However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes.Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with 13C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods.We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.The online version contains supplementary material available at 10.1186/s12859-022-04742-7. By this, FBA bypasses the need for reaction kinetic parameters facilitating to construct metabolic models on a genome scale without determining these experimentally demanding parameters [13C tracer based data, it led to considerably good flux predictions [Gaining insight into the metabolic fluxes can lead to a better understanding of how cells maintain their metabolic state and how they metabolically adapt to their microenvironment. It has led to astonishing discoveries such as considerably increased production yields after metabolic engineering –4, improtrometry , 13–15, trometry , 17. Howtrometry , 18–20. trometry  has beentrometry . FBA canrameters –25. It arameters , 12, 26.rameters , 27–33. rameters –36. Thoudictions , 31–33. dictions  or the mdictions ). AlthouStaphylococcus aureus [13C tracing experiments are difficult [Bacillus subtilis (B. subtilis) using publicly available gene expression data studied at different carbon source conditions [Nutrition is essential for any cell to provide building blocks for maintenance and proliferation and to generate energy. Being able to identify carbon sources can support finding targets to treat microbial pathogens causing infectious diseases. Specifically, pathogenic micro-organisms are challenging to treat when hiding inside host cells. For example, osteomyelitis is an infection in the bone marrow and is mainly caused by s aureus –39. It iifficult  as the onditions .B. subtilis strain BSB1 was used. BSB1 is a tryptophan prototrophic derivative of strain 168. The data was taken from the original publication  [13C isotope labeling experiments of the same eight carbon source conditions (Table S4 from [Published microarray gene expression data of the  S2 from ). The dailis168 . B. subtyruvate) . To vali S4 from ). In the13C tracer based metabolic flux data from a time-series experiment of two nutritional shifts, i.e., the shift from glucose to glucose plus malate and the shift from malate to malate plus glucose [13C metabolic flux data were generated using the same experimental protocol as for the first dataset. B. subtilis was grown in minimal medium on a single carbon substrate until an OD600 of 0.5 was achieved. Then, the other substrate  was added to the culture to assess the bacterial behavior at 0 (before the addition of the other substrate), 5, 10, 15, 25, 45, 60, and 90 minutes after the other substrate was added. Both gene expression and 13C metabolic flux data were taken from the BaSysBio database (https://basysbio.ethz.ch/openbis/basysbio_openbis.html). This data is denoted as the second dataset in the following.To validate our model with a separate, unknown dataset, we used publicly available gene expression and B. subtilis168 available from the BiGG Models database [13C metabolic flux data from Chubukov et al. [We used the gene expression data of the first and second datasets. It had been pre-processed by computing the median of the estimated transcription signal of all probes assigned to one corresponding gene , 40. Thedatabase  . To efficiently compare prediction results from our approach with the 13C metabolic flux data, we determined if we could fit the 13C metabolic flux data to the metabolic model in R. The solution from the 13C model needed to be a feasible solution complying with all set constraints. However, initial trials showed that we could not find any solution in the solution space when we tried to fit flux values from the 13C metabolic flux data allowing only one exchange reaction flux to be non-zero, i.e., from the specific transporter of the corresponding carbon source. In turn, it was possible to find a feasible solution when we also allowed fluxes from other exchange reactions besides the exchange reaction of the corresponding carbon source to enter the system. Although the solution was found, the flux values from other exchange reactions were substantially high, which was unrealistic. Hence, we set up an optimization problem to find a reasonable boundary for each of these exchange reactions by letting the solution deviate from 13C metabolic flux data by maximal 0.1. After optimization, we obtained a sum of fluxes from other exchange reactions for each different condition. We then compared these values and applied the lowest possible value (sum of fluxes = 0.688) restricting the influx of all other metabolites (being not the metabolite of the corresponding condition) into the cell. The list of all exchange reactions besides the designated carbon sources of the corresponding minimal medium is provided in Additional file To develop a mixed-integer linear programming based model, we transferred the iYO844 model of 13C labeling data from the original publication. This gold standard data was available for 40 reactions, mostly covering central energy metabolism [B. subtilis in the control medium (B. subtilis grown in LB medium) [versus its expression in the control medium. The Benjamini-Hochberg method was used to correct for multiple testing across all genes [As described below, we benchmarked our models with a well-defined gold standard, i.e., flux values based on the tabolism . Hence, ll genes . The p-v13C flux data  to (3), we formulated the optimization problem byThe formulated objective function is a trade-off between two optimization criteria. The first term, equation . The prementclass2pt{minimThe second term in formula , \documementclass2pt{minimAfter comparing the sum of sums of absolute values of fluxes mentclass2pt{minimc based on the data from Chubukov et al. and Buescher et al. [A biomass constraint obtained from the growth rate was set for each condition r et al. , 14. Thewhere v et al.  across aTo correctly map the expression data to the metabolic flux, we needed a realistic estimate of the lower and upper bounds for the reactions in the model. At different steady-state conditions, the feasible minimum and maximum flux within the solution space can differ from the initially set lower and upper bounds of each reaction. FVA is a well-known technique to determine flux ranges . We applentclass1pt{minimaquations . FVA minentclass1pt{minima(formula ).Absolute values of The first reaction mentclass2pt{minimAfter optimizing the objective function in formula , the totThe inner iterative process terminates for reaction i. The next reaction in the list is selected by setting i = i+1, and the algorithm proceeds with step d).The algorithm terminates if the total model mapping discrepancy becomes stable, or all reactions are processed.In general, doing this for every reaction should narrow down the flux range of each reaction. However, we observed that the boundaries did not differ substantially after performing FVA for every core and associated reaction. To further reduce the solution space and limit its flexibility, we developed an iterative approach. The approach was part of the training scheme to narrow down the flux ranges; hence, we applied this only to the training data. The method was termed Iterative Feasible Flux Space Reduction (IFFPR). A figure of the workflow is given in the Additional file We terminated the process before the algorithm reached the end of the list. As the algorithm processed around 80% of the reactions in the list, the total model mapping discrepancy became stable Fig. . This waIn constraint-based modeling, the thermodynamic loop law can get violated. The loop law is similar to Kirchhoff’s second law for electrical circuits . It statTo solve this problem within the FBA framework, Schellenberger et al.  introducExternal reactions are not regarded. Applying a maximal flux value threshold (threshold = 0.01) for TIL to be allowed, the set of reactions subject toequation –14) is  is 11\doEquation  forces tmentclass2pt{minimquations  togetherquations , followequations –14). Th. Th\docu13C tracer analysis and quantified the similarity by Pearson’s correlation and normalized error as described in the publication of Machado and Herrgard [The overview of the entire process is illustrated in Fig. Herrgard . Furtherth percentile of expression values in the matrix as zero and as one otherwise [To benchmark LPM-GEM, we compared its prediction performance with three well-established methods, i.e., pFBA , iMAT 3, 33, andtherwise , 54. Alstherwise , 54.www.r-project.org). The Cobra toolbox version 3.0 and Matlab version R2019a (www.mathworks.com) were used to obtain the initial stoichiometric matrix, lower and upper bounds, reversibility information, metabolite and reaction names from the initial metabolic network, and benchmarking flux predictions from iMAT, mCADRE and pFBA (see Results). All further analysis was performed using R. The Gurobi optimizer version 9.0.2 (www.gurobi.com) was used to solve mixed-integer linear programming problems. For assessing the carbon sources, z-scores were calculated for each transporter of the eight (eight-carbon-source study) or two  different carbon sources across all conditions as a means to compare the results of the predictions across all the corresponding transporters. This enabled us to compare also transporters with lower differences in their fluxes among the different conditions to transporters with higher differences.All analyses were performed using R version 3.3.3 (Reducing the search space employing Iterative Feasible Flux Space Reduction (IFFPR),Applying our method RED-TIL, andPenalizing high flux contributions of non-core and non-associated reactions.Thermodynamically infeasible loops are problematic for constraint-based modeling leading to incorrect flux distributions , 30, 55.All strategies improved our models as described in the following (the improvement by strategy (3) is described in a lower section .For all fitted reactions (core and associated reactions) of the model, we performed flux variability analysis (FVA) to reduce the maximal and minimal flux boundaries. However, we observed that for many reactions, the resulting flux ranges did not substantially differ from the original upper and lower bounds. These high flux ranges were expected not to reflect realistic situations and may facilitate using TILs by the optimization procedure when fitting the model to the transcription profiles. Hence, we developed a new method to iteratively reduce the flux boundaries from FVA by comparing the discrepancy between the fluxes derived from the expression values and the optimal feasible flux when obtaining newly adjusted maximal . After optimization, we assessed our prediction results of the eight transporter reactions  to identify the major carbon source for each condition. For this, we compared the z-scores of the corresponding carbon source transporters in each condition to predict the primary (highest z-score) and secondary (second highest z-score) carbon sources  and generated context-specific metabolic models employing RED-TIL and IFFPR. This was done across each of the eight carbon sources . Besides the core reactions, for which Overall, our method could well predict the carbon sources based on gene expression profiles of the respective conditions.In addition to optimizing the fitting of gene expression to the fluxes of the reactions, we also considered minimizing the sum of all predicted fluxes from reactions which were either core reactions or associated reactions to restrict the optimizer employing thermodynamically infeasible loops . We tested different strengths of the penalty , and we selected the same core reactions for iMAT and mCADRE. For pFBA, no gene expression data was used as the method does not require them. We followed the standard protocols from these three methods  and compared their flux predictions to the gold standard (13C tracer derived fluxes), i.e., to all 40 reactions [13C data was available.For benchmarking, we compared our method with three other well-known methods , we assessed the flux prediction performance based on biomass and the known carbon sources. We calculated Pearson’s correlation coefficients (r) and normalized errors  between versus the measured fluxes (13C metabolic fluxes) is provided in Additional file We then compared the flux predictions of LPM-GEM with iMAT, and mCADRE. The scatterplot of the predicted fluxes of all three methods  B. subtilis in eight different steady-state conditions. However, in a natural environment, the bacteria may need to switch from one carbon source to another. Particularly, glucose and malate are preferred carbon sources for which such a switch may occur [B. subtilis was grown on a single substrate leading to a steady-state-like initial condition. Then, the other substrate was added. Transcription profiles and 13C flux data were generated in a time series until the shift was performed (at an endpoint at 90 min) according to the authors of the original study [So far, we showed how we trained and tested our model utilizing gene expression data to predict carbon sources for ay occur , 57. We Next, we investigated how our model predicted the time-series of the shifts and again compared the predicted fluxes of the malate and glucose transporters with the fluxes from the gold standard across all time points  to the unknown data from the validation set , the model correctly identified the carbon sources at baseline and at the endpoints of the carbon source shifts. For the prediction of the time lapse, the model predicted the time-lapse behavior of the main nutrients for the shift from malate to malate plus glucose correctly. However, the model had major difficulties for the shift from glucose to glucose plus malate, and this will be discussed below.B. subtilis. We identified the correct major carbon sources for all eight conditions based on the corresponding gene expression profiles. LPM-GEM was developed to be used as a comparative method to identify the major carbon sources and was tested here for single or two carbon sources. In a typical application, LPM-GEM may be also applied to investigations in rich media conditions. As a future aspect, the performance of LPM-GEM may be tested with data from different rich media conditions by e.g. comparing the predictions with the consumption rates of the cells from the supernatant. Moreover, for most reactions in substrate uptakes, glycolysis, and TCA cycle, the flux prediction results correlated well with 13C metabolic flux data. We could very well model the major intracellular changes in carbon metabolism when the carbon sources changed, and particularly the direction of fluxes and the switch between glycolysis (using NAD-dependent GapA) and gluconeogenesis (using NADP-dependent GapB).We established a novel method employing gene expression profiles to estimate metabolic fluxes in a systems view. During implementing the method, we observed that the optimization to fit the fluxes to expression profiles utilized thermodynamically infeasible loops (TILs), leading to low prediction performances. We addressed this issue following three different strategies. First, we came up with a novel approach (IFFPR) for reducing the upper and lower bounds by iteratively reducing the flux ranges of each considered reaction. Furthermore, we recognized that using an existing well-established method to reduce TIL (ll-COBRA) was very powerful, but it was quite CPU intensive for our purposes. We addressed this issue and introduced our new method RED-TIL as an alternative method. ll-COBRA formulates one large problem and searches for an optimal solution in a predefined-thermodynamic feasible region. In turn, employing a bottom-up design, RED-TIL splits the overall problem into smaller problems by detecting a TIL in the optimal solution, excluding it from the solution space, and re-optimizing the solution iteratively until no TIL (within a certain limit) is detected. While the results were comparable, the computational speed for RED-TIL was considerably faster. On average, RED-TIL removed TIL from the relevant solution space three times faster than ll-COBRA. Such a speed-up was relevant for our study as we needed to generate solutions for a larger range of different parameter settings, particularly for optimizing the upper and lower bounds for each reaction when running IFFPR. As a third means to reduce TIL, we penalized the sum of fluxes of non-core reactions. All three methods to reduce TIL improved our predictions. Using these methods, we set up an FBA model based on a linear fit between the expression of the encoding genes for an enzyme and its predicted flux. We aimed to predict the main carbon source for the model organism We benchmarked our method with the well-known methods . Without prior knowledge of carbon sources, pFBA failed to distinguish flux profiles. The method only showed a good flux prediction performance when carbon sources were known. It was then excluded from further comparisons as it did not suit the principal aim of the study . Compared to iMAT and mCADRE, our method yielded better flux predictions and, on average, better predictions of the carbon source. A reason for this may be that our method requires no binarization/discretization of the transcription profiles. iMAT, mCADRE, and other approaches , 31–33 n13C tracer derived flux from the original study). Although gene expression data is scalable and easy to obtain compared to 13C metabolic flux data [13C tracer derived flux data was available. In principle, our method enables using any set of enzymes as core reactions which are of particular interest. Still, also here, the selection needs a sufficient biological background knowledge to circumvent studying unrealistic scenarios which may be due to the fact that the enzymes under study are not regulated on the gene expression level or are not the relevant pacemakers in the studied conditions. Even though LPM-GEM showed a distinctive better flux prediction performance compared to other methods tested , a limitation of our approach is that it needs more CPU runtime to build the models. For our set of core reactions, this was not limiting our analyses. However, future studies need to be performed to test if models basing on much larger sets of core reactions can also be built in reasonable time.Using the model trained with the dataset of the eight carbon sources, we validated our approach with a second publicly available dataset in which a shift in the carbon source from glucose to glucose plus malate and from malate to malate plus glucose was investigated . Our modlux data –36, it plux data . We obseB. subtilis. Our study supports the idea that FBA analysis based on gene expression profiles can serve as an alternative to 13C tracer analysis. Since our approach adjusts flux levels continuously to gene transcript levels, it circumvents defining thresholds and discretization of the expression data. Still, one should be aware of the limitations when studying processes that are not regulated by gene transcription.We have introduced a novel computational approach integrating gene expression profiles into a metabolic network supported by new methods to reduce TIL. With this, we could well predict the carbon sources of Additional file 1. LPM-GEM supplementary information.


Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis, iJT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed iJT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like iJT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria. Bacillus subtilis is the best-studied Gram-positive bacterium and has promising industrial applications1. The organism has been widely used in industrial applications, including for the production of antibiotics, enzymes, and vitamins2. Furthermore, B. subtilis serves as a model in studies of gut3 and soil microbiome4, for our understanding of sporulation and cell differentiation, biofilm formation, as well as to unravel pathogenicity in related pathogens6.B. subtilis allowed reconstructing one of the first bacterial genome-scale metabolic models (M-models)7. The M-model consists of a network of all known metabolic reactions, resulting in high prediction accuracy of gene essentiality, growth on different carbon and nitrogen substrates, and gene-knockout phenotypes8. Even though this network can accurately predict metabolic responses to nutrient levels and gene knockouts10, enzyme production costs, and protein secretion are out of the scope of the M-model. Moreover, the biomass composition is a constraint in M-models, thus limiting predictions about variations of biomass precursor abundances11. Therefore, it is impracticable to simulate stress conditions that involve shifts in gene expression or alterations in biomass composition with the M-model. While M-model coupling with metabolomics data has been successfully employed to analyze variations in biomass composition13, no a priori prediction of these shifts has been possible with bacterial M-models.The tremendous amount of omics and physiological data available for 14. The models of metabolism and gene expression (ME-models) link enzyme production profiles with metabolic reaction fluxes, thus assigning additional protein biosynthetic costs to metabolism. Now, predicted metabolic fluxes account for optimal proteome composition at specific growth conditions. ME-models can also be associated with the chaperones framework to simulate changes in the proteome in response to temperature or metal availability18. The first ME model was reconstructed for Thermotoga maritima18, with explicit definitions of necessary coupling constraints for complex usage, transcription, translation, and mRNA degradation. The next ME-models were reconstructed for Escherichia coli, the first being published by Thiele et al.19, which then underwent three subsequent updates, namely iOL1650-ME14, iJL1678-ME17, and iJL1678b-ME20. The last of these was released with a new standard on ME-model reconstruction, called COBRAme, upon which this work was based.A new generation of computational models enables linking gene expression mechanisms to metabolic reactionsB. subtilis str. 168, iJT964-ME, based on the available M-model iYO8447, gene annotation in BsubCyc21, and extensive manual curation of transporters and secretory pathways. We show the increased predictive capability of iJT964-ME to simulate gene essentiality, stress-induced biomass composition variation, and shifts in gene expression. Furthermore, we deployed the ME model to accurately predict enzyme production under various conditions, showcasing its ability to assist protein production strategies.Here, we reconstructed the ME model of iJT964-ME was performed by adapting the available metabolic modeling packages COBRAme20, COBRApy22, and ECOLIme20. COBRAme functions were altered to be compatible with Bacillus subtilis gene and protein nomenclature used in GenBank, FASTA, and other files  contains 964 genes, 6282 reactions, and 4208 metabolites  than in iYO844, since reversible reactions are split into forward and reverse subreactions.Reconstruction of es Table . The restes Fig. . A detaiiJT964-ME includes new transcription and translation reactions, which correspond to 28% of the total reactions. An additional 23% of reactions represent complex formation (including generic complexes), post-translational modification, and tRNA charging, and the remaining reactions (21%) account for exchange and demand reactions. While most exchange reactions are kept the same as in iYO844, 1,081 new demand reactions were added to account for RNA degradation. To test the quality of the ME-metabolic network, we ran a high-throughput phenotypic analysis of 87 carbon sources based on experimental results from ref. 7, which were initially used to validate iYO844. iJT964-ME and iYO844 simulations achieved a Matthews Correlation Coefficient (MCC) of 0.454. Considering the base metabolic network of the model is fully inherited from iYO844, it is expected that growth calls on different substrates varies little to none. Major changes, however, are to be expected in predictions at the level of gene expression and protein synthesis. Complete lists of flux distributions for the M- and ME- models are provided in Supplementary Data actions 2% account6), which is not the case for iJT964-ME. On the other hand, the rest of the predicted metabolic fluxes are distributed similarly across the network in both iYO844, with each reaction carrying a comparable flux in both models, yielding an overall Pearson correlation coefficient (R2) of 0.81 , with 32% of them being essential in the growth on glucose as predicted by iJT964-ME  and 40% in Precision. The superior performance achieved by ME-models can be explained by the emergent metabolite-metabolite and metabolite-protein dependencies that arise when flux in the network is permitted to alter gene expression profiles.The addition of gene expression reactions into the network of -ME Fig. . The extB. subtilis is a versatile organism that can metabolize a wide range of different carbon and nitrogen sources. This results in the model containing many transporters, though most of them are not active under specific growth conditions. For example, if glucose is supplemented in minimal medium the transporter for glucose-6-phosphate will be prioritized. The next significant groups of non-essential genes correspond to carbohydrate and amino acid metabolism. Even though both biomass precursors are essential for growth, B. subtilis contains several alternative pathways to synthesize them. This is especially the case for carbohydrate metabolism, as only 6% of its genes were predicted to be essential and relate mostly to the pentose phosphate pathway (PPP) and glycolysis reactions. In a similar way, alternative pathways render some reactions in nucleotide metabolism, lipid metabolism, membrane synthesis, and cell wall synthesis non-essential.Figure rpsT was observed to not be essential23; however, since it is annotated as a subunit of the ribosome, it is essential to carry out translation in the ME-model. As a result of this limitation, false positives are higher among the core expression machinery . The trp operon (trpEDCFBA-hisC-tyrA-aroE) codes for enzymes that carry out tryptophan biosynthesis. It has been suggested that trp upregulation is caused by ethanol inducing a decrease in tryptophan concentrations25, although it has not been fully explained yet. We performed simulations to predict ethanol stress and give a mechanistic insight into gene expression phenomena in B. subtilis.Stress by increased concentrations of fermentation products, e.g., alcohols or short-chain fatty acids, is one of the principal stresses to overcome in industrial settingsiJT964-ME contains transport reactions that simulate the flow of ethanol through the cell membrane via diffusion as experimentally observed. We simulated ethanol diffusion rates between (0 and 0.5 mmol/gDW/h). In agreement with the recent differential expression results25, the model predicted the increase in tryptophan synthesis due to increased ethanol uptake . Sodium is moved through the membrane via active transport, so transport fluxes in the model trigger transporter expression. However, sodium importers are downregulated  by the FDA5, is a widely used cell factory for the production of proteins due to its accelerated metabolism and highly