Photorhabdus luminescens—the source of modern Lux reporters—while literature data has been used for LuxEC. Importantly, the data show clear evidence for previously unreported product inhibition for the LuxAB reaction. Model simulations show that predicted bioluminescent profiles can be very different from changes in gene expression, with transient peaks of light output, very similar to light output seen in some experimental data sets. By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activity from the bioluminescence. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology.The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has also been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for euthanasia. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a computational approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. This approach is based upon a new mathematical model for Lux activity, that includes the actions of LuxAB, LuxEC and Fre, with improved mechanisms for all reactions, as well as synthesis and turn-over of Lux proteins. The model is calibrated with new experimental data for the LuxAB and Fre reactions from  Bioluminescent reporters are used in many areas of biology as fast, sensitive and non-destructive measures of gene expression. They have been developed for bacteria, adapted now for other kinds of organisms, and recently been used for whole cell biosensors, and for real-time live animal models for infection without the need for euthanasia. However, users of Lux technologies rely on the light output being similar to the gene expression they wish to measure. We show that this is not the case. Rather, there is a nonlinear relationship between the two: light output can be misleading and so limits the way that such data can be interpreted. We have developed a new computational method that, for the first time, allows users of Lux reporters to infer accurate gene transcription levels from bioluminescent data. We show examples where a small decrease in light would be better interpreted as promoter being switched off, or where an increase in light would be better interpreted as promoter activity for a longer time. These genes, including their order (luxCDABE), are conserved in all lux systems of bioluminescent bacteria. An additional gene (luxF), with homology to luxA and luxB, is located between luxB and luxE in some species. The light emitting reaction, catalysed by the LuxAB complex, involves the oxidation of FMNH2 and the conversion of a long chain fatty aldehyde  to its cognate acid, with the emission of blue-green light. LuxC, D and E together form the fatty acid reductase complex, involved in a series of reactions that recycle the fatty acid back to aldehyde. In E. coli and other species, Fre has been shown to be the enzyme responsible for flavin reduction back to FMNH2.The lux operon contains genes for the bacterial bioluminescent reaction , 2: luxAGene expression can be measured by cloning a promoter of interest upstream of the lux operon, and interpreting the bioluminescence from bacteria containing such constructs as a measure of transcription , 4. ThisHowever, this light is an integrated signal of transcription, mRNA half-life, translation and protein turn-over, the bioluminescence reaction kinetics and substrate availability and cycling. As a consequence, absolute transcription activity cannot be directly inferred from the data generated. This is a limitation in the current use of Lux technologies. For example, some studies using Lux reporters have observed fluctuations in light output –16, and Photorhabdus luminescens LuxAB that we have purified. An important part of our approach is to fit the models directly to the time series experimental data, which can be thought of as a  factorial experiment with varying concentrations of both FMN and NADPH. This allows for the simultaneous inference of all parameters in complex kinetic models, which is an advantage over traditional chemical kinetics techniques that only use the maximal velocity.In this paper we show that detailed mathematical models for bioluminescence can be used to relate bioluminescence to promoter activity. We developed a new model that addresses limitations of previous work , whose s2 as an energy source. This is an energetically transient species with a short half-life. In vivo it is supplied to the luciferase complex for immediate consumption by the redox activity of the Fre enzyme, which converts NADPH and FMN to FMNH2 and NADP+. We measured the kinetics of the Fre reaction ; in all cases, the initial NADPH concentration was 200μM. The initial velocity of the reaction increased with increased FMN, leading to different kinetics in the three curves, with the steady state being reached more rapidly with increased FMN concentration.The luciferase reaction uses FMNHreaction  by recorμM, 10μM and 100μM  kinetic reactions, we combined components of the coupled Fre-LuxAB reaction. We measured light output arising from different initial concentrations of FMN: 10nM, 100nM, 1nd 100μM . For allto 400uM . Productth LuxAB , with onThe new mathematical model includes three chemical reactions: the Fre reaction for flavin recycling; the LuxAB reaction for light production; and the LuxEC reaction for aldehyde recycling. It also contains a further equation to describe the turnover dynamics of the Lux proteins:R0 is constant and ρ0 = τR0. Control of the bioluminescent reactions is assumed to lie with the most rapidly turned-over protein. This is modelled by setting P(t) to represent all of the Lux proteins and using the same rate of synthesis and turn-over for all proteins. The protein production T(t) encompasses transcription, translation and mRNA degradation and is supplied as a model input; it is assumed that the lux mRNA is at quasi steady state. The value of the protein turn-over rate γL has been inferred from Lux data as 0.378h−1  over the model fit previously reported  overnight cultures were grown aerobically at 37°C in in Tris Minimal Succinate medium  for plasmid maintenance Bacterial pellets were washed once in TMS without sodium succinate (TM) and resuspended in an equal volume of TM. These were diluted 1/50 into fresh TM supplemented with filter sterilized sugars (Glucose or glucose-6-phosphate) supplemented with Erythromycin (5μgml−1).The promoter of the Universal hexose transporter (rminator  using a N4220 pSB09 overniμl) were placed into the wells of a 96-clear-bottom microtiter plate (Porvair) and incubated at 37°C in a Tecan Genesis Pro microplate reader. Optical density (600nm), fluorescence (485ex/510em) and bioluminescence (RLU) readings were taken at 30 min periods over the course of the experiment.For growth and reporter gene measurements, replicate samples  decreases while cells are still in exponential phase (corresponding OD measurements). We estimate the turn-over rate for each such curve  by fitting a linear line to the log transformed data. The histogram of estimated values are shown in In order to estimate the kinetic parameters for all three reactions, we used a similar Bayesian approach based on Markov Chain Monte Carlo (MCMC), in which a sub-model relevant to each reaction is used along-with corresponding experimental data. We used an adaptive version of Metropolis-Hasting MCMC algorithm with global scaling  in orderλ = 100) for kinetic parameters, while more informative gamma priors ) for degradation rates. This reflects our a priori knowledge that the kinetic parameters (ratio of kinetic rate constants) are all positive real numbers. For those parameters which are common to the inference in both the Fre and LuxAB models, prior estimates for the LuxAB inference were generated by fitting exponential distributions to the posterior estimates derived from the the Fre inference.Unless otherwise specified, we used un-informative exponential priors (θ), we simulate the species of interest (stored in vector Y′), and use the corresponding experimental data in vector Y to give the likelihood function:We define the likelihood of the parameters, for any of the models using in τ, in case of Fre and LuxEC inference, while for LuxAB data, we estimated the noise variance from the replicates. Details on the derivation of the Gibbs step is provided in A separate Gibbs step is introduced for the sampling of noise precision n is an exponential distribution with mean value equal to the current height at the previous point n−1. At each step, a point is chosen at random, and a new height is proposed, as described in Green 1995 [A Monte Carlo approach was used to infer promoter activity from light readout. The promoter input function is modelled as a series of K heights at fixed positions. A Martingale prior distribution is used , so thateen 1995 . The likS1 Text(PDF)Click here for additional data file.


Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as  E. coli. This method could be used to quantify the enzyme cost of many other pathways and explain why evolution has selected some low-yield metabolic strategies, including aerobic fermentation in yeast and cancer cells. Furthermore, future metabolic engineering projects could benefit from our method by choosing pathways that reduce the total amount of enzyme required for the synthesis of a value-added product.“Enzyme cost”, the amount of protein needed for a given metabolic flux, is crucial for the metabolic choices cells have to make. However, due to the technical limitations of linear optimization methods, this cost has traditionally been ignored by constraint-based metabolic models such as Flux Balance Analysis. On the other hand, more detailed kinetic models which use ordinary differential equations to simulate fluxes for different choices of enzyme allocation, are computationally demanding and not scalable enough. In this work, we developed a method which utilizes the full kinetic model to predict steady-state enzyme costs, using a scalable and robust algorithm based on convex optimization. We show that the minimization of enzyme cost is a meaningful optimality principle by comparing our predictions to measured enzyme and metabolite levels in exponentially growing  PLOS Computational Biology Methods paper.This is a E. coli, efforts to exhaustively map metabolic networks are only nearing completion on the stoichiometric level. Our understanding of metabolic fluxes, their dynamic regulation and their connection to cell fitness is far from perfect /[A] < Keq where the reaction’s equilibrium constant Keq is determined by the Haldane relationship, B]/[A] < 1, i.e., ln[B] − ln[A] < 0, putting a linear boundary on the feasible region (B]/[A] ≈ Keq), the enzyme demand E2 approaches infinity. Beyond the boundary ([B]/[A] > Keq) no positive flux can be achieved (grey region). Such a threshold exists for each reaction , i.e., the driving force is positive whenever Q < Keq, and it vanishes if Q = Keq. How is this force related to enzyme cost? A reaction’s net flux is given by the difference v = v+ − v− of forward and backward fluxes, and the ratio v+/v− depends on the driving force as v+/v− = eΘ. Thus, only a fraction v/v+ = 1 − e−Θ of the forward flux acts as a net flux, while the remaining forward flux is canceled by the backward flux  and allosteric regulation. Thus, one must model these factors using the available kinetic information [E, forward catalytic constant −1), driving force , and on kinetic effects such as substrate saturation or allosteric regulation. If all active fluxes are positive, reversible rate laws like the Michaelis-Menten kinetics in The prediction of optimal metabolite and enzyme levels can be extended to models with general rate laws and complex network structures. In general, enzyme demand depends not only on driving forces and ormation , 40, or ormation , 42. Theηrev and ηkin  to emphasize this fact. The cost function is defined on the metabolite polytope To determine the enzyme demand of an entire pathway, we sum over all reactions: ld cause , 45. Thuq reflects a trade-off between fluxes to be realized and enzyme expression to be minimized, where the relation between fluxes and enzyme levels is not fixed, but depends on metabolite log-concentrations x. Wherever trade-offs exists in biology, it is common to assume that evolution converges to Pareto-optimal solutions [The cost function olutions , i.e. caenzyme cost minimization (ECM), is a convex optimization problem and can be solved with local optimizers. Enzyme demand and enzyme cost functions, for single reactions or pathways, are differentiable, convex functions on the metabolite polytope. This convexity holds for a variety of rate laws, including rate laws describing polymerization reactions [q(x) has a broad optimum as in KM values), can thus be handled in two ways: either, by using a simplified EMC function that does not require this parameter, or by relying on parameter values chosen by the workflow.The resulting method, which we call eactions , and eveη ≤ 1, simplified enzyme cost functions with fewer parameters can be obtained. For example, ηrev = 1 would imply an infinite driving force Θ → ∞ and a vanishing backward flux, ηkin = 1 implies full substrate saturation, as well as full allosteric activation and no allosteric inhibition . In these limiting cases, enzyme activity will not be reduced, and enzyme demand will be given by the capacity-based estimate Beyond minimizing the total enzyme cost, one can also use ECM to analyze the individual enzyme demands. When the metabolite levels are known, the demand can be directly calculated and each efficiency factor in kcat values, equilibrium constants, or even KM values), one may choose between different cost functions with different data requirements: EMC0 , EMC1 , EMC2 , EMC3 , and EMC4 functions . Details of the simplified EMC functions are given in Depending on the data available  and equilibrium constants, which need to be known. This factor approaches infinity as reactions reach equilibrium (i.e. where Θl → 0), which is what forces reactions away from equilibrium during cost minimization −1  is that they are based on reaction , 49 and n.ac.il/ . As in tlike MDF  and mTOWlike MDF  have beeηkin = 1. Just like the reversibility factor ηrev, the kinetic factors ηsat and ηreg can be used to define tighter constraints on metabolite levels. However, unlike ηrev, the kinetic terms may take various forms and contain many kinetic parameters. To obtain simple, but reasonable formulae in EMC3, we first consider rate laws in which enzyme molecules exist only in three possible states: unbound, bound to all substrate molecules, or bound to all product molecules. Metabolites affect the rate only through the mass-action terms S = ∏i(si/KMi) (for substrates) and P = ∏jpi/KMj (for products), and the degree of saturation is determined by ηsat = S/(1 + S + P), where the formula effectively has two Michaelis-Menten constants: one for substrates and one for products . EMC3 represents a balance between complexity and requirement for kinetic parameters, and is a practical cost function if simple, realistic rate laws are desired. The EMC4 functions, finally, represent general rate laws and ηkin can take many different forms depending on mechanism and order of enzyme-substrate binding. Again, for simplicity, we resort to analyzing only a small set of relatively general templates for EMC4, known as convenience kinetics [The next logical step is to relax the assumption that kinetics  or modulkinetics . NeverthE. coli central metabolism, containing three major pathways: glycolysis, the pentose phosphate pathway, and the TCA cycle (see −15 liters) [10-scale) corresponds to a typical fold error of 10RMSE = 2.6. In line with the measured protein levels, the predicted enzyme levels tend to be larger in glycolysis than in TCA and pentose phosphate pathway, reflecting the larger fluxes and less-favorable thermodynamics. All predictions including metabolite concentrations, thermodynamic forces and c/KM ratios can be found online at the accompanying website www.metabolic-economics.de/enzyme-cost-minimization/.To benchmark our optimality-based prediction of metabolite, we applied ECM to a model of  liters) . EMC4 prWe note that predicted enzyme levels become more accurate as more complex cost functions are used, with a prediction error decreasing monotonically from 1.35 with EMC0 to 0.42 with EMC4. The capacity-based enzyme cost (EMC1) assumes that enzymes operate at full capacity . Taking all of these aspects into account would create very detailed models but at the price of considerably increasing the demand for data. Here, we obtained a flexible modeling method by combining the two main modeling approaches, constraint-based and kinetic modeling, in a new way: with fixed metabolic fluxes, kinetic models are used to determine a cost-optimal state. The tiered approach in ECM allows for different levels of detail, which can easily be matched to the amount of existing data. The minimal requirement for running ECM is to have a metabolic network with given steady-state fluxes, while the maximal requirement would be a fully parameterized kinetic model. The method applies to individual metabolic pathways and, theoretically, entire metabolic networks. No matter if we model