions. Synthetic lethality – an extreme case of epistasis – was successfully predicted for some genes using FBA already in 2007; however, these authors could correctly predict only 7 out of 29 previously described synthetic lethals, corresponding to a recall of only 24%32. Two further studies in 2015 compared FBA predictions of synthetic lethality to experimental observations in yeast38 and E. coli39, confirming that only a minority of observed synthetic lethal interactions can be predicted successfully.While the 23, diploid-based synthetic lethality analyses with microarrays19, synthetic dosage-suppression and lethality screens18, and epistatic miniarray profiles22. The most comprehensive estimates of epistasis are available for the baker’s yeast Saccharomyces cerevisiae33, obtained through SGA. Szappanos et al.33 were the first to compare quantitative epistasis predictions from FBA and MOMA with high-throughput experimental data, examining 67,517 pairs of non-essential yeast genes . They also found that only a minority of empirically observed interactions can be successfully predicted. For negative epistatic interactions, at 45% precision , they obtained a recall (percentage of observed interactions that are correctly predicted) of 2.8%. While the recall can be increased to slightly above 4% by lowering the prediction threshold, this comes at the cost of many false positive predictions, associated with a drastic reduction of precision to below 6%. For positive interactions, Szappanos et al. obtained a recall of 12.9% at a precision of around 10%, which could not be improved much further by lowering the prediction threshold. Furthermore, the prediction quality could only be improved marginally by an automated model refinement procedure33. These results suggest that the physiological responses of yeast to double gene knockouts are not sufficiently captured by computational methods based on yield maximization such as FBA and MOMA. A later study that calculated epistasis from a new “function-loss cost” metric did not result in significantly improved predictions of the same data34.Several experimental platforms for the high-throughput detection of epistasis have been developed, among them synthetic genetic arrays (SGA)37. Both FBA and MOMA predictions ignore the protein cost of enzymatic reactions, which arises from the necessary investment of cellular currencies, such as ATP and carbon, into enzyme production. Furthermore, it has been suggested that enzymes and the protein translation apparatus compete for the limited intracellular concentration space, a suggestion consistent with the observation that total cellular protein concentrations appear to be approximately constant across conditions40. In particular the latter constraint, summarized under the term (macro-) molecular crowding, has been explored in detail in the literature43. Instead of a largely arbitrary constraint on the uptake of a limiting nutrient, FBA models with molecular crowding limit cell growth by imposing a maximal mass concentration of enzymes, which in turn limits the total flux through the reactions the enzymes catalyze. Note that FBA and related constraint-based models do not consider internal metabolite concentrations explicitly, and thus FBA with molecular crowding methods calculate the enzyme concentration necessary for a given reaction flux v as [E] = v/keff, with a constant effective rate constant that is often approximated through the enzyme turnover number kcat43.Why did the methods tested – FBA and MOMA – perform so poorly when predicting epistatic interactions? FBA captures epistasis based on the maximal biomass yield of the single and double mutants. MOMA assumes that the redistribution of reaction fluxes relative to the FBA wild-type solution is minimized upon the genetic perturbation45. Accordingly, the fitness effect of a non-essential enzyme knockout will depend not only on the stoichiometry of the catalyzed reaction (which is what FBA considers), but also on the enzyme’s kinetics .Could molecular crowding be responsible for epistatic interactions? FBA considers different yields of pathways, but pathways also differ in their kinetics, such that the same overall flux may require much more protein investment in one pathway compared to an alternative pathway; such differences in pathway costs of fluxes are believed to be the origin of overflow metabolismTwo toy examples for positive and negative epistasis are given in Fig. 37 suffer from a second problem. FBA solutions are generally redundant, i.e., multiple flux distributions lead to the same biomass yield. Thus, the distance of the MOMA to the FBA flux distribution may depend strongly on the particular FBA solution returned by the numerical solver of the wild-type optimization problem. A straightforward possibility to rectify this problem is to use the wild-type flux distribution returned by parsimonious FBA (pFBA), which attempts to minimize protein investment at a given biomass yield46 and has been shown to perform well in predicting the effects of single gene knockouts47.Previous applications of MOMAin silico predictions of epistasis34 can be improved by correcting the shortcomings discussed above, we compare the double gene knockout data for yeast in ref.33 to epistasis predictions from (i) FBA; (ii) FBA with molecular crowding, using two slightly different algorithms ; and (iii) MOMA starting from the pFBA solution for the wildtype flux distribution.Here, to test if the poor performance of previous S. cerevisiae epistasis data for metabolic genes identified in Szappanos et al.33. This data was generated using synthetic genetic array (SGA) screens. We excluded genes deemed to be essential by the metabolic model or blocked in the model. This resulted in 291 negative and 123 positive interactions among 71,994 non-essential gene pairs.We used a high-confidence subset of S. cerevisiae metabolism, we used the metabolic reconstruction yeast7.6 (https://sourceforge.net/projects/yeast)48. Following the authors of ref.33, we removed a set of genes from the metabolic model  to mimic the strain background used in the experiments; we also used the same definition of the growth medium as in ref.33, which mimics the experimental conditions33. The resulting, strain-specific model encompasses 904 metabolic genes associated with 3,326 reactions.To model sybil, a computer library for efficient modelling of metabolic networks49 in R50. Among other methods, sybil implements FBA, pFBA , MOMA, and diverse methods for genome-scale simulations of genetic perturbations.We performed all simulations using v12, and the two single gene knockouts, v1 and v2; in each case, all fluxes through reactions for which one of the knockouts is essential are forced to zero. We convert the biomass yield values to fitness estimates by dividing them by the wild-type biomass yield, vWT: Wi = vi/vWT. The fitness of the single and double mutants then allows the calculation of epistasis as33:FBA identifies a flux distribution across the metabolic network that maximizes biomass yield under the constraints given by (i) the stoichiometry of enzymatic and transport reactions and (ii) lower and upper bounds on individual fluxes. The upper bounds on individual enzymatic fluxes are meant to reflect maximal enzyme capacity, and hence FBA could in principle also take enzyme kinetics into account; however, as enzyme capacities are generally unknown, the upper bounds are typically set to a value that is effectively infinite. Lower bounds on individual enzymatic reactions are set to zero for reactions deemed irreversible, and are (effectively) set to negative infinity for reversible reactions. Bounds on exchange reactions reflect maximal nutrient uptake or excretion rates. To estimate epistasis with FBA, we need to calculate the maximal biomass production yield of the double gene knockout, 37. Previous applications of MOMA to epistasis predictions minimized the distance to an arbitrary FBA solution returned by the linear solver of the FBA problem33. As FBA flux distributions are highly degenerate, we instead use the parsimonious FBA  solution to the wild-type problem46, which should lead to biologically more relevant results47. Following previous applications33, we minimize the Manhattan rather than Euclidean distance between wild-type and knockout flux distributions, which results in a linear optimization problem (lMOMA). As for FBA, epistasis was then estimated from the difference between the double knockout fitness and the product of the single knockout fitnesses (Eq. (Minimization Of Metabolic Adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. MOMA employs quadratic programming to identify the closest point (in terms of its Euclidean distance) in the permissible flux space of the knockout to the wild-type flux vectorses (Eq. ).43 is an algorithm for performing FBA with molecular crowding42. MOMENT extends FBA by adding a global constraint on the total mass concentration  of enzymes:i, [Ei] is the molar concentration of enzyme i per gram dry weight,mi is the molar mass of the enzyme, and C is an upper limit on the total enzyme mass per gram dry weight. The limit imposed by Eq. (43 set this limit to C = 0.27 (g enzymes/gDW) based on a fit between observed and predicted growth rates, suggesting that metabolic enzymes are responsible for about half of the total protein mass43. Note, however, that due to the linearity of the biomass production rate in C, C cancels in the ratio of knockout/wildtype biomass reaction fluxes, and hence its numerical value has no influence on the epistasis predictions