The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same  core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available  13C-isotopic labeling data and a computationally inexpensive parameterization algorithm into a kinetic parameterization pipeline to ensure that the resulting kinetic model (k-ecoli74) conforms to experimental data. We show how the use of an identical metabolic network for flux elucidation and kinetic parameterization influences predictive capabilities.Microbial production hosts are used for production of a wide range of commodity chemicals. Improving the conversion efficiency of microbial strains is critical to the economic viability and the continued push towards the use of environmentally neutral bioprocesses as a means for producing the chemicals society depends on. Metabolic models have played a key role in helping us to predict metabolic behavior in response to environmental and genetic perturbation that can maximize efficiency. Recently, kinetic models of metabolism have re-emerged as a means for characterizing metabolism, offering improvements over their stoichiometric counterparts in both the type of information that can be gleaned from them, and in prediction accuracy. Despite recent developments, a lack of raw experimental data needed for flux elucidation and, subsequently, kinetic parameterization, and high computation cost have prevented the development of a uniform workflow for construction of the most informative kinetic models. Here, we have incorporated raw  However, probabilistic glucose isotope tracer data. The atom mapping model assembled from the atom transitions gleaned from Leighty and Antoniewicz  < 1) and reverse reaction flux (0 < vr < 10000)) allowed elementary kinetic parameters to assume a broad range of values (0 < k < 1000000). As a result, mutant strain metabolite fold changes also had the potential to assume large values upon steady-state evaluation that were not always physiologically relevant. This occured when the magnitudes of the estimated forward and reverse elementary kinetic parameters for any elementary step in the network were different by several orders of magnitude. The fold changes predicted for aspartate in the Δpgi, Δrpe, and Δzwf strain reflected this, as the elementary parameters for production and consumption of aspartate by ASPTA differ by several orders of magnitude. Small ASPTA free enzyme fractional abundance in those strains relative to other enzyme complexes ensured aspartate concentration had to be large to recapitulate flux. Another reason for large metabolite concentrations was to help to characterize flux redirections in mutant strains. In Δgnd, 6pg and kdpg concentrations were increased to values >1000% of the wild-type concentration to help characterize the increased ED pathway flux that was dependent upon their concentrations, and only observed in the Δgnd strain. In Δpgi, extracellular glucose concentration was increased because glycolysis was blocked and growth rate was low, causing the model to predict an accumulation of glucose. In other instances, large metabolite concentrations were an artifact of the assumptions made in the modeling framework. kdpg was only produced and consumed in the ED pathway, and was not an inhibitor of any reactions. As a result, in the Δedd and Δeda knockout strains, because only EDD or EDA enzyme level was forced to zero, kdpg was able to assume large values as long as the net flux through the reaction that was not knocked out in the pathway was zero. These predictions are a limitation of the elementary decomposition approach for kinetic parameterization in the absence of metabolomics training data, as the mathematical framework of K-FIT allows for flexibility in metabolite concentration predictions when only fluxes are fitted.While the majority of metabolite concentration values estimated by k-ecoli74 fell within ranges consistent with experimental data , the looE. coli generated with a reduced metabolic network. Our first attempt at parameterizing a kinetic model using a reduced network was with a set of fluxes generated using the minimal metabolic network used by Ishii et al. [To test how using the kinetic parameterization pipeline changes training data recapitulation and impacts kinetic model predictive capabilities, we parameterized a kinetic model with metabolic network coverage identical to k-ecoli74, but using flux data for wild-type and the seven mutant strains of i et al. . K-FIT, The reduced network used for flux elucidation, therefore, was a network simplified from the k-ecoli74 metabolic network see . It did 13C-MFA-elucidated fluxes in reduced network mutant training datasets. When compared to the flux ranges predicted by k-ecoli74, the flux ranges predicted by the core model parameterized with reduced flux dataset were consistent across glycolysis, PP pathway, and TCA cycle. However, due to deviation from k-ecoli74 training data observed in the wild-type strain upon flux projection, both the directionality and magnitude of amino acid synthesis and degradation reactions was impacted across all strains used for parameterization. In the projected wild-type flux distribution, reverse flux through SERD-L was required to generate a flux distribution at metabolic steady state with minimum violation of the bounds defined using the reduced network 95% confidence intervals. As a result, SERD-L flux was predicted to be reversed across all strains used for parameterization. In Δzwf, Δgnd, and Δrpe, the magnitude of SERD-L flux was so large that the lumped serine synthesis reaction was also predicted to carry reverse flux. GLYCL and threonine degradation were also required to carry negative flux in order to minimize deviation from the reduced network 95% confidence flux bounds in the wild-type strain. Because glutamate did not re-enter central carbon metabolism as 2-oxoglutarate (akg) during flux elucidation, glutamate demand across all strains was significantly reduced when compared to k-ecoli74 training data. The behavior of the parameterized model reflected these discrepancies. The most notable underpredictions occurred in Δpgi, Δrpe, Δzwf. In each of these strains, GLUDy flux was underpredicted by more than 50%. The observed discrepancies between the model generated using a projected wild-type flux distribution and the flux distribution used for k-ecoli74 indicates that the information lost due to the absence of atom mappings and stoichiometric information for omitted and simplified reactions is significant. When flux information for these reactions is generated via constraint-based flux projection in a manner consistent with that employed by Khodayari et al. [Predictions by the model parameterized with the reduced network flux dataset were inconsistent with k-ecoli74 predictions for several reactions peripheral to central carbon metabolism. i et al. , 32 fluxTarget product overproduction was evaluated using k-ecoli74 for seven metabolites using nine engineered strains not used during parameterization. All genetic intervention strategies, target metabolites for overproduction, target reactions used to estimate product yield, predicted product yields, and comparisons with experimental values and k-ecoli457 predictions are reported in Kinetic model predictions for malate and acetate yields were consistent with experimental observations. In both cases, the kinetic model outperformed k-ecoli457 in estimating product yield. In the case of malate, although an approximate three-fold increase in the predicted value was observed compared to the experimental value, TCA cycle flux is required for strain viability, and therefore only a fraction of that flux would be directed towards malate secretion. The reported value is, therefore, a maximum theoretical yield. These results indicate that k-ecoli74 is better suited for predicting central carbon metabolite yields under glucose-rich batch conditions than k-ecoli457.E. coli [As expected, L-valine yield was underpredicted by k-ecoli74 by an order of magnitude (similar to k-ecoli457). This underprediction was due to the absence of an excretion pathway known to exist in E. coli , and thek-ecoli74 overpredicted yield for metabolites not included in the network for all six strains tested. This was due to the use of central carbon drains as proxies for pathways not included in the model. This overprediction was expected, since only a fraction of central carbon flux can be directed towards branched pathways if the strain is viable due to the need for carbon flux towards biomass precursors synthesis and energy generation reactions. In three of those six strains, the engineered strain product yield was higher than the wild-type product yield, indicating a favorable re-direction of flux resulting from the genetic perturbations. k-ecoli457, however, outperformed k-ecoli74 when predicting yields in those strains. This was due to the inclusion of pathways peripheral to central carbon metabolism in k-ecoli457, and anaerobic conditions included in the training data which delivered significant flux towards lactic acid secretion, allowing for the generation of a kinetic parameter set better suited to predict lactic acid yield.The reduced model was only capable of predicting metabolite yields for five of the nine model validation strains tested. In both naringenin overproduction strains and in the glucaric acid overproduction strain, the reduced model predicted no carbon uptake or growth. In the acetate overproducing strain, the model became unstable upon downregulation of RPI expression, and a steady-state flux distribution could not be reached in the perturbed state. The 2,3-butanediol yield prediction was similar to k-ecoli74 prediction, as was L-valine yield, and lactic acid yield under ACKr downregulation. k-ecoli74 outperformed the reduced model significantly, however, when predicting lactic acid yield under the ACKr knockout condition. This indicates that k-ecoli74 is more sensitive to small changes in enzyme level than the model parameterized with the reduced network. The reduced model outperformed k-ecoli74 significantly when predicting malate yield. Reduced model malate yield was within 9% of the experimental value, while k-ecoli74 overpredicted malate yield by 180%. Overall, the reduced model had difficulty predicting metabolite yields when TCA cycle, PP pathway, or oxidative phosphorylation enzyme levels were perturbed. Thus, the feasible prediction space of the core model parameterized by the reduced flux dataset is substantially reduced compared to k-ecoli74, as these pathways represent significant components of core metabolism and potential targets for perturbation strategies that kinetic model of core metabolism are designed to predict.Metabolite yield variability was also assessed by comparing the upper and lower bounds of metabolite yields predicted by the three top performing model with experimental ranges. To test k-ecoli74’s ability to predict flux in strains not used for training and determine the impact of parameterization with and without unique flux phenotype in the training data, leave-one-out cross validation was performed. For each cross-validation, a unique kinetic model was parameterized while removing one mutant strain from the training data at a time. Parameterizations were either initialized until a model was generated with SSR less than the best identified model with all data included, or initialized until the recovery rate for a single model fell below the recovery rate obtained during the primary parameterization. In the latter case, the best model identified was used for cross validation analysis. Optimality was tested after each parameterization to ensure a local minimum was reached. Number of parameterizations, recovery rates and minimum SSR for each cross