Obtaining flux estimates nevertheless still required frequent manual reinitalization to obtain a good reproduction of the experimental data and, moreover, did not optimize on all observables simultaneously (metabolites and isotopomer concentrations). In our contribution we focus on measures to achieve faster and robust dynamic flux estimation which leads to a high dimensional parameter estimation problem. Specifically, we address the following challenges within the PWA problem formulation: (1) Fast selection of sufficient domains for the PWA flux functions, (2) Control of over-fitting in the concentration space using shape-prescriptive modeling and (3) robust and efficient implementation of the parameter estimation using the hybrid implicit filtering algorithm. With the improvements we significantly speed up the convergence by efficiently exploiting that the optimization problem is partly linear. This allows application to larger-scale metabolic networks and demonstrates that the proposed approach is not purely theoretical, but also applicable in practice.The design of microbial production processes relies on rational choices for metabolic engineering of the production host and the process conditions. These require a systematic and quantitative understanding of cellular regulation. Therefore, a novel method for dynamic flux identification using quantitative metabolomics and  Thesein vitro, but the validity of estimated kinetic parameters has been shown to be limited for reconstruction of the observed in vivo metabolic network properties [In vivo, only the whole network response can be analyzed, making the identification of kinetics highly challenging. Pioneering in vivo research [Kinetic metabolic regulation is a result of complex interactions between enzymes, substrates and allosteric effectors. Enzyme kinetics can be investigated operties . In vivoresearch  used stie), substrate and effector concentrations (ci) and kinetic parameters (θ) and are typically non-linear. The latter can be either derived from mechanistic assumptions e.g., Michaelis-Menten kinetics, or be non-mechanistic e.g., power law leading to parameters with no physical meaning [These models use kinetic formalisms to describe the flux as a function of enzyme activity ( meaning .(1)v=e⋅(1)The model equations  are strongly dependent on each other, which means the system can hardly be solved from a decomposition and commonly also leads to highly correlated parameters ,9.(2)a priori, which means that the mechanism of every enzyme and all interactions between metabolites and enzymes involved in the regarded metabolic network have to be selected before parameter optimization. Especially, the kinetic formats of each reaction in larger metabolic networks are yet unknown, unclear, or shall be deduced from the captured observables.The kinetic functions have to be chosen There are mainly two inherent conceptual drawbacks of kinetic modeling:i.e., the nullspace, however, the methodology requires selecting a kinetic model a priori and cannot directly identify dynamic metabolic fluxes. Regardless of those challenges, the number of uncertain reaction mechanisms in a kinetic model leads to a rapidly increasing number of models to be compared, together with the challenges of parameter estimation in non-linear systems, which result in putative non-convex optimization landscapes that quickly reach computationally infeasible scales.One solution that has been used to address this issue was to define a family of different models, also referred to as ensemble modeling ,11,12,13a priori assumptions of kinetics  strong enough to cover a significant (metabolite) concentration space for good identification of the kinetic parameters .•The metabolomics should preferably have a complete coverage of the regarded intracellular metabolic response, e.g., intracellular concentrations, which moreover have to be quantitative.•13C labeled substrate for improved flux tracing, as the concentration information alone is mostly not sufficient to identify all intracellular fluxes [Use of a r fluxes ,23.Crucial for every modeling approach is sufficient information for the identification of the model parameters from the observables . The idedy-state  and the 13C labeled internal standards [13C enrichment information.These experimental requirements are partly conflicting, especially when concentration measurements are performed using tandards , as they(1)13C labeling from a single experiment.They generate repetitive concentration patterns in time, allowing for dense sampling from multiple cycles as well as application of (2)The feast/famine perturbation includes both: the transient from limitation to excess as well as a return to limitation in a short timeframe of minutes.(3)In this setup, the starting metabolite concentrations of each cycle are the same as the endpoint, which means there is no net metabolite accumulation during one cycle .In this work, we build on one dynamic setup that has been demonstrated to be advantageous in facilitating concentration and labeling measurements—there cyclic and block-wise feeding are used leading to so-called feast-famine conditions ,26. Compi.e., improving data density and accuracy.These properties are beneficial for flux identification, as the concentration and enrichment information can be captured over several cycles, In the piecewise affine flux estimation approach, the choice of the number and positions of the breakpoints is crucial for a successful approximation of the flux profile in time and they directly determine the achievable convergence of the flux approximation towards the real fluxes as well as the number of parameters to be identified from the observables.The method requires that the breakpoints for all flux functions in the metabolic network have the same set of breakpoints. To best capture the different response of all the metabolites and fluxes a satisfying compromise of breakpoints has to be found. Changes in the (net) in- and out-fluxes of a metabolite pool influence the metabolite concentration and it is assumed that the major changes in metabolic (net) fluxes are reflected if the flux approximation can reproduce the concentration profile well.13C labeling aside. If the reproduction of enrichment measurement is not satisfactory, additional domains can be introduced at any time in the optimization workflow. Moreover, a trade-off between the number of parameters to be estimated and the convergence that can be achieved with the PWA flux functions has to be found. Especially, a too high number of breakpoints can significantly increase the tendency of the system towards overfitting.With this assumption, an optimization for the placement of breakpoints (number and times) can be performed using the less complex linear metabolite balances and the concentration observables leaving the in silico data, the properties of the domain selection are studied. A small reaction network, further referred to as the spiral model, is simulated under feast-famine conditions and the approximation is compared to the noise-free measurements of the model with respect to:(1)The number of breakpoints and(2)The placement of breakpoints.Using t = 126 s) reduces the sum of squares about fourfold (134.1). Nevertheless, the approximation of the concentration profiles is visually not yet sufficient. To compare the goodness of fit we use the R2 and adjusted t0 = 0 s and endt = 140 s). Further, the Akaike information criterion (AIC) is calculated and summarized in R2 = 0.77).In the spiral network, six metabolite pools are observed  at 13 different time points during a feast/famine cycle of 140 s. The first and last breakpoints are fixed . With only these breakpoints, no reasonable approximation can be obtained see  and the t2 = 25 s, t3 = 131 s), a sum of squares of 15.1 (R2 = 0.97) is reached and the concentration measurements can be reproduced within the expected error range of 10%. Addition of another, third free breakpoint further reduces the RSS to 2.3 (R2 = 0.99). Next to these global optima, local minima are observed.With a second free breakpoint (best choice t2 = 23 s and t3 = 132 s (RSS = 16.9). The territory of approximation error (RSS) has several local minima (data not shown). The second best combination of breakpoints was obtained with t2 = 2 s and t3 = 30 s (RSS 25.3).When two free breakpoints are placed, the best combination was obtained with R2 = 0.996) is found for a combination with t = , the second cluster has a minimum of 4.6 at t2 = 2 s, t3 = 14 s, t4 = 83 s  laborious and computationally hardly feasible on standard computer hardware. From a practical standpoint, it is desirable to start with a simple model, as extension of a model is usually more straightforward than a model reduction, moreover, a smaller number of parameters can often be better identified from the observables [Clearly, the breakpoint optimization will lead to an optimal sequence of breakpoints for the flux functions in terms of the chosen objective function Rervables .2 or AIC, can help in finding a minimal set of breakpoints. However, also using those criteria does not necessarily guarantee a sufficient representation of the concentrations, i.e., occurrence of overfitting and no negative concentrations. Having in mind those practical limitations of the domain selection, we implemented constraints in the approach that allow computing flux functions, even with a suboptimal choice of breakpoints and shape-prescriptive constraints.Here the use of objective functions penalizing the number of parameters, like adjusted Rt =  is discussed. Two major flaws can be observed  Metabolite A shows overfitting in the last domain. Negative concentrations are undoubtedly not feasible and cannot be accepted; moreover they compromise the numerical integration of the isotopomer balances being detrimental to the numerical robustness of the forward simulation. To enforce non-negativity quadratic inequality, constraints are introduced. This measure prevented all metabolites to reach negative concentrations  Neguxes see . MoreoveP. chrysogenum was re-evaluated. The metabolic network for flux identification, further referred to as PenG model, comprises 17 balanced metabolites and 28 fluxes. Five free breakpoints were determined, leading to a total of seven breakpoints, i.e., t = (0 s 18 s 36 s 90 s 185 s 230.5 s 360 s). Because of the dependency of the last and first domain (feast/famine setup), 28 × 6 = 168 flux values need to be estimated.To demonstrate the practical performance of the optimization approach, a previously described dynamic labeling experiment with The simulations have been performed over three consecutive feast/famine cycles. As a starting point for the parameter optimization, a feasible parameter set derived from the best fit for the metabolite concentration measurements (including the respective constraints) was used.We first applied a damped Quasi Newton optimization algorithm as reference convergence performance . We founNull-space-based sampling indeed significantly increases the convergence  and shapi.e., the gradients become very small. This inefficiency could be reduced by the implementation of a threshold for the improvement between subsequent iterations Indeed, this measure  improves the convergence significantly, especially for the first couple of iterations. However, it has to be noted that the thresholds can increase the risk of obtaining a local optimum. The strategy that has been implemented to prevent such behavior is to restart the complete sequence of stencil scales after failure of a stencil.It can be observed that the implicit filtering algorithm leads to a step-wise convergence . This beThe flux estimation follows a workflow, which will be described in detail in the following subchapters. First step is the domain selection procedure selecting the breakpoints for the flux functions. This is followed by definition and introduction of the non-negativity (prevents negative concentrations) and shape-prescriptive constraints. For use of the final step, first a feasible initial iterate has to be computed as implicit filtering requires this as an input. The final step performs the actual flux identification with incorporation of the enrichment data.in silico Spiral model was used as described previously [The eviously .−1. The feed contained minimal medium with a glucose concentration of 15 g/L. After several cycles, repetitive offgas and DO measurements were obtained, a biomass concentration of 5.7 g/L was obtained (average over the cycle). Samples for intracellular metabolites were withdrawn using a rapid-sampling device [13C-labeled glucose. The enrichment was monitored during three consecutive cycles using rapid sampling and MS analysis.For the practical PenG example, concentration and labeling measurements as well as the metabolic network stoichiometry were obtained from a feast/famine cultivation of P. chrysogenum as previously reported . In brieg device , quencheg device ,28. AfteN.The modeling approach is based on balances for metabolite concentrations and enrichments quantified by isotopomers  ,29. For g can be derived describing the balance of each single isotopomer concentration  x of the network:Based on the atom transitions and the stoichiometry, a non-linear function i.e., mass-isotopomer fractions.Integration of the isotopomer balances will yield the time-course of the isotopomer concentrations, which can be used to calculate the observables, j valid for all fluxes in the considered metabolic network, partitioning the flux function vi into number of breakpoints minus one domains in time:Using piece-wise affine functions for the flux description changes some properties of the equation system. The piece-wise affine flux functions are defined in time by a set of breakpoints ti at the breakpoint tj and defined strictly positive (so a reversible reaction will have two fluxes). Flux values between breakpoints are calculated by linear interpolation on its two adjacent breakpoints. Therefore a metabolic network with i fluxes and j breakpoints has i × j parameters to be estimated. As will be discussed later, this number can decrease for specific experimental setups that introduce additional constraints on the concentrations or flux functions. This definition is also valid for higher order piecewise-defined flux functions as long as they are to be defined unique on the breakpoints, e.g., smooth quadratic splines, but will not be discussed in the scope of this paper.The parameters of this function are the value of respective fluxes vj by linear combinations according to the stoichiometry matrix which can be further divided into known bv and unknown nv fluxes [With the previous definition of the flux functions in time, the metabolite mass balances can be formulated locally at every breakpoint n fluxes .