efficient secretory pathways. One of the most prominent biotechnological application of B. subtilis is the production of amylase for a variety of materials and detergents industries31. Amylase production and secretion in this organism has been extensively optimized, mainly through random mutagenesis and variation in starch-feeding strategies32.33. While M-models can accurately capture the biosynthetic mechanism of metabolic compounds, optimization of enzyme synthesis is not feasible by these models. ME-models on the other hand have great potential for the simulation and optimization of protein synthesis and secretion. In this work, we manually curated the translocation pathways of B. subtilis 31 and high (0.195 h−1) growth rate32. Simulations were then performed with starch as the carbon source, which triggers the secretion of amyE in the model. Starch uptake rates were set to fit the experimental growth rate described in the mentioned kinetic studies. Then, we sampled the solution space close to the optimal solution  to generate a distribution representing biologically relevant secretion rates robustly34 (see “Methods”). The predicted secretion rates at both conditions were compared with the experimental data  on the simulated data. As expected, both growth conditions cluster tidily in the PCA plot shown in Fig. B. subtilis growth and amylase secretion in starch, we further designed an in silico experiment to isolate the effect of amyE expression at a constant growth rate.Under low growth, the model overestimates the amylase secretion rate, while We tested what groups of reactions would significantly drive the overexpression of amyE by fixing the growth rate at the lowest of the high growth conditions and forcing amyE overproduction until reaching the highest secretion rate. With this new sampling dataset, we performed a PCA that generated a profound insight into the effect of amyE overexpression on the network. The two largest components . A few more amino acids are called in Fig. Figure B. subtilis was grown in M9 medium supplemented with starch and different amino acids (see “Methods”), and the resulting amylase expression was quantified using enzymatic activity as a proxy for protein content (see “Methods”), as described in the previous studies36. A complete summary of sensitivity, composition, and experimental values is provided in Supplementary Data 405) and biomass concentration (OD600) are provided in Supplementary Data We aimed to test the effect of outliers in Fig. −1, it being the only amino acid with a negative predicted effect. The observed amylase activity , with a mean ratio of −0.227 (mmol Cys) (mmol Amylase)−1, which is in sync with the single optimum shown in Fig. Predicted low-sensitivity amino acids, such as tryptophan, phenylalanine, tyrosine, valine, and isoleucine, were also observed to induce a significantly lower increase in amylase activity. Interestingly, the model predicted that cysteine would have a negative effect of −0.23 (mmol Cys) (mmol Amylase)ity Fig.  with addB. subtilis, in terms of energetic level and macronutrient content. It can also be argued that higher amylase secretion rates favor the uptake of starch, and thus, biomass production. However, this is not the case for the negative effect of cysteine, which indicates it is not related with its nutritional value. Further, aspartate, glutamine and glutamate, showed an amylase secretion effect comparable to asparagine . While ME-models are the first generation of models with the ability to predict adapted amino acid synthesis profiles, numerous biological processes at the level of kinetics and regulation are not considered. Leucine is one of the amino acids with the lowest solubility in water, which significantly decreased the amount of amino acid that we could supplement in the samples, and thus reduces the driving force for its uptake in vivo. This physical limitation of leucine might have impacted the amylase activity increase.Inevitably, discrepancies were encountered between model simulations and observed amylase activity variations. Out of four predicted outliers, leucine did not show a significant influence on amylase secretion  as compared to iYO844.ME-models can improve the prediction of flux distributions while overcoming inherent issues present in M-models. One of these issues that are most difficult to overcome is the prediction of metabolic loops with fluxes above biologically relevant ranges. In previous studies, loops have been addressed by coupling thermodynamic constraints25 computed and characterized sets of co-regulated genes across a microarray dataset and identified signals with a lack of prior literature: (i) activation of tryptophan synthesis by ethanol stress and (ii) downregulation of arginine synthesis by salt shock. By simulating these conditions, we demonstrate that our model recapitulates these effects, without adding further constrains to the model, such as varying biomass reaction coefficients12 or condition-specific flux constraints13. With regard to (i), our model supports potential mechanisms involving increased amino acid synthesis for ethanol processing and efflux enzymes, and a potential role of folate degradation. For (ii), we observed decreased arginine synthesis as a result of overall downregulation for amino acid synthesis as expression flux shifts toward carbohydrate metabolism. It is worthy to note that in all cases, iYO844 could not capture any change in amino acid metabolism due to it being directly constrained by the fixed requirements in the biomass reaction.Recent advances in transcriptomic analysis using machine learning tools have led to hypotheses about amino acid metabolism, which presented excellent questions for us to explore with our model. The study by Rychel et al.iJT964-ME could be employed to predict protein secretion at different growth conditions. For this, we reproduced two previous reports of amylase secretion at high32 and low31 growth rate conditions in silico. The model successfully predicted the secretion rate of amylase within the experimental reported range, though the secretion rate under low growth was overestimated. Overall, the dependence of growth rate on the required secretion rate of amylase was accurately captured. We performed a PCA to identify critical drivers of the metabolic change between these two conditions. As expected, the response to a higher expression of amylase was mixed with the global increase in metabolic activity. This showed the main drivers to be essential reactions related to biomass precursor synthesis, energy metabolism, and carbon source breakdown.In a final analysis, we assessed whether Thereupon, we isolated the response of amylase overproduction by simulating an increase in amylase secretion at a fixed growth rate. The new PCA unraveled key metabolic pathways directly related to amylase composition, with the biosynthetic pathways of the most abundant amino acids in its sequence having the highest weights in the principal components. However, the link between composition and weight in the principal component was not direct, as the amino acids with the highest weight were not always the most abundant. Out of the four predicted outliers with outstanding effect on amylase secretion, only one observation diverged from the simulations. Further, low-sensitivity amino acids were correctly predicted, with the notable case of cysteine, which was correctly predicted to negatively affect amylase secretion.B. subtilis. Its wide scope and ability to capture expression changes have improved gene essentiality predictions, shed light on recent hypotheses relating amino acid metabolism and stress, and explored the capacity to secrete industrially relevant proteins. This model can serve as the basis for unraveling further questions about metabolism and has the potential to be the foundation on which to optimize heterologous protein expression in this important model organism and cell factory.The iJT964-ME model represents a significant advancement in the metabolic modeling of 22 and COBRAme20. Models were solved using the package SOLVEme39. The E. coli reconstruction package ECOLIme20 was adapted with B. subtilis gene expression machinery, complexes, and translocation pathways. In brief, every reaction in a template core metabolic network (M-model) is coupled with the consumption of the enzyme that catalyzes it. Similarly, enzyme production pathways  are coupled with the corresponding catalyzing enzymes. The link of the reactions is performed with coupling coefficients, which represent the usage requirement of the catalyzing enzyme per unit flux of reaction.Reconstruction was performed in Python 3.6, using the reconstruction packages cobrapy 0.5.4iYO844, along with its gene-reaction rules . Then, the QuadMINOS40 solver was called to solve the LP and assess feasibility in quad-precision.Metabolic and gene expression flux distribution was predicted following the same protocol and software used in the reconstruction of the 7 with the template M-model iYO844. Only those carbon substrates that were already present in the model were included in this analysis, which leaves 88 