validation are listed in Δpgi was the only strain for which a cross-validation model with SSR less than the best model parameterized with all strains could not be identified. Δrpe, Δpgi, and Δgnd performed the worst during cross-validation, as there were phenotypic attributes of these strains that were unique when compared with the other training datasets. In each of these cases, the cross-validation model was incapable of predicting glucose uptake accurately, and the associated error propagated throughout the entire metabolic network. Δpgi, the cross-validation model predicted negligible glucose uptake because the PP pathway kinetic parameters were not well suited for carrying the entirety of flux from g6p. The Δrpe cross-validation model wasn’t well-suited for predicting the reduced glucose uptake observed experimentally in the absence of similar training data. Rather than predicting reduced glucose uptake, the Δrpe cross-validation predicted increased glycolytic flux compared to experimental fluxes and increased ED pathway flux to compensate for the partial blockage of the nonoxidative PP pathway. Because the training data in the Δgnd cross validation lacked a strain delivering significant flux through the ED pathway, the PP pathway and glycolytic kinetic parameters were unable to accommodate the full amount of glucose uptake observed, and the ED pathway was incapable of carrying significant flux. Δeda and Δedd cross validations models each performed well due to their phenotypic similarities, which ensured that a similar phenotype was included in the training data when one was left out. The resulting models were able to predict the flux distributions of interest well, even though they were the worst fitting strains when all sets were included in the training data. Δfbp and Δzwf each performed marginally well when compared with the other strains. In Δfbp, glycolytic reactions and GLUDy had the highest associated residual errors. The Δfbp cross-validation model predicted an increase in glucose uptake flux relative to the wild-type strain, which was propagated through glycolysis. The excess carbon fed into the network was sinked towards glutamate and glutamate-derived amino acid synthesis, rather than the metabolic byproduct, acetate, resulting in the high residual error for GLUDy. In Δzwf, a decrease in glucose uptake rate was predicted by the cross-validation model relative to the wild-type strain. This decrease in carbon availability was propagated through glycolysis, where all reactions had high residual error. Similar to Δfbp, GLUDy was the central carbon sink reaction with the highest residual error to compensate for the low glucose uptake flux. The results indicate that the model isn’t able to predict metabolic fluxes well when the strains being predicted have phenotypic characteristics that are drastically different from the training datasets.In a priori achieve due to the scarcity of experimental data. Metabolic flux magnitude and directionality dominant in the datasets is often reflected in resultant kinetic parameters, causing inaccurate prediction of unique flux redirections in the context of those used for training data. This implies that parameterization results must be carefully interpreted and flux datasets revealing unique flux redirections may have to be more heavily weighted during the parameterization process.Overall, the cross-validation results, as well as the full parameterization results, parameterization with reduced flux dataset results, and yield prediction results highlight the large influence that mutant flux dataset selection has on the value of the inferred parameters. Ideally, one would want to assemble a set of mutant flux datasets that uniformly perturb the flux in all major pathways. Unfortunately, this is difficult to To assess the necessity of regulatory mechanisms included in k-ecoli74 and identify nonessential regulations, we compared the locally approximated standard deviation of the inhibition parameters to their parameter values. Regulations were identified as nonessential if the parameter range characterized by the standard deviation had a lower bound of zero. E. coli metabolism have either relied on experimentally determined kinetic parameters gleaned from a database and a combination of metabolomics and fluxomics data gleaned from a number of sources [The kinetic parameterization pipeline developed in this study and applied to the development of k-ecoli74 is unique compared to other frameworks used for kinetic model construction in that a single metabolic network was used for flux elucidation and kinetic parameterization. Recent kinetic models of  sources , or reli sources , 32. A cKm parameters were derived from the regressed elementary rate constants and compared to those generated using a previously developed core E. coli kinetic model [Km parameters calculated from both models and experimental ranges, indicating that the optimal set of kinetic parameters for predicting in-vivo flux differs from the optimal kinetic parameters determined from in-vitro experiments. While no definitive reason can be identified based on the results of this study, possible reasons include differences in physiological states in vivo and in vitro (such as macromolecular crowding effects in vivo) [When ic model , signifiin vivo) –93, and in vivo) ). The deE. coli model performed by Khodayari et al. [E. coli model with metabolic network conservation analysis and model stability check performed by Greene et al. [The use of the K-FIT algorithm has improved parameterization time by almost an order of magnitude over EM-based parameterization of a core i et al. . Whereasi et al.  for a sie et al. . Greene E. coli core metabolism constructed using an elementary decomposition approach, the model developed here shows improvement in fitness to 13C-MFA-derived flux distributions. In this study, 86% of fitted fluxes fell within a single standard deviation of their corresponding 13C-MFA value. The ensemble modeling-parameterized core kinetic model of E. coli, parameterized using the same number of mutant strains, estimated 78% of fitted flux values within a single standard deviation of 13C-MFA values [Compared to the previously developed ensemble modeling-parameterized kinetic model of A values .13C-MFA  can have on the resulting kinetic model and prediction accuracy. k-ecoli457 was parameterized using flux distributions for wild-type and mutant strains grown primarily under chemostat conditions with growth rate fixed at a uniform, arbitrarily low value (0.2h-1) [13C labeling data for this study . As a result, k-ecoli457 overpredicted malate yield by more than 540% and under predicted acetate yield by 75%, while k-ecoli74 overpredicted malate yield by only 180% and predicted acetate yield within 18% of the experimental value. While k-ecoli74 performed better than k-ecoli457 in predicting product yields for metabolites in central carbon metabolism, it was limited in its ability to predict yields for metabolites outside of the k-ecoli74 network. k-ecoli457 outperformed k-ecoli74 when predicting yields for 2,3-butanediol and glucaric acid due to the inclusion of pathways peripheral to central carbon metabolism in the model. k-ecoli457 also outperformed k-ecoli74 when predicting lactic acid yields. This was because the k-ecoli457 training data included datasets generated under anaerobic conditions which exhibited significant flux towards lactic acid secretion. The limited coverage of k-ecoli74 also limited the number of metabolite yields that could be effectively predicted. Whereas k-ecoli457 was able to predict metabolite yield for 24 metabolites across 320 genetic conditions, k-ecoli74 was only able to predict yields for seven metabolites across nine of those conditions. This was due to the non-inclusion of reactions that were perturbed in mutant strains and the non-inclusion of precursor metabolites to the pathway producing the metabolite of interest.A comparison of the yields for acetate and malate predicted by k-ecoli74 and those predicted by k-ecoli457 highlight the impact that the biological conditions of the cell culture used to generate the labeling data required for for (0.2h-1) , 94. The(0.2h-1) . Experim(0.2h-1)  and mala(0.2h-1)  yields wOur results from parameterization with a reduced flux dataset indicate that there is potential for significant discrepancies between kinetics models parameterized using fluxes inferred using the same network compared to fluxes inferred from a simplified model. The differences in the directionality and magnitude of amino acid synthesis and degradation reactions between k-ecoli74 and the kinetic model parameterized with reduced flux dataset demonstrate this. The reduced flux dataset parameterization also show a clear loss in predictive capabilities when the full stoichiometric model is not considered during flux elucidation, as the model parameterized with the reduced flux dataset failed to predict a feasible flux distribution for four of nine validation strains tested. Thus, the inclusion of the full metabolic network in the flux elucidation step of the kinetic parameterization pipeline was essential to successful k-ecoli74 parameterization.Δrpe and Δgnd, and in the Δrpe cross-validation, 12.5% of solutions yielded SSR values within 10% of the best model when all data was used in fitting. This indicates that the existence of unique phenotypic behavior in mutant strains can lead to the existence of a large number of local minima and decreased recovery rate. It is important to note that our criteria for reproducibility was stringent. The maximum average square residual deviation from the best model was 0.05 per reaction flux. In both cross-validation parameterizations and the primary k-ecoli74 parameterization, many models were generated that produced similar flux distributions to the best model, but did not satisfy the specified reproducibility criteria.The flux data used for kinetic parameterization also had an impact on the recovery rate of the best solution using the gradient-based methodology. Only 0.6% of solutions recovered agreed with our best model, despite all solutions satisfying local optimality criteria . Recovery rate increased to 4% and 7%, respectively, when cross-validation was performed for E. coli, a number of issues still exist. To construct a genome-scale kinetic model using 13C-labeling data for parameterization, a comprehensive set of genetic knockout strains across not only upper glycolysis and the PP pathway, but also lower glycolysis and the TCA cycle is required to generate the informative parameter sets for peripheral pathways branching from lower glycolytic and TCA cycle metabolites . Another limitation requiring attention is that kinetic models constructed using elementary decomposition methods [i.e. glucose-rich batch culture, mid-exponential growth phase), a systematic method for updating enzyme level according to growth rate or growth condition would allow for a broader range of applications. This would be a step towards the development of mechanistic kinetic model of metabolism with universal application.While the developed kinetic parameterization pipeline addresses some issues that have slowed the development and application of kinetic models of metabolism in strain design and lays a framework for kinetic model scale-up in  methods  are able methods , 96. It  methods , 97, conS1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(DOCX)Click here for additional data file.S4 File(XLSX)Click here for additional data file.S5 File(DOCX)Click here for additional data file.S6 File(DOCX)Click here for additional data file.S7 File(DOCX)Click here for additional data file.S8 File(XLSX)Click here for additional data file.S9 File(XLSX)Click here for additional data file.


Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications.S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network . The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources . Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle.In this study, our goal was to characterize the metabolic profile of S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens.Our results show that The online version of this article (10.1186/s12859-019-2826-1) contains supplementary material, which is available to authorized users. Serratia marcescens, and utilize RNAseq data to achieve a better understanding of its metabolic network.With recent advances in high-throughput data and bioprospecting, the breadth of novel and interesting biochemistry continues to expand. In terms of the large amount of data and resources available, one current challenge is to interpret raw data into knowledge that can help better understand complex biological systems. Genome-scale metabolic modeling (GSMM) provides gene-protein-reaction level specificity and can combine metabolic network with fundamental genomic and biochemical information. However, there are still a number of limitations . For insS. marcescens is unique among enteric bacteria in many aspects. It secretes extracellular DNase, gelatinase, lipase, several proteases, a red pigment (prodigiosin), chitinases and a chitin binding protein. It is believed to be one of the most efficient chitin-degrading bacteria in the environment [S. marcescens Db11 contains ten chitinase-related proteins [S. marcescens chitinolytic mechanisms [S. marcescens chitinase genes have been cloned into model bacterial species (e.g. Escherichia coli) [S. marcescens. This may be due to the complexity of chitin degradation systems and synergy between multiple enzymes [S. marcescens may be developing S. marcescens rather than moving its chitinases into other systems by heterologous expression.ironment , 3. S. mproteins  leading chanisms –8. Due tia coli) , 47. The enzymes , 13. HenS. marcescens Db11, the sequencing of its genome in 2014 sets up a milestone towards understanding this industrially applicable microbe [S. marcescens are listed in Table The ability to produce chemicals of industrial importance using inexpensive chitinolytic biomass has been a recent focus , 49, 50. microbe . The iniS. marcescens Db11, we believe that it can be developed into a facile chitinolytic system for consolidated bioprocessing. Recently, we established molecular tools for genetic engineering this organism using: 1) an in-frame gene deletion approach based on endogenous exonucleases by introducing linear DNA fragments and 2) a shuttle vector that contains functional replication origin and expression elements (e.g. promoter and RBS) [S. marcescens. In the same study, the ChiR overexpression S. marcescens produced 1.13 g/L 2,3-butanediol from 2% crystal chitin. The ability to convert chitin into biochemicals makes this organism an interesting candidate for a chitin-based CBP [S. marcescens to facilitate future engineering applications.In studying and RBS) . These aased CBP , 18, 49.A reconstructed GSMM can provide a framework to understand cellular processes and can be used to find target for focused metabolic engineering to yield products of biotechnological value . The mosv is the reaction flux vector, ai and bi are the constraints based on the flux vi of the reaction i [where, Z is the flux towards objective function, e.g. biomass production and production optimization, S is stoichiometry of the reactions as matrix, action i .S. marcescens Db11 and its chitinolytic regulation systems. Therefore, a genome-scale metabolic model, iSR929, consisting of 1185 reactions, 1164 metabolites and 929 genes was constructed. The constructed model was analyzed for 1) accuracy compared to experimental results; 2) prediction of essential genes; 3) metabolic differences under different carbon growth conditions. We also proposed three potential chemical compounds for metabolic engineering implementation.After compiling biochemical information into a draft model, a second step of model curation can be done by integrating high-throughput omics data to realize “content in context” . A varieS. marcescens Db11 [S. marcescens, hereafter denoted iSR929, was constructed. The reaction database used for drafting the model includes but is not limited to sources such as KEGG [iSR929 contains 1185 reactions representing the function of 929 genes and 1164 metabolites .Based upon the genome sequence of ens Db11  and avai as KEGG , BiGG [2 as KEGG , rBioNet as KEGG  and MBRo as KEGG . iSR929 iSR929 by functional categories is represented in Fig. S. marcescens appears to have the capacity to synthesize all 20 amino acids [iSR929 reflects this. Other large groups of reactions include subsystems related to carbohydrate metabolism , cofactor and vitamin metabolism  and nucleotide metabolism (including reactions related to purine and pyrimidine synthesis). The model also includes reactions relating to the synthesis of lipids, including fatty acid, glycerophospholipid, and glycerolipid metabolism.The breakdown of no acids , and iSRiSR929 model, we simulated growth of S. marcescens by applying FBA, assuming minimal media conditions with one of three possible carbon sources . We then compared model predictions to experimentally observed growth rates and fermentation product secretion profiles of S. marcescens grown in batch culture. In addition to the carbon source, the in silico minimal medium used for simulations contained water (H2O), ammonia (NH4), sulfate (SO4), phosphate (pi), calcium (Ca2), ferrous iron (Fe3), hydrogen sulfide (H2S), potassium (K), magnesium (Mg2), pantothenate (pnto-r), and nicotinate D-ribonucleotide (nmn). In each of the three simulation conditions , we applied progressively more experimentally determined constraints associated with by-product secretion rates to determine how closely the computational results could match the experimental results given the possibility of alternate optimal solutions. Figure To test the predictions of the iSR929 using glucose as a carbon source and the other minimal media components (as described above), and constraining the glucose uptake rate to its experimentally observed value for batch growth (10.53 mmol/gDW/h). Gene essentiality results are shown in Fig. S. marcescens genes included in iSR929 were predicted to be essential. We also examined which subsystems of iSR929 contained the highest percentage of essential reactions (‘vulnerable subsystems’). Among the most vulnerable subsystems are amino acid metabolism, nucleotide metabolism, lipid metabolism, and metabolism of cofactors and vitamins.A comprehensive analysis of in silico single gene deletions were conducted with S. marcescens Db11 were prepared from cell cultures under four conditions: M9 medium with glucose, M9 medium with N-acetylglucosamine, M9 medium with glycerol, and LB medium. Overall, the average coverage per gene (4831 genes)  separately, where a reaction flux is computationally constrained to 1000 if the corresponding gene’s RPKM is over 850.56 and a reaction flux is 0 if the gene’s RPKM is less than 3.00. Raw data of reaction flux can be found in Additional file N-acetylglucosamine, there were 132 active reactions. Among these 132 active reactions, the majority of the reactions were: carbohydrate metabolism (39), amino acid metabolism (28), lipid metabolism (6), cofactor metabolism (9), and nucleic acid metabolism (10). When growing on glycerol, there were 146 active reactions. Among these 146 active reactions, the functional categories were: carbohydrate metabolism (44), amino acid metabolism (41), lipid metabolism (6), cofactor metabolism (8), and nucleic acid metabolism (7).Simulations were then run on the The main active pathways for the metabolism of glucose as a sole carbon source were shown in Fig. N-acetylglucosamine as a sole carbon source was shown in Fig. N-acetylglucosamine, S. marcescens is believed to phosphorylate transported N-acetylglucosamine by a membrane type II phosphotransferase (SMDB11_0473). The SMDB11_0473 gene expression level in the N-acetylglucosamine condition (RPKM 967.20) was 12.09- and 5.13-fold than those under glucose (RPKM 80.23) and glycerol (RPKM 188.36) conditions, respectively, supporting that N-acetylgluosamine is likely phosphorylated intracellular. The carbon flows through glycolysis metabolism from fructose-6P to regenerate glucose-6P. The glucose-6P flows to the pentose phosphate pathway to yield pyruvate. Then, the majority of the carbon flux was distributed into purine and pyrimidine metabolism, butanoate metabolism , pyruvate metabolism , amino acid synthesis, and lipoprotein synthesis.The main active pathway for the metabolism of N-acetylglucosamine condition. The SMDB11_4022 expression level in the glycerol condition (RPKM 4535.25) was 76.6- and 63.6-fold higher than those of glucose (RPKM 59.20) and N-acetylglucosamine (RPKM 71.28) condition. The gene expression level of SMDB11_3886 in the glycerol condition (RPKM 1683.89) was 59.9- and 54.7-fold higher than those of glucose (RPKM 28.10) and N-acetylglucosamine (RPKM 30.78) condition. The main active flux goes to glycolysis, pentose phosphate pathway, amino acid synthesis, and pyruvate metabolism.The main active pathways for the metabolism of glycerol as a sole carbon source were shown in Fig. N-acetylglucosamine condition, the TCA cycle is not active. The gene expression level of the TCA cycle pathway under N-acetylglucosamine, glucose or glycerol condition were mapped in Fig. N-acetylglucosamine condition, those corresponding genes under glucose or glycerol condition are generally higher. In particular, the SMDB11_0505 gene, which encodes a citric acid synthase that converts acetyl-CoA to citric acid, may play the key role in determining the model prediction since under glucose or glycerol condition, the SMDB11_0505 mRNA level are both above the high threshold, while under N-acetylglucosamine condition, the SMDB11_0505 mRNA level is below the high threshold.According to model simulation results, we have examined metabolic fluxes differences in the carbon central metabolism, namely, glycolysis, pentose phosphate pathway, and TCA cycles, shown in Additional file N-acetylglucosamine were lower than those for growth on glucose or glycerol, providing some support for the predicted lower TCA cycle flux results  as precursors for amino acids synthesis, and synthesis of nucleotides (e.g. purine and pyrimidine) comes from the ribose-5P generated from the pentose phosphate pathways since N-acetylglucosamine is an ideal carbon source for amino-sugar and nucleotides synthesis (e.g. C:N ratio 8:1).To test for differences in TCA cycle flux, measurement of intracellular citric acid and isocitric acid levels can be used as indicators of TCA cycle activity . Experimlts Fig. b and c. Based on metabolic flux analysis predicted by the model, the common consumption/re-generation of NADH under the three carbon source conditions are supported in the amino acid metabolism, butanoate metabolism and pentose phosphate metabolism; the common consumption/re-generation of NADPH under the three carbon source conditions are supported from the glycolysis and gluconeogenesis metabolism, one carbon pool by folate, and transport; common pathways associated with ATP consumption and generation under the three-carbon source conditions are supported from amino acid metabolism, one carbon pool by folate, pyruvate metabolism and transportation of molecules, shown in Additional file S. marcescens under N-acetylglucosamine as a sole carbon source, NAD+/NADH tends to be a main cofactor compared to NADP/NADPH. It is worth noting that under N-acetylglucosamine as a sole carbon source condition, additional NADH cylces can be achieved in the pyrimidine metabolism, and pentose and glucuronate interventions by a nucleotide phosphorylase and a 3-dehydro-L-gulonate 2-dehydrogenase. This is in agreement with the observance shown in the carbon metabolism map that nucleotide metabolism and pentose phosphate pathways are highly active when using