according to Eq. .MOMENTmentclass2pt{minim51 is a general implementation of FBA with molecular crowding that largely implements the MOMENT algorithm43, but improves on MOMENT by explicitly considering multifunctional enzymes. ccFBA is implemented in R50 and builds on the sybil package49; it is distributed on CRAN (https://cran.r-project.org) and has briefly been described in ref.52. We replaced the iMM904 yeast model distributed with ccFBA with the yeast 7.6 model adapted to the experimental data (see above). The resulting ccFBA model contains experimental kcat values for 535 enzymes; for the remaining enzymes, we use the median of the 535 known values51, kcat,med = 11.5. The same model and parameters were used for the MOMENT (see previous subsection). The proportion of biomass devoted to metabolic enzymes was set to C = 0.27 as in MOMENT43, and epistasis was calculated accordingly.ccFBA36 (FBA); (ii) a linear version of minimization of metabolic adjustment37 that finds the knockout flux distribution most similar to the pFBA prediction for the wildtype flux vector (lMOMA); (iii) metabolic modelling with enzyme kinetics43, an implementation of FBA with molecular crowding that approximately accounts for enzyme kinetics (MOMENT); and (iv) a modified implementation of MOMENT with a more realistic consideration of multifunctional enzymes52 (ccFBA). To obtain an overview over the differences between the tested methods, we first classified gene pairs into those showing negative epistasis (ε ≤ −0.0001), positive epistasis (ε ≥ +0.0001), or no epistasis (|ε| < 0.0001).For each pair of non-essential genes contained in the metabolic model, we calculated Epistasis (Eq. ) based oThe Venn diagrams in Fig. et al.33. Figures The large numbers of unique predictions of individual methods could potentially indicate that each method captures distinct aspects of physiological responses to the knockouts. To test this possibility, we compared the epistasis predictions by the three methods to the high-confidence experimental epistasis data set provided by Szappanos The negative interactions jointly predicted by MOMENT and ccFBA are also confirmed in 24 out of 262 cases (9.2%), indicating that predictions based on the concept of molecular crowding that are robust to changes in method details tend to be more reliable than those that are not. These predictions include 21 confirmed cases not predicted by FBA , indicating that combining standard FBA with MOMENT/ccFBA may improve the recall achievable in FBA predictions compared to FBA alone.in silico) and precision  at a given cutoff |ε| by the method indicated by the colour. Higher cutoffs mean that only predictions of strong epistasis are deemed reliable, and lead to lower recall and generally to higher precision; lower cutoffs deem more epistasis predictions reliable and consequently lead to higher recall but generally lower precision. The data points in the insets  represent the same predictions, but report the false positive rate (the fraction of in silico predictions that are not confirmed by the data) instead of the precision. Higher cutoffs |ε| lead not only to lower recall, but also to lower false positive rates; lower cutoffs increase both recall and false positive rate. The distance of data points to the diagonal (which represents random “predictions”) indicate the accuracy of predictions at this cutoff, where prefect predictions would lie in the top left corner.The cutoff of |ε| = 0.0001 for epistasis used to select the predicted interacting gene pair sets in Fig. For negative interactions, all four methods show a similar relationship between recall and precision Fig.  and betwImportantly, even with the most generous cutoffs, the highest recall reachable by any of the methods is at or below 33%. Thus, two thirds of experimentally observed epistatic interactions are not detectable by any of the constraint-based methods tested, regardless of how many false positives we are willing to accept. To achieve recall values above 20%, we have to accept false positive rates of more than 8% for negative and 2% for positive interactions; given the high number of comparisons made , this means that true predictions of epistasis are drowned in a sea of false predictions. At a more reasonable false positive rate of 1%, the highest achievable recall values are around 12% for negative interactions and 19% for positive interactions.53. In contrast, the strength of constraint-based methods may lie more in qualitative predictions: FBA has been demonstrated to accurately predict gene essentiality, i.e., genes whose knockout is lethal55. The likely reason is that knockout lethality often arises from the inability to produce a biomass component without the knocked out reaction, i.e., from an effect of the knockout on metabolic network topology rather than on kinetics, regulation, or biomass yield. Thus, it might be reasonable to expect that constraint-based methods also perform well when predicting synthetic lethals, i.e., gene pairs where the single mutants are viable but the double mutant is not. In disagreement with this expectation, previous studies showed recall values below 25% for the FBA prediction of synthetic lethals38. However, these observations were based on the analysis of small numbers of experimentally confirmed synthetic lethals drawn from diverse studies, and thus it seems advisable to compare model predictions of synthetic lethality to a systematic, genome-wide screen of metabolic genes.To predict epistasis scores for viable double mutants, we need to calculate fitness values quantitatively for the single and double knockouts. It is conceivable that the underwhelming performance of constraint-based methods to predict genetic interactions Figs , 3 is du33, we selected non-essential gene pairs with experimentally confirmed negative epistasis  and with very low double mutant fitness (f < 0.2). Only 146 out of a total of 207,060 non-essential gene pairs represented in the model and assayed by Szappanos et al.33 were labeled as synthetic lethal according to this definition.To identify pairs of synthetic lethal genes in the raw data from ref.f < 0.2) for the computational epistasis predictions, we recover only 4 (FBA), 4 (lMOMA), 0 (ccFBA), and 0 (MOMENT), respectively, of the experimentally confirmed synthetic lethal pairs. This corresponds to recall values below 3%. For FBA, lMOMA, and MOMENT, recall cannot be improved by choosing less stringent cutoffs, as long as we require negative epistasis. For ccFBA, we can obtain 6 true positive predictions if we relax the double mutant fitness cutoff to f < 0.55 while requiring negative epistasis (ε < −0.0001). These findings confirm the earlier results on smaller datasets of synthetic lethals38: constrained based methods appear no better at predicting synthetic lethality than at predicting epistasis in general.When using the same cutoffs  for only 535 out of 4,594 protein-associated reactions, and it is conceivable that an improved parameterization may lead to improved prediction accuracy. However, none of the tested methods could correctly predict synthetic lethal interactions, which in most cases probably arise from changes in network topology rather than from enzyme kinetics; this failure suggests that the problem is more fundamental.The essence of FBA with molecular crowding, as implemented in MOMENT and ccFBA, is the incorporation of a tradeoff: the expression of one pathway reduces the cellular resources available for other pathways. This interdependence between pathways in terms of available resources may underlie at least some epistatic interactions, and may hence contribute to explaining why these methods were able to expand the set of correctly predicted interactions Fig. . Both meA second potential explanation for the observed underperformance of constraint-based methods is the influence of regulatory feedbacks. Regulatory interactions evolved in the ancestors of the wild-type strain as responses to environmental conditions. Changes in metabolite concentrations resulting from the knockouts may be mis-interpreted by the cell’s regulatory system as environmental cues, and may thus lead to regulatory responses that cause suboptimal metabolic network usage. Such “inappropriate” regulatory responses might lead to large discrepancies between mutant physiology and predictions by optimization-based methods ignorant of regulatory circuits.If true, the hypothesis advanced in the last paragraph has important implications not only for the utilization of constraint-based methods to predict experimentally observed epistasis, but also for the biological interpretation of double knockout mutant physiology. If the observed effects of double knockouts are in large part due to regulatory responses, they may provide little information on the interaction of the gene products in wildtype physiology.Supplementary Dataset 1Supplementary Dataset 2