carbon substrates. Detailed results are shown in Supplementary Data As a way of checking the sanity of the metabolic network, we reproduced the carbon substrate analysis performed by Oh et al.41 for B. subtilis and EcoCyc42 for E. coli. Gene functions were assigned as annotated in the subsystem of the reaction they catalyze. Metabolic subsystem annotation was taken from iYO844, and gene expression subsystems were assigned according to the catalyzed reaction types , True Negative Rate (TNR), False Discovery Rate (FDR), Matthews Correlation Coefficient (MCC), Precision, and Coverage. The definitions were as follows:7. Transcription and translation levels were defined as a ratio of the flux at a specific condition to the maximal flux in the whole dataset to normalize and highlight trends.Ethanol is a small polar molecule that can readily diffuse through the cell membrane. Therefore, ethanol uptake was modeled with no enzymatic coupling. The exchange reaction of ethanol was opened with lower and upper bounds equal to a defined uptake rate . Then, exchange reactions were constrained with random values within the calculated bounds in every sampling iteration. This solution space was sampled 100 times.In the model, amyE is transcribed (transcription_TU8J2_1134_from_BSU25200-MONOMER), translated (translation_BSU25200), secreted through the sec pathway (translocation_BSU03040), and finally used in the hydrolysis of extracellular starch (AAMYL_1_FWD_BSU03040-MONOMER). Secretion rates, in units of mmol gDW32 and low31 growth rate. In both studies, the authors reported time-course biomass, X (g L−1), and amylase activity, A (mU mL−1). Amylase activity required conversion to mass concentration for direct comparison with model predictions. Thus, we converted activity to amylase concentration, C (mg mL−1), using a typical range for B. subtilis’ amylase specific activity43 of 153.7 (minimum) to 245 (maximum) U mg−1. Then, growth and amylase secretion rates were obtained using a linearized model.Validation of secretion rates was performed in data collected from two previously reported experimental datasets, at a highX) and amylase (C) in batch culture, where μ is the growth rate and v is the amylase secretion rate. Integration for 32 and low31 growth rate conditions were 0.81 and 0.98, respectively. For the amylase secretion rate (ν), integration for Equations  and 8)  8) are tt in Eq.  yields tAmylase overexpression was performed by setting the growth rate constant at the minimum rate exhibited in the distribution of samples at a high growth  is a resourceful unsupervised machine learning method that identifies key underlying variables that drive the variance in the samples. We used PCA to identify key reactions that explain the variance between high and low growth, as well as within the samples at different levels of amylase overexpression. The flux data were pre-processed by calculating z-scores and then fed to the scikit-learn method sklearn.decomposition.PCA.B. subtilis strain 168 was struck out on LB agar plate overnight. One colony was inoculated in LB medium for 16 h at 37 °C, pelleted and resuspended in M9 medium. For amylase production testing, the cell resuspension was inoculated into M9, M9 + 0.2% starch (through 0.22uM filter), and M9 + 0.2% starch + amino acid in a 96-well plate at the starting inoculum of OD600 = 0.07 and final volume of 200ul. All amino acid concentrations were at 20 mM when possible, based on solubility data provided by the manufacturer (Millipore Sigma). Exceptions are Asp (3.4 mM), Glu (5.8 mM), Ile (14 mM), Leu (3.8 mm), Phe (16.3 mM), Trp (6.6 mM), Try (0.3 mM). All growth conditions were set up in triplicates. After 24 h at 37 °C, amylase activity and production were determined using the Sigma-Aldrich® Amylase Activity Assay Kit (MAK009). The protocol was followed exactly as suggested by the assay kit manufacturer. OD405 was measured at 24 h, and the value of the starch-only sample was subtracted from the value of each amino acid- supplemented sample. Amylase activity was reported as nmole/min/mL (milliunits), considering that one unit of amylase is the amount of amylase that cleaves ethylidene-pNP-G7 to generate 1.0 mmole of p-nitrophenol per minute at 25 °C.31 (low amylase secretion) and ref. 32 (high-amylase secretion). Accordingly, amylase secretion rates were centered around 1.36e-8 and 4.4e-7 mmol Amylase gDW−1 h−1, allowing for a 10% variation. Sampling was performed with 100 points, and outliers were removed with 95% confidence. Significance in the change of cysteine synthesis rate distributions across both conditions was assessed with a two-tailed t test. The sensitivity of cysteine synthesis to amylase expression, in units of Sampling was performed in two different amylase secretion conditions, corresponding to amylase secretion rates calculated from ref. Supplementary InformationFlux distributions of the M- and ME-models under different substratesSensitivity, amino acid composition and observed effect in amylase activity of amino acids.Raw measurements of amylase activity with amino acid supplementationSampling flux distributions at low and high amylase secretion rates.Raw measurements of biomass concentration with amino acid supplementation


Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies.Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard  Cupriavidus necator H16 that is linked to the BioCyc database, which provides an efficient way of application, model update, integration of experimental data and network visualisation for other researchers. Using our model, we demonstrate how integrating experimental observations, including Transposon-directed Insertion site Sequencing (TraDIS) and omics data, can be used to compensate for the lack of regulatory, kinetic and thermodynamic information in GSMs, and thus improve model accuracy. Importantly, we found that TraDIS in vivo screening and GSM analysis are complementary approaches, which can be used in combination to provide reliable gene essentiality predictions. Overall, our results offer an informed strategy for the deliberate manipulation of C. necator H16 metabolic capabilities, towards its industrial application to convert greenhouse gases into biochemicals and biofuels.Genome-scale metabolic models (GSMs) provide a tool for unravelling the complex metabolic behaviour of bacteria and how they adapt to changing environments and genetic perturbations, and thus offer invaluable insights for biotechnology applications. For a GSM to be used efficiently for strain development purposes, however, the model must be easily readable and reusable by other researchers, whilst being able to predict metabolic behaviour with a high level of accuracy. In this work, we developed a GSM for  First, we used the experimental data reported in ’ in some of the reactions, which is to account for the possibility of different fatty acids being consumed. We therefore manually added a copy of these reactions to the submodel ‘PHL.spy’ file for each of the fatty acids found in E. coli in a separate submodel. The LPS composition can vary in different bacteria, therefore including it as a separate submodel allows easy curation of the reactions. To our knowledge, the LPS composition has currently not been defined for C. necator, and so was assumed to be the same as the LPS composition in E. coli in iCN1361.We included the generic pathway to lipopolysaccharide (LPS) biosynthesis available in the MetaCyc PGDB (v 21.0) for via Blast+ or the KEGG database and added to the ExtraReacs.spy module. Similarly, gap-filling was carried out to fulfil growth in the model on feedstocks for which experimental evidence exists for C. necator H16.Due to the incompleteness of the BioCyc PGDB, essential reactions for producing the biomass precursors were absent from the model. These missing reactions were identified C. necator H16 was added to a separate submodel. These reactions all include the suffix ‘_tx’, which makes them easily identifiable for setting constraints on uptake and production. The prefix ‘x_’ was also added to metabolites that were in the extracellular compartment. Notably, we also represent PHB storage using an artificial transport reaction in this submodel.A transport reaction for each metabolite known to be consumed or produced by i.e., isoenzymes), and rely on only 1 of the genes being active. Reactions associated with isoenzymes are represented using an ‘OR’ relationship. To identify the gene-relationships for C. necator, we first used the PyoCyc module in ScrumPy to automatically derive a list of genes associated to each reaction. The genes associated with a reaction were defined as a complex only if they were present in the same operon. If the genes are being expressed simultaneously then it is highly likely they form a complex. The information regarding which operon a gene was contained in, was also automatically extracted from the PGDB database using the PyoCyc module in ScrumPy. The remaining genes were then assumed to be acting as isoenzymes. Any reaction that had been added to the model through the initial automatic construction but was not essential for a known