Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.The purple non-sulfur bacterium  Rhodopseudomonas palustris actively participates in the carbon and nitrogen cycles by fixing carbon dioxide (CO2) and molecular nitrogen (N2), as well as transforming nitrate into N2. This photosynthetic bacterium has been extensively studied and is considered a model microorganism for elucidating carbon and nitrogen fixation, H2 production, denitrification, aromatic compounds degradation, anoxygenic photosynthesis and the production of complex polymers like polyhydroxyalkanoates. Constraint-based metabolic models are systems biology tools capable of predicting an organism’s phenotype under several growth conditions. The metabolic model of R. palustris Bis A53, iDT1294, accurately predicts growth rates of over 350 carbon and nitrogen sources  under different oxygen and light conditions. Furthermore, iDT1294 precisely simulates how R. palustris switches between photoheterotrophic and photoautotrophic metabolic modes over time, depending on nutrient availability and light conditions. The model represents the most comprehensive M-model for a purple non-sulfur bacterium to date, thus aiding the elucidation of R. palustris’ metabolic roles in different environments. Rhodopseudomonas palustris is a photosynthetic Gram-negative purple non-sulfur bacterium (PNSB) of the family Bradyhizobiaceae. It can attain a wide range of metabolic states and is considered one of the most versatile microorganisms  [2 as nitrogen sources, achieving a minimum accuracy of 74% and a maximum value above 98%.c growth . To expldustries ,72. BaseiDT1294 was further validated using growth with organic acids and glycerol under anaerobic conditions in the light, employing experimental data from Govindaraju (2019) [10P) were calculated comparing the experimental growth and consumption rates against the model estimations across the time.u (2019)  to evaluu (2019)  was applu (2019) . To caliiDT1294 demonstrates a strong correlation R2 coefficient (98.6%) for the growth rate samples. For most of the conditions analyzed, the model accurately predicted lag and exponential phase trends with clear mismatches of the starting point of the stationary phase. Additionally, substrate consumption across the conditions was predicted correctly according to the dynamic simulation time points generated through dFBA model analyses (R2>97%). In some specific cases , the consumption of carbon sources was incorrectly estimated since the COBRA Toolbox algorithms assume constant uptake fluxes, ignoring the biological regulation and uptake restrictions of the organism. Surprisingly, iDT1294 was capable of accurately predicting growth rates after depletion of the available carbon sources. When the carbon source is depleted, R. palustris utilizes CO2 as the carbon source (carbon fixation). For instance, under malate and malate-lactate conditions nutrients were depleted after 100 hours and CO2 became the sole carbon source available; iDT1294 accurately predicted the change in growth rates for these scenarios. Based on the statistical correlation parameters for growth rate and substrate consumption values, our comprehensive model is established as a suitable tool to estimate and analyze the dynamic metabolic behaviors of R. palustris Bis A53 under photoheterotrophic conditions.R. palustris under four distinct metabolic modes. We generated a high-quality, manually curated, meticulously validated metabolic model of R. palustris Bis A53. Initially, the R. palustris model was reconstructed based on three principal model references from BiGG [Synechocystis sp. PCC 6803 and Synechococcus elongatus PCC 7942) and a well-known Gram-negative bacterium (Escherichia coli K-12 substr. MG1655). BLASTp parameter values were optimized by reconstructing multiple draft models employing the three template models. While there is no clear consensus on which BLASTp criteria optimize the metabolic reconstruction process, previous studies have reported similar BLASTp cut-offs to reduce the amount of false positive (wrong gene assignations) and false negative (missing genes) calls in the GPR associations [We have reconstructed a comprehensive genome-scale metabolic model for a well-studied PNSB using semiautomatic strategies. This work represents a systems biology modeling approach to elucidate the metabolic capabilities of rom BiGG  selectedciations ,54,55. Hciations , the corciations , KBase [ciations  and CarvR. palustris Bis A53 M-model contains 2,721 reactions and 2,123 metabolites associated with 1,294 genes. The resulting model was highly curated using semiautomatic and manual strategies, including GPR-specific compartment associations. iDT1294 was successfully validated using over 350 different carbon and nitrogen sources under several growth conditions varying O2 content (aerobic and anaerobic) and presence of light (light and dark), achieving the highest growth phenotype accuracy , is the first M-model capable to predict steady state and dynamic phenotypic data utilizing a wide range of carbon and nitrogen sources under the four R. palustris metabolic modes in three cellular compartments.Our accuracy  comparedtal data , demonstiDT1294 accurately predicts the growth rate values of R. palustris Bis A53 using 190 carbon and 95 nitrogen sources under aerobic chemoheterotrophic dark conditions . The model contains all required reactions and constraints to simulate the BOF representing the growth of the organisms successfully. We have designed an R. palustris-specific BOF, which