There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet–microbiome, microbe–microbe and host–microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions. The human gut microbiome consists of trillions of microorganisms such as bacteria, archaea, and unicellular eukaryotes ,2. Most 2, CH4) [+ T cells [The gut microbiome acts as an auxiliary metabolic organ. Several complex carbohydrates, not digested by the host intestinal enzymes, are passed to the microbial community, which are then metabolized in the large intestine ,15. The 2, CH4) ,17,18. G2, CH4) . Studies T cells . It alsoOur understanding of the gut microbiome and its role in health and disease has considerably improved with the advent of high-throughput meta-omics technologies. The wealth of data generated by the gut microbiome research, however, begs the development of novel computational tools and mathematical models. Such tools have already enabled researchers to begin exploring complexities of the gut microbiome . SeveralGenome-scale metabolic modeling (GSMM), a constraint-based mathematical modeling approach has been increasingly used to study gut ecosystems, attempting to elucidate the microbial metabolic interactions with each other and their host ,24,25,26Herein, we review the role of GSMM in understanding microbial metabolism in the human gut, with a focus on how GEMs have been used to infer diet–microbiome, microbe–microbe and host–microbiome interactions under physiological conditions. We discuss metagenomics profiling, and how meta-omics datasets can be used for building condition-specific personalized community models of gut microbiota. We further summarize the available tools for metagenomic profiling and GSMM. Finally, we highlight and emphasize the experimental techniques and data required to validate the GEM-based predictions. Early colonization of the gut microbiota in infants is vital for shaping of the intestinal ecosystem at a later age ,32. ThesLactobacillacea, Veilonellaceae and Helicobacterceae co-occur in stomach, Bacillaceae and Streptococcaceae in the small intestine, and Bacteroidaceae, Clostridium, Lactobacillaceae and Bifidobacterium in the colon [The distribution of microbes along the gastrointestinal (GI) tract is non-random, in that, certain species of microbes are co-localizing. he colon . Dysbioshe colon ,36, colohe colon , type 2 he colon ,25, cardhe colon , non-alche colon ,40 as wehe colon .Metagenomics shotgun sequencing  and 16S Various computational tools and pipelines have been developed for these sorts of purposes. MOCAT2, for example, provides automated annotation of non-redundant reference catalogues from 18 databases covering various functional categories . HMP UniFunctional annotation of metagenomics datasets poses several challenges in itself ,54. AlthA rapid increase in use of shotgun metagenomics, the availability of model organisms, and the number of meta-omics datasets in public repositories, gives an opportunity to develop metabolic reconstructions of human gut microbes. These reconstructions can be converted into quantitative mathematical models that can be used to study metabolism at the genome scale ,56,57,58In a GEM, uptake or secretion of certain metabolites over time (denoted as their ‘flux’), enzymes/transcript abundances and ON/OFF gene expression can be constrained using information from datasets generated by quantitative fluxomic, metabolomic, transcriptomic and proteomic experiments. By applying these constraints, GEMs can be contextualized to a particular state or condition. These condition-specific/contextualized models can provide information about the activity of metabolic pathways, metabolite flux, cellular growth, and provide estimates of the overall metabolic capacities of these gut microbes. GSMM use FBA , a constBacteroides caccae ATCC 34185. Moreover, these metabolic reconstructions have been used to infer metabolic diversity of microbial communities. The AGORA framework can be coupled with, for example Recon 2, a generic reconstruction of human metabolism, which in turn can be used to study host–microbiome interactions. AGORA reconstructions are publicly available via the Virtual Metabolic Human (VMH) [https://vmh.life/). In addition, BiGG Models [http://bigg.ucsd.edu/) and the Human Metabolic Atlas [http://metabolicatlas.org/) are other open access knowledge bases for metabolic reconstructions. GSMM has been applied to study gut microbial metabolism and its interactions with the host. Recently, AGORA  was published, which carried out semi-automatic metabolic reconstruction of 773 human gut bacteria  . The autan (VMH)  databaseic Atlas  (http://https://kbase.us/) and ModelSEED [http://modelseed.org/) are the web-based servers for automatic reconstruction of microbial GEMs by integrating genome sequences and/or metagenomics datasets. The COnstraint-Based Reconstruction and Analysis (COBRA) [Kbase  (https:/odelSEED  (http:// (COBRA) ,60,61 an (COBRA)  toolboxe (COBRA)  extends  (COBRA) . In a metabolic model, numerous genes and metabolites are associated by way of metabolic pathways deemed to be thermodynamically feasible. These models are formalized and applied over the entire microbiota community model . VariousShotgun metagenomics and 16S rRNA data have guided the selection of representative microbes (species or strains) in a community . IntegraContext-based, personalized microbiota models have already been used to study various conditions ,56,61,84Food metabolomics datasets detailing dietary constituents have been used to constrain the nutrient uptake rates of microbiota models . Diet acBacteroides enterotype whereas plant-based polysaccharides are associated with a Prevotella enterotype [Diet is the direct regulator of microbial metabolism in the gut ecosystem; dietary patterns have profound effect on gut colonization and the shaping of the gut microbiome during the early stages of life . Westernterotype . Mostly,terotype . Bacteroides thetaiotamicron, Eubacterium rectale and Methanobrevibacter smithii, when subjected to different gut ecosystems [GSMM has already begun to be used to help improve mechanistic understanding of gut microbial metabolism and its dietary interactions ,25,26. Cosystems ,22. Receosystems . This meMicrobial species or strains with high abundances in samples are often selected for pairwise or community modeling ,26. Two Bacteroides spp. and R. gnavus can cooperatively produce UDCA [Pairwise analysis of microbes in the community has determined their metabolic relationships when introduced to different types of diets ,26,83. Huce UDCA .Alternatively, correlation-based co-occurrence topological networks looking at abundant metagenomic species can be developed ,89. SuchClostridium, Bifidobacterium, and Bacteroides, and altered 4-cresol levels in human urine have been associated with weight loss in IBD [Gut microbiota can harvest nutrients and energy from the diet. During these processes, small molecules (metabolites) are produced. Some of these metabolites can be beneficial for host and microbial symbionts ,18,84. Os in IBD . The pris in IBD . Also, BGEMs have been expanded to study metabolism in humans. Human generic metabolic reconstructions such as Recon 1  and the The Microbiome Modeling Toolbox , deploye+ effector T cells , CD8+ T cells (cytotoxic) and macrophages undergo metabolic reprogramming during proliferation and differentiation processes [In a different context, the microbiome-induced immune response is currently well established. An imbalance in gut microbial composition has been linked to inflammatory and autoimmune diseases ,105,106.rocesses . The macrocesses . Metabolrocesses , might gTo establish the biological relevance of metabolic models, the congruence between model predictions and experimental data is of utmost importance. GEM-based predictions can be validated by existing data, knowledge and bibliographical evidence. For instance, metabolites secreted by gut microbiota can be compared with the concentrations of metabolites found in fecal and blood samples ,25. FurtIntegration of meta-omics datasets and genome-wide metabolic reconstructions provide a framework for interrogating and suggesting mechanistic workings of diet-microbe-host metabolic interaction. However, such integrative methods are still evolving and require extensive and robust experimental validation. Profiling and culturing gut microbes at the strain level, under controlled conditions, remains challenging. Recently, an integrated approach involving targeted phenotypic culturing, WGS, phylogenetic analysis and computational modeling has succeeded in culturing a substantial portion of bacteria previously declared to be ‘unculturable’ under laboratory conditions. This approach identified 137 bacterial species, including novel species isolated from pure cultures . Furthermax, Km, Kcat) [In studies of gut microbial communities, there is increasing interest in mechanistic approaches, in contrast to solely genome-centric approaches. Correspondingly, GSMM is widely used as a preferred computational method for studying gut microbial metabolism and its interaction with the host. Additionally, GEMs can be contextualized and personalized using longitudinal meta-omics datasets, providing a snapshot of metabolic processes over time. Personalized microbiota models may help to reduce the costs of clinical studies, predict markers and contribute to the development of potential treatments at either the individual patient level, or for a defined patient group ,113. Manm, Kcat) ,116. AlbAs indicated in this review, GSMM and CBA have provided computational tools and frameworks to study metabolism of gut microbiota. These tools guided researchers to study and identify the metabolic functions of individual microbes in the gut community. It also helped to infer their spatial dynamics, environmental interactions and metabolic resource allocations under a certain condition. We believe that, a combination of several computational and experimental approaches, may reveal the complex and diverse structure of the human gut microbiome and its underlying interactions with the host metabolic machinery. It might bridge the gaps in gut microbiome research and thereby, enhance our knowledge of human gut microbiota under health/disease conditions.