metabolic function and for which a gene association could not be determined, was removed from the model. Missing gene annotation for essential reactions were checked in KEGG and UniProt and added to the model if available.The gene-reaction relationships are defined in a GSM using boolean logic ,61. A reN, in which rows represent the m metabolites and columns represent the n reactions. The entries in N correspond to the stoichiometries of the metabolites in the reaction. Negative and positive entries are used to represent metabolite consumption and production in the reaction, respectively. The changes of metabolite concentrations can be modelled using a system of kinetic equations, such thatv is a vector of length n corresponding to the fluxes through reactions and x is a vector of metabolite concentrations of length m. Nv = 0). For most metabolic networks, the number of reactions exceeds the number of metabolites (n>m), and results in multiple solutions existing for this coupled system of equations. Constraint based approaches can be applied to explore possible flux distributions.The set of reactions in the GSM are represented mathematically using a stoichiometric matrix, Z, whilst also satisfying a set of constraints. The objective function can be any linear combination of fluxes multiplied by a vector of weights (Z = cTv). Additional constraints are added to the LP to reduce the feasible solution space. Knowledge of reaction directionality and metabolite uptake and production rates are incorporated into the LP as lower and upper bounds on reactions , for example. The LP is formulated as follows:In this work, we identified feasible flux distributions using flux balance analysis (FBA) . FBA fini.e., the flux towards the biomass equation) as the objective function. The optimal growth rate is then fixed as an additional constraint. The second LP is then solved with minimisation of the total sum of fluxes as the new objective function:c contains a weight of 1 in the position corresponding to the biomass reaction and zero elsewhere. To mimic minimal media in aerobic conditions, we constrained both LPs to allow for the free uptake of sulfate, phosphate, ammonium, oxygen, water and protons. All other transport reactions had their lower bounds set to zero.In this work, we assumed that bacteria evolve to optimise their cellular growth, whilst also minimising their enzymatic burden. This method is called parsimonious flux balance analysis (pFBA) and involves solving two LPs . StandarThroughout the curation stage, we tested whether the model could synthesise each precursor in the biomass composition. To test an individual precursor, we added an artificial transport reaction that consumed the metabolite. The LP in Eq  was thenhttps://github.com/SBRCNottingham/CnecatorGSM/tree/main/MEMOTE_repo/sbrc_cnecator_gsm), which shows the results of these consistency checks.We checked that the model obeyed the law of mass and energy conservation. First, we verified that the model was not capable of synthesising energy-equivalents without the input of electrons. To do this, we modified the LP in Eq , to maxi-1·h-1. The uptake rate of all other carbon sources in the model were fixed to zero. Maximisation of the biomass equation was then set as the objective in the pFBA simulation : the reaction expression was determined by summing the RPKM value of all isoenzymes.Reactions with ‘OR’ GR-relationships : the reaction expression was determined using the minimum RPKM value of all genes in the complex.Reactions with ‘AND’ GR-relationships  and (2).th percentile were associated with high activity, similar to the approach carried out in  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. 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MaranasAssociate EditorPLOS Computational BiologyJason PapinEditor-in-ChiefPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: SummaryIn this paper, the authors reconstructed a genome-scale metabolic model (GSM) for Cupriavidus necator H16 using metabolites/reactions IDs from BioCyc database. The model is validated against multiple experimental data such as growth and 13C metabolic fluxes. The authors then performed TraDIS experiments to assess gene essentiality and compared with GSM predictions, and false positive essential genes were further verified experimentally, which revealed that some TraDIS limitations can be complemented by GSM predictions. The GSM is then integrated with transcriptomics data to build two condition-specific models. Flux sampling was used to predict flux changes during nitrogen limitation condition, and another computational approach was used to predict transcriptional or post-translational regulations. Overall, the experimental approaches and analysis are very thorough, and the paper is well-written. The major concern I had is that the authors were benchmarking their model against an old RehMBEL1391 model while an updated model [1] was published recently. A few comments are listed as follows for the authors to consider to strengthen their manuscript.CommentsMajorhttps://github.com/m-jahn/genome-scale-models).- Regarding the model reconstruction, it would be more helpful to the readers if the authors can provide some comparison with the published models. For example, iCN1361 has 1292 reactions, while the number of reactions in the first RehMBEL1391 model is 1391 and the recent RehMBEL1391 model is 1360. Can the authors add some explanation on why certain reactions are added/removed? A good example is in the readme of the updated iCN1361 repos that include a list of changes made compared to the previous model  as corrected by [1].- The latest RehMBEL1391 study [1] performed gene essentiality experiments but used a different experimental approach (Tn-seq). It also included gene essentiality data from formate/succinate carbon source experiments. It is recommended to include these datasets to test the GSM predictions as well. Since the experimental approach to determine gene essentiality is different, any limitations of TraDIS mentioned by the authors in pages 17-19 did not shown up in the Tn-seq study?- Page 25: iMAT can use transcriptomics data or proteomics data to build condition specific models. Since genome-wide proteomics data is also available in the latest study [1], the authors may want to compare the model they built with proteomic-based iMAT model to find any discrepancies.- Page 28 lines 627-639: The conclusions the authors made about CBB cycle and CO2 fixation are different from the analysis of [1]. The conclusion from the latest RehMBEL1391 is that “CO2-reassimilation through Rubisco does not provide a fitness benefit for heterotrophic growth, but is rather an investment in readiness for autotrophy”. Can the authors provide a more detailed analysis to explain the different predictions made by the two models?- In the proteomics data from [1], phaC enzyme level is low compared with phaA or phaB. Is it possible that the feedback inhibition is not the major limitation in f16 condition but the enzyme level of phaC? In other words, is it possible for the computational approach/analysis to be done at the pathway level to make sure that feedback inhibition is the only reason as suggested by the author?https://github.com/maranasgroup/iRhto_memote- Page 8 lines 184-185: It is great that the authors are using MEMOTE for validation tests and generated a report in the github repository. However, the github repository is not organized using the memote format. For example, there should be a data folder to store the gene essentiality, growth, and media data. Moreover, the repository can be used to track model changes since new experimental results are often leading the model improvement [2]. It is therefore recommended that the authors follow the same standard to create a memote generated structure. A good example is Minor- Page 6 lines 135 - 148: It is possible that some metabolites/reactions in C. necator H16 do not exist in BioCyc. Therefore, it might make more sense to use a more general identifier . This will allow the model to use information from other comprehensive databases  that are not added into biocyc. Can the authors comment on whether they have encountered this scenario during their model reconstruction?- Page44 formulation (3): the objective function should be minimizing of 1-norm of flux vector.- There are still 25 unbounded reactions based on memote report, can the authors verify that they are not forming thermodynamically infeasible cycles?References[1] Jahn, Michael, et al. "Protein allocation and utilization in the versatile chemolithoautotroph Cupriavidus necator." bioRxiv (2021).