exponentially growing cells, microbial cells in stationary phase, or non-growing eukaryotic cells, the sum of enzyme costs per unit flux is a meaningful objective for pathways used by the cell. Although similar approaches exist in dynamic modeling , 56 and 1. Solving the enzyme optimality problem in metabolite space One way of modeling the cost and benefit of enzymes is to study kinetic models and to treat enzyme levels as free variables to be optimized. However, this calculation can be hard because enzyme profiles may lead to one, several, or no steady states, and the resulting optimality problem can be non-convex. By fixing fluxes and using metabolite concentrations as our primary variables, we drastically simplify this optimization problem. Flux directions and the second law of thermodynamics impose constraints that define a set of feasible metabolite profiles, the metabolite polytope. This polytope is used here as a space for screening, sampling, and optimizing metabolic states; accordingly bounds on metabolite concentrations or driving forces can be easily formulated as linear constraints. Using log-concentrations as free variables, and given a (steady and non-steady) flux distribution, we can parametrize the set of metabolic states very easily: we simply consider all feasible metabolite profiles and compute, for each of them, the corresponding enzyme profile by taking the inverse rate laws. With enzyme levels as free variables, parameterizing the set of metabolic states would be much more complicated.2. Convexity The metabolite polytope not only provides a good search space, but it also facilitates optimization because enzyme cost is a convex function of the metabolite log-concentrations , it wou3. Separable rate laws disentangle individual enzyme cost effects To assess how different physical factors shape metabolic states, we focused on separable rate laws, which lead to a series of easily interpretable, convex cost functions. The terms in these functions represent specific physical factors and require different kinetic and thermodynamic data for their calculation. By neglecting some of the terms, one obtains different approximations of the true enzyme cost. The more terms are considered, the more precise our predictions about metabolic states becomes . Fo for MDF , pathway for MDF , and Con for MDF . Thermod for MDF , 57, 58 5. Kinetics-based flux cost functions for flux balance analysis Accordingly, results from ECM can be used to improve flux analysis [kcat values and enzyme sizes) could be adjusted by dividing them by efficiency factors obtained from our workflow. In FBA , flux cost or enzyme demand are linear functions of the fluxes. Enzyme Cost Minimization allows us to compute plausible prefactors for this formula from detailed knowledge of enzyme kinetics: by rearranging ηrev = ηkin = 1 and that cost is expressed in terms of protein mass in Daltons Apw = vpw/q.analysis , 23 by d6. Embedding ECM into flux analysis Furthermore, ECM could be “embedded” into FBA by screening a finite set of possible flux distributions, characterizing each of them by quantitative cost (using ECM) and choosing the most cost-favorable mode. Since we now know that any metabolic state that has maximal specific rate is an elementary flux mode [lux mode , it woulLactococcus lactis, which uses the exact same enzyme expression profile for completely different anaerobic growth modes [Lactococcus lactis experiments. This stands in contrast to aerobic fermentation in E. coli, which seems to be explained well by predictable shifts in protein allocation [ECM is based on the central assumptions that the metabolic states of cells are cost-optimized and that cost arises from cellular protein levels. Both assumptions are of course debatable. There is ample evidence that cells assume apparently sub-optimal states in order to maintain robust homeostasis or to gain metabolic flexibility for addressing future challenges . For exath modes : slow grlocation .minimal protein investment that would be required to support a certain metabolic state. In metabolic engineering, such predicted investments may be used to rule out potential, but uneconomical metabolic pathways. On the other hand, ECM can be used as a background model to be compared to more complicated optimality-based cell models. Such comparisons can allow us to quantify the impact of other fitness objectives in units of “protein cost”, to learn which objectives can best explain cellular behavior, and to describe non-optimality as a deviation from a presumable cost-optimal state.As these examples show us, the importance that certain cells attribute to saving on protein costs is highly variable and, in some cases, can be negligible: for instance, when protein levels are already low or when protein demands change quickly and unpredictably. Moreover, random fluctuations in protein levels will be tolerable as long as the impact on fitness is not very high. Nevertheless, we think that a simple principle of cost optimality as in ECM can be a useful heuristics. On the one hand, it can reveal the h(E)). In particular, metabolite levels may be under additional constraints or optimality pressures because they appear in pathways outside our model, which may favor high or low levels of the metabolites. Also chemical molecule properties, such as hydrophobicity or charge, may affect the preferable metabolite levels in cells [b(c)(x) that penalize low ATP levels  and sufficient substrate levels (for saturation efficiency). Trade-offs between these requirements, together with predefined bounds, will shape the optimal metabolite profiles [kcat values, we may expect a strong pressure on sufficient driving forces and substrate levels.profiles : in a li3. Enzyme profiles reflect global effects of enzyme usage If enzyme profiles follow a cost-benefit principle, costly enzymes should provide large benefits. Such a correspondence has been predicted, for example, from kinetic models in which flux is maximized at a fixed total enzyme investment [vestment : in optiBeyond the analysis of central metabolism, ECM can be applied to select candidate pathways in metabolic engineering projects. A prediction of enzyme demands or specific activities  or non-stationary (e.g. from dynamic 13C labeling experiments [l ⋅ vl > 0), and thus l = 0). Close to these faces, enzyme cost goes to infinity.A metabolic network with given flux directions, equilibrium constants, and metabolite bounds defines the eriments ). If theE, its forward catalytic constants x. While the enzyme level and are concentration-dependent, unitless, and can vary between 0 and 1. The reversibility factor ηrev depends on the driving force , and the equilibrium constant is required for its calculation. The saturation factor ηsat depends directly on metabolite levels and contains the KM values as parameters. Allosteric regulation yields additive or multiplicative terms in the rate law denominator, which in our example can be captured by a separate factor ηreg. In general, ηsat and ηreg can be combined into one kinetic factor ηkin, as depicted in According to  factors . In Fig v, and contains the terms from the rate law in inverse form multiplied by the enzyme burden hE. The left-hand part of the equation, ηkin can be split into 1/ηsat ⋅ 1/ηreg. By omitting some of these factors, one can construct simplified enzyme cost functions with higher specific rates, or lower enzyme demands  that are non-linear, but convex over E. Some EMC functions are strictly convex  holds with a < sign instead of ≤), while others are not (e.g. EMC2). The most simplified EMC functions are actually constant (as in EMC0 and EMC1). To find an optimal state, we choose an EMC function and minimize the total enzyme cost within the metabolite polytope. Optimal metabolite profiles, enzyme profiles, and enzyme costs are obtained by solving the enzyme cost minimization (ECM) problemEnzyme cost minimization (ECM) uses a metabolic network, a flux profile q (defined in q(x) and the metabolite polytope itself are convex, ECM is a convex optimization problem. The optimal enzyme levels depend on external conditions and have to be recalculated after any change in external metabolite levels. There are cases where q(x) is convex, but not strictly convex, and therefore λ is a small, heuristically chosen weighing factor .E. coli model, ATP is one such important boundary metabolite: if we allowed for a lower