Arabidopsis) primary metabolism and show that we can realistically reproduce and quantify heterosis in a highly complex trait (the rate of biomass production). The results demonstrate that additive effects coupled to the complex patterns of epistasis generated by a large metabolic network are sufficient to explain most or all the heterosis seen in typical F1 hybrids. Such models provide a simple approach to exploring and understanding heterosis and should assist in designing breeding strategies to exploit this phenomenon in the future.Heterosis, or hybrid vigour, is said to occur when F1 individuals exhibit increased performance for a number of traits compared to their parental lines. Improved traits can include increased size, better yield, faster development and a higher tolerance to pathogens or adverse conditions. The molecular basis for the phenomenon remains disputed, despite many decades of theorising and experimentation. In this study, we add a genetics layer to a constraint-based model of plant ( Heterosis (hybrid vigour) plays a major role in many crops and livestock used for food production. However, the molecular basis for the increased biomass production in hybrids remains unclear. We propose a computational approach that integrates genetic constraints into a genome-scale metabolic model in order to simulate and better understand this phenomenon. The approach was able to reproduce the effects of selection pressure and heterosis. Analysing a large set of predicted metabolic fluxes in hybrid plants and their parents provided an unprecedented level of detail on the causes of heterosis in this simplified system. We could show that heterosis naturally emerges from multi-locus epistatic effects inherent across a complex biological network. Simple additive effects are sufficient to create significant heterosis without the need for specific biological mechanisms linked to heterozygosity. Animal and plant breeders have benefited from hybrid vigour to produce increasingly more effective strains. In the USA, the use of hybrid maize has contributed to a dramatic six-fold increase in yield since the 1930s.3 Its benefits have stimulated extensive study into improving the design and selection of commercially valuable hybrids (reviewed in ref. 4). However, the molecular explanation for the phenomenon remains a topic of lively discussion. Early studies showed that heterosis was proportional to the genetic distance between two parents.7 More recent work has added considerable detail about how the genetic landscape of the parental lines might influence heterosis.9 Several competing but not mutually exclusive genetic models have been proposed in an attempt to provide some mechanistic explanation of the phenomenon.As early as 1876, Charles Darwin provided the first scientific observations that interspecific hybrids tended to show more vigour than intraspecific hybrids.dominance hypothesis holds that independent sets of deleterious alleles accumulate over time and particularly during inbreeding of parental lines.7 In these conditions, dominant alleles originating from one parent would complement inferior alleles from the second parent, thus resulting in a phenotypic improvement. The overdominance hypothesis suggests the existence of allelic interactions that induce greater expression of heterozygous loci in hybrids.11 Neither of these hypotheses involve interactions between non-allelic loci. Additional hypotheses that do involve such interactions have been proposed. Pseudo-overdominance11 is the appearance of overdominance due to repulsion-phase linkage of dominant alleles.4 The effects of epistasis on heterosis are also becoming increasingly clear as the complexity of the biological networks underlying multigenic traits becomes better understood.12 Epistasis not only shapes which loci can express heterosis but it can also mimic overdominance.13 All of these mechanisms could, and probably do, contribute to heterosis. However, debate continues over which are the more important, because of the difficulty in experimentally distinguishing these intertwined genetic effects.The 23 Such studies have provided much data but perhaps fewer clear explanations than might be hoped, with different studies reaching different conclusions about which molecular mechanisms predominate.The potential complexity of the mechanisms contributing to heterosis has encouraged attempts to take a ‘systems’ approach to the topic, with large-scale molecular profiling to analyse gene expression and biochemical phenotypes.24 We take this approach a step further by adding a simple genetics layer to a genome-scale model of plant primary metabolism. This combined model demonstrates plausible levels of heterosis under a set of realistic assumptions based on simple additive effects coupled to epistasis.An alternative approach to treating such complexity is to attempt to model it mathematically or computationally. Modelling allows deliberate simplification of the system to make interpretation of the results easier, either by reducing the number of components, or their interactions, or both. Genetic models of heterosis have been studied for many years, but generally leave out downstream biological interactions that intervene in the complex interplay between genotype and phenotype. Only a few attempts have been made to bridge this gap and link genetic models of heterosis to biological networks that can simulate the extent of potential epistatic interactions between loci, and these have been limited to small-scale networks.Arabidopsis metabolism25 such that each reaction is assigned to a genetic locus, and each locus can be assigned a collection of alleles, allowing the simulation of diploid and polyploid individuals. The original metabolic model was designed for flux balance analysis, in which steady-state metabolic fluxes are calculated for each reaction under external metabolic constraints on the network (e.g. limitations on the rate of photosynthesis). In general, the metabolic fluxes are optimised to maximise the desired output, for example, biomass production. In our additions to this model, we added internal genetic constraints. Each allele consists of a constraint that can be applied to a reaction in order to confine its metabolic flux. The rationale behind this is that the genetic information determines the level of expression and thus the activity of the enzymes in the metabolic network. In the simulations described here, this genetic information acts in cis, that is, we have included no trans-acting regulatory factors. This simplification does not prevent heterosis appearing in the simulations because many cross-locus interactions are already provided by the metabolic network.Standard descriptions of genome-scale metabolic networks do not generally incorporate any notion of genetic variability or heterozygosity, thus to study heterosis, an additional layer of genetic information is required. We modified a general model of 26 was then performed on the network to calculate an envelope of alternate fluxes for each reaction that could maintain optimal biomass production. Allele-specific constraints were then generated using the FVA as a guide . Each individual within the population was assigned two randomly chosen alleles at each locus . In simulated crosses, alleles at each locus were transmitted randomly, independently and unaltered; that is, we did not attempt to simulate genetic linkage, mutation or any epigenetic effects. By default, we assumed that the genetic effects of multiple alleles are additive, that is, we took the average genetic constraints from the alleles at one locus to set a single constraint for each reaction.The constraint attached to each allele was chosen randomly, as follows. First, the generic model was optimised for the maximisation of biomass production , resulting in a reference flux distribution (RFD). Flux variability analysis (FVA)Prior to any further calculations, the flux constraints assigned to each individual were normalised such that the sum total of all constraints corresponds to the sum total of all the fluxes in the RFD. Thus, the total amount of ‘enzyme’ that any individual can produce is fixed in our simulations. This constraint was introduced to avoid trivial results where rate of biomass production is simply dependent on the total flux capacity . Holding total flux capacity constant forces any changes in rate of biomass production in the simulations to be dependent in changes in metabolic efficiency—producing more interesting outcomes. The biomass production network was then optimised for each individual using the constraints specific to that individual. Thus, for each reaction in the network, we obtain four flux values : the two potential flux constraints encoded by the alleles at the locus, the genetic flux constraint calculated from these allele values  that was used during flux balance analysis (henceforth referred to as the enzyme capacity) and the calculated flux through the reaction following optimisation of biomass production (henceforth referred to as the computed flux).To model the genetic composition of the inbred lines used in conventional breeding programs, we simulated the effect of selection by inbreeding 40 independent heterozygous populations over 50 generations  in at least one F1 