[2] Dinh, Hoang V., et al. "A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data." Metabolic engineering communications 9 (2019): e00101.Reviewer #2: The manuscript presents an exemplary work on genome-scale metabolic reconstruction. The draft model from an automated pipeline was curated extensively with consistency tested thoroughly. The model was validated against growth physiological data, 13-C MFA data, transposon mutant library for gene essentiality prediction and phenotypes of particular knockout mutants under questions. Transcriptomics data were then used to identify potential interactions between different pathways and regulatory control. Overall, the model captures the experimental data very well and generates insight into the metabolism of the organism being studied. This will be a useful tool for predicting metabolic engineering strategies for Cupriavidus necator H16.Minor comment:Only two minor comments:- When analyzing the correlation between different pathways with the integration of transcriptomic data . Are those correlations truly the results of integrating the transcriptomic data or are they possibly just the network properties of the model? In other words, are the correlations significant compared to the base model not with transcriptomic data integrated? Comparing this might give a clearer picture.- Is the manually curated PGDB in BioCyc format available online? This will help a lot to share the results especially to non-computational people.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. 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Read more information on sharing protocols at  23 Feb 2022AttachmentResponseLetter_PCOMPBIOL-D-21-02005R1.pdfSubmitted filename: Click here for additional data file. 14 Apr 2022Dear Dr Pearcy,We are pleased to inform you that your manuscript 'A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomics data reveals metabolic insights for biotechnological applications' has been provisionally accepted for publication in PLOS Computational Biology.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Jason PapinEditor-in-ChiefPLOS Computational Biology***********************************************************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have addressed all of my comments in details. In particular, the authors have included the comparison with a recently published Cupriavidus necator H16 model and updated their model/analysis using the new biomass, gene essentiality, and other data in the published model. I recommend the paper to be accepted to PLOS Computational Biology journal.Reviewer #2: The authors have satisfactorily addressed the comments.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. 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Purine nucleosides play essential roles in cellular physiological processes and have a wide range of applications in the fields of antitumor/antiviral drugs and food. However, microbial overproduction of purine nucleosides by de novo metabolic engineering remains a great challenge due to their strict and complex regulatory machinery involved in biosynthetic pathways.Bacillus subtilis. The metabolic flux was analyzed to predict two key backflow nodes, Drm (purine nucleotides toward PPP) and YwjH (PPP–EMP), to resolve the competitive relationship between biomass and purine nucleotide synthesis. In terms of the purine synthesis pathway, the first backflow node Drm was inactivated to block the degradation of purine nucleotides, which greatly increased the inosine production to 13.98–14.47 g/L without affecting cell growth. Furthermore, releasing feedback inhibition of the purine operon by promoter replacement enhanced the accumulation of purine nucleotides. In terms of the central carbon metabolic pathways, the deletion of the second backflow node YwjH and overexpression of Zwf were combined to increase inosine production to 22.01 ± 1.18 g/L by enhancing the metabolic flow of PPP. By switching on the flux node of the glucose-6-phosphate to PPP or EMP, the final inosine engineered strain produced up to 25.81 ± 1.23 g/L inosine by a pgi-based metabolic switch with a yield of 0.126 mol/mol glucose, a productivity of 0.358 g/L/h and a synthesis rate of 0.088 mmol/gDW/h, representing the highest yield in de novo engineered inosine bacteria. Under the guidance of this in silico-designed strategy, a general chassis bacterium was generated, for the first time, to efficiently synthesize inosine, adenosine, guanosine, IMP and GMP, which provides sufficient precursors for the synthesis of various purine intermediates.In this study, we designed an in silico-guided strategy for overproducing purine nucleosides based on a genome-scale metabolic network model in Our study reveals that in silico-guided metabolic engineering successfully optimized the purine synthesis pathway by exploring efficient targets, which could be applied as a superior strategy for efficient biosynthesis of biotechnological products.The online version contains supplementary material available at 10.1186/s13068-022-02179-x. As essential metabolites in organisms, purine intermediates participate in the metabolic synthesis of nucleic acids, energy and amino acids , 2. AmonBacillus is known to be superior for the fermentation of nucleosides with advantages of a safe producer, few byproducts, and a simple separation process [B. subtilis 168, inosine titer was increased to 6 g/L by disrupting the conversion of IMP to AMP and XMP, the degradation of inosine, and the repressor PurR and 5’-UTR of the pur operon [prs, purF and purA in B. subtilis XGL has been reported to increase PRPP concentration and pur operon transcription, which achieved adenosine accumulation to 7.04 g/L [guaA and optimized the growth condition to increase adenosine titer from 7.40 to 14.39 g/L in B. subtilis A509 [Escherichia coli is another industrial strain that is applied to nucleoside synthesis and has been reported to produce 6.7–7.5 g/L inosine by overexpressing the prs and purF genes with inactivation of the purA, deoD, purF, purR, add, edd, pgi and xapA genes [purA and deoD genes in B. subtilis W168 [ process , 12. In r operon . Furtherr operon . Under glis W168 . The flulis W168 , 16. Ovelis W168 , 17. TheE. coli, B. subtilis, Corynebacterium glutamicum, and Saccharomyces cerevisiae [iBsu1103V2 model for B. subtilis accounts for 1103 open reading frames and 1451 metabolic reactions involving 1156 metabolites. Compared with the available models in B. subtilis, it has been improved considerably at predictions of metabolites and viability of mutant strains in rich defined medium and glucose minimal media. Thus, the iBsu1103V2 model provides an effective tool to conduct rational guidance for metabolic engineering and systems biology research in B. subtilis [With the rapid development of whole-genome sequencing and multi-omics technologies, genome-scale metabolic network models (GEMs) have been developed by constructing metabolic networks with the aid of computer technology , 19. Basrevisiae , 21–23. subtilis .iBsu1103V2 model to analyze and predict the novel targets for increasing IMP flux without influencing biomass [In this study, we take advantage of the  biomass , 26. An B. subtilis. Among various purine nucleosides, inosine is directly synthesized from IMP through a one-step reaction . Then, the purine nucleoside phosphorylases (PNPs) DeoD and/or PupG, which are involved in the degradation of nucleosides, were inactivated to generate the engineered strains PN02 (W168 ΔpurA ΔdeoD), PN03 (W168 ΔpurA ΔpupG) and PN04 (W168 ΔpurA ΔpupG ΔdeoD) , PN03 (8.88 ± 1.10 g/L), and PN04 (9.54 ± 1.19 g/L) with improved yields (0.009 to 0.039 mol/mol glucose), productivities (0.009 to 0.133 g/L/h), and synthesis rates (0.002 to 0.038 mmol/gDW/h) , respectively  based on the drm gene and the transaldolase encoded by the ywjH gene, while strategy 2 was designed to knock out drm and the fructose bisphosphate aldehyde carboxylase encoded by fbaA  and the transport systems  deletion to construct the engineered strains PN05 (PN01 ΔPdrm), PN06 (PN01 drm*) and PN07 (PN01 Δdrm), respectively . The mRNA expression levels of the pupG gene of the PN06 and PN07 strains were upregulated 2- to 7-fold compared to the original strain PN01, but it could not be detected in PN05 due to promoter deficiency  produced high inosine and low hypoxanthine, which were similar to the control strains PN05 and PN05-s0 (PN05 lacA::Pxyl -xylR). In contrast, dramatically decreased inosine titer and increased hypoxanthine production were detected in the drm expression strain PN-5-s1 (PN05 lacA::Pxyl -drm-xylR). Combined with the above results, Drm, not the purine nucleoside phosphorylase PupG, exhibited a major effect on increasing inosine production and decreasing its degradation  with different secondary structures were selected to replace the original promoter Ppur of the purine operon. Among these promoters, Pctc formed the least stem–loop structure with the highest translation initiation efficiency of 35619.97 AU. The stem–loop structure of Pveg is far from the ribosome binding site (RBS) and the start codon with an initiation efficiency of 19842.93 AU. The promoters P43 and PgsiB, close to RBS and the initiation codon, formed a relatively complex stem–loop structure, which could result in a negative impact on translation initiation. P43 forming the most stable stem–loop structure, had the lowest translation initiation efficiency of 1274.25 AU, whereas PgsiB had a translation initiation efficiency of 15147.28 AU.The purine operon is strictly regulated by transcription initiation repression and ribosome-mediated switch in the cell