ATP level, ATP could be produced at a lower enzyme cost because of the more favorable driving forces. If we do not fix the ATP concentration, but define an allowed range, ECM would choose the lowest possible ATP level; thus, if the allowed ATP range is too broad, no meaningful predictions can be expected. In a model of ATP-consuming biosynthesis pathways, the situation would be exactly the opposite: here, it is a high ATP level that would lead to higher driving forces and to lower enzyme requirements. Whole-cell models contain both types of pathways—ATP-producing and ATP-consuming ones. In such a model, ECM could predict some meaningful compromise, i.e. an intermediate ATP level that minimizes the enzyme cost of ATP production plus the enzyme cost of biosynthesis. Since in our central metabolism model there are only 3 reactions that produce or consume ATP, it is unlikely that so few reactions would be representative of the cost tradeoff between the dozens of enzymes that use ATP in the full metabolic network. Therefore, we chose to fix the ATP level to its measured value (∼3 mM).A key point in ECM is the choice of metabolite levels on the model boundary. If we predefine all these metabolite levels, our pathway will be “isolated” from the rest of the network, and any information about the surrounding network can be safely ignored. In our E. coli’s proteome, this effect is probably not so severe. In any case, this problem can be easily fixed by imposing constraints or fixed concentrations for central metabolites such as pyruvate .Our use of small-scale pathway models, which ignore most of the metabolic network, is therefore justified: as long as we predefine all metabolic fluxes and all metabolite levels at the pathway boundary, pathways can be modeled separately and the models can later be combined without any adjustment. This makes the ECM approach fully modular. All the input parameters  are directly obtained from measurements without any further tuning. By setting all relevant fluxes and boundary metabolite concentrations to their measured values, we isolate our submodel from any effects that the surrounding network might have on predicted enzyme cost. Finally, there may be metabolites that are “free” in a model, but that affect enzymes that are not in the model . By neglecting these enzymes, we ignore some of the complex compromises between them, and the predicted metabolite concentration may be wrong. In our specific case of central metabolism, whose enzymes comprise a very large fraction of q) and choose a tolerable cost qtol . This defines a tolerable region in Evolution could tolerate non-optimal enzyme costs; this tolerance depends on population dynamics and can sometimes be quite significant, e.g. in small isolated communities. To compute realistic tolerance ranges for the ECM problem, we start from the optimum . Errors or uncertainties in these constants will cause errors or uncertainties in the predicted enzyme profiles. To estimate these uncertainties, we considered a joint distribution of all model parameters, describing both the uncertainties of individual parameters and the correlations between dependent parameters. This probability distribution was directly obtained from parameter balancing ; then we sampled metabolite profiles in a much wider range, by sampling convex combinations of extreme points in the metabolite polytope . As shown in effective parameters, which may differ from “ideal” parameters, e.g., by crowding effects. However, since measured parameter values are usually incomplete and inconsistent, parameter balancing [To predict enzyme and metabolite levels in metabolic pathways we developed an automated workflow . In a coalancing  is used alancing .E. coli central metabolism  were obtained from the BRENDA database , and a complete, globally consistent parameter set was determined by parameter balancing. During ECM, all metabolite levels were limited to predefined ranges, and the levels of cofactors and some other metabolites were fixed at experimentally known values. To compute tolerances for predicted metabolite and enzyme levels, we defined an acceptable enzyme cost, one percent higher than the minimal value, and determined ranges for metabolite levels that agree with this cost limit. The enzyme cost function accounts for protein composition, giving different costs to different amino acids. However, models with equal cost weights for all proteins, or with size-dependent protein costs yielded similar results (results are provided on the website). Data, model, and MATLAB code for ECM can be obtained from www.metabolic-economics.de/enzyme-cost-minimization/.The model shown in S1 Text(PDF)Click here for additional data file.


Escherichia coli polyculture collectively expressing 15 exogenous or modified pathway enzymes from diverse plants and other microbes. This synthetic consortium-based approach enables the functional expression and connection of lengthy pathways while effectively managing the accompanying metabolic burden. The de novo production of specific anthocyanin molecules, such as calistephin, has been an elusive metabolic engineering target for over a decade. The utilization of our polyculture strategy affords milligram-per-liter production titers. This study also lays the groundwork for significant advances in strain and process design toward the development of cost-competitive biochemical production hosts through nontraditional methodologies.Fermentation-based chemical production strategies provide a feasible route for the rapid, safe, and sustainable production of a wide variety of important chemical products, ranging from fuels to pharmaceuticals. These strategies have yet to find wide industrial utilization due to their inability to economically compete with traditional extraction and chemical production methods. Here, we engineer for the first time the complex microbial biosynthesis of an anthocyanin plant natural product, starting from sugar. This was accomplished through the development of a synthetic, 4-strain  To efficiently express active extensive recombinant pathways with high flux in microbial hosts requires careful balance and allocation of metabolic resources such as ATP, reducing equivalents, and malonyl coenzyme A , as well as various other pathway-dependent cofactors and precursors. To address this issue, we report the design, characterization, and implementation of the first synthetic 4-strain polyculture. Division of the overexpression of 15 enzymes and transcription factors over 4 independent strain modules allowed for the division of metabolic burden and for independent strain optimization for module-specific metabolite needs. This study represents the most complex synthetic consortia constructed to date for metabolic engineering applications and provides a new paradigm in metabolic engineering for the reconstitution of extensive metabolic pathways in nonnative hosts. Microbial communities are ubiquitous in nature. In much the same way that multicellular eukaryotic organisms have evolved to contain specialized organelles that work together to seamlessly perform specialized tasks, communities of unicellular organisms have evolved similar divisions within their populations, such that microbial consortia amount to more than simply a sum of individual parts –5. These–The study and application of natural microbial consortia have been topics of interest in the scientific literature for decades –8; howev–9–de novo production of flavan-3-ols and anthocyanidin-3-O-glucosides in microbial hosts , the predominant red pigment in strawberries  represents a major milestone for the de novo production of phenylpropanoic acids , while the remaining 8 were targeted for caffeic acid production (TAL and HpaBC overexpression). The effect of the endogenous gene supplementation plasmid pCS-TPTA was also tested but did not show significant titer improvements for any of the tested combinations  and one caffeic acid production strain (R2) from the initial screen were subjected to further optimization to determine the full potential of these modules in monoculture. Through course optimization of the induction point, inducer concentration, production temperature, and carbon source, the highest-titer production to date was realized for both p-coumaric and caffeic acids at 2.51 ± 0.03 and 1.03 ± 0.02 g/liter, respectively  intermediate, was observed from all caffeic production modules