population. These reactions included both internal reactions and transporters associated with the production of biomass components. Thus, as would be expected, heterosis in these simulations appears to be due to increased metabolic flux through pathways relevant to biomass production. This can be shown formally by calculating the coupling between each reaction and the production of biomass.27 Indeed, 90% of the highly correlated reactions were found to be coupled with biomass production  is maximised. The computed flux through this objective function provides a highly multigenic trait indicative of overall performance. Detailed analysis of the rest of the metabolic network provides useful information regarding which reactions are contributing to heterosis. In order to identify reactions contributing to the increased performance in the F1, for each F1 population we calculated the correlation coefficient between the magnitude of heterosis in each F1 individual and the computed flux for each reaction in the network . This represents the proportion of synthesised enzyme that is actually active. Figure The presence of many reactions in the network operating at below 100% efficiency implies the presence of metabolic bottlenecks. These bottlenecks are imposed by the direct genetic constraint on certain metabolic steps. However, they also impact the rest of the network by forcing upstream or downstream reactions to operate at rates below their maximal capacities. These bottlenecks are partially relaxed in F1 hybrids, as illustrated by the reactions whose efficiency improves. An example provided in Fig. It is evident that in the context analysed here, heterosis is largely not dependent on heterozygosity at any locus , but rather on epistatic effects across two or more loci. If this is the case, then it should be possible to ‘fix’ the improvements obtained in the F1 generation by selecting for the optimal combinations of parental alleles over subsequent generations. Therefore, we continued to select for biomass production in the F2 and ensuing generations produced by selfing Fig. . The dep29 The models also demonstrated biologically plausible levels of heterosis, averaging around 10% of increased metabolic flux, which is in the range of what is commonly observed when crossing typical inbreds. The models also reproduced the collapse of heterosis in the F2 generation. Inspection of the reaction fluxes in the models provides a rational explanation for the mechanisms responsible for the observed improvement in hybrid F1 plants and allows the investigation and quantification of the contribution of different aspects of the genetic models.Despite substantial efforts to define a universal theory explaining the phenomenon, the understanding of the molecular basis for heterosis has remained elusive. By reproducing and explaining several, if not all, aspects of heterosis, the modelling approach presented in this study can shed some light on this long-standing question. The models reproduced the progressive gain in performance and reduction in heterozygosity under intense selection over several generations and the subsequent plateau as the best alleles become fixed in the population.Although the results we obtain appear to mimic heterosis, an obvious question is whether the models we describe accurately reflect a significant component of the processes underpinning heterosis in living organisms. Our models are necessarily greatly simplified, but we believe they do reproduce the most salient features of the genetics of heterosis. The results of any computational model are dictated by the choices made in its design; we believe we have made sensible and even conservative decisions in the choices of the metabolic and genetic models, and in the structure of the populations and the selection regime. As an example of a conservative choice, we used FVA analysis to set the range of the initial alleles within the population . Without using FVA to restrict the choice of starting alleles, individuals could contain strongly sub-optimal alleles (i.e. would be effectively ‘mutants’ in a genetic sense). We felt this would not be a good starting point for simulating heterosis in natural or breeding populations where recurrent selection will have eliminated almost all strongly sub-optimal alleles even prior to the creation of inbred lines. By reducing the ‘genetic variability’ in the starting populations, the use of FVA to set the bounds reduces the magnitude of the potential heterosis, but results in a more plausible simulation.21 This, of course, does not rule out that dominance or overdominance might play important roles in specific instances, and this is supported by genetic studies specifically looking for loci underlying heterotic effects.30 Epistatic effects are hard to fully account for in such genetic studies if they involve multiple loci. Similarly, previous attempts to include epistasy in computational models of heterosis have by and large been limited to effects between two or only a few loci . The importance of epistasy and biological networks in shaping heterosis has been discussed at length12 but has been difficult to examine in practice. The originality of connecting a simple genetic model to a large metabolic network, as we have done, is in the capture of the complex epistatic effects created by the topology of the network. It is clear that in our simulations, effects involving multiple loci far outweigh the effects of any individual locus, or even of all individual effects summed together. Models such as those we describe should make it much easier to investigate the contribution of epistasy to heterosis.An important finding is that simple additive genetic effects are entirely sufficient to provide significant levels of heterosis without any requirement for dominant or overdominant genetic effects . Most experimental observations of gene expression in F1 hybrids have found that additive expression is by far the most common case.Arabidopsis, which showed that the improved phenotypic traits obtained in an F1 hybrid could be stabilised through the generation of the so-called ‘hybrid mimic’ lines.32If inter-locus interactions are more important than intra-locus interactions for heterosis (and in our models this is certainly the case), then the prediction is that the gains in the F1 generation can be fixed in homozygous inbreds that preserve the best parental allele combinations at different loci. We showed that in the models this is relatively simple to achieve by recurrent selection over a few generations. This is greatly facilitated in our simplified system by the lack of any genetic linkage that would interfere with the reassortment of parental alleles. Nevertheless, the results recapitulate a study in 33 By allowing a system-wide view, computational models provide valuable insights into complex biological processes or phenomena such as heterosis. Our models provide an approach for rigorously quantifying various contributions to heterosis by different genetic or metabolic processes. We hope that such models can ultimately be applied to improve the rational design of breeding and selection programs to maximise heterosis for agronomically relevant traits.Despite the good match of the model outputs to experiment observations of heterosis, there are many ways in which these models could be improved, or at least extra complexities could be added that may be needed to more finely simulate specific biological contexts. Our model did not incorporate any notion of genetic linkage, allowing alleles to be inherited completely independently, which would lead to unrealistic rates of allele segregation and selection, but should not greatly affect the conclusions we draw here. More importantly, the model is lacking several layers of network complexity: notably there are no ‘regulatory genes’, and the metabolic model similarly lacks any feedback loops or metabolic regulation. These deficiencies should not alter the main concepts and conclusions demonstrated in this study, but may make it more difficult to apply such models for real-world purposes . Nevertheless, we believe that this would be an interesting and rewarding direction to take this research in the future, as promising results in other studies have been achieved even without the benefit of explicit models of molecular interactions.Arabidopsis thaliana presented in ref. 25 The model consists of 549 reactions, 407 metabolites and 6 sub-cellular compartments. The composition and topology of the network were not altered, except for the integration of allele-specific constraints as described below. We used a parsimonious flux balance analysis (pFBA) to simulate growth in all the experiments. This method uses a two-step optimisation in which the growth rate is optimised using traditional flux balance analysis, followed by the minimisation of the total flux through all the reactions.34 Minimising the total flux limits the number of possible solutions and improves the predicted flux distribution by representing an efficient enzyme usage within the network. The original model provides three different biomass compositions: carbon-limiting, nitrogen-limiting, and optimal growth conditions (under which biomass accumulation is only limited by photon flux into photosynthesis). The latter was