(5)N=[Nn is square and nondegenerate, the matrix can be inverted and a unique solution of unknown fluxes can be identified from the concentration transients and the known fluxes.If Ni.e., they contain parallel pathways, cycles or reversible reactions.In practice most metabolic networks are underdetermined with respect to the number of unknown fluxes f, This means the metabolic network does not contain enough measurable (known) fluxes in order to identify all fluxes. In this case Nn spans a null space (kernel), which contains all linear combinations of its basis vector(s) describing the infinite combinations of fluxes that lead to the same solution of This is called practical non-identifiability and also directly explains why isotopic tracers are needed in order to identify all flux functions of such metabolic networks.v, thus u on the same breakpoints as v:The right hand side (rhs) c0:This function can easily be integrated , leading to a continuous, quadratic solution for the concentration in time with the initial concentrations i,ju; with available concentration measurements, a linear regression can be performed to obtain the best estimate of u. The measurements cM at timepoints tM can be obtained by generation of a matrix Y:As can be seen, this solution is moreover linear with respect to WM) then reads:The weighted linear regression problem (with weight matrix Solving the optimization problem13C tracer.) with the inner loop solving Equation (12). and the outer loop iterating on the breakpoints minimizing the weighted residual sum of squares on the concentration observables Rc using the PSwarm optimization algorithm [For the flux functions the location of the breakpoints in time has to be selected as also derived in . Best prlgorithm   can lead to issues during the optimization. Especially, cases that impair the stability of the numerical integration have to be prevented to ensure maximal robustness of the high-dimensional parameter estimation: (1) Negative values in the approximation of the concentration profile; (2) Overfitting. Additionally, the experimental setup could enforce certain conditions that should be reflected in the parameter estimation, like in the case of the feast/famine setup, the first and last domain (beginning and end of the cycle) have to be equal.(1)In a stable feast-famine regime the metabolite concentration at the end of one cycle has the same concentration as in the beginning (of the next cycle).(2)Similarly, the flux at the end of the feast famine cycle has to be the same as in the beginning. Otherwise no stable, repetitive cycles were obtained.In the previous paragraph, the solution for the best estimate of the concentration profile with the given breakpoints has been demonstrated. In the special case of a feast/famine experiment the solution should additionally fulfill two (linear) constraints:u (and c0). A column vector C is generated, representing a column of matrix Y for the timepoint endt:The concentration at the end of the cycle is the result of the integration using the parameters Integration of the constraint is obtained using Lagrange multipliers λ:v1 = vj. The vector C (and b) are extended by an additional row:A comparable approach is taken to introduce the flux constraint As can be seen this equality constraint reduces the number of parameters by one per balanced metabolite.In order to ensure numerical stability of the integration for the isotopomer equations, it is crucial that computed negative approximated concentrations are detected and eliminated. In order to find a sufficient constraint the piecewise quadratic concentration pattern has to be evaluated in every domain. In the convex case, the minimum of the vertex value has been computed, whereas in the concave case it has to be checked for roots in its interval. These computations are trivial but correspond to a nonlinear (quadratic) inequality constraint.In practical applications overfitting has to be prevented. Reasons for overfitting can be (1) noisy data, (2) outlier or leverage points, (3) suboptimal choice of domains for flux functions, (4) suboptimal sample time points, (5) systematic error in observables or (6) missing data. Overfitting will lead to flux functions with potentially too high gradients. Additionally, because metabolite pools are often closely linked, the overfitting will propagate through the metabolic network, leading to an ill-posed optimization landscape hindering the parameter estimation progression and biased flux functions.One obvious approach is to optimize the domain selection as discussed. Nevertheless, there are limits to the reduction of domains as all flux functions are defined on the same domains, and a trade-off between overfitting prevention and achievable convergence of the flux functions has to be found. It has to be noted that the proposed linear solution of the domain selection can only be applied to a subset of the observables (the concentration measurements), while non-linear differential equation systems are required to obtain the labeling profile. Consequently, the domains are chosen on a subset of the available data leading to a risk that the domain selection yields a good approximation but potentially not the global optimum for all data.Shape constraints can be applied to “guide” the optimization procedure using shape primitives; together with the least squares objective function. This additional information on the shape of the (quadratic) concentration patterns has several benefits:(1) It can facilitate higher convergence orders of the flux functions; (2) The constraints restrict the search space and prevent that the optimization algorithm to converge in local optima in a noisy optimization landscape; (3) The chosen constraints do not introduce additional (unobserved) parameters; (4) The evaluation of the constraints is computationally very cheap even at large scale.Shape constraints are (with exception of the non-negativity constraint) linear inequality equations, which can be implemented rapidly and intuitive in the PWA framework as linear inequality constraints on the parameter vector. This also means that feasible parameter sets can be computed almost instantly using linear programming. It has to be noted that shape constraints can also be introduced for metabolites where no observables are on hand  which are exceptionally prone to overfitting as they do not contribute to the objective function. A strategy that has been used in the Penicillium model was to assume non-observed pools to follow the same dynamics as their surrounding metabolites.u at the breakpoints adjacent to the respective domain.When constraining the monotonicity in a domain four cases have to be considered for PWA flux functions: (1) Monotonous decreasing, (2) Monotonous increasing, (3) Switch from monotonous increasing to monotonous decreasing and (4) Switch from monotonous decreasing to monotonous increasing See . In the i.e., forcing the inflection point to the respective middle knot. Generally, convexity and curvature can be constrained independently. Nevertheless, in the case that a change of monotonicity is enforced, also the curvature is constrained. It is good practice to check the constraints for feasibility, e.g., by using a linear programming solver, e.g., linprog in MATLAB.A direct switch from monotonous increasing to decreasing and the other way round is only possible if the rhs between those neighboring domains was zero, leading to a double-inequality constraint which is equivalent to an equality constraint, In special cases a more stringent constraint than the inequality constraints could be needed. Here, the slope of the concentrations at the breakpoints can also directly be constrained using an equality constraint:i.e., the (net) influx equals the outflux at all times  was chosen, whereas a constant a was introduced as scaling factor to weight the two iR, which will lead to different points on the respective pareto frontier; it has been set to 1.317 to normalize for the different number of observations in either dataset. This leads to a multi-objective optimization problem and the L2 global criterion was used to define the overall objective function on previously computed aspiration values (best fit on either dataset) for the two residual sum of squares (cR and xR).The final flux (parameter) estimation problem has to minimize the error subject to two sets of objectives: (1) The concentration measurements and (2) The Computing the aspiration values on first sight seems an unnecessary additional effort, but it has to be noted that the best fit of the concentration data is already available from the domain selection procedure and a good approximation of the enrichments can usually be computed fast, neglecting the concentration observables.In practice this has the advantage that the whole dataset can be inspected for outliers and leverage values and those observations can be removed or downweighted. This procedure will lead to a better posed optimization landscape and can speed up the overall parameter estimation process considerably. If no good estimate for the errors is available, those could also be estimated using, e.g., an iteratively reweighted least squares approach like robust regression. With the current scope to analyze the practical optimization convergence, a simplifying assumption of an error of 5% of the average value within each dataset was used.stencil  in the parameter space comparable to a pattern search or direct search algorithm. From the collected points in the iteration, projected gradients are computed, which are then used to perform a quasi-Newton iteration. In case no improvement was found using the quasi-Newton iteration, the solver will move the center of the stencil to the best sampled point. If no better point was sampled, the actual stencil scale is adjusted (decreased) or the optimization is terminated. In this work, the quasi-Newton iteration with the SR1 Hessian update was used.The implicit filtering algorithm has been applied as provided (version for MATLAB)  with minImfil has to be initialized with a strictly feasible initial parameter set, which was computed using the MATLAB fmincon solver. In contrast to fmincon, using imfil, constraints have to be implemented as so-called hidden constraints, i.e., a parameter set outside the feasible territory is rejected and a failure is returned to the algorithm  are likely to lead to a better value in objective function and (2) are known to fulfill the given constraints together with the central finite differences. Feasible directions have been implemented in the algorithm using the A powerful set of such directions can be directly derived from Equation (7). Samples within this null space will have the same rhs u, and therewith the concentration profile as the current best iterate, but still a different solution of the isotopomer balances. Using this stoichiometry-based null space is further referred to as null-space-based sampling.13C labeling measurements to be identified. This parameter space reduction also eliminates the putative trade-off between concentration and enrichment observable residuals in the multi-objective function . In order to find the best overall optimum on all observables the central finite differences for all parameters are sampled together with the null space basis vectors.This approach allows iterating in the lower dimensional space of fluxes (parameters) see , which cDynamic flux identification requires advanced experimental and computational approaches, especially experimental setups that allow for the simultaneous, respective serial measurement of intracellular concentrations and labeling enrichments. For the evaluation and interpretation of this experimental data, modeling and parameter estimation are crucial and require attention to obtain a good approximation of metabolic network fluxes.Metabolic networks show an inherent correlation of (flux) parameters and common optimization approaches fail to identify the proper search direction. To solve this issue, we applied PWA flux functions and a two-staged optimization that resulted to be feasible also for larger networks. Especially, the implementation of implicit filtering-based optimization algorithms increased the convergence speed and basically eliminates manual intervention by frequent re-initialization of the optimization. The proposed implementation allowed for the obtaining of a reasonable estimate for 168 parameters within 20,000 function evaluations. Key for fast convergence were (1) implementation of null space sampling and (2) shape constraints that prevent overfitting and reduce the search space.i.e., each approximated flux profile can be reproduced and tested using different kinetics formats for a small network as discussed in [Once the flux functions in time have been identified, these together with the respective effector concentration measurements allow for testing of different biological hypothesis on the regulatory mechanism. In contrast to the classical reaction kinetic approach, the identification of kinetic parameters can now be decoupled from the overall metabolic network, ussed in .