N-acetylglucosamine. Particular ATP consumption and generation pathways in S. marcescens under N-acetylglucosamine condition include glycine, serine and threonine metabolism and pyrimidine metabolism.When growing S. marcescens possesses interesting native metabolic capabilities  and can potentially be engineered for biochemical production. Based on the aforementioned experimental observations and computational simulation results, we selected three chemical compounds  as potential production targets and illustrate pathway designs for the production of each.S. marcescens Db11 is a native 2,3-butanediol producer. Direct production of 2,3-butanediol from crystal chitin by S. marcescens Db11 have been experimentally validated and the model iSR929 shows 2,3-butanediol production across all growth conditions [S. marcescens 2,3-butanediol producers [S. marcescens Db11 harbors only two 2,3-butanediol dehydrogenases ) and relatively small amount of -2,3-butanediol dehydrogenase was expressed compared to the meso-2,3-butanediol dehydrogenase, indicating meso-2,3-butanediol is the major product.nditions . The proroducers , 38, S. n-butanol. The iSR929 incorporates most of the reactions present in the butanoate metabolism as inferred from genomic and biochemical data. Besides the 2,3-butanediol synthetic pathway, two active reactions (R01171 and R01975) were found under glucose growth conditions and one active reaction (R01171) was observed under glycerol growth conditions. One consideration of the computational simulation is that butanoate is a product of a secondary pathway and S. marcescens may not overproduce genes associated with this pathway during normal exponential growth. Thus, it is not necessarily surprising that growth simulations of a wild-type strain with no genetic manipulation may not show flux through the butanoate metabolism pathways. The proposed n-butanol synthetic pathway and associated gene expression levels in S. marcescens Db11 are shown in Fig. n-butanol production is partially complete but is missing the enzyme required for the last reaction . A bifunctional alcohol dehydrogenase (AdhE2) from Clostridium acetobutylicum is reported with highest activity of alcohol production [S. marcescens. Addition of an alcohol dehydrogenase would theoretically enable production of n-butanol, but it is also likely that additional pathway optimization would be necessary to improve flux through the necessary reactions as expression of the remaining endogenous butanoate genes is relatively low (e.g. SMDB11_4664 acetoacetate-CoA transferase).The second target of interest is a biofuel chemical, oduction  and can N-acetylneuraminic acid which is not natively produced by S. marcescens Db11. Compared to glucose-based N-acetylneuraminic acid synthesis (demonstrated in E. coli [N-acetylglucsaomine/chitin as a carbon source has the following advantages: 1) it requires only two steps for N-acetylneuraminic acid synthesis from N-acetylglucosamine compared to glucose and 2) no additional nitrogen source is needed [S. marcescens on N-acetylglucosamine showed that the enzyme (SMDB11_0473) associated with uptake of N-acetylglucosamine is highly active (RPKM 967.20). After transport, SMDB11_0473 (N-acetylglucosamine transferase) is a reversible N-acetylglucosamine transferase that can generate intracellular N-acetylglucosamine [N-acetylglucosamine can be converted to N-acetylneuraminic acid by additional two biochemical reactions (N-acetylmannosamine 2-epimerase and N-acetylneuraminic acid synthase) [N-acetylneuraminic acid may require carefully balance the heterologous pathways since heterologous expression may generate metabolic burden [The third chemical target of interest is  E. coli ), use ofs needed , 41. Grocosamine , shown iynthase) . Furtherc burden .iSR929, for the chitinolytic bacterium Serratia marcescens. The model contains 929 genes, 1185 reactions and 1164 metabolites. Initial experimental testing showed that the iSR929 model is able to reasonably predict quantitative phenotypes for S. marcescens. Integrating transcriptomic data for growth on different carbon sources  to the model provides insight into metabolic difference that are condition-dependent including different utilization of the TCA cycle for growth on N-acetylglucosamine. Three biochemical targets  were proposed as potential metabolic engineering implementation targets and pathway activities were given. Overall, our results provide insight into the metabolic function of S. marcescens and reveal potential targets for engineering S. marcescens for biochemical production.In this study, we constructed a genome-scale metabolic model, Serratia marcescens Db11 was purchased from the Caenorhabditis Genetics Center  [S. marcescens strain was grown in M9 medium or LB medium supplemented with various carbon sources. Carbon sources in the M9 medium were used at the following concentrations: glucose, 5 g/L; N-acetylglucosamine, 5 g/L; glycerol 5%. S. marcescens pre-cultures were grown in LB medium at 30 °C and 250 rpm overnight. 2.5% seed culture was inoculated at 50 mL M9 minimum medium with a 250 mL Erlenmeyer flask at 30 °C, initial pH 7.5, and 220 rpm for growth experiments. Stock cultures of S. marcescens were maintained at − 80 °C in 26% (v/v) glycerol.edu/CGC) . The S. S. marcescens GSMM is a compile of metabolic reactions occurring in S. marcescens, compiled based on biochemical information from genome annotations [S. marcescens genes were adapted to select reactions from a set of database reactions.The core otations  and expeotations , 30, 44.The FBA simulations were run using in-house python scripts based on the linear programming algorithm according to previous publications , 32, 33.Serratia biomass equation is available; therefore, we modified E. coli biomass equation by including components that are essential for Serratia growth [To this end, no a growth –53. DetaFBA-GAP was used to fill metabolic gaps where reactions are missing in the initial reconstructed model. The gap-filling was conducted according to methods described previously , 24, 25.Constraint-based models can be used to analyze in silico gene essentiality through running FBA . BrieflyS. marcescens Db11 was extracted from cells harvested during mid-log phase. A QIAGEN  RNeasy Mini kit along with the proper RNA protect reagent was used. Then, the mRNA was isolated, enriched, and reverse transcribed into cDNA. The sequencing of cDNA was conducted using paired-end reads using Illumina Hiseq 2500 . The detailed information of the RNASeq data can be found in a previous publication [S. marcescens are listed in Additional file The total RNA of lication . In addiS. marcescens culture density was generally quantified at OD600 using a Biomate3 UV/VIS spectrophotometer .The measurement of N-acetylglucosamine, glucose, glycerol and all metabolic end-products  were analyzed by HPLC described in details in our previous publication [The concentration of lication .iSR929 model was analyzed using a mixed integer linear programming algorithm approach [The integration of RNASeq data with the approach , In brieiSR929 using the gene-protein-reaction (GPR) relationships to generate lists of reactions predicted to be high or low fluxes [Where g is the relative expression level as determined by RNAseq (e.g. RPKM) and γ is the threshold value that was manually set. The resulting gene states were then integrated into w fluxes .S. marcescens cell growth was stopped at 2.5 h (OD600 about 0.4) and cells were collected by centrifuging at 4 °C, 15,000 rpm, and 10 min. Cell pellets were lysed using a commercial detergent BugBuster . Citric acid and isocitric acid assays were conducted according to the manual’s instruction. 10 μL supernatant samples were added in a total volume of 100 μL reaction. Citric acid and isocitric acid concentrations were determined at 570 nm and 450 nm by a VERSAmax microplate reader , respectively.Intracellular levels of citric acid and isocitric acid were evaluated using a citrate assay kit  and an isocitric acid kit , respectively. In brief, Additional file 1:Table S1. Average RPKM values of S. marcescens gene expression level at three different growth conditions. Table S2. Number of genes excluded/included after the lower/upper bound cutoff. Table S3.S.marcescens iSR929 reaction flux values after running Ruppin algorithm. Table S4. NAD+/NADH associated reactions under three carbon source conditions. Figure S1. Representative figure of gene expression level distribution under M9 glucose medium growth condition. Figure S2. Relative gene expression levels of reactions in the metabolic map of (A) Glycolysis (B) Pentose Phosphate Pathway of S. marcescens. (ZIP 898 kb)Additional file 2: Model file. (TXT 116 kb)Additional file 3: Biomass objective function file. (TXT 1 kb)Additional file 4: Exchange file. (TXT 2 kb)Additional file 5: GPR file. (TXT 199 kb)


Escherichia coli O:113) administration. Before and after induction of immunotolerance, circulating CD14+ monocytes were isolated and assessed for antimicrobial functions, including cytokine production, oxidative burst, and microbial  killing capacity, as well metabolic responses to ex vivo stimulation. Next, the effects of altered cellular metabolism on monocyte functions were validated in vitro. Ex vivo lipopolysaccharide stimulation induced an extensive rewiring of metabolism in naive monocytes. In contrast, endotoxin‐induced immunotolerant monocytes showed no metabolic plasticity, as they were unable to adapt their metabolism or mount cytokine and oxidative responses. Validation experiments showed that modulation of metabolic pathways, affected by immunotolerance, influenced monocyte cytokine production, oxidative burst, and microbial  killing in naive monocytes. Collectively, these data demonstrate that immunotolerant monocytes are characterized by a loss of metabolic plasticity and these metabolic defects impact antimicrobial monocyte immune functions. Further, these findings support that the changed cellular metabolism of immunotolerant monocytes might reveal novel therapeutic targets to reverse sepsis‐induced immunoparalysis.Secondary infections are a major complication of sepsis and associated with a compromised immune state, called sepsis‐induced immunoparalysis. Molecular mechanisms causing immunoparalysis remain unclear; however, changes in cellular metabolism of leukocytes have been linked to immunoparalysis. We investigated the relation of metabolic changes to antimicrobial monocyte functions in endotoxin‐induced immunotolerance, as a model for sepsis‐induced immunoparalysis. In this study, immunotolerance was induced in healthy males by intravenous endotoxin (2 ng/kg, derived from  Endotoxin‐induced immunotolerant monocytes are characterized by a loss of metabolic plasticity and these metabolic defects impact antimicrobial monocyte immune functions. A low human leukocyte antigen–antigen D related (HLA‐DR) expression on monocytes coincides with an increase in nosocomial infections and higher mortality.In the past decade, it has become clear that during sepsis immune function and cellular metabolism are closely interlinked.E. coli LPS) to model sepsis‐induced immunoparalysis. Monocytes were studied before, during, and 1 week after endotoxin‐induced immunotolerance to characterize metabolic changes associated with immunotolerance and long‐lasting metabolic reprogramming following endotoxin challenge. First, the metabolic profile and immunological functions were characterized ex vivo before immunotolerance, during immunotolerance and 1 week after immunotolerance. Second, metabolic pathways found to be affected were validated for their impact on antimicrobial functions in naive monocytes.In the current study, the primary aim was to better understand the potentially defective adaptation of monocyte metabolic responses during immunoparalysis. To this end, a standardized protocol22.1E. coli O:113, Pharmaceutical Development Section of the National Institutes of Health, Bethesda, MD, USA) or placebo . From the original study of 30 volunteers (NL54870.091.15),Experimental human endotoxemia was used as a model for