both on solid as well as in liquid media. This observation is consistent with current literature and is assumed to be the primary factor limiting caffeic acid production  was conserved, with the fraction of the new module being varied to result in the first account of a functional synthetic four-strain polyculture. This microbial consortium enabled, for the first time outside of plants, the production of the anthocyanidin-3-O-glucoside callistephin from glucose (Our previous successes using polycultures for the production of flavonoids motivated the further application of this technology to expand what is currently possible  pathway . Transfo glucose .P < 0.05) of callistephin from glucose was achieved across a wide range of inoculation ratios, while conserving the optimum fermentation conditions from previous experiments.Addition of two additional enzyme overexpressions, MatBC, to the previously published C5 module further highlights the flexibility of the polyculture platform for rapid expansion and modification. These enzymes enable the uptake of externally supplemented malonate and subsequent activation to malonyl coenzyme A , a key and limiting substrate for the chalcone synthase (CHS) enzyme . Signifide novo production of various late-pathway flavonoid metabolites demonstrates the power of such approaches over traditional monoculture metabolic engineering efforts  is key to minimizing difficulty in this area. We do envision future applications, especially with products that are membrane associated or lipophilic, where significant effort will need to be performed to ensure transport to and from strain modules. Additionally, in systems where transport must be engineered, this limitation can also be used as an advantage to limit substrate availability to promiscuous enzymes in off-target strain modules.de novo production of high-value specialty flavonoids and anthocyanin chemicals.We have shown high sensitivity to small (less than 5%) changes in initial community composition, which readily facilitates fine-tuning of consortium productivity but could also lead to many unknowns upon scale-up to fed-batch, high-cell-density cultures. Additionally, as subsequent microbial modules are appended to the polyculture, its further expansion is limited by a fixed total biomass to be distributed across all modules. This shortcoming of polyculture-based strategies is minimized when an obvious rate-limiting module is identified, but it can be more pronounced when multiple limiting/bottlenecking modules exist and deplete space available for the more efficient modules in the pathway. Despite these limitations, we have demonstrated the ability of our polyculture-based approach to outperform previously reported efforts to enable the de novo production of flavan-3-ols for the first time outside of the native plant hosts. Further expanding on this polyculture theme, we incorporated a fourth module (Antho) containing the anthocyanadin synthase (ANS) and 3-O-glycosyltransferase (3GT) genes. Using all four modules, we also showcased, for the first time outside of plants, the production of the anthocyanidin-3-O-glucoside callistephin from glucose. This complicated feat was facilitated by the modularity of the polyculture platform, which conserves the genetic optimization of each module and only requires basic fermentation optimization to achieve peak production. The use of this polyculture-based approach has enabled a system complexity only rivaled by a few other success stories in the metabolic engineering literature , we demonstrate the terature , 34. OurE. coli DH5α was used to propagate all plasmids. BL21star(DE3), BL21star(DE3)ΔsucC ΔfumC  on pTrc-RgTALsyn  to create the constitutive expression plasmid pXylA. The PxylA promoter, although xylose inducible in B. megaterium, was shown previously to be constitutively expressed in E. coli  in pXPA-FapO-eGFP (PGAP promoter) (We replaced the T7-lac feature on pETM6 with the P E. coli . To thisromoter)  at restr10.1128/mBio.00621-17.1TEXT S1 TEXT S1, DOCX file, 0.05 MB.gBlock sequence for cloning pXylA. Download Copyright © 2017 Jones et al.2017Jones et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the d-thiogalactopyranoside). In the case of the phenylpropanoic acid production strains, 125-ml nonbaffled shake flasks containing 25 ml of medium were used to confirm small-scale screening studies, allow for more frequent sampling, and limit evaporation effects on final titer.The small-scale cultivation protocol was adapted from Jones et al.  with onlO-glucosides, where 518 nm was used. A representative high-performance liquid chromatography (HPLC) chromatogram for anthocyanin quantification is shown in t test with a 95% confidence interval.Analysis methods were slightly adapted from Jones et al. . A 25-μl10.1128/mBio.00621-17.2FIG S1 FIG S1, PDF file, 0.2 MB.HPLC chromatogram for callistephin quantification. Traces for the commercially available standard (Std) and 4-strain polyculture supernatant are shown. Download Copyright © 2017 Jones et al.2017Jones et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen’s metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments.The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of  Mycobacterium tuberculosis, the etiological agent of tuberculosis, to study host-pathogen interactions with a multi-omics approach. We collected metabolomics and complete transcriptome RNA sequencing  data of the infected macrophages, integrated them in a genome-wide reaction pair network, and identified metabolic subnetworks in host cells and M. tuberculosis that are modularly regulated during infection. Up- and downregulation of these metabolic subnetworks suggested that the pathogen utilizes a wide range of host-derived compounds, concomitant with the measured metabolic and transcriptional changes in both bacteria and host. To quantify metabolic interactions between the host and intracellular pathogen, we used a combined genome-scale model of macrophage and M. tuberculosis metabolism constrained by the dual RNA-seq data. Metabolic flux balance analysis predicted coutilization of a total of 33 different carbon sources and enabled us to distinguish between the pathogen’s substrates directly used as biomass precursors and the ones further metabolized to gain energy or to synthesize building blocks. This multiple-substrate fueling confers high robustness to interventions with the pathogen’s metabolism. The presented approach combining multi-omics data as a starting point to simulate system-wide host-pathogen metabolic interactions is a useful tool to better understand the intracellular lifestyle of pathogens and their metabolic robustness and resistance to metabolic interventions.Nutrient acquisition from the host environment is crucial for the survival of intracellular pathogens, but conceptual and technical challenges limit our knowledge of pathogen diets. To overcome some of these technical roadblocks, we exploited an experimentally accessible model for early infection of human macrophages by IMPORTANCE The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen’s metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments. The standard method to probe intracellular pathogen uptake of a specific substrate is based on infection phenotypes of auxotrophic strains  to evaluate the potential contribution of either organism to each of the assessed metabolite pools.t test) of ion intensities revealed that 233 (5%) of all detected ions showed a significant increase  in the presence of the maximally assayed, nonphysiological bacterial load of 20 bacteria per single THP-1 cell compared to THP-1 alone , Homo sapiens  (M. tuberculosis Mtb Lipid Database (http://mrl.colostate.edu/mtb/) and MycoMass (http://www.brighamandwomens.org/research/depts/medicine/rheumatology/Labs/Moody/default.aspx) databases of mycobacterial lipids   , and then total) . Out of cificity . Since o10.1128/mSystems.00057-17.1FIG S1 M. tuberculosis (ratio 1:20). (A) Volcano plot of pairwise comparison of all ions with KEGG H. sapiens (hsa) annotations using an unpaired t test. (B) Volcano plot of pairwise comparison of all ions with KEGG M. tuberculosis (mtu) annotations using an unpaired t test. (C) Volcano plot of pairwise comparison of all ions with lipid database annotations using an unpaired t test. (D) Volcano plot of the intersection of increasing ions with KEGG H. sapiens (hsa) and lipid database annotations. Annotations were filtered for ion adducts. All data come from at least three biological replicates and two technical replicates. FC, fold change; FDR, false-discovery rate. Download FIG S1, EPS file, 1.7 MB.Comparison of the THP-1 metabolome with the mixed metabolome of THP-1 cells and Copyright © 2017 Zimmermann et al.2017Zimmermann et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00057-17.7TABLE S1 TABLE S1, XLSX file, 3.6 MB.Metabolomics data of irradiated tubercle bacillus spiking experiment and THP-1 infection time course experiment. Pathway enrichment analysis results of metabolomics infection time course data. Download Copyright © 2017 Zimmermann et al.2017Zimmermann et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the THP-1 cells were infected at a multiplicity of infection (MOI) of 5, and immediate bacterial replication was comparable to replication in previous reports , based o2 fold change| > log2 1.5; FDR < 0.05). Third, we applied a significance threshold for the comparison between the changes in the infection experiment versus control experiment (FDR < 0.05). These three subsequent steps led to the identification of consistently and significantly changing  metabolites during the 48 h of infection   , 46.