Clostridium acetobutylicum has been used to produce butanol on an industrial scale through acetone-butanol-ethanol (ABE) fermentation. A genome-scale metabolic (GSM) model is a powerful tool for understanding the metabolic capacities of an organism and developing metabolic engineering strategies for strain development. The integration of stress-related specific transcriptomics information with the GSM model provides opportunities for elucidating the focal points of regulation.Clostridia are anaerobic Gram-positive Firmicutes containing broad and flexible systems for substrate utilization, which have been used successfully to produce a range of industrial compounds. In particular, C. acetobutylicum ATCC 824, iCac802. iCac802 spans 802 genes and includes 1,137 metabolites and 1,462 reactions, along with gene-protein-reaction associations. Both 13C-MFA and gene deletion data in the ABE fermentation pathway were used to test the predicted flux ranges allowed by the model. We also describe the CoreReg method, introduced in this paper, to integrate transcriptomic data and identify core sets of reactions that, when their flux was selectively restricted, reproduced flux and biomass-formation ranges seen under all regulatory constraints. CoreReg was used in response to butanol and butyrate stress to tighten bounds for 50 reactions within the iCac802 model. These bounds affected the flux of tens of reactions in core metabolism. The model, incorporating the regulatory restrictions from CoreReg under chemical stress, exhibited an approximate 70% reduction in biomass yield for most stress conditions.We describe here the construction and validation of a GSM model for The regulation placed on the model for the two stresses using CoreReg identified differences in the respective responses, including distinct core sets and the restriction of biomass production similar to experimental observations. Given the core sets predicted by the CoreReg method, remedial actions can be taken to counteract the effect of stress on metabolism. For less well-known systems, plausible regulatory loops can be suggested around the affected metabolic reactions, and the hypotheses can be tested experimentally.The online version of this article (doi:10.1186/s13068-014-0144-4) contains supplementary material, which is available to authorized users. Clostridium, anaerobic Gram-positive Firmicutes, contain broad and flexible systems for substrate utilization [Clostridium acetobutylicum has been the model organism for the production of butanol on an industrial scale through the acetone-butanol-ethanol (ABE) fermentation [The organisms of the genus and class lization . Their ilization . In partentation .ABE fermentation is biphasic in nature; the acidogenic, exponential growth phase is characterized by the production of butyric and acetic acids, while the solventogenic stationary phase is characterized by the production of the ABE solvents. Production of acids and the resulting drop in the culture pH during the acidogenic phase drives the transition towards solventogenesis ,2. TheseAn important asset for understanding the metabolic capacity of an organism and deciding on metabolic engineering interventions is a genome-scale metabolic (GSM) model . These mC. acetobutylicum [C. acetobutylicum produces butanol and byproducts such as acetate and butyrate. More recently, two GSM models of C. acetobutylicum ATCC 824 have been developed [, one-sixth of the number of genes coded on its genome). The Senger and Papoutsakis model [C. acetobutylicum. The tricarboxylic acid cycle (TCA cycle), known to operate in a non-cyclic bifurcated manner, was recently shown to use Re-citrate synthase to produce α-ketoglutarate via citrate [13C-metabolic flux analysis (13C-MFA) data that both α-ketoglutarate dehydrogenase (α-KGDH) and succinate dehydrogenase (SDH) are inactive during the acidogenic phase [One of the earliest metabolic reconstructions was, in fact, a model of utylicum . A smallutylicum . These eeveloped ,21. Thesis model . A largeis model . Howeveric phase . In contic phase . While GA number of approaches have been proposed to incorporate regulatory information into GSM models. Regulatory flux balance analysis (rFBA) introduces Boolean constraints for gene expression into flux balance analysis (FBA) by linking the regulators to their targets in an iterative fashion ,27. The C. acetobutylicum ATCC 824, iCac802, validation with experimental data. New reactions and pathways absent in earlier models include an updated TCA cycle, a completed fatty acid synthesis pathway, and additions to the purine, pyrimidine, and cobalamin biosynthetic pathways. The iCac802 model along with the corresponding GPRs and metabolite information is available as SBML and excel files in Additional files iCac802 and predict nexus points of regulation, that underlie cellular response to the physiological stressors butanol and butyrate [In this paper, we describe the construction of a second generation genome-scale reconstruction of butyrate .iCac802 for C. acetobutylicum ATCC 824 spans 802 genes and includes 1,137 metabolites participating in 1,462 reactions. All reactions present are elementally and charge balanced. GPR associations were determined from the available functional annotation information and homology predictions accounting for monofunctional proteins, multifunctional proteins, isozymes, and protein complexes. The model was curated to remove any thermodynamically infeasible cycles, as detailed in the iCac802 model statistics and those of all other published models for C. acetobutylicum are shown in Table iCac802 has 64% more genes and 84% more reactions than the McAnulty et al. model [iCac802 contains a citrate synthase leading to a partial and bifurcated TCA cycle , which  [et al. . The lat [et al.  as reactrvations  and iCac13C-metabolic flux analysis (13C-MFA) [13C-MFA data revealed that four reactions (pyruvate carboxylase (PC), fumarate hydratase (FH), pyridoxal phosphate synthase (PLPS), and alanine-glyoxylate (AGT)), which were originally removed to eliminate thermodynamically infeasible cycles, carried flux in the organism, and therefore, they were reinserted in the model. The cycles were instead eliminated by removing three alternate reactions , succinate dehydrogenase (SDH), and malate dehydrogenase (MDH)) and by modifying the directionality of two others: succinyl-coenzyme A synthetase (SCS) was made reversible, and aspartate ammonia-lyase (ASPA) was restricted to the production of fumarate from aspartate. Figure The model was extensively tested to ascertain that it is capable of replicating flux ranges and phenotypes that have been documented for the wild-type (WT) organism and its mutants. The model predicted flux ranges were compared with experimental flux values from 13C-MFA) . The 13C13C-MFA analysis [−1 h−1. FVA was performed while constraining the growth rate to the WT value of 0.32 h−1 for C. acetobutylicum grown in complex media [C. acetobutylicum was grown in defined media by Au et al. [13C-MFA data [−1. Upon reapplying FVA with the biomass yield constrained to 0.07 h−1  was performed on the core carbon metabolism reactions, and the flux ranges were compared to the values obtained by the analysis . These eex media . The comu et al. , exhibitu et al. . In addiMFA data  was coll13C-MFA data, the model’s responses to gene knockouts and varying environmental conditions were also tested. The model was used to analyze the effect of increasing the size of the NADH pool on the production of various acids and solvents. It has been shown experimentally that an increase in the level of NADH leads to a concomitant increase in butyrate, solvents, and hydrogen production  in C. acetobutylicum [iCac802 in order to test the impact of glycerol as a carbon source. The increased availability of reducing equivalents showed a similar affect, as having no redox constraint in the model by allowing for free interconversion between NAD + and NADH or NADP + and NADPH resulted in an increase in butyrate, solvents, and hydrogen production, as seen in Table The model was also queried with respect to the ability to co-utilize glycerol. Glycerol as a highly reduced carbon source (its degree of reduction is 4.67 compared to 4.0 for glucose) allows for the generation of more reducing equivalents which drive the production of butyrate and alcohols . While utylicum . It is iutylicum ,5. BasedC. acetobutylicum by forcing the transition from acidogenic to solventogenic fermentation . CO inhibits the hydrogenase arresting H2 production  were subsequently calculated by fixing both the growth rate and restricting the acids/solvents to the FVA calculated maximum and minimum values. Yield ranges were determined by evaluating the ratio of acids/solvents to the corresponding minimum nutrient flux. Mutants ∆ack and ∆ptb reduce (but do not eliminate) acetate and butyrate production by removing acetate kinase (ACK) and phosphotransbutyrylase (PTB) activities, respectively . Regulatory constraints on the iCac802 model were imposed using the transcriptomic data from Wang et al. [g et al.  in the fWhen regulation was imposed on the model, the biomass yield decreased by approximately 70% for all stress conditions except for the low-level butyrate stress, where the biomass yield was unaffected. For each one of the stress conditions we identify the reactions (core set) for which the application of regulatory constraints is sufficient to reproduce the flux ranges seen in the model when all regulatory constraints are imposed. Core sets of reactions were identified for each of the stress conditions by comparing flux bounds of the regulated model with the imposed regulatory constraints . The presence of this reaction can be related to the regulation of the DNA replication and repair mechanism which is initiated to protect the DNA from any damage owing to the oxidative stress component of the butyrate stress . Howeveret al.  have repet al. . This absol operon  [The addition of butyrate in the media leads to earlier onset of solventogenesis with higher butanol production ,48, whicfA-ctfB) ,5. The CC. acetobutylicum could provide superior insight into predicting phenotypes for the development of strains with higher tolerance to stressors and higher production of desired products. Thus, a more stress resilient strain of C. acetobutylicum may be engineered by improving these cellular functions.A GSM model is a powerful tool that serves as a framework to visualize the changes captured from transcriptomic or proteomic data at the global metabolic level. The strength of such a model relies on the inherent characteristic capabilities to predict phenotypes from genotype. The proposed CoreReg method managed to elucidate focal points of regulation (core sets) on metabolic pathways. The core sets represent likely nexus points of regulation under stress conditions, as they can broadly propagate the regulatory effect to the stress affected pathways. Interestingly, different stressors and levels elicited different metabolic responses, as also corroborated by the DNA microarray data. The prediction of phenotypes and the corresponding regulation that leads to the phenotype along with model performance can be greatly enhanced by the development of a whole cell model. This would include the integration of regulatory knowledge derived from gene expression, transcription factors and their binding sites, regulation, and post-transcriptional regulation in the form of small non-coding regulatory RNA (sRNA) into GSM models. With the recent reconstruction of a transcriptional regulatory network  and the