sepsis‐induced immunoparalysis, as extensively described in previously published studies.2.2Blood was diluted (1:1) in PBS. PBMCs were isolated from blood by differential density gradient centrifugation over Ficoll‐Paque PLUS . Cells were washed twice in PBS and resuspended in RPMI 1640 culture medium Dutch modification , supplemented with pyruvate , glutamax , and gentamicin . Classical monocytes were subsequently isolated from the PBMC fraction using magnetic beads labeled with anti‐CD14 . Finally, cells were counted using the Sysmex XN‐450 automated differential hematology analyzer .2.35 cells/well; Eppendorf, Hamburg, Germany) and stimulated with RPMI or E. coli LPS  for 24 h at 37°C and 5% CO2. Monocyte cytokine responses were measured in supernatants of 15 endotoxin‐challenged subjects and 6 placebo subjects.Isolated CD14+ monocytes were plated in 96‐well tissue treated flat‐bottom plates (1.0 × 102.3.16 cells/tube in a volume of 1 ml). Cell pellets were snap frozen in liquid nitrogen and shipped to Metabolon . Quantitative measurements of metabolite concentrations were assessed in classical monocytes at baseline, 4 h, and 7 days following endotoxemia. Metabolon performed the identification and quantification of amino acids, carbohydrates, lipids, nucleotides, microbiota metabolism, energy metabolism, cofactors and vitamins, xenobiotics, and novel metabolites.Metabolic profiling was performed on ex vivo LPS‐restimulated and unstimulated CD14+ monocytes isolated from 5 randomly selected volunteers that were infused with endotoxin. Samples were placed in sterile micro centrifuge tubes . For these experiments, the total production of reactive oxygen species (ROS) generated by monocytes was measured in flat‐bottomed white tissue‐culture treated 96 well plates  To every well, 20 μl of 3‐aminophthalhydrazide, 5‐amino‐2,3‐dihydro‐1,4‐phthalazinedione  in HBSS (Gibco) and 0.5% BSA (Sigma‐Aldrich) were added. Monocytes were seeded directly in white 96‐wells plates (1.0 × 105 cells/well) and stimulated with 10% human serum opsonized live E. coli , S. aureus , C. albicans , or RPMI. In all experiments, the chemiluminescence was measured every minute, at 37°C, by using a BioTek Synergy HT reader  for 1 h.In the NL61136.091.16 endotoxemia cohort, oxidative burst capacity was determined in isolated CD14+ of 10 male volunteers (2.3.3+ monocytes (1 × 105 cells/well) of 5 male volunteers were exposed to live C. albicans  for 24 h at 37°C and 5%. CO2 at baseline, 4 h after endotoxin challenge, and 7 days following endotoxin administration. After incubation, all cells were lysed in water and serial dilutions of lysates were plated on Sabouraud agar plates. The Candida killing capacity of CD14+ monocytes was quantified by counting remaining colony‐forming units (CFUs), after overnight incubation at 37°C and 5% CO2. The percentage of killed microbes was calculated as 1 − (CFU remained after incubation with microbes/CFU determined before incubation with microbes) × 100.To determine the intracellular killing capacity of pathogens after experimental endotoxemia, CD142.4The local ethical board at the Radboud University Nijmegen  approved blood sampling for the in vitro validation studies using metabolic inhibitors. Venous blood was drawn from healthy volunteers after the healthy volunteers provided written informed consent. The effect of metabolic modulation on immune function was studied in experiments using PBMCs. Briefly, venous blood samples were drawn from healthy volunteers after written informed consent was obtained. PBMCs were isolated by density gradient centrifugation over Ficoll, as described above. Metabolic inhibitors were selected based on the metabolic changes observed during endotoxin‐induced immunotolerance. First, various metabolic pathways were modulated, either inhibited or stimulated, for 24 h. (An overview is presented in Table 2.4.15cells/well) were stimulated with heat‐inactivated E. coli , S. aureus , and C. albicans  for 24 h. Cytokines were measured in supernatants to assess the effects of metabolic modulation on cytokine responses.After 24 h preincubation with modulators, PBMCs (5.0 × 102.4.25 cells/well) were discarded. Subsequently, the PBMCs were stimulated with 200 μl in 10% human serum opsonized live E. coli , S. aureus , C. albicans , or RPMI. ROS production was measured with a luminol oxidation assay, similarly as described above for ex vivo experiments.Similarly, the effects of metabolic modulation on oxidative burst were determined. Supernatants of seeded PBMCs  for 24 h at 37°C and 5% CO2. After incubation, all cells were lysed in water, and serial dilutions of lysates were plated on Sabouraud agar plates . The Candida killing capacity of the PBMCs was quantified by counting remaining CFUs overnight incubation at 37°C and 5% CO2. The killing capacity was calculated as described before.After removal of the metabolic modulators, PBMCs were washed twice with warm PBS in order to prevent interference of the metabolic inhibitors and stimuli with the live 2.4.52. Immediately before measurement 1.5 μl PI (Invitrogen Molecular Probes) was added. Cells were measured using a CytoFLEX cytometer (Beckman Coulter).To determine the effect of the inhibitors and stimuli of specific metabolic pathways on cell viability in PBMCs, apoptosis was assessed using Annexin‐V and propidium iodide (PI) staining. Cells were stained for 15 min in 300 μl RPMI containing 1 μl Annexin‐V (Biovision) and 5 mM CaCl2.5Cytokine measurements of all experiments were performed in collected supernatants that were stored at −20°C until cytokine measurements were performed. TNF‐α, IL‐1β, IL‐1Ra, IL‐10, and IL‐6 levels were measured using commercially available ELISAs according to the protocol supplied by the manufacturer .2.6sem or median, based on their distribution. One‐way ANOVA for repeated measurements was used to compare statistical differences for the means of the respective time points. Dunn's multiple comparison test was used as a post hoc test. The Wilcoxon signed rank test was performed to compare means for significant differences of metabolic modulation in validation experiments. Calculations and statistical analysis were performed with GraphPad Prism Version 5.03  All computational analyses for metabolome data analysis were performed in the R programming language.P‐values were calculated using a 2‐sided paired t‐test. A Benjamini‐Hochberg false discovery rate procedure was used to correct these P‐values for type I errors.P‐value < 0.05 was considered statistically significant with *P < 0.05, **P < 0.01, and ***P < 0.001.The data are represented as the mean ± 33.1Intravenous endotoxin administration induces immunotolerance in circulating monocytes in healthy volunteers, and these otherwise highly responsive cells are rendered irresponsive to ex vivo re‐challenge with LPS.E. coli and S. aureus  killing, and cell viability were determined. Metabolic pathways affected by immunotolerance were systematically targeted by inhibitors/modulators at crucial steps in the metabolic pathways  showed a trend in reduced C. albicans‐induced TNF‐α and IL‐1β production   Fig. B–D.3.4E. coli, S. aureus, and C. albicans after inhibition or simulation of various metabolic pathways in monocytes. ROS release was diminished upon inhibition of glycolysis with 2DG, PPP with 6AN, and glutaminolysis with BPTES or C968. In contrast, inhibition of lactate production with sodium oxamate (SOX) augmented ROS release. Increasing TCA‐cycle activity with DCA increased oxidative burst in response to C. albicans exclusively. Albeit being an antioxidant, N‐acetylcysteine (NAC) also increased oxidative burst in response to microbial stimulation  exhibited a substantial impact on Candida killing in naive monocytes . DPI is a frequently used and potent ROS inhibitor mediated by flavoenzymes, in particular NADPH oxidase.C. albicans yeasts, confirming the link between ROS and killing  killing. During endotoxin‐induced immunotolerance, most major metabolic pathways failed to be modulated in monocytes upon ex vivo LPS restimulation. Even 7 days after endotoxin‐induced immunotolerance, major metabolic pathways including glycolysis and the TCA cycle were not inducible. This implies a sustained defect in metabolic reprogramming following in vivo exposure to a bolus endotoxin infusion. Validation in naive immune cells showed that these pathways were essential for the immune response to microbial ligands, highlighting the influence of metabolic modulation on essential antimicrobial functions in immunotolerance.This study revealed impaired plasticity of monocytes to modify their metabolism in response to immunogenic stimulation following in vivo endotoxin‐induced immunotolerance. This was accompanied by reduced cytokine responses and ROS release, which in some individuals was associated with impaired microbial  killing capacity. At the same time, metabolic modulation induced high variability in intracellular killing between donors. This can partly be explained by the experimental design, in which inhibitors are washed away after the preincubation period to prevent interference with the metabolic pathways of C. albicans. This probably enabled complete or partial restoration of indirectly targeted metabolic pathways, like glycolysis (2DG) or glutamine metabolism, as these compounds can compete with nutrients in the culture media like glucose, pyruvate, and glutamine. In contrast, direct targeting of enzymes like GLS‐1 inhibition by BPTES or C968 may induce less transient metabolic reprogramming.Oxidative burst is crucial for antimicrobial defense.Glutamine has gained clinical interest as it has regulatory effects on immune cell function and may serve as a beneficial supplement in critically ill patients.S. aureus or E. coli stimulation. This was similar for DCA upon C. albicans stimulation. These beneficial effects support the potential of these modulators as novel candidates to reverse immunoparalysis and will be discussed further in this context.Remarkably, DCA (TCA cycle stimulator) augmented cytokine responses to all microbial stimuli in our study. Similar effects were observed after modulation with NAC (glutathione synthesis). After exposure to NAC, monocytes also demonstrated augmented oxidative burst upon First, NAC is commonly known as a ROS scavenger and appreciated for its capacity to reduce oxidative stress and organ damage as well as anti‐inflammatory effects. NAC was introduced as an antioxidant in SIRS (systemic inflammatory response syndrome) and sepsis cohorts, but was ineffective in reducing mortality.Second, DCA was used in a clinical trial to treat hyperlactatemia, which included patients with septic shock, but did not improve clinical outcome.Our study has some limitations. First, metabolomic profiling was performed in a small number of participants. Therefore, validation experiments were focused on metabolic pathways rather than on single metabolites. We assume that parallel to pathway analyses, individual metabolites may help to identify novel metabolic therapeutic targets. Second, our study population was within an age range of 18–35, whereas 64.9% of cases of sepsis occurs in patients at or over the age of 65.To conclude, this study provides essential insights into metabolic changes in immunotolerant monocytes, and we report a loss of metabolic plasticity in these cells. By modulation of metabolic pathways in naive immune cells, we validated the concept that metabolic pathways changed in immunotolerance can affect antimicrobial functions. Furthermore, several metabolites and metabolic pathways were identified that might represent future therapeutic targets to reverse sepsis‐induced immunoparalysis. As such, this study opens up new therapeutic avenues for sepsis‐induced immunoparalysis.The authors declare no conflicts of interest.Figure S1: Changes in metabolites during endotoxin‐induced monocyte tolerance.Figure S2: Effect of metabolic modulation on PBMC viability.Click here for additional data file.Table S1. Differentially regulated metabolites following ex vivo LPS stimulation.Click here for additional data file.