includes the principal metabolic constituents of a PNSB, employing experimental and theoretical calculations based on genomic and proteomic annotations. BOF optimization was tested under the four metabolic modes of this organism, demonstrating the model elasticity to precisely predict the metabolic mechanisms using the available nutrients in the environment. The resulting predictions were confirmed by experimental validation using Biolog plates  and aromatic compounds assimilation for steady-state conditions and organic acid substrate consumption to validate dynamic growth and metabolites assimilation datasets. We estimated statistical parameters from the high-throughput phenotypic data validation, achieving values higher than 90% regarding phenotype growth accuracy. Compared to other R. palustris metabolic models, iDT1294 showed higher precision in true positive and negative predictions for both carbon and nitrogen sources. With this information, we identified metabolic pathways employed by this bacterium to consume different types of carbon and nitrogen compounds. Despite the "Swiss army knife metabolism", R. palustris could not metabolize several amino acids as carbon and nitrogen sources; almost 50% of the true negative growth estimations were amino acids. While the organism contains general ABC branch-amino acid transporters to transfer these amino acids inside the cell, it lacks the required enzymes to transform these amino acids into core metabolic intermediates and thereby utilize them as an energy source. In some cases , the necessary enzymes and transporters can be produced by the model. The discrepancy can be explained by non-metabolic mechanisms , which are out of the scope of the metabolic model capabilities. Compared to other prokaryotic metabolic models’ accuracy, iDT1294 displays similar statistical parameter values for both carbon and nitrogen analyses with a global accuracy of 90% and MCC maximum value of 0.86 [R. palustris Bis A53 M-model was curated to consume aromatic compounds under anoxygenic photosynthesis. Most of the resulting simulations achieved a quantitative accuracy close to 80%, demonstrating quantitative and qualitative precision using uncommon carbon sources. These estimations are of particular interest since R. palustris can be found in many wastewater and bioremediation consortia which contain such varied metabolites. While the M-model accurately predicted utilization of several carbon and nitrogen sources under changing O2 concentrations and light conditions, it still possesses some inherent limitations due to the composition behind the metabolic model. For instance, metabolic models cannot predict the partial expression of the nitrogenase complex and changes in enzyme activity due to O2 concentrations. These tightly regulated processes, e.g., transcriptionally, translationally, or allosterically regulated, are out of the scope of GEMs but could be partially recapitulated by including all the expression and allocation features in a subsequent model of metabolism and gene expression (ME-model) of the organism [iDT1294 can be employed to understand how R. palustris Bis A53 metabolism varies over time under several different conditions. of 0.86 ,54,59. Forganism ,75. FinaiDT1294 successfully predicts qualitative and quantitative growth behavior of R. palustris under a wide range of conditions, such as with or without oxygen present, in the presence or absence of light, under molecular nitrogen-fixing conditions, in the presence of aromatic compounds, and metabolizing a variety of different carbon and nitrogen sources. Moreover, the model predicts combinations of these conditions to determine possible metabolic fluxes and pathways employed by the bacterium. Thus, iDT1294 represents a valuable computational tool to elucidate how R. palustris can successfully establish itself in so many different environments, such as wastewater or plant roots, and manages to metabolize varies carbon and nitrogen sources, as well as aromatic compounds, in the presence or absence of light.R. palustris Bis A53 was obtained from The NCBI Reference Sequence database . Protein sequences were aligned to build the initial draft model using the BLAST algorithm for protein homology. This first draft was reconstructed using The COBRA [R. palustris Bis A53 metabolism, four reference models were selected according to the proteome comparison and metabolic capabilities from the BiGG Database [Synechocystis sp. PCC 6803, model iJN678 [Synechococcus elongatus PCC 7942, model iJB785 [Escherichia coli K-12 substr. MG1655, model iML1515 [Azotobacter vinelandii DJ, model iDT1278 [iDT1278 was mainly employed as a template model to obtain the nitrogen metabolism. The initial draft model from the photosynthetic organisms and iML1515 was built using optimized BLAST parameters . A sensitivity analysis was performed to determine which specific BLAST parameters’ values maximize the number of true positive homology calls and minimize the number of false negative and positive calls in the Gene-Protein-Reaction (GPR) associations. The determination of the optimal values involved the assessment of each BLAST parameter and the effect of the three parameters interactions. The resulting optimal BLAST parameter values were employed to determine the GPR associations of the Gram-negative template models. Template models contained reactions associated with carbon and nitrogen fixation, photosynthesis, amino acid catabolism, lipid metabolism, nucleotide pathways, and sugar degradation. The generated draft model