systematically used as the objective function in our simulations. Given the stoichiometric matrix of the network (S), flux balance analysis computes the vector of fluxes v, using the assumption that the system is at steady state:We used the metabolic network of Sirrev. Thus, each reaction is constrained to carry a positive flux and the total flux is minimised subject to the optimal biomass production rate:In order to minimise the total flux, all the reversible reactions are split into two irreversible reactions, resulting in the stoichiometric matrix First, a pFBA was performed on the original model, resulting in an RFD. The analysis was set up to maximise the flux through the objective function (biomass) while minimising the sum of all the fluxes in order to limit futile cycles.FVA was used to compute the maximum and minimum fluxes for each reaction that maintain a minimum fraction (95%) of the optimum biomass production rate. Alternative FVA minimum fractions (90% and 99%) were also tested, the results are available in Supplementary Fig. Each reaction was attributed a pair of alleles (to simulate a diploid organism). In this context, an allele corresponds to a random variable following the discrete uniform distribution over the set of possible fluxes within the FVA range of a given reaction.Finally, reactions were constrained by assigning numerical values their upper or lower bound . Exchange reactions and biomass reactions were left unconstrained in all the simulations. For all the other reactions, the constraint corresponds to the average of its allele values, normalised such as the sum of the constraints across all loci is equal or lower than the sum of all the fluxes in the RFD. The normalisation ensures that all the individuals have a similar metabolic capacity.On the initial parents, metabolic constraints attached to each allele were generated as follows:The degree of heterosis of an F1 hybrid was defined as its biomass production rate divided by the average biomass production of the two parents, that is, mid-parent heterosis.To simulate the inheritance of genetic material, one allele from each parent was randomly selected for each reaction. The new pair of alleles was associated with the corresponding reaction in the resulting network. The reaction bounds were then constrained and normalised as described previously.To simulate the effect of selection pressure, individuals were ranked based on their biomass production rate and the top 5% were selected. These best-performing individuals were then used as parents to produce the following generation. Each generation consisted of 500 individuals. This process was repeated until the number of desired generations was reached. During the production of these increasingly inbred lines, selfing was prevented .F1 individuals were obtained by randomly sampling 30% of the inbreds from two independent parental lines and crossing them together until the number of desired F1 hybrids was reached . The two parents were always from distinct populations; however, several F1 individuals could come from the same combination of parents. Such siblings will contain different combinations of alleles as the parents are not completely homozygous. The F2 generation was generated in a similar manner; 30% of the individuals from an F1 population were randomly selected and crossed with each other. In our implementation, an F2 individual always results from a cross between two distinct F1 parents  from the same population. To maximise the diversity of F1 and F2 hybrids, we used all possible combinations of crosses between our 40 inbred populations, resulting in 780 independent F1 and F2 populations containing 390,000 individuals in each generation.35 and the GNU Linear Programming Kit (http://www.gnu.org/software/glpk).The simulation of the metabolism of many individuals over multiple generations requires significant computing resources. To avoid scaling problems, the workflow was designed for parallel high-performance computing. We used a ‘divide and conquer’ design paradigm to break the computing tasks into single sub-problems corresponding the model optimisations. The entire workflow was written in Python. The simulations were performed using the COnstraint-Based Reconstruction and Analysis (COBRA) Python packageFurther information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationReporting Summary


Phytophthora infestans leads to extensive yield losses in tomato and potato cultivation worldwide. To effectively control this pathogen, a thorough understanding of the mechanisms shaping the interaction with its hosts is paramount. While considerable work has focused on exploring host defense mechanisms and identifying P. infestans proteins contributing to virulence and pathogenicity, the nutritional strategies of the pathogen are mostly unresolved. Genome-scale metabolic models (GEMs) can be used to simulate metabolic fluxes and help in unravelling the complex nature of metabolism. We integrated a GEM of tomato with a GEM of P. infestans to simulate the metabolic fluxes that occur during infection. This yields insights into the nutrients that P. infestans obtains during different phases of the infection cycle and helps in generating hypotheses about nutrition in planta.Late blight disease caused by the oomycete pathogen  Phytophthora infestans causes potato and tomato late blight, a disease that is a serious threat to agriculture. P. infestans is a hemibiotrophic pathogen, and during infection, it scavenges nutrients from living host cells for its own proliferation. To date, the nutrient flux from host to pathogen during infection has hardly been studied, and the interlinked metabolisms of the pathogen and host remain poorly understood. Here, we reconstructed an integrated metabolic model of P. infestans and tomato (Solanum lycopersicum) by integrating two previously published models for both species. We used this integrated model to simulate metabolic fluxes from host to pathogen and explored the topology of the model to study the dependencies of the metabolism of P. infestans on that of tomato. This showed, for example, that P. infestans, a thiamine auxotroph, depends on certain metabolic reactions of the tomato thiamine biosynthesis. We also exploited dual-transcriptome data of a time course of a full late blight infection cycle on tomato leaves and integrated the expression of metabolic enzymes in the model. This revealed profound changes in pathogen-host metabolism during infection. As infection progresses, P. infestans performs less de novo synthesis of metabolites and scavenges more metabolites from tomato. This integrated metabolic model for the P. infestans-tomato interaction provides a framework to integrate data and generate hypotheses about in planta nutrition of P. infestans throughout its infection cycle.The oomycete pathogen  Phytophthora infestans, the causal agent of late blight disease on tomato and potato, which leads to significant yield losses worldwide  can be used to help understand cell metabolism. GEMs represent cell metabolism as a cell-scale network of biochemical reactions, typically distributed over several cellular compartments, that connects the uptake of nutrients to the production of biomass precursors , 21. Ass27–10.1128/mBio.00454-19.5FILE S1TEXT S1, PDF file, 0.2 MB.Principles of metabolic modeling and explanation of flux coupling analysis. Download Copyright © 2019 Rodenburg et al.2019Rodenburg et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. infestans . For instance, according to recent insights into the mitochondrial localization of glycolytic enzymes , carbon metabolism (n = 61), purine and pyrimidine biosynthesis , and ABC transporters (n = 42), which indicates that at least 42 metabolites are known ABC transporter substrates. Of the 520 introduced transport reactions, 179 could carry a flux, meaning that the respective metabolites can both be produced by the tomato plant and assimilated by P. infestans. The integration of the two GEMs resulted in an integrated metabolic model of P. infestans-tomato interaction, comprising a total of 4,695 reactions, 4,303 metabolites, and 4,578 genes   . In scenompounds , in cont10.1128/mBio.00454-19.1FIG S1P. infestans biomass precursors, as follows: Biomasspinf, P. infestans biomass precursors; Min vpinf, scenario II, minimization of P. infestans flux; Max vpinf, scenario III, maximization of P. infestans flux; and Min vpinf ∪ vtransport, scenario IV, minimize both P. infestans and host-pathogen transport flux. The biomass precursors and imported nutrients in each scenario are listed in FIG S1, TIF file, 0.2 MB.Overlap in numbers of imported nutrients in three flux scenarios composed based on different objective functions and the set of Copyright © 2019 Rodenburg et al.2019Rodenburg et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. infestans) and simulate extreme circumstances not likely found in nature. The true pool of imported nutrients will likely be a combination of the pools predicted in each individual scenario. A comparison of the nutrients imported in all four scenarios revealed sets of nutrients in common between scenarios. There are 14 nutrients found in three out of four scenarios, i.e., II, III, and IV , while thiamine pyrophosphate (TPP) is imported in scenarios