C. acetobutylicum ATCC 824, iCac802, and the use of transcriptomic data to apply additional constraints on reaction flux bounds using the CoreReg method. These constraints were calculated for varying levels of butyrate and butanol stress and were used to identify core sets of reactions whose changes in flux values can explain broadly all observed changes in metabolism.In this paper we have described the creation of a second-generation genome-scale metabolic model for CoreReg was able to differentiate between the two stressors, with a larger restriction on biomass for butanol stress. The core sets for butanol stress contain reactions in arginine and amino acid metabolism, while the butyrate stress core sets contain reactions in arginine and pyrimidine metabolism. These results corroborate previous findings concerning the down-regulation of arginine metabolism and regulation of DNA replication under stress conditions. Given transcriptomic data for other stressors or environmental conditions, the CoreReg method can be used to predict both the metabolic response and candidate focal points of regulation in terms of core sets. If there exists an available mechanistic description of the regulation, a remedial action can be taken to counteract the effect of stress on metabolism . In cases where the regulation mechanism is less well known, CoreReg results could be used to design plausible regulatory loops around the affected metabolic reactions. These regulatory hypotheses can then be tested experimentally.iCac802 entailed the following processes: 1) identification of biotransformations using previous models and homology searches; 2) assembly of reaction sets into a genome-scale metabolic model and subsequent conversion into a computations-ready format; 3) identification and removal of thermodynamically infeasible cycles; and 4) evaluation and improvement of model performance when compared to in vivo growth phenotypes.The general principles of the metabolic model reconstruction process have been previously described -51. ConsC. acetobutylicum ATCC 824 [We began metabolic model reconstruction by parsing the existing genome-scale models for ATCC 824 -22 and tATCC 824 . We alsoATCC 824 , to parsATCC 824 . We examATCC 824 . All reaet al. [Model curation began with the removal of thermodynamically infeasible cycles present in the model. Flux variability analysis (FVA) was performed with no biomass constraints to identify unbounded reaction fluxes. The number of unbounded reactions was reduced by restricting the directionality of certain reactions using Model SEED’s calculated values of the reaction’s Gibbs free energy. If the entire range, including error, was more than 4 kcal/mol removed from zero, the reaction was restricted to the direction specified by the free energy . The metet al.  was thenet al. . During et al. .13C-metabolic flux analysis (13C-MFA) data.Of the cycles found, those involving only two reactions were examined first. These were composed of equivalent reactions, that is, reactions which both converted a given set of reactants to products or vice versa using the same co-factors, varying only in their directionality. One of the two identified equivalent reactions was subsequently removed based on a preponderance of literature or annotation information concerning its directionality. Cycles containing more than two reactions were formed due to the presence of pathways that recycled metabolites consumed or produced by an alternate route with a zero net metabolic cost. Reactions in these cycles were reviewed, and those with no literature or annotation reference were either removed or had their directionality restricted. Figure After reviewing all of the cycles in the null basis with up to five participating reactions, the null basis was reevaluated. After fixing the smaller cycles, the same review process was extended to larger cycles. This reevaluation process was iterated until no more cycles were detected using FVA. A total of 101 reactions were removed from the model, and 58 had their directionality restricted to stop the involvement of 404 reactions in thermodynamically infeasible cycles.Flux balance analysis (FBA) was performed to obtain the maximum attainable growth rate under the given constraints .\documenSij is the stoichiometric coefficient for metabolite i in reaction j. vj represents the flux of reaction j, while j. N and M represent the overall sets of metabolites and reactions, respectively.where FVA was used to identify reactions present in thermodynamically infeasible cycles.No constraints were placed on the biomass in order to identify all thermodynamically infeasible cycles. This analysis was performed iteratively for all metabolites.13C flux data [in vivo gene knockout data from the literature [The model predictions were tested against experimental lux data , experimlux data ,38,40,41terature ,43,45,57While the acidogenic phase was simulated in the model solely through the inclusion of required nutrients, additional constraints were required for the solventogenic phase. The solventogenic phase of Clostridia is characterized by the uptake of acetate and butyrate along with the reduction of carbon flux towards amino acids . Thus, tet al. [Regulation was incorporated into the model by a stepwise procedure that modified the minimum and maximum flux bounds of reactions based on fold change values of corresponding gene expression values by a new method called CoreReg. Gene expression data for each stress condition (GSE48031 and GSE48039), obtained from Wang et al. . TranscrStep 1: FVA is performed on the unregulated (UR) model to obtain lower Step 2: FVA upper and lower bounds for the unregulated model are multiplied by the fold change value (cj) obtained using the transcriptomic data. The lower R) model are evaluated as follows:Note that the updated lower bound is non-zero only for reversible reactions, effectively lowering the maximum possible flux value in the reverse direction.Step 3: FBA is performed on the unregulated and the regulated model to obtain maximum biomass yields Step 4: FVA is performed at max biomass R) model to obtain lower Step 5: Reactions j whose flux bounds are equal to the regulatory constraints  for the previously determined k core sets are set to one, thus removing their regulatory role in the model. The process is repeated from Step 2 with the value of k increased by one.


Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and—unexpectedly—to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the  Mammals require a source of THF in the diet but plants and most microbes make THF de novo. Although many variations in the THF biosynthesis pathway have recently been discovered  by the C9–N10 bond, makes them inherently unstable. Folates are sensitive at physiological pH to oxidative or photooxidative scission to release the pterin and p-aminobenzoylglutamate (pABA-Glu) moieties , or 6-hydroxymethyldihydropterin (H2-HMPterin), whose side chains are oxidatively labile in vitro  have been detected in various bacteria and are sometimes secreted in large amounts  as a side-reaction, (ii) that cells repair this damage via the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK), and (iii) that folate biosynthesis is upregulated in response to PanB-inflicted damage.We present here comparative genomic, genetic, and metabolic modeling evidence that (i) the pantothenate (vitamin B5) biosynthesis enzyme ketopantoate hydroxymethyltransferase (PanB) cleaves 5,10-methylenetetrahydrofolate (CHhttp://www.ncbi.nlm.nih.gov/) were routinely used. Sequences were aligned using Clustal Omega  . Physical clustering was analyzed with the SEED subsystem coloring tool or the SeedViewer Compare Regions tool    Edgar,  on the c) Edgar,  using Ma) Edgar, . The phyE. coli strains used in this study included MG1655 (Coli Genetic Stock Center), BW25113  was routinely used for cloning.Bacteria were grown at 37°C on Luria Bertani (LB) medium (BD Diagnostics Systems) or on M9 minimal medium  was obtained from the ASKA collection of E. coli clones  with sulfathiazole (0–1 μg/ml) or trimethoprim (0–1.5 μg/ml). Plates were incubated at 37°C overnight. To test the effect of ΔpanB on sensitivity to folate inhibitors, the same assay was performed with wild-type BW25113 and BW25113 ΔpanB::kan with the exception that chloramphenicol was not added to the media and all minimal media contained 1 μM pantothenate.Sensitivity to antifolates was also measured by dilution drops onto various concentrations of antibiotic. Cultures were grown in 5 ml M9-glucose medium with chloramphenicol overnight at 37°C with shaking. The following day, the strains were diluted 1:20 in fresh M9-glucose medium and grown at 37°C with shaking until the cultures reached approximately AfolB, folE, folK, and panB were analyzed in MG1655 harboring either empty vector pBAD33 or the panB overexpression plasmid pCA24N::panB. Three biological replicates of each were grown overnight in M9-glucose medium with chloramphenicol at 37°C with shaking. The following day the cultures were diluted 1:100 in fresh M9-glucose medium with chloramphenicol and grown at 37°C with shaking. IPTG was added to 0.2 mM when the culture was at A600 0.2 and the induced cultures were harvested at A6001.0. RNA was extracted from each culture with the QIAGEN RNeasy Protect Bacteria Mini Kit, the concentration was determined by NanoDrop spectrophotometer, and RNA was stored at −80°C. cDNA was synthesized from 300 ng RNA using the iScript Select cDNA synthesis kit (Bio-Rad) using the provided random primer mix. Quantitative PCR was performed using the MyiQ2 Real Time PCR Detection System (Bio-Rad) using iTaq Universal SYBR Green Supermix (Bio-Rad). Amplification parameters were 95°C for 3 min followed by 40 cycles of 95°C for 10 s, 59°C for 30 s with fluorescence measurement, followed by the establishment of a melting curve. Data were analyzed with the IQ5 software (Bio-Rad) and the Bio-Rad Expression Macro. Expression was normalized against two reference genes: rssA (b1234) and rpoA (b3295). All primers used for the RT-PCR are listed in Table Expression levels of folK1) and ACIAD2407 (folK2) were PCR-amplified with primers ACIAD3062-xbaI-5 (5′-CTA CTCTAGAATGAGCATAACCACCTATAT CG-3′) and ACIAD3062-SalI-3 (5′-CATTGTCGACCGCGT TATTTTTCAACCCAG-3′) and ACIAD2407-xbaI-5 (5′-CAT TTCTAGATTGAACGCCAACGCCACAATTTT TG-3′) and ACIAD2407-SalI-3 (5′-CAATGTCGACGAATT ATGATGAAGAAATCAC-3′), respectively. Acinetobacter baylyi ADP1 genomic DNA was used as the template for PCR. PCR products were digested with XbaI and SalI, purified with the Zymo Clean and Concentrator-5 kit, and ligated into XbaI/SalI-digested pBAD24. The ligation mixtures were used to transform E. coli GC10 and transformants were selected on LB with ampicillin. pBAD24::folK1 and pBAD24::folK2 were confirmed by sequencing and the plasmids were renamed pJJT115 and pJJT107, respectively.ACIAD3062  or dT. If the folK gene on the plasmid complements the ΔfolK, the colonies appear after overnight growth at 37°C on LB with ampicillin and arabinose without dT.pJJT115, pJJT107, or pBAD24 were transformed via electroporation into panB expression plasmid was constructed as follows. The panB gene was amplified from genomic DNA of strain W3110 by PCR using primers EcPanBp19Fwd (5′-CTA GAAGCTTATGACAGGAAACAGCTATGAAACCGACCACCATCT C-3′) and EcPanBp19Rev (5′-GATCGAATTCTTAATGGA AAC-TGTGTTCTTCG-3′), and cloned into the HindIII and EcoRI sites of pUC19  were grown on M9 glucose (0.4%) and 10 μM pantothenate. Cells were harvested when A600 reached 1.0. For panB overexpression analyses, the strains (wild type