Phytophthora infestans, the causal agent of late blight disease, affects potatoes and tomatoes worldwide. This plant pathogen has a hemibiotrophic lifestyle, having an initial biotrophic infection phase during which the pathogen spreads within the host tissue, followed by a necrotrophic phase in which host cell death is induced. Although increasing information is available on the molecular mechanisms, underlying the distinct phases of the hemibiotrophic lifestyle, studies that consider the entire metabolic processes in the pathogen while undergoing the biotrophic, transition to necrotrophic, and necrotrophic phases have not been conducted. In this study, the genome-scale metabolic reconstruction of P. infestans was achieved. Subsequently, transcriptional data  was integrated into the metabolic reconstruction to obtain context-specific (metabolic) models (CSMs) of the infection process, using constraint-based reconstruction and analysis. The goal was to identify specific metabolic markers for distinct stages of the pathogen's life cycle. Results indicate that the overall metabolism show significant changes during infection. The most significant changes in metabolism were observed at the latest time points of infection. Metabolic activity associated with purine, pyrimidine, fatty acid, fructose and mannose, arginine, glycine, serine, and threonine amino acids appeared to be the most important metabolisms of the pathogen during the course of the infection, showing high number of reactions associated with them and expression switches at important stages of the life cycle. This study provides a framework for future throughput studies of the metabolic changes during the hemibiotrophic life cycle of this important plant pathogen. Phytophthora infestans   of the infection process to identify important metabolic markers in the pathogenic phases of this oomycete. Furthermore, we tested the hypothesis of a significant change during infection at the level of metabolism. The omics data selected for this study responded to the criteria of quality and comprehensiveness. The genomics data of the T30-4 P. infestans strain were chosen for the analysis because it represents the best annotated genome for the species   and flux variability analysis (FVA)  database as the primary source of information (Table Context-specific models (CSMs) of the infection process for on Table .The TIGER toolbox  were established. Then based on those active reactions, calculations of pan, core, accessory, and unique reactomes were performed. The definitions for each one is as follows: (i) Pan reactome: reactions (and pathways for the reactions) present in all CSMs compared; (ii) Core reactome: reactions shared among the CSMs compared; (iii) Accessory reactome: reactions present in more than one CSM but not in all; and (iv) Unique reactome: reactions present in only one CSM. From now on, when unique, core and accessory are referred in the context of metabolic pathways, it does not imply that a whole pathway is unique, accessory or core, but it means that the reactions found in each set belonging to these pathways could be unique, accessory or core. It is a way to contextualize the reactions at the level of metabolic pathways.These calculations were performed at three levels. The first level, evaluates the whole model based on the reactions that are active in each strain using Venny 2.1.0  were computed. The linkage matrix with Euclidean distance based on Ward's method was calculated Ward, . A dendrP. infestans accounted for 1,530 metabolites participating in 1,571 reactions  cover 7% of the coding gene space. The pathways with more reactions in the reconstruction of P. infestans were associated with glycerophospholipid metabolism, then fructose and mannose metabolism, and glycine, serine, and threonine metabolism  reactions were obtained for the two strains (core reactions) while the number of pathways in the core model was 144 Table . The patP. infestans strains, 06_3928A and T30-4, were compared against each other. Table The two n = 5: five treatments: RSA and four time points) for that strain.In order to characterize each stage of development for each strain, the pan and core reactomes as well as the accessory and unique pathways were retrieved for each strain from the different treatments, for the nine CSMs Table . In thisThe pathways with more reactions active in FBA for each strain are depicted in Figure in planta . Some of the reactions had positive while others had negative fluxes (mmol/gDW/h) implying a different directionality of the reactions  to thrive in the treatment under study. Table ta Table . Furtherns Table .P. infestans strains are shown in Figure Hierarchical clustering heatmaps for Four groups of reactions were identified. The host group corresponded to those reactions that are not active in RSA medium and become active at early time points after inoculation Table . The bioWhen the pathogen is inoculated, as expected, several reactions of the metabolism are altered (their metabolic fluxes changed). The pathways with more reactions associated belonged to the purine and pyrimidine metabolism, the glycerophospholipid metabolism, and some reactions involving isomerase enzymes of the glycolysis/gluconeogenesis metabolism Table . In the Phytophthora genus' cell walls  did show some interesting metabolic markers. First, there was a change in the biosynthesis of some amino acids and the synthesis of 1,3-beta-glucan, the main component of the In the necrotrophy phase of infection, the glycerophospholipid metabolism was altered, their metabolic fluxes changed, but this time with more active fluxes than in the host category Table . For theA comparison was made between the model of Rodenburg et al.  iSR1301. For theIn the case of transport and exchange reactions, they were analyzed separately to determine the correspondence between them, because they did not have the same database nomenclature. Only a correspondence of 7 exchange and transport reactions between the two models was found. Additionally, when we analyzed in detail the reconstruction made by Rodenburg et al.  we foundT. gondii  of several life stages of this oomycete inside the host. The CSMs represent the subset of reactions from the genome-scale metabolic model (GSM) that show fluxes that are active in a given context , but none of the stages of the infection process (infection on leaflets) were included  is not active in the zoospore stage  for studying the pathogen are now available as well as metabolic markers in all the main life-cycle stages Both reconstructions started with similar genomic data and used similar predictive software to obtain the list of reactions that constituted the whole reconstruction before the CSMs. Rodenburg et al.  compartmThe novelty of our work with regards to Rodenburg's from a biological point of view is the use of the modeling and analysis of the distribution of fluxes in our CSMs to understand the differences of the life stages during the plant's infection. Indeed, our CSMs cover the plant infection, the most important phase of a pathogen's life cycle, including the biotrophy necrotrophy life stage. Furthermore, we performed different comparisons between two strains and the life stages including the identification of pan, core, unique, and accessory reactomes through the modeling of the CSMs. These comparisons and modeling contributed to the understanding of the dynamics of pathogenicity under the brake-accelerator hypothesis  in the core, pan, and accessory reactomes when the nine CSMs were pooled and/or when the strains were compared. Changes in the fructose concentrations in the host tissues have been previously shown (Judelson et al., in planta, when compared to growth on RSA medium in both strains (Table This study revealed the importance of the fructose/mannose metabolism during infection of ns Table . This enns Table .P. infestans.Although most of the predicted fluxes by the models have been validated with experimental data, there are groups of fluxes showing inconsistencies between real and modeled data (Kjeldsen and Nielsen, P. infestans at different stages of development, growth, and pathogenesis of this plant pathogenic oomycete. For example, sugars like fructose, amino acids such as arginine, glycine, serine, threonine, and folate, among others, are metabolites that could influence successful host colonization. Network analyses allowed the development of a new tool to design new experiments to comprehend the complete metabolism of plant pathogens in planta. Despite the complexity of the metabolism of P. infestans at each life stage, a framework for future research that seeks to further understand the hemibiotrophic life cycle of P. infestans is now available. We are also aware that the use of two different strains can add some noise to the analyses but our approach at the pathway level permitted to assess some variation at the specific reactions used by each strain.In conclusion, the metabolic reconstruction and the construction of CSMs provide important novel insights into the whole metabolism of SR, AG, IV, and DB designed the study. IV, DH, M-JR, and DB performed the analyses. All authors contributed to writing the manuscript and approved its final version.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.