−4)  and phosphatidylinositol phosphates (FDR = 5 × 10−4) . This isrculosis , 48. Thirculosis , 50, thrrculosis , and by rculosis . The latrculosis .−6), nucleotides (FDR = 2 × 10−3), and sugar phosphates (FDR = 4 × 10−4) (13C-tracing experiments proposed the uptake of various amino acids by intracellular M. tuberculosis (−4)  . These c × 10−4) , 19  . Moreoverculosis . The avasis (−4) . The uptsis (−4) . OverallTo obtain further insights into host-pathogen interactions, we investigated whether transcriptional adaptations of both host and pathogen during infection reflect multinutrient acquisition of the bacteria. Analogous to metabolomics, the transcriptome was analyzed at 0, 4, 24, and 48 h p.i. Complete transcriptome analysis was performed on rRNA-depleted total RNA by microbe-enriched dual RNA sequencing  . The phy10.1128/mSystems.00057-17.8TABLE S2 M. tuberculosis (Mtb) and human gene expression upon infection. Analysis of mycobacterial transporter expression during THP-1 infection. Download TABLE S2, XLSX file, 3.2 MB.Dual RNA-seq data of the THP-1 infection time course experiment. Pathway enrichment analysis results of both altered Copyright © 2017 Zimmermann et al.2017Zimmermann et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 fold change| > log2 1.5; FDR < 0.05 compared to uninfected samples) and performed pathway enrichment analysis using KEGG and TBDB pathway definitions , as recently applied to proteomics data , and other cholesterol derivatives were also depleted during infection , converting methylmalonyl-CoA to succinyl-CoA in the tricarboxylic acid (TCA) cycle, was downregulated , which becomes toxic unless further metabolized , 59. Expegulated . These dynthesis .Cholesterol degradation illustrates the pathogen’s activation of catabolic pathways to utilize host metabolites, which are consequently depleted in host cells. On the basis of a comparable activation pattern, we provide evidence that tubercle bacilli utilize glycine and sugar phosphates through the glycine cleavage and rhamnose biosynthesis pathways, respectively  and B.10.1128/mSystems.00057-17.2FIG S2 M. tuberculosis. (A) Glycine cleavage pathway in M. tuberculosis. Metabolites are indicated by circles, and genes are indicated by ovals. Red color indicates increase or upregulation , and blue color indicates decrease or downregulation ; all data come from at least three biological replicates. For the metabolite traces, mean fold change values from at least three independent experiments are shown for the infection experiments (blue and red) and the control experiments (gray) with 95% confidence intervals (t test). The dashed lines correspond to |log2 FC| = log2 1.5. Decreasing levels of glycine indicate that it is a potential carbon source of bacteria, whereas genes in the glycine cleavage pathway are upregulated. (B) Decreasing levels of sugar phosphates indicate potential uptake of these compounds by intracellular M. tuberculosis. Genes in the polyketide sugar biosynthesis pathway, which is fueled by sugar phosphates, are upregulated. Pathway maps are adapted from pathway maps in the TBDB database with permission from the publisher. (C) Decreasing levels of pyrimidine biosynthesis intermediates indicate potential uptake of these compounds by intracellular M. tuberculosis. Genes corresponding to the initial steps of de novo pyrimidine biosynthesis from the pentose phosphate pathway are downregulated, whereas genes coding several nucleotide salvage enzymes are upregulated. The pathway map was adapted from a pathway map in the KEGG database (pyrimidine biosynthesis)   with perCopyright © 2017 Zimmermann et al.2017Zimmermann et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 fold change <– log2 1.5; FDR < 0.05), whereas the levels of the corresponding end products decreased over time    and D. Trophages . As a code novo synthesis from the pentose phosphate pathway intermediates was also decreased, whereas enzymes of the nucleotide salvage pathway were increased  given the possibility to take up different nutrients from the phagosome. This corresponds to an estimated mycobacterial doubling time of 24 h, which matches our experimental data based on CFUs indicating roughly two replications during the 48-h infection experiment. Fifth, following the rationale that gene expression and fluxes through an enzyme are loosely related, we used transcript data to formulate an optimization function based on activation and repression of genes, as previously described  and mycobacterial models . Third, al model , whereasescribed . Brieflyin vitro culture conditions , which is in agreement with previous experimental findings . No constraints were used for the biomass fluxes. Flux values are normalized by the sum of bacterial uptake fluxes. Download FIG S3, EPS file, 0.7 MB.Temporal analysis of predicted nutrient fluxes between host and bacteria in the genome-scale metabolic model. To obtain the flux distribution, up- and downregulated transcripts were used to constrain the optimization function in the iMAT procedure  for each set of constraints on the lower bound of mycobacterial and host cell biomass flux, ranging from 0 to 50% of either maximal value . For all parameter sets, fatty acids, amino acids, and cholesterol were predicted to be consumed . The 36 sets of constraints on the mycobacterial biomass function (x axes) and host biomass function (y axes) were tested. Flux values are normalized by the sum of bacterial uptake fluxes. Download FIG S4, EPS file, 0.9 MB.Activity of predicted nutrient fluxes between host and bacteria in the genome-scale metabolic model to the constraints on biomass fluxes. Heatmaps represent the fraction of each group of nutrients among the predicted mycobacterial carbon uptake fluxes. Each solution was obtained by the iMAT procedure including up- and downregulated transcripts . (A) Two constraint values on THP-1 biomass flux with a flexible constraint on the mycobacterial biomass flux. (B) Two constraint values on mycobacterial biomass flux with a flexible constraint on the THP-1 biomass flux. Flux values are normalized by the sum of bacterial uptake fluxes. Download FIG S5, EPS file, 1.3 MB.Sensitivity of predicted nutrient fluxes between host and bacteria in the genome-scale metabolic model to the constraints on biomass fluxes. To obtain the flux distribution, up- and downregulated transcripts were used to constrain the optimization function in the iMAT procedure . Each FBA  parameter set is represented with a pair of constraints on the THP-1 and mycobacterial biomass fluxes. The biomass fraction is calculated by dividing the nutrient uptake flux by the nutrient contribution to the corresponding biomass flux in the bacterium. Download FIG S6, EPS file, 1.2 MB.Analysis of nutrient fractions used for biomass formation depending on the constraints on the genome-scale host-pathogen metabolic model. To obtain the flux distribution, up- and downregulated transcripts were used to constrain the optimization function in the iMAT procedure  corresponding to Copyright © 2017 Zimmermann et al.2017Zimmermann et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the M. tuberculosis