MG1655 harboring the vector pUC19 or containing panB) were grown on M9 glucose (0.4%) with added Amp. IPTG (0.5 mM) was added at A600 of 0.3. Three independent colonies for each were cultivated. Only the cultures overexpressing panB had filaments (cell lysis) after adding IPTG (repeated twice independently). Cells were harvested when A600 reached 1.0. Protein contents of harvested bacterial cells were determined using the Pierce bicinchoninic acid (BCA) assay kit. Pterins were extracted, converted to their oxidized forms, and analyzed by HPLC with fluorometric detection as described , and the proposed PanB side reaction, the model included exchange reactions that simulate the production of H2-HMPt and pABA by reactions outside the selected scope of the model. Similarly, exchange reactions were added to simulate the continuous dilution of THF and CH2-THF that occurs during cell growth . Increasing the kinetic constant in the mass-action kinetic equation will increase the instantaneous flux of the associated reaction, but then the substrates of the reaction are consumed faster than they are produced, causing a decline in substrate concentration, and restoring flux to its original steady-state value. In this case, the steady-state flux through the Fol[KPCA] reactions will be the flux entering the pathway via the Hvmax values exceed the net steady-state flux that these reactions must achieve (vnet + vPanB).The model was also insensitive to the saturation state of the FolK, FolP, FolC, FolA, and PanB reactions. All of these reactions were modeled as completely unsaturated reactions, essentially following mass action kinetics. We chose mass action kinetics for these reactions, as opposed to unsaturated Monod kinetics, because this reduces the number of parameters, simplifies the model, and produces equivalent behavior when the enzyme is in a completely unsaturated state. However, we did determine that modeling these reactions as saturated enzymatic reactions results in similar behavior and steady states so long as the 2-THF. The ratio and magnitude of these rate constants dictated how close this reaction was to equilibrium, as well as what the equilibrium ratio of THF and CH2-THF would be. The total steady-state concentration of THF and CH2-THF was controlled by the value of vnet and the kinetic parameters on the drain reactions for THF and CH2-THF. The THF and CH2-THF drain reactions represent the consumption of these compounds during cell growth and dilution. Because the value of vnet was fixed, the only adjustable parameters governing the total THF and CH2-THF concentration were the kinetic parameters on the drain reactions. These parameters were adjusted to ensure that the total concentration of THF and CH2-THF predicted by the model matched experimental data under all conditions studied. Given these constraints, these kinetic parameters were not flexible. Rather, only a single set of values could be found to produce the optimal fit to the experimental data.In contrast, the kinetic parameters on the GlyA reaction exercised a large degree of control over the ratio of steady-state concentrations for THF and CH2-HMPt, as well as the magnitude of the recycle flux back to H2-HMPt. This flux controls the dynamic response of the folate pathway intermediates, including the initial dip that occurs in the H2-HMPt concentration immediately following the induction of the PanB enzyme, which we have proposed as the cause of the overall induction of the folate pathway. We tuned the kinetic parameters on the PanB reaction in our model to fit the experimentally measured concentrations for H2-HMPt. We currently model this reaction as unsaturated following irreversible mass action kinetics, but behavior would be similar if the reaction followed saturated Monod kinetics.The other key parameters in the model are the kinetic constants on the PanB reaction, which control the steady state concentration of HfolK). This observation had already been made when the subsystem was first constructed with far fewer genomes  and 18% harbor a single folK gene that clusters with panB   a single folK gene clusters with both panB and folB  in plants and yeast  Figure . FurtherB Figure . To elimB Figure . Very siB Figure . These aB Figure . The sepfolK, both have 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase activity in vivo, the folK1 and folK2 genes of A. baylyi ADP1 were tested for the capacity to complement the dT auxotrophy a ΔfolK strain of E. coli. The folK homolog found next to panB  robustly complemented the dT auxotrophy, whereas the one next to folB  did not .In order to test whether, from genomes with two copies of t Figure . This fiB Figure . This wapanB gene encodes the CH2-THF-dependent enzyme ketopantoate hydroxymethyltransferase (EC 2.1.2.11). This suggested an explanation for the observed association between panB and folK based on the proposed reaction mechanism of PanB. Powers, Snell, and coworkers first purified this enzyme and showed that it is a class II aldolase  . In path A, the other reaction product is 2-dehydropantoate; in path B, α-ketoisovalerate is regenerated along with an unstable bis-hydroxymethylpterin, which spontaneously loses formaldehyde to give H4-HMPterin. The H4-HMPterin formed by either path could re-enter the folate pathway via FolK after spontaneous oxidation to the dihydro form  and trimethoprim (a dihydrofolate reductase inhibitor).If a side-reaction of PanB cleaves CHF Figure , then ovpanB was tested by transforming either E. coli strain BW25113 or MG1655 with the ASKA clone expressing panB under control of the lac promoter (ppanB) or with an empty vector (pBAD33) control. Sensitivity to trimethoprim and sulfathiazole was assessed for each strain by measuring the halos of inhibition of different concentrations of each drug. The strains overexpressing panB were more sensitive to both trimethoprim and sulfathiazole resulting in larger zones of inhibition .To assess whether deleting 6 damage that propagates to CoA and folate metabolism .Growth phenotypes can be caused by very indirect effects, as shown in recent studies on mutants affected in vitamin BpanB (as a pUC19 construct) in E. coli MG1655 indeed led to accumulation of 6-hydroxymethylpterin in both medium (five-fold) and cells (three-fold)  Figure . Also, tl Figure . The rats Figure . Note ths Figure .panB. As shown in Figure 2-THF  that were partially offset by decreases in 5-formyl-THF and 5,10-methenyl-THF/10-formyl-THF . The folate pools that increased were largely polyglutamyl forms . Upon IPTG induction, the expression of panB increased over E. coli MG1655 harboring empty vector by up to 6000-fold. When panB induction was 6000-fold, the expression of folK, folB, and folE increased by about 10-fold , however, the expression of all three folate genes was essentially unaffected (not shown). Variation in induction of panB presumably reflects the stochastic nature of induction, as documented for the lac promoter both experimentally and through modeling . This is consistent with the kinetic and thermodynamic data available for these reactions . While measured values are not available for many of the metabolite concentrations and kinetic parameters in the model, these parameters are restricted by the need to replicate experimental observations. Once complete, we simulated the model under wild-type conditions, demonstrating that the steady-state predicted by the model was consistent with all the experimental observations.Kinetic parameters and metabolite concentrations were selected for the model to fit physiological conditions and replicate the wild-type concentrations measured for HF Figure . The mod2-THF (and THF) concentration began to drop due to the larger flux through the PanB side reaction. This led to a rise in the concentrations of H2-HMPt , which in turn led to a rise in the flux through the THF synthesis pathway, leading to the slow recovery of the THF concentration back to—but not exceeding—the wild-type values. Thus, this simulation successfully replicates the rise in H2-HMPt concentration that accompanied the overexpression of PanB in our experiments, but it fails to replicate the rise in THF concentrations, meaning the overexpression of PanB alone cannot explain all experimental observations.Next, we simulated the overexpression of PanB by increasing only the kinetic constant of the mass-action kinetic equation for the PanB folate-cleaving reaction (which integrates the enzyme concentration of PanB), leaving all other model parameters the same. In this simulation, all metabolite concentrations started at the steady-state values predicted from the wild-type model Figure . Immedia2-HMPt and pABA at the same time as we increase the kinetic constant of the mass-action kinetic equation for PanB kinetic constant. In this scenario, we still see an initial dip in THF concentrations, which accompanies the rise in H2-HMPt, but this time, the THF concentrations recover more rapidly and ultimately rise beyond the wild-type levels and reach new steady-state values that closely resemble those observed in the PanB overexpression experiments  was almost ten-fold less than the increase in pterin production (4.7 nmol mg−1 protein), all of it found in the medium. Thus, an increase in intracellular folate cleavage triggers a doubling of pterin production, most of which flows not to folate synthesis but out into the medium.Overexpression of PanB caused expected and unexpected effects. Expectedly, levels of 6-hydroxymethylpterin and total pterins increased. Unexpectedly, so did folate levels. However, as kinetic modeling showed, the folate increase can be accounted for by a very reasonable assumption, viz. that depletion of certain folates (or conceivably the buildup of folate damage products) leads to upregulation of the folate pathway. he observed increase in folate production appears to be the only case so far reported of a folate pathway regulatory response to a physiological perturbation  grants MCB-1153413.