Chlorella sp. HS2 is explored through optimization of the parameters for its heterotrophic cultivation. First, carbon and nitrogen sources were screened in PhotobioBox. Initial screening using the Plackett-Burman design (PBD) was then adopted and the concentrations of the major nutrients  were optimized via response surface methodology (RSM) with a central composite design (CCD). Upon validation of the model via flask-scale cultivation, the optimized BG11 medium was found to result in a three-fold improvement in biomass amounts, from 5.85 to 18.13 g/L, in comparison to a non-optimized BG11 medium containing 72 g/L glucose. Scaling up the cultivation to a 5-L fermenter resulted in a greatly improved biomass concentration of 35.3 g/L owing to more efficient oxygenation of the culture. In addition, phosphorus feeding fermentation was employed in an effort to address early depletion of phosphate, and a maximum biomass concentration of 42.95 g/L was achieved, with biomass productivity of 5.37 g/L/D.The heterotrophic cultivation of microalgae has a number of notable advantages, which include allowing high culture density levels as well as enabling the production of biomass in consistent and predictable quantities. In this study, the full potential of  They are also a promising alternative energy source due to their potentially higher oil content, growth rate, and ability to accumulate high amounts of lipids in comparison to conventional crops and lignocellulosic types of biomass. Today, autotrophic growth is the most common procedure for cultivating microalgae due to their photosynthetic ability and the added benefit of sequestering atmospheric carbon dioxide.Microalgae have been highlighted as a renewable biomass resource for biofuel over the last few decades due to a number of advantages they have over terrestrial biomass2 sequestration. The primary bottleneck that limits the productivity of all photoautotrophic cultivation systems is the light penetration2 and absence of light at night3. Light penetration is often poor inside photobioreactors (PBR) and open raceway ponds (ORP), and the penetration further falls with increasing cell density and depth due to self-shading effects. Light/dark cycle is also inevitable for outdoor cultivation, and it was reported that respiratory effect at night causes a decrease in the biomass produced in the day period3. In addition, the outdoor cultivation systems also suffer from inconsistent biomass productivity due to the fluctuations of light intensity and temperature across the day/night cycle and seasonal changes5. Furthermore, most common outdoor cultivation systems such as open raceway ponds frequently suffer from contamination problems. Due to the nature of open systems, it is nearly impossible to maintain a monoculture of a single target microalgal strain due to the high susceptibility of these systems to be contaminated with undesirable foreign species6. Lastly, concentration of cell culture dictates the costs of harvest, extraction, and purification process of the product, in which case the higher culture density of heterotrophic cultivation provides further advantages of reduced downstream processing costs7.Although microalgae-based biofuels have many advantages, further research and development are necessary to overcome the current problems of low productivity and high cost. The commonly studied process for algal biomass production is outdoor photoautotrophic cultivation, due to the ease of scalability and the added benefit of CO1. One of the major advantages of heterotrophic cultivation is that the culture can achieve more reliable and predictable biomass productivity in comparison to the photoautotrophic systems, due to the consistent availability of the energy source in the form of organic carbon uptake9. Moreover, additional advantages of heterotrophic cultivation include a cost-effective and relatively simple mode of operation. While additional costs would occur for the usage of carbon sources, there have been many researches which use waste and wastewater containing the massive amount of organic carbon for simultaneously reducing the costs of both algal cultivation and wastewater treatment11. Of course, contamination issues would occur in the outdoor cultivation using organic carbon sources, however, it could be controlled and minimized by using extremophiles12. With successful cultivation using the techniques above, it is possible to obtain a high-density culture that is unobtainable under photoautotrophic modes, which allows economic harvesting and easier downstream processing for large-scale production13. In some cases, fed-batch cultivation can achieve culture density of over 70 g/L, far exceeding that of the photoautotrophic cultures8 and considering that centrifugation which is one of the common harvesting methods brings about 100 g/L cell density, then it surely saves the costs for the harvests15. Lastly, heterotroph based massive cultivation could make it possible to produce value-added products which have higher prices at least greater more than biofuel18. While it has been estimated that the costs of carbon sources account for up to 80% of the total operating costs in the heterotrophic cultivation, the above-mentioned advantages can easily compensate for the increased costs19.In this light, heterotrophic cultivation has been proposed as an alternative method for industrial-scale algal biomass and biofuel productionChlorella sp. HS2 was recently isolated from Jeongbang falls in Jeju, Korea. It has the ability to achieve extraordinary culture density (5.91 g/L) and biomass productivity (656.7 mg/L/D) under photoautotrophic conditions20. In addition, it is capable of tolerating high-level of salinity (0–5%), and being able to proliferate under a wide range of temperature (14–46 °C) and pH (3.0–10.5)21. Due to these outstanding growth and environmental resistance characteristics compared to other microalgae, investigating how this strain would perform under heterotrophic fermentation holds significant interest.24. However, statistical optimizations were limited to phototrophic cultivation of microalgae, and the application for heterotrophic cultivation has been seldom reported. Most of the previous statistical analyses on heterotrophic cultivation were mainly for producing value-added products instead of focusing on general biomass productivity26.Statistical analysis has been applied on algal biomass and lipid production a lot, because it is an efficient way to optimize complex and various culture conditions including nutrient, temperature, pH and so onChlorella sp. HS2 in order to explore the potential of green algae fermentation for biomass and biofuel production using Plackett-Burman design (PBD)27 and the response surface methodology (RSM) with the central composite design (CCD)29. The optimized parameters were then experimentally verified on the flask scale and then on the final scale, a 5-L fermenter, for further optimization. Lastly, biomass and lipid productivity was further improved by enhancing the nutrient uptake using a P-feeding strategy.Thus, in this study, we optimized the heterotrophic cultivation of promising strain of Chlorella sp. HS2, glucose, sucrose, sodium acetate, and lactic acid were tested. Glucose was chosen as the candidate because it is the most common substrate in industrial fermentation processes30. Sucrose is an inexpensive carbon from waste molasses8. Acetate and lactate were one of the most common carbon sources for growing many microorganisms, including microalgae2. Cell growth using four different carbon sources with 1, 5, 10, and 20 g/L concentrations were examined with optical density at 680 nm and it was calculated as dry cell weight  using Eq. 31 and cells had lower efficiency to use sucrose directly or degraded fructose than glucose. As a result, Chlorella sp. HS2 has less capability to use sucrose in this condition and resulted in just half the growth compared to that obtained with the same concentration of glucose. Sodium acetate and lactic acid were the third and fourth carbon source for Chlorella sp. HS2. In the case of sodium acetate, it has the ability to buffer the pH32, but a high dosage of it could modify the pH to an undesirable range for Chlorella sp. HS2. In a similar manner, lactic acid could modify the pH below the optimum range for Chlorella sp. HS2 and showed almost no growth. As a result, among the carbon sources, glucose has been utilized as the primary carbon source in microorganism based industries because it is metabolized in the form of energy and a carbon source with minimal cost. Chlorella sp. HS2 appeared to easily metabolize glucose and thereupon provide good biomass production. In terms of economic feasibility, glucose is relatively more cost-effective than the other carbon sources because it is produced on a large scale industrially and has a high substrate yield in the cultivation industry33. In addition, glucose constitute large part of reasonable carbon sources from agricultural and food waste water34. Therefore, optimization study based on glucose will help better understand the cultivation using various reasonable carbon sources also35. Thus, under these circumstances, glucose was selected as a heterotrophic carbon source for Chlorella sp. HS2.In order to determine the substrate that is most favored by Chlorella sp. HS2. Sodium nitrate was chosen as the candidate because it is commonly used as a nitrogen source for Chlorella cultivation36. Urea is one of the cheapest nitrogen sources and is considered a valuable nitrogen source to achieve high lipid productivity37. Ammonium chloride is meanwhile the preferred nitrogen source for most microalgae, as all other forms of nitrogen must be converted to ammonium for accessible nitrogen forms38. Finally, yeast extract is an important nitrogen source for all microorganism growth as well as microalgae growth39. Each nitrogen source  were added to a BG11 medium containing 10 g/L of glucose and cell growth was examined. The results are provided in Fig. 2. However, different concentrations of dosages did not show significant differences in cell growth and the highest cell growth (0.25 g/L growth with 750 mg/L urea) was even less than the minimum result of sodium nitrate (0.28 g/L growth with 125 mg/L sodium nitrate). Yeast extract contains not only nitrogen source in the form of amino acids but also carbon and phosphorus sources, therefore it usually yields higher cell density than the other nitrogen sources. However, in this screening test, maximum cell growth was achieved under 500 mg/L yeast extract, and further additions reduced the cell growth. Therefore, considering the substrate yield and economic feasibility of nitrogen sources, sodium nitrate was selected for the heterotrophic cultivation of Chlorella sp. HS2.In addition to the carbon source, sodium nitrate, urea, ammonium chloride, and yeast extract were tested for the heterotrophic cultivation of Chlorella sp. HS2. In addition, all eleven nutrient sources in glucose added BG11 media with sodium nitrate were evaluated in twelve combinations via PBD27. Tables 4∙7H2O, Na2CO3, and ferric citrate all positively influenced cell growth. However, CaCl2, citric acid, and Na2 EDTA were found to have negative effects. Although some of the trace metal solutions displayed a negative effect, the error range is considerably large due to the very low concentrations of the trace metals. Trace metals were not removed from the list of media components given that their presence is still necessary for cell growth. While the choice of phosphorus source did not have a critical effect on the overall cell growth, monopotassium phosphate was observed to have a greater influence on the response  in comparison to dipotassium phosphate. Additional comparison between monopotassium phosphate and dipotassium phosphate was conducted in the flask scale . MgSO4∙7H2O plays an important role in the formation of the chloroplast of green algae, and therefore MgSO4∙7H2O was included in the initial RSM. However, when it was present above a certain concentration, MgSO4∙7H2O was found to not have a remarkable influence on cell growth and was hence excluded as the main factor. Ferric citrate was also shown to have a relatively large effect according to the PBD results, but it had less meaning to find the optimal concentration due to its low concentration in the medium. Therefore, dipotassium phosphate was chosen as a key nutrient for optimization along with glucose and sodium nitrate.Based on the results of the carbon and nitrogen screening test described above, glucose and sodium nitrate were selected as the main nutrients for BG11 application on heterotrophic ale Fig.  because 3 factorial design points