appears to follow a multiple-substrate fueling strategy during the infection of macrophages, utilizing a large variety of different nutrients ranging from amino acids to carbohydrates and lipids . The 59 genes that became “nonessential” under conditions of multisubstrate availability belonged mostly to amino acid metabolism (44 genes) (in vivo replication under multisubstrate utilization conditions were mainly found in membrane (27 genes), amino acid (17 genes), nucleic acid (29 genes), and lipid metabolism (18 genes). Our results suggest that there are fewer genes truly essential for mycobacteria during infection, thus narrowing down options of potential drug targets in mycobacterial metabolism. Supported by the recently discovered growth inhibitors of intracellular M. tuberculosis targeting cholesterol metabolism  . Of thes4 genes) . The 103tabolism , we beli6 cells/well in a six-well plate were differentiated for 24 h using culture medium containing 40 ng/ml phorbol 12-myristate 13-acetate. The cells were then washed with fresh culture medium and incubated for 48 h. Exponentially grown M. tuberculosis H37Rv  was pelleted , washed twice with phosphate-buffered saline (PBS), and resuspended in THP-1 culture medium. THP-1 cells were infected at a multiplicity of infection (MOI) of 5. For mock infections, gamma-irradiated M. tuberculosis H37Rv was used . The M. tuberculosis culture medium suspension was added to the differentiated cells and subsequently incubated for 4 h. The infection mix was removed, and the cells were washed twice with prewarmed PBS and incubated further using fresh medium. RNA isolation and metabolite extraction were performed at designated time points postinfection (p.i.).The THP-1 cell line  was maintained in Roswell Park Memorial Institute medium 1640 supplemented with 10% (vol/vol) fetal calf serum, 2 mM glutamine, 1 mM sodium pyruvate, and 0.05 mM 2-mercaptoethanol in a humidified 5% carbon dioxide atmosphere at 37°C. An estimated 5 × 10m/z range of 50 to 1,000 following published protocol (Homo sapiens) and mtu (M. tuberculosis), the Human Metabolome Database (HMDB) , frozen on dry ice, and stored at −80°C until further processing. Metabolites were extracted three times with 70% ethanol at 78°C for 2 min, and the three extracts were pooled, dried under vacuum at ambient temperature (22°C), and resuspended in 0.5-ml water for metabolic analysis. Untargeted measurements of metabolites was performed by direct injection mass spectrometry using a quadrupole time of flight instrument (Agilent 6550 Q-TOF) with the following settings: negative mode; 4-GHz high-resolution mode; scanning the protocol . Negative (HMDB) , and Mtbe (HMDB) . Raw inte (HMDB) . In brieM. tuberculosis-infected THP-1 cells without enrichment was isolated by abrasive particles in a reciprocal shaker with TRIzol . Cold 4 M GITC was added to the monolayer, and the cells were transferred to a 1.5-ml screw-cap tube. After centrifugation, the pellet was resuspended in residual GITC and mixed with 1 ml TRIzol containing 20 µg/ml linear acrylamide, followed by incubation for 5 min at RT. Bacteria were disrupted by bead beating . The sample was centrifuged for 1 min at 4°C and 13,000 rpm, and the supernatant was transferred to a 2-ml screw-cap tube containing 200 µl chloroform, mixed, and incubated at RT for 5 min. After centrifugation at 4°C and 13,000 rpm for 10 min, RNA was extracted from the aqueous phase using the Qiagen RNeasy minikit including an on-column DNase digestion (Qiagen). The quality and quantity of total RNA were assessed using an Agilent 2100 bioanalyzer (Agilent Technologies) and a NanoDrop 1000 spectrophotometer.TruSeq RNA Sample Preparation v2 Guide (Bar-coded RNA-seq libraries were prepared according to the v2 Guide  without http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Fastq files were imported in pairs into the CLC Genomics Workbench  running on a CLC Genomics Server . The imported reads were mapped to the mycobacterial genome H37Rv (NC_000962) and Homo sapiens genome hg38 (GRCh38) with the RNA-seq tool. For transporter analysis, RNA-seq data were mapped to a set of mycobacterial transporters compiled from the Transporter Classification Database (TCDB) . The following parameters were used for mapping: mismatch cost of 2, insertion cost of 3, deletion cost of 3, length fraction of 0.8, similarity fraction of 0.8, maximum number of hits for a read of 10, and failed reads were removed. Mapping reports were generated for each sample and exported from the CLC Genomics Workbench in csv format for further analysis. Unique read counts were normalized by a size factor calculated for each sample as the median of the ratios of the detected gene counts to the counts of an averaged sample. The averaged sample was calculated as the geometric mean across all samples . Metabolomics mixed samples from infection and control experiments at 4, 24, and 48 h p.i. were compared to uninfected human samples using a t test with unequal variances . Fold changes of the infection versus control experiments at 4, 24, and 48 h p.i. were compared using a t test with unequal variances. RNA-seq nonenriched mixed samples at 4, 24, and 48 h p.i. were compared to the uninfected human samples; RNA-seq enriched mixed samples were compared to the mycobacterial samples using a negative binomial model (Matlab function nbintest with constant variance link and pooled/nonpooled variance for enriched/nonenriched samples) . P values were corrected for multiple hypotheses testing by calculating the false-discovery rate (FDR) by the Benjamini-Hochberg procedure. For functional group and pathway enrichment, metabolite classes were defined as in the HMDB database. Pathway descriptions were downloaded from KEGG and TBDB databases. Metabolites passing the threshold of |log2 fold change| > log2 1.5, FDR < 0.05, and FDR(change to control) < 0.05 and transcripts passing the threshold of |log2 fold change| > log2 1.5 and FDR < 0.05 were ranked according to P value or fold change at each time point. Enrichment analysis was performed with the Fisher exact test for each subset of size varying from 1 to the total changing set size, and the smallest P value was retained for each pathway, as proposed in the gene set enrichment analysis (GSEA) method  for quantitative and statistical analysis. For differential analysis, human samples with spiked bacteria at a ratio of 0, 5, 10, or 20 bacteria per human cell were compared to the human samples without bacteria using ) method . P valuehttp://rest.kegg.jp/). An in-house script in Python (Python 2.7) was used to retain only main reaction pairs. For each mycobacterial gene, a list of metabolites involved in reactions associated with this gene was created. A gene-gene connectivity matrix was built based on the metabolites associated with genes . K-shortest path script based on Yen’s algorithm  was used to calculate the shortest paths between two genes based on the connectivity matrix. All paths of length three and more were visualized in Cytoscape software (Cytoscape 2.8.3). Reaction directions were downloaded from KEGG (kgml pathway files) with an in-house Matlab script.All computational analyses were done in Matlab (MathWorks), if other tools are not mentioned explicitly. For KEGG reaction pair network analysis, the KEGG reaction pair list, KEGG reaction pair-mycobacterial gene association list, and KEGG compound list were downloaded from KEGG API (application programming interface) (http://systemsbiology.ucsd.edu/InSilicoOrganisms/MacTB) and extended with metabolites and reactions listed in http://bigg.ucsd.edu/models/iNJ661) were used to update the gene-protein-reaction (GPR) information (2 fold change| > log2 1.5 and FDR < 0.05 for at least one time point were defined active or inactive. Mixed-integer linear programming (MILP) minimizing the flux through inactive reactions and maximizing the flux through active reactions was defined as described in reference Flux balance analysis with RNA-seq constraints: the joint macrophage-mycobacteria model was downloaded from the repository (ormation . Flux baE-MTAB-5287. Metabolomics data are available in http://www.imsb.ethz.ch/research/sauer.html). All numerical data used for all main and supplemental figures are provided in the supplemental tables.Raw sequence reads of the RNA-seq analysis can be downloaded from the EMBL-EBI European Nucleotide Archive under accession no.