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Systematic investigations of complex pathological cascades during ischemic brain injury help to elucidate novel therapeutic targets against cerebral ischemia. Although some transcription factors (TFs) involved in cerebral ischemia, systematic surveys of their changes during ischemic brain injury have not been reported. Moreover, some multi-target agents effectively protected against ischemic stroke, but their mechanisms, especially the targets of TFs, are still unclear. Therefore, a comprehensive approach by integrating network pharmacology strategy and a new concatenated tandem array of consensus transcription factor response elements method to systematically investigate the target TFs critical in the protection against cerebral ischemia by a medication was first reported, and then applied to a multi-target drug, Danhong injection (DHI). High-throughput nature and depth of coverage, as well as high quantitative accuracy of the developed approach, make it more suitable for analyzing such multi-target agents. Results indicated that pre-B-cell leukemia transcription factor 1 and cyclic AMP-dependent transcription factor 1, along with six other TFs, are putative target TFs for DHI-mediated protection against cerebral ischemia. This study provides, for the first time, a systematic investigation of the target TFs critical to DHI-mediated protection against cerebral ischemia, as well as reveals more potential therapeutic targets for ischemic stroke. Cerebral ischemia or stroke is a serious neurological disease, which can lead to broad cerebral injury and result in high disability and mortality rates in many countries567891011A computational approach, such as a network pharmacology-based1415Large-scale quantitative profiling of TFs during ischemic brain injury can be another good choice to comprehensively investigate their roles in cerebral ischemia. However, due to the low abundance of TFsRadix Salviae miltiorrhizae and Flos Carthami tinctorii, has been shown to be effective in protecting against ischemic stroke, its pharmacological mechanisms, especially the putative multi-targets of TFs, are still unclear. This study provides the first systematic investigation of the TFs critical to DHI-mediated protection against cerebral ischemia, as well as reveals more potential therapeutic targets for ischemic stroke.In this study, an approach by integrating network pharmacology strategy and the catTFREs method to systematically investigate the target TFs critical in the protection against cerebral ischemia by a medication was first reported, and then applied to a multi-target conventional drug for coronary heart disease and cerebral ischemia, Danhong injection (DHI)202122Concatenated tandem array of consensus transcription factor response element (catTFRE) DNA was synthesized by Genscript . Biotinylated catTFRE primers were synthesized by Sigma . Dynabeads (M-280 streptavidin) were purchased from Invitrogen. Sequencing grade porcine trypsin was obtained from Promega . Nuclear extract prep kits were purchased from Thermo Fisher. Danhong injection (DHI) was obtained from Shandong Danhong Pharmaceutical Co., Ltd . All other chemicals were of analytical grade reagent. Deionized water (R > 18.2 MΩ) used for all experiments was purified by using Millipore purification system .Male C57BL/6 mice (Charles River Laboratories), six-eight weeks of age, were treated by intraluminal occlusion using monofilament for preparation of the permanent middle cerebral artery occlusion (MCAO) model24Based on the literatures about the processes after cerebral ischemia2627To evaluate the protection effect of DHI against cerebral ischemia, each MCAO mouse was administered 5 mL/kg/time DHI immediately after MCAO-operated by intraperitoneal injection (i.p.), then twice per day, respectively in the morning and evening, for eight consecutive days. For control, each sham mouse without MCAO was also administered 5 mL/kg/time DHI, and the usage is same as above described.Nuclear extracts of the mouse brain were prepared by using nuclear extract prep kits (Thermo Fisher) according to the protocols of manuals. Biotinylated DNA was pre-immobilized on Dynabeads and then mixed with nuclear extract. The mixture was supplemented with EDTA/EGTA to a final concentration of 1 mM and adjusted with NaCl to 200–250 mM total salt concentration. The solution was then incubated at 4 °C for 2 h. The supernatant was discarded and Dynabeads were washed with NETN  twice followed by PBS solution twice. Beads were digested overnight with trypsin.Tryptic peptides were separated on a C18 column  with a flow rate of 350 nL/min, and were analyzed by LTQ-Orbitrap Velos (Thermo). The MS conditions were as the followings: Nano-spray ion source was used. A spray voltage of 1800 V was applied, with no sheath gas flow and with the ion transfer tube at 350 °C. The mass spectrometer was programmed to acquire in a data dependent mode. The survey scan was from m/z 375 to 1600 with resolution 60,000 at m/z 400. The 50 most intense peaks with charge state 2 and above were acquired with collision induced dissociation with normalized collision energy of 35%, activation time of 5 ms, one microscan and the intensity threshold was set at 500. The MS2 spectra were acquired in the LTQ normal scan mode. Proteins were identified by Proteome Discovery version 1.3 using MASCOT search engine with percolator against the mouse RefSeq protein database (updated on 11-17-2014). The mass tolerance was set to be 20 ppm for precursor. As for the tolerance of product ions, Velos was set as 0.5 Da. Oxidation (Met), Acetyl (N terminus) was chosen as variable modifications; carbamidomethyl (Cys) was chosen as a fixed modification; and one missed cleavage on trypsin was allowed. Both peptide and protein level false discovery rates (FDRs) were controlled lower than 1%. Transcription factors (TFs) were assigned based on TFClassin silico protein digestion with a PERL script, and all fully tryptic peptides between 6 and 30 amino acids were counted while missed cleavages were neglected.Intensity-based absolute quantification (iBAQ)-based protein quantificationshttp://tcm.cmu.edu.tw/, Updated in Jun 28, 2012), which is currently the largest non-commercial TCM database worldwide. Totally, structural information of 101 compounds for Radix Salviae miltiorrhizae and 22 compounds for Flos Carthami tinctorii were collected from this database.Structural information (*.mol or *.sdf files) of the chemical components of each herb in DHI were obtained from TCM Database@Taiwanhttp://www.drugbank.ca/, version: 3.0) and the Online Mendelian Inheritance in Man (OMIM) databasehttp://www.omim.org/, Last updated: October 31, 2013). After deleting redundancy, 62 known therapeutic targets for the treatment of ischemic stroke were collected in this study. Please see detailed information in Known therapeutic targets for ischemic stroke were collected from DrugBank databasehttp://www.reactome.org/, Version 37) and Stringhttp://www.string-db.org/, Version 9.1) were used for the collection of PPI data.Two existing PPI databases, including ReactomeRadix Salviae miltiorrhizae and Flos Carthami tinctorii were predicted by MetaDrug from GeneGo, Inc. MetaDrug predicts the putative targets for certain chemical components using three methods: (1) Based on the MetaBase database, which contains compound-protein interactions; (2) Based on QSAR predictions of protein target affinity from the included models that define a limited number of potential targets for novel molecules and/or their metabolites submitted for analysis; (3) Based on a similarity search for the structure and its major metabolites against the database of existing structures and their targets. Putative targets for certain compounds are inferred through structurally similar compounds in the database .Putative targets of the chemical components contained in Chemical components contained in DHI, the corresponding putative targets, known therapeutic targets for ischemic stroke and the differentially expressed TFs in MCAO models and controls were used to construct two interaction networks: putative targets-known therapeutic targets-TFs network and chemical components-major putative target-major TFs network. Navigator software (Version 2.2.1) and Cytoscape (Version 2.8.1) were utilized to visualize the networks.Four topological features for each node in the network, including ‘Degree’, ‘Betweenness’, ‘Closeness’ and ‘K value’ were calculated to evaluate the topological importance of DHI putative targets and the differentially expressed TFs in MCAO models and controls. For each node “i” in these two networks, “Degree” was defined as the number of links to node i; “Betweenness” was defined as the number of edges running through node i; “Closeness” was defined as the inverse of the sum of node i distances to all other nodes. “Degree”, “Betweenness” and “Closeness” correlate with a protein’s topological importance in the PPI network2, 50 ng poly(dI-dC), 0.05% NP-40, and incubated with 10 fmol of biotin-labeled DNA probe for 15 min at room temperature. DNA probe/protein complexes were then separated by 5% native polyacrylamide gel at 100 V for 60 min, followed by transferred to a nylon membrane , and visualized by chemiluminescence according to the manufacturer’s instructions (GE ImageQuant LAS 4000mini).Nuclear extracts of the mouse brain were prepared by using nuclear extract prep kits (Thermo Fisher) as above mentioned. EMSA was performed by using the LightShift chemiluminescent EMSA kit  according to the protocols of manuals. The sense strand sequences of the oligonucleotides for the EMSAs are as follows: ATF1, 5′-ATGACGTCAATGACGTCAATGACGTCA-3′, Mut-DNA of ATF1, 5′-ACGATGTCAACGATGTCAACGATGTCA-3′; PBX1, 5′-ATCAATCAAATCAATCAAATCAATCAA-3′, Mut-DNA of PBX1, 5′-ACATATAACACATATAACACATATAAC-3′. Nuclear protein (6 μg) was mixed with 10 × binding buffer (Thermo Fisher), 2.5% glycerol, 5 mM MgCl−ΔCt. Then relative differences of gene expression among groups were expressed as relatively changes, setting the values of sham mice as one. The assays were carried out in triplicate and the results were analyzed by one-way analysis of variance, and significant at P < 0.05.qPCR was performed with primers listed in First, we measured the changes in TF activation in the brain after ischemic injury using the catTFREs method and the intensity-based absolute quantification (iBAQ)-based quantification approach, which had been demonstrated to be accurate in protein quantification3038262739Among the 250 identified activated TFs, 47 TFs were expressed only in sham controls, while 19 were expressed only in ischemic brains . Moreove2From the fold changes of the quantified activated TFs in ischemic brains compared with the controls , we also2Next, using network pharmacology strategies, TFs critical to DHI-mediated protection against cerebral ischemia were computationally predicted. First, a total of 660 putative targets were predicted for 57 chemical components contained in DHI. Detailed information regarding the putative targets and corresponding chemical components contained in DHI is listed in The network consists of 553 nodes  and 2204 edges. Four topological features, ‘Degree’, ‘Betweenness’, ‘Closeness’ and ‘K value’ (defined in EXPERIMENTAL SECTION), were chosen to identify major putative targets and major differentially expressed TFs. In total, we identified 175 major nodes, the ‘Degree’, ‘Betweenness’, ‘Closeness’ and ‘K value’ of which are all larger than the corresponding median values. Among these major nodes, there are 140 major putative targets and 14 major differentially expressed TFs. The interaction network of 175 major nodes is shown in To experimentally validate the predicted target TFs crucial to the DHI-mediated protection against cerebral ischemia, first, the pharmacological effects of DHI on MCAO mice was evaluated. As shown in To further investigate the pharmacological effects of DHI on MCAO mice, another group of mice that received DHI were euthanized six hours after MCAO operation and their brains were stained with TTC. As shown in 2223Based on the reliable protective effect of DHI against cerebral ischemia, we further experimentally verified the TFs that were critical to this protection by applying the catTFREs method and the accurate iBAQ-based quantitative approach to comprehensively measure the corresponding change in TF activation in pooled samples from three ischemic brains after DHI treatment. Meanwhile, to further confirm these TFs as critical, we conducted a parallel test by administering the same amount of DHI to sham mice without MCAO, followed by a high-throughput analysis of the corresponding changes in TF activation using the same method mentioned above. Moreover, to attain accurate quantitative analysis, peptide-spectrum match (PSM)From the results of the computational prediction and the experimental verification of TFs critical to DHI-mediated protection against cerebral ischemia, we found that pre-B-cell leukemia transcription factor 1 (PBX1) and cyclic AMP-dependent transcription factor 1 (ATF1) were authenticated by both approaches. PBX1 binds the sequence 5′-ATCAATCAA-3′ and may act as a