with six-star points and six replicates at the central points, thus requiring 20 experiments in total41. The coded and natural values of the variables are shown in Table 242. The interrelationships between the dry cell weight (g/L) and the levels of key nutrients were found to be well represented by the following quadratic equation with a correlation coefficient R2 of 0.9050,After the initial profiling of the major and minor nutrients, the concentrations of glucose, sodium nitrate, and dipotassium phosphate were optimized by employing the RSM and CCD. For the CCD, a second-order polynomial RSM model consists of 2Respective contour plots and three-dimensional response surface curves described by the regression model are presented to explain the effects of the key nutrients Fig. . Each reChlorella sp. HS2 was cultivated under the proposed optimum concentrations of glucose, sodium nitrate, and dipotassium phosphate, which were tested in order to validate the RSM model. Under non-optimized conditions containing 72 g/L glucose in BG11 medium, the maximum biomass of 5.85 g/L was obtained =10.56) was three times greater than the tabulated F(=3.14), which shows that the model is a good predictor of the experimental results44. Furthermore, the relatively high correlation coefficient (R2 = 0.9050) and low P-value (P <0.001) provided strong evidence of the high significance of the regression model45.The accuracy of the RSM model Eq.  was analChlorella sp. HS2 cultivated in the optimized BG11 medium presented a remarkable increase in DCW from 5.85 to 18.13 g/L. However, considering that cells were not able to fully utilize all the glucose in the medium   to 0.81 g/L/D (7th day) and subsequently showed a similar value (0.90 g/L/D). This decrease was mainly because cells showed less biomass productivity than changes in lipid content, which showed an increase.As expected, /D) Fig. . Howeverays Fig. . Phosphaays Fig. . The totThe fermenter scale cultivation gave 1.57 times more biomass yield and 10 g/L higher consumption of glucose. Although screened and optimized conditions were applied, cells could not completely consume the glucose, mainly because of the depletion of phosphate. It is noteworthy that even though screening and statistical optimization were applied, additional optimization is required at a specific scale. Therefore, further optimization was conducted in the form of phosphate feeding.51. This phenomenon makes it difficult to precisely determine the amount of phosphate that is used and to control the balance among the C, N, and P sources48. In addition, RSM data showed that more addition of dipotassium phosphate led to lower biomass yield, and therefore a phosphate feeding strategy was applied. Although most fed-batch fermentation processes involve the feeding of a carbon source, the P-feeding process is feasible for fed-batch fermentation52. The initial medium used was BG11, which is identical to that in the previous experiment, and 1 mL of concentrate containing 0.22 g/L of phosphorus was pulse-fed every two days until the glucose was depleted.RSM data showed that the optimum dipotassium phosphate feeding amount was 0.22 g/L. In microalgae, phosphates are commonly known to be depleted first due to microalgae quickly uptaking any excess phosphorus in the environment for sequestration in the form of phosphate granulesth day) as the glucose consumption rate increased from 6.4 g/L/D (without phosphate feeding) to 9.0 g/L/D (with phosphate feeding). Enhanced glucose utilization could be explained by the fact that greater phosphate availability under pulse-feed culture conditions allowed cells to increase nitrogen uptake  over the cultivation, as shown in Fig. ake Fig. . All the48. The other is that more of the carbon flux was involved in de novo protein synthesis and cellular growth than in lipid accumulation  was among the highest ever reported. However, the overall lipid content was found to be noticeably lower (16.0%) than that in other studies (26.3–72.0%).The overall performance of biomass production from the system that used ae Table . In termChlorella sp. HS2 had identical C:N ratio (21.8) in the medium, different biomass yields (35.3 and 43.0 g/L) and lipid contents (26.3 and 16.0 g/L) were obtained. As for two studies involving Chlorella vulgaris and Chlorella sorokiniana, the C:N ratios in the medium were comparable (19.2 and 17.6 each), but they reported totally different biomass yields (4.27 and 1.75 g/L) and lipid contents (30.0 and 39.2%). In the case of Chlorella protothecoides, Li et al. and Chen & Walker used different C:N ratio in the medium, but obtained showed similar biomass yields (13.9 and 15.3 g/L) and lipid contents (72.0 and 64.1%). Therefore, it appears that C:N ratio in the medium alone is a poor indicator to predict the biomass and lipid yields, and specific optimization is needed for each target product and selected strain under given conditions. In this study, BG11 medium  has been optimized using PBD and RSM with the primary objective of maximizing the rate of biomass productivity. While the lipid content was lower than the values reported in most instances of heterotrophic fermentation, the possibility of increasing the lipid content further can be explored with Chlorella sp. HS2. These steps would involve a potential two-stage cultivation mode with nutrient starvation after supplemental glucose fed-batch fermentation, combined with increased delivery of disolved oxygen (DO) for more effective utilization of the supplemental glucose.It is noteworthy that different C:N ratio within the biomass (based on the consumption) results in different lipid content and biomass yield even within the same system as shown in Figs. Chlorella sp. HS2. An RSM analysis based on CCD successfully determined the optimal concentrations of glucose, sodium nitrate, and dipotassium phosphate, which were 72 g/L, 8 g/L, and 0.22 g/L, respectively. A subsequent scale-up of the model to a 5-L fermenter showed that the culture underwent possible phosphorus deficiency, resulting in incomplete utilization of the carbon and nitrogen sources. This problem was addressed via P feeding fermentation using pulse-supplementation of phosphorus, which resulted in a maximum biomass concentration of 42.95 ± 0.05 g/L. This led to the conclusion that the heterotrophic cultivation of Chlorella sp. HS2 is a highly promising method for the production of high-density algal cultures.In this study, PBD was employed to determine the effects of various nutrients contained in BG11 on the heterotrophic growth of Chlorella sp. HS2 was isolated by Dr. Hee-Sik Kim at the Korea Research Institute of Bioscience and Biotechnology (KRIBB) in Korea. The culture was maintained in a BG11 medium containing (g/L) 1.5 NaNO3, 0.04 K2HPO4∙2H2O, 0.075 MgSO4∙7H2O, 0.036 CaCl2∙2H2O, 0.006 citric acid, 0.02 Na2CO3, 0.006 ferric ammonium citrate, 0.001 Na-EDTA, and 1 mL of the trace metal A5. The trace metal A5 solution consisted of (g/L) 2.86 H3BO3, 1.81 MnCl2∙4H2O, 0.22 ZnSO4∙7H2O, 0.39 NaMoO4∙2H2O, 0.079 CuSO4∙5H2O, and 0.05 CoCl2∙6H2O. The seed culture, which used BG11 medium with 10 g/L glucose, was maintained in a dark condition at 32 °C and 120 rpm.Green alga Chlorella sp. HS2 was conducted using PhotoBiobox 55. Photobiobox is an incubator invented to keep the same condition (temperature and air supply) for all wells on 96 well plate and it makes possible to compare the effect of certain additives precisely. For the carbon source screening, glucose, sucrose, sodium acetate, and lactic acid were tested. Glucose, sucrose, sodium acetate, and lactic acid, with respective concentrations of 1, 5, 10, and 20 g/L, were added into each 1 L BG11 medium containing 1.5 g sodium nitrate and sterilized with an autoclave. For the nitrogen source screening, sodium nitrate, urea, ammonium chloride, and yeast extract were tested. Each nitrogen source, where the concentrations were 125, 250, 500, and 750 mg/L, was added into each BG11 medium containing 10 g/L glucose and sterilized with an autoclave. Microalgal culture was inoculated in 250 μL wells of a 96-well glass plate (working volume: 200 μL) and it was covered to prevent evaporation55. Cells were cultivated for 120 hours under a dark condition at 37 °C and mixed using the auto-mixing function in Photobiobox. Cell growth was initially measured by optical density at 680 nm (OD680 nm) using Plate reader . And dry cell weight (DCW) was calculated based on the predetermined calibration curve using the correlation between dry cell weight and OD680 nm, which is represented by Eq. Screening of carbon and nitrogen sources for heterotrophic For the correlation, cells were cultivated in the flask scale and then OD and DCW were measured three times daily based using plate reader and method below, respectively.Chlorella sp. HS236. After the selection of three significant factors using the PBD, a factorial response surface methodology (RSM) was utilized to obtain information about the important effects and the interactions between the selected variables with a positive influence on cell growth. In this study, the central composite design (CCD) was employed for the RSM. In RSM, glucose, sodium nitrate, and dipotassium phosphate was used to obtain the respective optimum concentration. Glucose concentration ranges from 25.36 g/L to 94.64 g/L and sodium nitrate concentration ranges from 5.07 g/L to 18.93 g/L and dipotassium phosphate concentration ranges from 0.06 g/L to 0.24 g/L.The Plackett-Burman Design (PBD) was used to select the statistically significant factors that are considered to influence the biomass concentration of F-test), a related probability P (F) assessment, and the coefficient of determination (R2), which measures the fit of the regression model. The response surface and contour plots of the model were used to evaluate the interactions between the important variables.During the carbon and nitrogen source selection, P-values were obtained via a Student's t-test between glucose and different carbon sources at the same concentrations, and a Student's t-test between nitrate and different nitrogen sources at the same concentrations. All the experiments were carried out in duplicate. The results were expressed as mean values and standard deviation (SD) between experiments. The analysis of variance (ANOVA) method was used for the statistical analysis of the model. The analysis includes a Fisher test  and 8 days (P-feeding). In the P-feeding cultivation, dipotassium phosphate was initially 0.22 g/L and 0.11 g/L per feed.For a fermentation test, the cells were grown in a 5-L jar fermenter  containing 3 L of the modified BG11. The filtered air was supplied at a 1.67 vvm flow rate, and temperature and agitation were kept at 37 °C and 300 rpm, respectively. The culture pH was held constant at 7 via the automatic addition of 1 N HCl. The initial concentration of the inoculum was 7.4 ×10To measure the dry cell weight (DCW), 1 mL of culture was centrifuged at 13,000 rpm for 3 min. The pellets were washed twice with DI water and dried at 90 °C for 24 h in an oven. To measure the total lipid amount, 30 mg of the cell was centrifuged at 4,000 rpm for 10 min. The pellets were washed twice with DI water and stored at −70 °C for two days and lyophilized for three days.56. The dried cell was grounded into a powder using a bead beater. Thirty mg of biomass was added into 10 mL of chloroform/methanol  and sonicated for 4 h. For the phase separation, 2.5 mL of DI water was then added and vortexed for 5 min and centrifuged at 4,000 rpm for 10 min. The lower hydrophobic phase was transferred to another glass vial through an organic solvent filter  using a syringe. Four mL of filtered hydrophobic phase was transferred to a pre-weighed aluminum weighing dish and underwent natural evaporation in the hood. The lipid content was calculated using Eq. Total lipids were determined using the modified Folch methodD: weight of the aluminum dish, WL: weight of aluminum dish with lipid, WS: weight of sample, VC: volume of chloroform, VP: volume of chloroform in the aluminum dish.W2SO4). The supernatant was passed through a 0.2 μm syringe filter , and the nitrate concentration was measured using ion chromatography . An anion column (Metrosep A Supp 5) was used to analyze the nitrate (NO3-) and the phosphate (PO4−) ions remaining in the culture medium. The eluent, which consisted of 3.2 mM Na2CO3 and 1 mM NaHCO3, was supplied at a flow rate of 0.7 mL/min into the column for the analysis57.The sugar concentrations were measured using a high-pressure liquid chromatograph  equipped with a refractive index detector (RI). The Aminex HPX-87H column  was maintained at 65 °C with a mobile phase (0.01 N HSupplementary tables and figures