T. erythraeum and used genome-scale modeling approaches to investigate carbon and nitrogen fluxes as well as growth and equilibrium population composition.Computational, genome based predictions of organism phenotypes has enhanced the ability to investigate the biological phenomena that help organisms survive and respond to their environments. In this study, we have created the first genome-scale metabolic network reconstruction of the nitrogen fixing cyanobacterium T. erythraeum with 971 reactions, 986 metabolites, and 647 unique genes. We then used data from previous studies as well as our own laboratory data to establish a biomass equation and two distinct submodels that correspond to the two cell types formed by T. erythraeum. We then use flux balance analysis and flux variability analysis to generate predictions for how metabolism is distributed to account for the unique productivity of T. erythraeum. Finally, we used in situ data to constrain the model, infer time dependent population compositions and metabolite production using dynamic Flux Balance Analysis. We find that our model predicts equilibrium compositions similar to laboratory measurements, approximately 15.5% diazotrophs for our model versus 10-20% diazotrophs reported in literature. We also found that equilibrium was the most efficient mode of growth and that equilibrium was stoichiometrically mediated. Moreover, the model predicts that nitrogen leakage is an essential condition of optimality for T. erythraeum; cells leak approximately 29.4% total fixed nitrogen when growing at the optimal growth rate, which agrees with values observed in situ.We created a genome-scale reconstruction of T. erythraeum colonies. Our predictions match both in situ and laboratory data, indicating that stoichiometry of metabolic reactions plays a large role in the differentiation and composition of different cell types. In order to realize the full potential of the model, advance modeling techniques which account for interactions between colonies, the environment and surrounding species need to be developed.The genome-metabolic network reconstruction allows us to use constraints based modeling approaches to predict growth and optimal cellular composition in The online version of this article (doi:10.1186/s12918-016-0383-z) contains supplementary material, which is available to authorized users. Trichodesmium is a genus of filamentous diazotrophic (nitrogen fixing) cyanobacteria that not only flourishes in this environment but provides bio-available nitrogen for surrounding species. Trichodesmium is responsible for fixing roughly 100 TgNy-1 of nitrogen annually   = 0.12 × [(A565 − A592) − 0.20 × (A455 − A592)] [Phycoerythrin is a protein-pigment complex that is abundant in  A592)] .Lipids were extracted using the Bligh and Dyer method . Chlorop18:C16:C14, etc.) was assumed to be the same distribution for every lipid subclass. Otherwise, lipid pathways were consistent with the methods described in the Cyanothece ATCC51422 model  = [0.01776(A646.6 − A750) + 0.00734(A663.6 − A750)].The only chlorophyll pigment o Harris . First, μL cells concentrated from 250 mL to 2 mL via filtration overnight and phycocyanin was measured in the resulting medium according to established techniques [2.70 mg.mL lysozyme was used to disrupt 740 chniques , 73.μL was disrupted using 2.70 mg/mL lysozyme overnight at 37 °C/proteinase K for 2 h at 55 °C instead of bead disruption. The concentration of DNA and RNA were assessed spectrophotometrically using 1 cm path-length extinction coefficients [DNA and RNA were extracted using MoBio UltraClean Microbial Isolation Kits  with 50 mL cells concentrated to 2 mL, of which 740 Leptolyngbya, Anabaena, Synechococcus, and Cyanothece to identify unannotated genes. Where these organisms failed to predict genome similarity, proteins displaying the function as the unannotated, or insufficiently annotated, reaction were BLASTed against all available organisms. This resulted in similarities to the organisms in Table Nitrosococcus oceani, Pleurocapsa sp. PCC 7327, Zymomonas mobilis subsp. NRRL B-12526, Amborella trichopoda, Dehalococcoides mccartyi VS, Nostoc sp. PCC 7524, and Candidatus Nitrosophaera gargensis. Photosynthesis was modeled similarly to other cyanobacterial genome scale reconstructions [and operator; redundant genes were assigned an or operator). These were done with consultation of KEGG [2, H2O, N2, etc.) [T. erythraeum genome and were non-essential, therefore they were removed. Irreversibility was assumed where databases indicated significant energetic unfavorability (such as in redox reactions) or where other models demonstrated consistent unidirectionality. Reactions were finally elementally and charge balanced using built-in COBRA functionality in conjunction with the PubChem Database [The genome scale network reconstruction was based on the publically available genome sequence  and an aructions , 59, 80  of KEGG , BioCyc  of KEGG , BRENDA  of KEGG , and Cya of KEGG . Finally2, etc.) . Manual Database . Small mDatabase .in situ data found in literature [2 and ammonium while exporting glycogen, diatomic oxygen, and diatomic nitrogen. Available nutrients, trace metals, and ions which are allowed to be used for growth were restricted according to the YBC-II medium formulation. Table Cyanothece sp. ATCC 51142, another photosynthetic diazotroph [-1 and maintenance related ATP was set so that the growth rate matched literature derived physiological data [-1 and 67.2 mmol (g DW h)-1 for diazotrophs by matching the growth rate in the dFBA model to the growth rate measured in literature. This is significantly higher than other reported values, but is justified by the lack of substantial light restrictions. It is assumed that light uptake restrictions and maintenance ATP are both contained within this constraint. All other nitrogen and carbon sources except for those specified in Table The genome scale metabolic network was used to predict fluxes using FBA . The conterature , 29. Twoterature , nitrogeazotroph , at 544 FBA and FVA were conducted using COBRA toolbox functions . The optThe cell-specific uptake and export rules as well as the seawater constraints generated a suite of constraints of the form :The cell-specific uptake and export rules as well as the seawater constraints generated a suite of constraints of the form:Diazotroph Cell Bounds:Phtotoautotroph Cell Bounds:It should be noted that seawater ingredients are assumed to be replete in this model.Steady state was assumed to make the model solvable:Essentially, this means that mass is conserved and all inputs exit either through the biomass equation or through export. The second major assumption was that the only outputs would be biomass, glycogen, or ammonium; energy or mass balancing mechanisms, such as through organic acid spilling, were assumed to be negligible because of efficient, exponential growth conditions.Gene deletions, reaction deletions, and Dead End reaction analysis were run using these constraints. These problem formulations were solved using the COBRA toolbox  in conju-1 h-1 for the photoautotroph and 67.2 mmol ATP (g DW)-1 h-1 for the diazotroph.dFBA was executed with the aid of the Dynamic Multispecies Metabolic Modeling (DyMMM) framework as a template . Both thμ and Δt are non-negative) by:For the purpose of this program, the final constraint is estimated , total growth rate (μT), and fixed nitrogen yield (YN → Env) were calculated by:Adjusted values are calculated for final concentrations and, during the next iteration, the new initial values are the final values from the previous iteration. This ends at a defined maximum time (400 h) and cell fractions are calculated as a function of biomass of cell type Since multiple steady states exist for population composition, growth rate, and substrate release rate, randomized initial cell concentrations  were generated using MatLab over 1000 iterations. Also, FBA was run individually on each cell type once more to determine the growth rate of each cell type in replete  conditions.T. erythraeum on different nitrogen sources was assessed by “opening” reaction boundaries for exchange reactions corresponding to the nutrient and assuming the nutrient was in excess. Then, the dFBA simulation was run assuming equilibrium starting composition. The growth profiles and growth rates were recorded in the same way as previous simulations. Where proteomics data did not infer transport of a particular compound, a simple, diffusion style transporter was temporarily added to the model.The hypothetical viability of