transcriptional activator of platelet factor 4 (PF4) in complex with homeobox protein Meis1 (MEIS1)To further verify the TF activities of PBX1 and ATF1, the electrophoretic mobility shift assay (EMSA) experiments were conducted. Both PBX1 and ATF1 showed a stronger binding in MCAO mice with DHI treatment than in the ones without DHI treatment. And the same result was also obtained between sham mice with and without DHI treatment a,b. FurtMoreover, from our experimental verification results, we also found out that, in addition to PBX1 and ATF1, six other TFs, nuclear transcription factor Y subunit gamma (NFYC), POU domain, class 3, transcription factor 1 (POU3F1), POU3F2, POU3F3, DNA-binding protein SATB1 (SATB1) and SATB2, may also be critical target TFs for DHI-mediated protection against cerebral ischemia. NFYC is the component of the sequence-specific heterotrimeric TF (NF-Y) that specifically recognizes a 5′-CCAAT-3′ box motif found in the promoters of its target genesTo further clarify the relationships of eight major target TFs with major putative targets of DHI and known therapeutic targets for ischemic stroke, we constructed a network of interactions among these genes as shown in 555657Radix Salviae miltiorrhizae and Flos Carthami tinctorii), 33 chemical components, 8 major TFs, 10 major putative targets of DHI, 2 known therapeutic targets, 27 other differentially expressed TFs and 145 edges, was constructed and shown in Radix Salviae miltiorrhizae and Flos Carthami tinctorii, respectively, had interactions with PBX1, NFYC and ATF1 through their corresponding putative targets, suggesting that these three major TFs might be more closely associated with the therapeutic effects of DHI acting on ischemic stroke.Finally, the chemical components-major putative targets-major TFs network, which contains 74 nodes, including 2 herbs , a conventional drug for coronary heart disease and cerebral ischemia. After changes in TF activation during the ischemic brain injury in mouse MCAO models were measured using the catTFREs method and an accurate quantitative approach, the target TFs that were critical to DHI-mediated protection against cerebral ischemia were computationally predicted by the network pharmacology strategy. Then, by combining the evaluation of the pharmacological effects of DHI on MCAO mice, the predicted target TFs were experimentally validated. The experimental results indicated that PBX1 and ATF1, along with six other TFs, are putative target TFs for DHI-mediated protection against cerebral ischemia. This study provides the first systematic insight into the target TFs critical to DHI-mediated protection against cerebral ischemia and reveals novel potential therapeutic targets for ischemic stroke.How to cite this article: Wei, J. et al. Systematic investigation of transcription factors critical in the protection against cerebral ischemia by Danhong injection. Sci. Rep.6, 29823; doi: 10.1038/srep29823 (2016).


The RNA World hypothesis offers a plausible bridge from no-life to life on prebiotic Earth, by assuming that RNA, the only known molecule type capable of playing genetic and catalytic roles at the same time, could have been the first evolvable entity on the evolutionary path to the first living cell. We have developed the Metabolically Coupled Replicator System (MCRS), a spatially explicit simulation modelling approach to prebiotic RNA-World evolution on mineral surfaces, in which we incorporate the most important experimental facts and theoretical considerations to comply with recent knowledge on RNA and prebiotic evolution. In this paper the MCRS model framework has been extended in order to investigate the dynamical and evolutionary consequences of adding an important physico-chemical detail, namely explicit replicator structure – nucleotide sequence and 2D folding calculated from thermodynamical criteria – and their possible mutational changes, to the assumptions of a previously less detailed toy model.emergent uniformity of metabolic replicator fitnesses imposed on the community by local group selection and attained through replicator trait convergence, i.e., the tendency of replicator lengths, ribozyme activities and population sizes to become similar between the coevolving replicator species that are otherwise both structurally and functionally different.For each mutable nucleotide sequence the corresponding 2D folded structure with minimum free energy is calculated, which in turn is used to determine the fitness components  of the replicator. We show that the community of such replicators providing the monomer supply for their own replication by evolving metabolic enzyme activities features an improved propensity for stable coexistence and structural adaptation. These evolutionary advantages are due to the extra viability of the metabolic replicator system that the present model adds to the MCRS concept of the origin of life. Surface-bound, metabolically coupled RNA replicators tend to evolve different, enzymatically active sites within thermodynamically stable secondary structures, and the system as a whole evolves towards the robust coexistence of a complete set of such ribozymes driving the metabolism producing monomers for their own replication.In the most general terms it is the surprisingly high This article was reviewed by Gáspár Jékely, Anthony Poole and Armen Mulkidjanian The RNA-World scenario of the origin of life is based on the hypothesis that the first evolvable ancestors of all recent life on Earth must have been RNA-like macromolecules ,2. Even dual nature of RNA: it carries genetic information in its nucleotide sequence, and RNA molecules of different sequences are capable of catalysing a seemingly infinite variety of different chemical reactions  range. Simulations lasted for 106 generations, after which the frequency of each replicator type , the average of each enzymatic activity A1, A2 and A3 and the average length of each replicator type  were recorded . The ecological and the evolutionary aspects of the simulations are treated in separate sections below.The mineral surface (the lattice of 300 × 300 sites) was seeded in all simulations with a random population of replicators at an initial frequency of 80%, leaving 20% of the sites empty. The length of the initial replicators was also random with per se in a random sequence pool. Obviously, the emergence of two or more “active centres” within the same random sequence  is much less likely but still occurs in 1-2% of the initial RNA pool. In most of the simulations the frequencies of mono-active replicators increase to their equilibria, while promiscuous enzymes almost completely disappear from the system after a temporary surge upwards  parameter: the smaller the penalty the longer the average replicator, which implies that it is easier to fit more than one active loops into its sequence. At the extremely low b2 = 0.001 the frequency of bi-active enzymes increases to about 10%, and even replicators with all three types of active loop remain in the system at a low abundance.In the random initial replicator population the proportion of enzymatically active replicators was low (slightly above 20%) with mono-active enzymes represented at the highest frequency, suggesting that enzymatically active secondary structures as defined by the rules of the model Figure  are not s Figure A. This bh = 3 × 3 = 9.Extremely small metabolic neighbourhoods are unlikely (or unable) to accommodate all the necessary enzyme activities and thus result in system collapse, because the number of catalytic activities is larger or just slightly smaller than the number of neighbourhood sites. This effect does not show up in our simulations, because system size  is 3, whereas the smallest metabolic neighbourhood size used in the simulations is 6 generations. The frequencies of enzymatically active replicators  parasites tend to be somewhat shorter than cooperating ribozymes, but the difference is not very large, even though length reduction is an efficient way to increase fitness through better replicability  that fosters parasite invasion, which is evident on Figure D the mono-active replicators are at an advantage over bi-active ones, because promiscuous enzymes are less efficient in both catalytic functions than mono-actives (cf. Eq. αi > 1), and bi-actives still need some mixing to avoid forming homogeneous patches on the surface and thus going extinct. Two of the three possible bi-active types  show up and then vanish during the first 3 x 105 generations . This suggests that the initial difficulty of assembling three different mono-active replicators in the same metabolic neighbourhood is circumvented by evolving bi-active ones that are later excluded by their own, more efficient mono-active mutants. Note that {2,3} type bi-active replicators do not show up in large numbers because they need to be longer to be stable, and they are expendable because the two shorter bi-active types can deliver all the three necessary enzyme functions. 2) Tri-active replicators need to be much longer than mono-actives to fit all the three active loops into their secondary structure. This makes them much less probable to replicate than shorter replicators in the same replication neighbourhood, so that rarely emerging tri-active mutants are very likely fast eliminated from the system by their competing neighbours.Figure i Figure A, the avi Figure B and theL Figure C – of thE) of a (folded) sequence defines the probability of the sequence being in the folded state  are traded off: increasing pfold decreases replicability  that is optimized during the evolution of the replicator community.The Gibbs free energy  and metabolically active replicators at the stationary state of the system may be surprising at first, but the explanation is simple. Mono-active replicators easily mutate to parasitic ones, and parasites are in principle free to mutate further towards increasing their direct fitness (by higher replicability and/or lower degradation). However, fast replicating parasites are strongly selected against locally, because they demolish metabolism in their own vicinities and starve to death. That is, parasites are quickly eliminated by local selection, and permanently reintroduced by mutation from metabolically active replicators. Thus the parasites we see in the system are the close sequence relatives of functional metabolic enzymes, representing their parasitic quasispecies  obtained with toy model simulations scanning a wide range of the  parameter space. The earlier study revealed a positive effect of increasing dispersal (D) which prevents the aggregation of conspecific replicators, and a negative effect of increasing h, though approximating the mean-field situation that is known to go extinct. The deleterious effect of larger h can be compensated by increasing D to some extent, but the compensatory effect is limited: at too large metabolic neighbourhood sizes the system collapses anyway . The parameter setting suggested by the Reviewer  is, unfortunately, practically not feasible in the explicit model, because even on a high-capacity grid computer it would take months to run a single simulation. The huge difference between the CPU time demands of the toy models and the present one is due to the “handling time” of sequence folding. However, the results of the toy models are likely to carry over to the explicit case in this respect, too, since in all other respects we experienced qualitative matches.Importantly, the authors should provide their code as an Additional file or deposit it to a public repository for others to