II and IV (minimizing fluxes). It is assumed that many oomycetes import thiamine to form TPP  and (inter)dependencies of reactions , also known as coupled reactions. Coupled reactions within a metabolic model can be identified using a method called flux coupling analysis . Fatty acid biosynthesis in oomycetes is associated with fungicide resistance . FC. FCP. inon pairs  involvineactions . One of nfestans . Some renfestans , implyinnfestans , thus ineactions . One of sistance  and enerP. infestans reactions that are coupled to a transport reaction, the more likely that the associated substrate is important for P. infestans. We selected the most frequently coupled transport reactions from the model and assessed their couplings to other tomato and P. infestans reactions  biosynthesis (2-dehydropantoate), de novo pyrimidine biosynthesis (S-dihydroorotate) (myo-inositol 3-phosphate). Notably, three transporter substrates were associated with glycerophospholipid metabolism (phosphodimethylethanolamine N−methylethanolamine phosphate and choline). It is conceivable that P. infestans can take up glycerophospholipid precursors to facilitate the formation of membrane lipids  , riboflae lipids . Seven Pubstrate . Among tP. infestans-tomato interaction, we integrated the model with dual-transcriptome data obtained from RNA, isolated from tomato leaves inoculated with P. infestans strain 1306 . The infections showed profuse sporulation at around 4 days postinoculation (dpi). In previous studies, it was found that in infections by this P. infestans strain, marker genes for biotrophic growth peak at 2 to 3 dpi, and those for necrotrophic growth peak at 4 to 5 dpi (P. infestans genome steadily increased over time (from 7% to 84%), the amount of reads mapping to the tomato genome decreased accordingly (from 87% to 8%)  . The tra% to 8%)  that cal% to 8%) , indicat% to 8%) , mostly f tissue . To obtaf tissue . This ref tissue , 16, 55.10.1128/mBio.00454-19.2FIG S2P. infestans and tomato. Download FIG S2, TIF file, 1.3 MB.Statistics of the RNA sequencing (RNA-seq) read mapping. (A) Per-base quality values for the Illumina reads  derived from the FastQC software. (B) Mapping efficiency per sample for Copyright © 2019 Rodenburg et al.2019Rodenburg et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. infestans reactions are enriched for several primary metabolic pathways, such as amino acid biosynthesis, glycolysis, and the TCA cycle. This could be a response to sugars in the apoplast that are still produced by photosynthesizing tomato leaf cells during the biotrophic phase of infection  under perturbations  is often referred to as robustness . We can oduction . This reoduction . This sual genes .10.1128/mBio.00454-19.6TABLE S1P. infestans-tomato model iSR4578, the associated pathways, formulas, and genes, and their occurrence in the 25 submodels. Reactions that were added during the reconstruction process are listed, as well as essential genes per submodel. Download Table S1, XLSX file, 1.0 MB.Reactions in the Copyright © 2019 Rodenburg et al.2019Rodenburg et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.00454-19.7TABLE S2Table S2, XLSX file, 0.01 MB.Nutrients imported per scenario (objective function). Download Copyright © 2019 Rodenburg et al.2019Rodenburg et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.00454-19.8TABLE S3Table S3, XLSX file, 0.2 MB.Flux coupling analysis results. The cross-species couplings and the flux couplings to transport reactions are shown. Download Copyright © 2019 Rodenburg et al.2019Rodenburg et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. infestans while colonizing tomato, we assessed the essentiality of the host-pathogen transport reactions in each submodel  for each host-pathogen transport reaction per submodel. The Copyright © 2019 Rodenburg et al.2019Rodenburg et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. infestans at 2 days and 12 h (2d/12h) and 5 days and 12 h (5d/12h) postinoculation. To relate the detected metabolites to metabolic fluxes, we hypothesized that metabolites strongly decreasing in abundance (log2 fold change <−2) between these two time points are produced in the submodel of 2d/12h, and metabolites that increase (log2 fold change >2) are produced in the submodel of 5d/12h  Adding metabolome data leads to an increase or decrease in reactions in various pathways. The analysis was done on two submodels, 2d/12h (left) and 5d/12h (right). Negative bars indicate reactions that were removed in the metabolome-guided (T + M) submodels compared to the transcriptome-only (T) submodels, and positive bars indicate reactions that were added. Download FIG S4, TIF file, 0.8 MB.Integration of metabolome data in the model. (A) Volcano plot of the metabolome data, highlighting metabolites that show a significant logCopyright © 2019 Rodenburg et al.2019Rodenburg et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. infestans to generate hypotheses about their relationship. Our metabolic model of the P. infestans-tomato interaction represents one of the few integrated pathogen-host metabolic models published to date  in 19 oomycete proteomes which were downloaded from FungiDB (2 June 2018)   (Table Sne 2018) , as prevne 2018) . We selesequence , masked b server  to predirthologs . To avoiP. infestans model identifiers, the reaction identifiers of the tomato model were matched to MetaNetX reaction identifiers for which the associated formula was retrieved. According to knowledge of thiamine biosynthesis in plants  using the RAVEN (v2)  and COBRd et al. , definedP. infestans isolate 1306. For the plant infection assays, a zoospore suspension was prepared as described previously  sequence reads were obtained using an Illumina NextSeq 500 platform, and quality was assessed using FastQC (v0.11.8) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The reads were independently mapped to the P. infestans T30-4 (RNA and metabolites were isolated from tomato leaflets (cv. New Yorker) inoculated with eviously  and adjuv0.11.8)   ultraperformance liquid chromatography (UPLC)-tandem mass spectrometry (MS/MS) using positive-ion mode electrospray ionization (ESI), RP-UPLC-MS/MS with negative-ion mode ESI, and hydrophilic interaction-UPLC-MS/MS with negative-ion mode ESI. The area under the curve method was used to quantify peaks. For each time point, four biological replicates were analyzed. Differential abundance of metabolites was determined using of <0.05 . We impuThe transcriptome-based submodels were generated using the INIT algorithm , which pijw is a weight for each reaction i and condition j in the full model, ijE is the transcript abundance value summarized per reaction , and x̄i is the mean expression of that reaction across samples. An inflation factor (+1) was added in the denominator to prevent inflated weights for extremely low expression values. According to Agren et al. (w of −5 ≤ ijw ≤ 10. Reactions with an expression lower than the average (thus negative weight) will be less likely to be included in the submodels, and reactions expressed above average (thus a positive weight) will be more likely to be included. Transport reactions were assigned a weight of −0.1, based on the hypothesis that these should not be “free” to include  but should only have a minimal influence on the submodel solution. Reactions without associated genes were given a weight of 0. For the MILP optimization, the flux of all biomass precursors of both tomato and P. infestans was constrained to be >1.n et al. , we mainPRJNA516028.Transcriptome data were deposited in the NCBI Sequence Read Archive under BioProject number 10.1128/mBio.00454-19.9TABLE S4Table S4, XLSX file, 0.7 MB.Gene expression values summarized per reaction and the quantitative metabolome data of the tomato leaf infection. Download Copyright © 2019 Rodenburg et al.2019Rodenburg et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