Fig. a. OptimiPpur in wild-type strain W168 was separately replaced by P43, Pveg, Pctc and PgsiB. The mRNA levels of the purE and purF genes were all upregulated 8- to 78-fold under the control of the replaced promoters, suggesting that the transcription levels of the purine operon were enhanced by promoter replacement . Therefore, Pveg could efficiently relieve the complex regulation of the purine operon and dramatically enhance the synthesis of purine nucleotides, which was selected to optimize the inosine engineered strains.To detect the effects of different promoters on the transcription levels of the purine operon and the synthesis of purine intermediates, the promoter Ppur in the engineered strains was replaced by Pveg to generate PN09 (W168 Ppur::Pveg), PN12 (PN01 Ppur::Pveg), PN13 (PN03 Ppur::Pveg) and PN14 (PN07 Ppur::Pveg). Flask cultivation showed that purine intermediates of inosine and hypoxanthine were all significantly increased in the inosine engineered strains (P < 0.01), further demonstrating that Pveg could efficiently enhance the synthesis of purine nucleotides , respectively  and PN07 (W168 ΔpurA Δdrm) strains were replaced with Pveg, the inosine production of PN12 and PN14 remarkably increased by 148% and 21%, respectively . The inosine titer of PN15 was significantly increased to 20.89 ± 0.69 g/L without an apparent reduction in cell growth , which increased inosine titer to 22.01 ± 1.18 g/L with improved yield , productivity (0.306 g/L/h) and synthesis rate   showed an improved synthesis rate without an obvious impact on cell growth in comparison with those of the control strain PN17 (PN16/pHT01-scaf). In the presence of an inducer, the strain PN18 showed a similar inosine synthesis rate and a slight reduction in cell growth  and PN20 (PN16 Ppgi::Pxyl) showed that pgi deficiency greatly inhibited cell growth . In contrast, cell growth was significantly enhanced and the inosine biosynthesis significantly decreased in the presence of xylose   encoding 11 enzymes is necessary for de novo synthesis of the precursor IMP of the purine intermediates in B. subtilis. However, transcription of this operon is dually regulated by a repressor protein encoded by the purR gene [purR gene, destroying the riboswitch structure, or optimizing the core sequence of the pur operon promoter region [43, Pveg, Pctc, and PgsiB) to eliminate the pur box sequence and riboswitch structure of the PurR recognition site in the original promoter region, which can simply and effectively relieve complex regulation. Combining the transcription level and translation initiation efficiency , which can be catalyzed either by glucose-6-P-1-dehydrogenase Zwf to form glucono-1,5-L-6P of PPP or by glucose-6-P isomerase Pgi to form F6P of EMP Fig. a. PrevioMUST, which identifies genetic modifications through maximizing the objective flux [−1 of cell growth rate and 0.081–0.096 mmol/gDW/h of inosine synthesis rate, closing to predictions of ROOM (0.243 h−1of biomass and 0.083 mmol/gDW/h of IMP flux). Furthermore, the fermentation experiment was carried out using the minimum medium (MM) that was usually used for in silico design  was dramatically lower than that of FM (0.172–0.237 h−1). These experimental data showed that the higher IMP flux would result in lower biomass in MM, indicating a competitive relationship between biomass and IMP flux as predicted by FBA. Compared to the glucose minimal medium, the rich medium has considerably improved the biomass of the final engineered strain, which still has the potential to maximize the product synthesis rate by fed-batch fermentation in further study.Based on the robustness analysis by constraint-based FBA, a competitive relationship between biomass and IMP synthesis suggests a rational engineering strategy is urgently required. Furthermore, many enzymes are responsible for multiple reactions in the glycolysis, gluconeogenesis, the nucleotides terminal pathways (purine/pyrimidine conversions) and the transport systems .The strains used in this study are listed in Additional file Kits for DNA purification/gel recovery and extracting genomic DNA, plasmid DNA, and RNA were purchased from TIANGEN Biotech . DNA polymerase, restriction enzymes, and dNTPs were purchased from New England Biolabs (USA). Antibiotics, inducers, and standard chemicals, such as ampicillin, kanamycin, chloromycetin, erythromycin, IPTG, xylose, purine bases, nucleotides, and nucleosides were purchased from Sigma-Aldrich (USA). Tryptone and yeast extract were purchased from Oxoid Company (UK). Other reagents for cell culture and fermentation medium were all analytical pure, and purchased from Beijing Modern Oriental Fine Chemical Co., Ltd (China).B. subtilis, the upstream and downstream DNA fragments flanking the targets purA, pupG, deoD, drm, Ppur, ywjH, pgi, and zwf were amplified using W168 genomic DNA as the template. The purified DNA fragments were jointed by splice overlap extension (SOE) PCR and then ligated with the vector pWYE486 using the digestion–ligation approach or NEBuilder HiFi DNA fragment rapid assembly kit . After verification by PCR and DNA sequencing, the recombinant plasmids were separately transformed into competent B. subtilis cells to generate engineered strains through two rounds of homologous recombination , respectively. The lower limits of the absorption rates for phosphate, sulfate, and NH3 are set to − 5 mmol/gDW/h. H2O, Ca2+, H+, K+, Mg2+, Na+ and Fe3+ are allowed to be freely transported as external metabolites for exchange  was used to analyze the metabolic flux distribution of nchanged . The calT is the objective function value, expressed as a function of the flux of each reaction; S is the m × n order stoichiometric matrix; v is the reaction flux; vmax is the upper limit flux of each reaction; and vmin is the lower limit flux of each reaction. In general, the accumulation rate of biomass was used as an objective function to calculate the metabolic flux distribution in the case of maximizing biomass.where C−1 in this study). Based on the iBsu1103V2 model, most purine nucleosides/nucleotides are predicted to be secreted out of the cell, including AMP, 3'-AMP, xanthosine, guanosine, GMP, guanine, adenosine, adenine and inosine. The initial calculation principle was designed as previously described [Genetic design through local search (GDLS) converts the bi-level optimization problem (metabolic fluxes of biomass and IMP synthesis) into a mixed-integer linear programming problem using MATLAB software and GLPK (GNU Linear Programming Kit) solvers. An effective, low-complexity multi-path search was carried out to find a set of locally optimal strategies in the solution space that maximizes the target metabolic flow under a certain growth rate of the strain  was determined by the smallest number of reactions, which could change the metabolic flow of the modified strain . These r600 reached 10–12, three milliliters of seed culture was inoculated into a 500-mL shake flask containing 30 mL of fermentation medium (FM) containing 120 g/L glucose, 16 g/L soybean meal hydrolysate, 14 g/L yeast powder, 15 g/L (NH4)2SO4, 4 g/L MgSO4·7H2O, 4 g/L K2HPO4, 0.01 g/L FeSO4, 0.1 g/L biotin and 20 g/L CaCO3. During the two-stage fermentation process, bacterial strains were first cultured for 12 h in a medium containing 3% xylose, soybean meal hydrolysate, yeast powder, (NH4)2SO4, MgSO4·7H2O, K2HPO4, FeSO4, biotin and CaCO3. Then, the sterilized glucose solution (800 g/L) was supplemented into the culture to a final concentration of 120 g/L. According to our previous method [To determine the production of purine nucleotide/base/nucleoside metabolites, the strains were pre-cultured in seed medium  at 32 °C and 220 rpm. Until the ODs method , the cel−∆∆CT method were used to verify and analyze qPCR data [Bacterial total RNA was extracted by an RNA isolation kit and NanoDrop 2000c  was used to determine the concentration of RNA sample, which was then subjected to cDNA synthesis using a FastQuant RT Kit. The primers for qRT-PCR are listed in Additional file PCR data . The tra600) using a spectrophotometer . The concentration of glucose was assayed using an enzyme electrode analyzer . After dilution and filtration of the culture supernatant and cell extract with ddH2O, nucleoside metabolites were determined using a high-performance liquid chromatography (HPLC) system  with an SB-AQ column  at 33 °C. Mobile phase A was 100% (v/v) methanol, whereas mobile phase B consisted of 0.5% (W/V) KH2PO4 at a pH of 4.5. HPLC was performed using a ratio of 90% A and 10% B at 1 mL/min with a monitor at 360 nm. All measurements were performed at least in triplicate and standard deviations (SD) were calculated from three independent experiments.Cell density was measured by determining the absorbance at 600 nm .Additional file 2: Table S1. The production yield, productivity, and conversion rate of inosine engineered strains. Table S2. Genes and reactions used in the iBsu1103V2 model. Table S3. The lower bounds and upper bounds of reactions based on the 13C flux data. Table S4. The targets possible targets for overproduction of IMP and inosine predicted by OptForceMUST.