reproduce or extend the model calculations.As the code is the result of a long process of development, and it will be further developed for later studies, we do not find it convenient to publish it at this stage. However, we are willing to send the code to the reviewer or to anyone for further studies or for reproducing our results, on an individual basis.Minor comments:pg 19–20 The discussion about the dynamics of parasites is repetitive in this section. I suggest to delete or shorten this part: “Mono-active replicators easily mutate to parasitic ones … their own vicinities and starve to death”.We have shortened this part.Typos:page 11: “we assume that different RNS molecules” change to RNACorrected.page 17: “i.e., larger metabolic neighbourhood) that fosters parasite invasion, which is evident on Figure Corrected.This is a very elegant study which has been explained in very accessible language. I found the results very insightful, and need say little other than that, for those interested in the RNA world, this is a paper well worth reading.We thank the Reviewer for their positive judgment of the study.For my money, the most exciting result is that this model suggests that catalytic promiscuity in early ribozymes may have been extremely short-lived. This bears thinking about, particularly given the view, popular in protein science circles that early enzymes were promiscuous (both in their substrate specificity and enzymatic reactions). It is also noteworthy that group selection appears as a feature of the model. This is broadly consistent with the cooperative networks that Lehman and colleagues observed for fragmented ribozymes (Nature 491:72–77). I would be interested to see a brief discussion of that work and how it relates to the authors’ findings.Lehman and co-workers showed that mixtures of RNA fragments that self-assemble into self-replicating ribozymes can form catalytic cycles and more complex networks. We agree that some aspects of our model show some similarity to some of Lehman’s, even though the two models (Lehman’s and ours) are essentially different in their basic assumptions. Ours assumes cooperation in the evolutionary sense, so that a complete metabolic neighbourhood  is capable of replicating a focal sequence which thus will have two identical copies locally (apart from mutations). The model of Lehman, on the other hand, assumes collective autocatalysis: in which the complex networks arise due to the fact that the members of the set catalyze each other’s formation, rather than replication. Yet, it is true that both models are prone to being parasitized and ultimately exterminated by parasitic replicators in a mean-field framework, both requiring some form of spatial structure as a potential defense: “Longer term evolutionary optimization would have required spatial heterogenity or compartmentalization to provide lasting immunity against parasitic species or short autocatalytic cycles.” ).http://dx.doi.org/10.1371/journal.pcbi.1002752) used a comparative genomic approach to look at whether there are ‘clues’ of the RNA world in modern organisms, so is perhaps more appropriate, given the sentence.Just a minor quibble about this statement in the Background: “recent organisms still carry reliable clues suggesting that RNA had played a central role both in the metabolism and in the genetics of very early forms of life ”. The papers cited here are both excellent, but address the more chemical aspects of the origin of RNA itself and of RNA catalysis. By contrast, the recent paper by Hoeppner et al. (PLoS Comp Biol 8: e1002752. Thanks for drawing our attention to the paper – we have cited it in the corresponding part of the text.Minor comments/typos (can be deleted from the review once addressed):Page 7, the use of the term “catching” - perhaps “binding” is more appropriate here. Enzymologists routinely talk about substrate binding and product release.We rephrased the sentence in a more clear way.Page 11, “hydroxil” should be hydroxylCorrected.P11, RNS should be RNACorrected.P26, “thedesing” should be the designCorrected.Figure Corrected.Könnyű and co-workers have attempted to fill the gap between purely mathematical, "toy" modeling of very early evolution and the physico-chemical realm within which such evolution may have proceeded. Specifically, the model of Könnyű and co-workers explicitly accounts for the primary and secondary structure of replicators. Hopefully, the authors would continue their efforts to model the physics and chemistry of the early evolution. Therefore, the comments below contain certain recommendations which could be realized either upon revising of the given manuscript or in the future work of the authors.Major comment:1) My major concern is the plausibility of the metabolic part of the model. The authors assumed that “the different RNA species cooperate to produce monomers for their own replication, and possibly also to supply other “common goods” for the replicator community”. Thereby, only three catalytic activities were assumed to be sufficient to perform all these functions in the model of the authors. Obviously, the assumption of only three catalytic activities is an oversimplification, which is quite understandable in the given context. In a wider context, however, the source of monomers and “common goods” for the first replicators is one of the open questions in the origin of life research. Apparently much more than three catalytic activities should have been simultaneously needed to produce different monomers and, in addition, the “common goods”. Furthermore, there is no evidence of ribozymes capable of synthesizing nucleotides from scratch; the chemistry of the RNA catalysis, as revealed so far, is not very encouraging in this respect. A possible solution for this conundrum is that monomers and other common goods (e.g. amino acids and sugars) could be initially produced in abiotic reactions [1–5]. Only later, step by step, the first replicating entities may have learned to synthesize nucleotides. Syntheses of nucleobases and amino acids were recently demonstrated in the solutions that contained formamide or urea and in the presence of UV-light, see  for reviews. Environments with high levels of amides/urea could be envisioned on the primordial Earth . The hypothesis of abiotic origin of monomers got a major boost after Sutherland, Powner and their co-workers succeeded in synthesizing nucleotides from scratch in geologically plausible, one-pot settings . In addition, these authors have shown that natural nucleotides were particularly UV-stable, so that their relative fraction selectively increased under UV illumination [10], in support of earlier theoretical predictions [12]. In such a case, the very first replicators would require only catalytic abilities of assembling abiotically formed monomers - a task that should have been feasible for ribozymes. Hence, scenarios of “abiotic syntheses” match the simplified approach of Könnyű and co-workers in that they imply only few catalytic activities. In a framework of such scenarios, however, an essential source of monomers for replication would be the decay of other replicators. Furthermore, the abilities to accelerate this decay by cleaving neighboring sequences (which would correspond to a reversion of the assembly reaction) as well as to use the fragments for building the own “body” would be extremely advantageous. A scenario of “abiotic syntheses”, of course, would require a separate model that would differ from the model in the given manuscript. Still, in the view of anticipated importance of replicator decay/cleavage, an analysis of the present model in relation to the decay processes, namely a consideration of the model outcome as a function of decay parameters (currently absent from the manuscript) might be of use for readers.We absolutely agree with the Reviewer in that even a very simple realistic metabolism requires a lot more different types of catalysts than postulated in our model. However, we are also sure that the only way such a metabolically competent ribozyme set could have evolved is through the retroevolutionary mechanism explained in some detail elsewhere [20–21], which must have started from abiotiocally produced monomers at its very beginning, exactly as the Reviewer suggests. We have inserted a paragraph into the Background section pointing this out explicitly and citing the relevant literature.Minor comments:s must be complemented by all three different enzymatically active molecules in its metabolic neighborhood , the set of h sites concentric on the site of the focal replicator s)” is not quite clear.2) The manuscript would benefit from a graphical presentation of the focal cell with its neighborhood. Without such a figure, the sentence “For a replication event to occur the focal replicator We added a figure that explains metabolic and replicator neighbourhood configurations.3) It is not clear how the model accounts for “the time consumption of “releasing” the product and “catching” the next substrate for catalysis”. The whole section on sub-additive effects is rather incomprehensible and showed be re-written in a more clear way.We rephreased it in a more clear way.4) The statement that "replication is possible only in the unfolded state" should be defined as one of assumptions of the model. Generally, it is possible to imagine that unfolding could proceed concurrently with replication. That is how replication occurs in our cells.Indeed, we cannot exclude the possibility of the simultaneous unfolding and replication of RNA molecules on the basis of first principles, but note that our postulate of the temporal separation of the two processes is in fact a worst-case assumption: the chance of catalytically active, low-energy folds to be replicated is small. Therefore if this assumption has any effect on the results, then it is negative, but we actually think that changing it may not alter the results in the qualitative sense.References[1] A.I. Oparin, The Origin of Life, Moskowskiy rabochiy, Moscow, 1924.[2] J.B.S. Haldane, The Origin of Life, in: C.A. Watts (Ed.) The Rationalist Annual 1929, pp. 3–10.[3] N.H. Horowitz, On the Evolution of Biochemical Syntheses, Proc. Natl. Acad. Sci. USA, 31 (1945) 153–157.[4] S.L. Miller, L.E. Orgel, The Origins of Life, Prentice Hall Englewood Cliffs, NJ, 1973.[5] A. Lazcano, S.L. Miller, On the origin of metabolic pathways, J. Mol. Evol., 49 (1999) 424–431.[6] S.A. Benner, H.J. Kim, M.A. Carrigan, Asphalt, water, and the prebiotic synthesis of ribose, ribonucleosides, and RNA, Acc Chem Res, 45 (2012) 2025–2034.[7] R. Saladino, C. Crestini, S. Pino, G. Costanzo, E. Di Mauro, Formamide and the origin of life, Phys Life Rev, 9 (2012) 84–104.[8] A.Y. Mulkidjanian, A.Y. Bychkov, D.V. Dibrova, M.Y. Galperin, E.V. Koonin, Origin of first cells at terrestrial, anoxic geothermal fields, Proc Natl Acad Sci U S A, 109 (2012) E821-830.[9] D.V. Dibrova, M.Y. Chudetsky, M.Y. Galperin, E.V. Koonin, A.Y. Mulkidjanian, The role of energy in the emergence of biology from chemistry, Orig Life Evol Biosph, 42 (2012) 459–468.[10] M.W. Powner, B. Gerland, J.D. Sutherland, Synthesis of activated pyrimidine ribonucleotides in prebiotically plausible conditions, Nature, 459 (2009) 239–242.[11] M.W. Powner, J.D. Sutherland, J.W. Szostak, Chemoselective multicomponent one-pot assembly of purine precursors in water, J Am Chem Soc, 132 (2010) 16677–16688.[12] A.Y. Mulkidjanian, D.A. Cherepanov, M.Y. Galperin, Survival of the fittest before the beginning of life: selection of the first oligonucleotide-like polymers by UV light, BMC Evol Biol, 3 (2003) 12.