Mycobacterium tuberculosis (Mtb) inside the phagosome, a compartment inside phagocytes for killing pathogens and other foreign substances. We have developed a combined model of Mtb and human metabolism, sMtb-RECON and used this model to predict the metabolic state of Mtb during infection of the host. Amino acids are predicted to be used for energy production as well as biomass formation. Subsequently we assessed the effect of increasing dosages of drugs targeting metabolism on the metabolic state of the pathogen and predict resulting metabolic adaptations and flux rerouting through various pathways. In particular, the TCA cycle becomes more important upon drug application, as well as alanine, aspartate, glutamate, proline, arginine and porphyrin metabolism, while glycine, serine, and threonine metabolism become less important. We modeled the effect of 11 metabolically active drugs. Notably, the effect of eight could be recreated and two major profiles of the metabolic state were predicted. The profiles of the metabolic states of Mtb affected by the drugs BTZ043, cycloserine and its derivative terizidone, ethambutol, ethionamide, propionamide, and isoniazid were very similar, while TMC207 is predicted to have quite a different effect on metabolism as it inhibits ATP synthase and therefore indirectly interferes with a multitude of metabolic pathways.Little is known about the metabolic state of  Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis, is an intracellular pathogen that thrives inside the phagosome of the host's macrophages  of metabolism have been proposed as efficient tools to explore bacterial metabolism, even in conditions difficult to access experimentally. Flux balance analysis (FBA) is a widely used approach to study GEMs and relies on the definition of an objective function that characterizes the metabolic objective of the organism under study  bounds on unidirectional  reactions.Model sMtb and RECON 2.2 were constrained as described in Rienksma et al. , using rAfter assigning counts to the reactions in both sMtb and RECON 2.2 a condition-specific biomass reaction was obtained for sMtb as described in Rienksma et al. . For RECfr. There exists a range wherein biomass formation and enzyme usage are balanced. An fr value of 0.8 was used for these calculations ; b represents a vector with zeros; l represents a vector with lower bounds for all fluxes and u represents a vector with upper bounds for all fluxes. The lower bounds for the nutrients available to Mtb in model sMtb-RECON were set according to the condition-specific biomass reaction obtained for the macrophage part.wherein Afterwards, the bi-objective optimization problem is altered such that the objective function value that was obtained is set as a constraint. A new optimization problem is formulated to calculate the minimal flux through the reaction(s) affected by the respective drug:subject to:w' represents the new objective function value and cd, i represents the drug weight vector for the current drug, i. Finally, a third optimization problem is formulated:Wherein subject to:fd is a value that is gradually lowered from 1 to 0 to represent increasing drug dosages, wherein a value of 1 represents no drug is applied or total ineffectiveness of the drug, and a value of 0 represents total effectiveness of the drug.Wherein Model sMtb-RECON has a total of 8,987 reactions and 13.4% are from Mtb. Model sMtb-RECON contains 6,373 metabolites and 2,605 genes, of which 16.5 and 35.7% are from Mtb, respectively. RECON 2.2 has 9 compartments in total (number of metabolites indicated between brackets): cytoplasm , extracellular space (770), Golgi apparatus (312), lysosome (291), mitochondrion (756), nucleus (161), endoplasmic reticulum (675), peroxisome (440), and the mitochondrial intermembrane space (1). No phagosome compartment is available in RECON 2.2. There is a lysosome, but Mtb is known to block phagosome-lysosome fusion, therefore, the metabolites in this compartment are not likely to be available as nutrients for Mtb. Mtb is however assumed to acquire access to the cytosol. As this compartment contains the majority of the biomass precursors for the host, Mtb is assumed to have access to these. The cytosolic biomass precursors in model RECON 2.2 are thus set as metabolites that can be taken up by the Mtb part of model sMtb-RECON. A list of the metabolites and their maximal uptake rates is given in Mycobacterium bovis BCG, a close relative to Mtb . Then the combined model was constrained using the dual RNA seq data and condition-specific biomass reactions were obtained, for host and pathogen, by maximizing each human biomass precursor one-by-one for both the Mtb and the human part of sMtb-RECON. Afterwards, the constraints placed on sMtb-RECON were removed and the condition-specific biomass reaction of the human part of sMtb-RECON was used as a proxy for nutrient availability for the Mtb part of sMtb-RECON. The maximum allowable uptake rates of the Mtb part were thus limited to the maximum obtainable fluxes for each human biomass precursor. Thereafter, all reactions affected by a drug were gradually constrained as metabolism starts to reroute.Using this method, uptake and secretion profiles can be predicted. This allows prediction of metabolites that are taken up or secreted even if in the starting model no additional constraints are imposed to limit uptake or secretion rates, apart from the oxygen uptake rate. Mtb encounters a hypoxic environment inside the host . A comprehensive list of predicted uptake and secretion rates is given in Mtb is predicted to take up and secrete a plethora of different metabolites at varying rates –5. A comAlmost all amino acids are predicted to be taken up by Mtb . Most noin vivo  that is taken up in a relatively high amount . It is k−1). These three metabolites are closely related, as phosphatidylcholine can be converted to DAG and choline phosphate by phospholipase C , triacylglycerol (TAG), and phosphatidylcholine are predicted to be taken up as well. Most notably, DAG is taken up in relatively large amounts .Of the 12 drugs in For TMC207, the flux through alanine, aspartate, glutamate, arginine, and proline metabolism becomes relatively low , while tThe specific growth rates gradually approach zero upon the application of the drugs. The application of TMC207 is predicted to result in a relatively faster drop to zero growth rate (at 80% restriction of the flux through the affected reactions instead of 100%) .Mtb is under considerable stress from the host during infection. This situation can worsen with the application of drugs. As the dosage of a metabolic drug(s) increases, so does the pressure on Mtb to circumvent the effects of the(se) drug(s) by rerouting metabolism. The percentage of restriction, as shown in The host-pathogen model first created by Bordbar et al. , iAB-AMØBoth RECON 1 and RECON 2.2 describe a relatively similar number of genes 1,496 and 1,675, respectively for. The increase in the number of genes in sMtb-RECON compared with iAB-AMØ-1410-Mt-661 is mainly due to the more detailed description of metabolism of Mtb provided by sMtb compared to iNJ661 (on which the iAB-AMØ-1410-Mt-661 model was based). Model sMtb was seen to outperform previously published models, among which iJN661, regarding predictions on gene essentiality, metabolic state, and drug associated phenotype. Additionally, sMtb includes pathways, such as the cholesterol degradation pathway, specifically relevant for intracellular growth and survival. sMtb-RECON inherits the respective improvements of sMtb and RECON 2.2.in vivo conditions of Mtb and in both cases, the biomass reaction was modified through incorporation of gene expression data. However, important differences appear. In the case of AB-AMØ-1410-Mt-661, the microarray data described gene expression levels in the host  and EsxB (CFP-10)  reductase is one of the most effective ways to eradicate Mtb. This enzyme catalyzes 2-trans-enoyl ACP reduction and catalyzes the final step in fatty acid synthesis and is involved in mycolic acid synthesis. Inactivation of InhA results in cell wall alterations and eventually lysis of the cell (Duan et al., M. bovis BCG 24 h post infection (Rienksma et al., Nevertheless, both mycolic acids and arabinogalactan are part of the condition-specific biomass reaction of Mtb. The condition-specific biomass reaction of Mtb is based on RNA sequencing data derived from The reason the predicted metabolic state of TMC207-affected Mtb differs from the other drugs is due to the function of AtpE as an ATP generating enzyme involved in respiration. ATP is on the one hand a direct biomass precursor in the condition-specific biomass reaction, but it is also required to synthesize almost all other biomass precursors. In addition, ATP is required to satisfy the non-growth associated maintenance constraint. As such, the effect of constraining AtpE is not nearly as straightforward as the effect of constraining InhA or EmbB. This effect can be seen in Model RECON 2.2 is a general model of human metabolism. A macrophage is however, a very specialized human phagocytic cell, which engulfs and digests pathogens in a specialized compartment, the phagosome. An important mechanism of pathogen killing by phagocytes involves generating the superoxide anion, which reacts with iron sulfur clusters in the pathogen, releasing iron and subsequently damaging DNA Hurst, . RECON 2Divalent metal cofactors such as iron, manganese, and zinc are essential for Mtb virulence (Zondervan et al., The question remains whether these predictions are accurate enough to warrant pinpointing specific genes and their corresponding enzymes as drug targets. Previous modeling efforts have shown a poor predictive power of essential genes, using a bi-objective optimization strategy (Rienksma et al., Understanding the metabolic rerouting upon drug administration can lead to the identification of new metabolic bottlenecks, the identification of new targets and in the long run the development of new therapies based on combination of drugs. Moreover, detailed analysis of the mechanisms deployed by Mtb to counteract the impact of drugs might offer insights on the role of genetic modifications related to the development of drug resistances.RR drafted the manuscript, conceived the study, and performed the data analysis. MS-D participated in drafting the manuscript and helped with the data analysis. PS and VM participated in drafting the manuscript. All authors contributed to the study design and critically read, revised, and approved the manuscript.VM is the majority owner and managing director of Lifeglimmer GmbH. All other authors declare no conflict of interest.