also contained genes from the template models, which were later removed during the model refinement stages.The genomic sequence of he COBRA  and The he COBRA  ToolboxeDatabase . Templatl iJN678  and Synel iJB785 . The sec iML1515 , and Azo iDT1278 . iDT1278Model refinement included manual curation of the GPR associations using bioinformatics databases and experimental information followed by gap-filling of new metabolic reactions not found in the BIGG Database based on genomic associations and experimental evidence.R. palustris metabolism using data from bioinformatics databases . Exogenous proteins assigned during the reconstruction stage were replaced with homologous sequences of R. palustris Bis A53 using the Enzyme Commission (EC) number as a reference to identify the metabolic function of the protein. For every subsystem of the model with exogenous proteins, the GPR associations were manually reviewed using KEGG, BioCyc, NCBI and PATRIC. When the sources disagreed about which protein should be associated to a reaction, a unique GPR was chosen by following the consensus among the databases and annotations. Additionally, reactions lacking any R. palustris Bis A53 proteins were checked through BLASTp. Candidate proteins from the R. palustris proteome were identified via BLAST against the proteins assigned to the GPR associations for the reaction in other microorganisms in the BiGG database. The BLASTp criteria were an e-value ≤ 1e-10, query coverage ≥ 80% and identity percentage ≥ 40%. A second step of manual curation was executed to confirm the correct assignation of the GPR associations [R. palustris. All validated reactions, metabolites, and GPR associations were distributed in three different compartments . Metabolites were labeled according to the corresponding position in the cell. Metabolites obtained from the photosynthetic model references were carefully renamed and properly allocated to the corresponding compartment of R. palustris Bis A53. Duplicated metabolites generated during the renaming and reallocation of photosynthetic metabolites were merged and integrated into a unique metabolite identifier. Transport reactions were added using the TransportDB database [In the first step of the manual curation process, reactions in the initial draft model with exogenous proteins in the GPR associations were identified and verified in ciations ,54,55. Tdatabase . Metaboldatabase . ReactioGap filling was performed in two steps, (1) gap filling of metabolic pathways already present in the manually curated draft model, and (2) gap filling by adding new metabolic pathways into the model obtained from different bioinformatic databases or experimental evidence from the literature.Gap analysis was performed to recognize which compounds were disconnected in the M-model and which reactions were lacking in the analyzed pathways. Initially, disconnected metabolites were determined through dead-end analysis algorithms. The dead-end metabolites were classified according to their disconnection motive . Subsequently, the involved reactions were identified to establish suitable connections of dead-end metabolites. Disconnected reactions were manually added using different bioinformatic databases . After these analyses, gap filling was employed to connect pathways  through the data retrieved. Finally, dead-ends were identified through KEGG and BioCyc pathway modules and synthesized using COBRA gap filling algorithms . A finalR. palustris Bis A53 from Machado et al 2018 and Navid et al., 2019 [R. palustris Bis A53-specific features, like bacteriochlorophyll biosynthesis and pathways for aromatic compound degradation, literature information using experimental evidence was employed to reconstruct these specific metabolic subsystems. Pseudoreactions with unspecific intermediates or used to represent transformations with no known pathway were not included in the M-model to avoid inaccurate predictions. All pathways of aromatic compound utilization were curated based on experimental evidence from Harwood and Ma [In the second step of gap filling, new pathways were added to the refined model using semiautomatic algorithms. Specifically, carbon (aerobic and anaerobic degradation of aromatic compounds and PHB production), nitrogen , and photosynthetic  metabolisms and their annotation. The names of new reactions and metabolites were assigned according to the BIGG database; reactions and metabolites with no information in BiGG were added to the model according to the EC Number information (through BRENDA ) and biol., 2019 ,68. For d and Ma ,72. A dein-silico single gene-deletion simulations (in-silico gene knockouts) to verify if the GPR associations are properly assigned using the COBRA and RAVEN Toolboxes. Next, we performed Mass and Charge Balance simulations of the COBRA Toolbox on the model to check for unbalanced reactions added during the model refinement. Unbalanced reactions were fixed by adding the correct formula and charge of each metabolite. Stoichiometric values assigned in every reaction were carefully reviewed and corrected for unbalanced reactions. Ultimately, the final model was analyzed by looking for ATP, NADH, and NADPH free energy production, by removing media nutrients (exchange reactions) and checking that they had zero flux.The final quality check was accomplished to guarantee correct GPR associations and suitably balanced reactions and metabolites. We performed R. palustris Bis A53 was purchased from the American Type Culture Collection (ATCC BAA-1125). R. palustris Bis A53 was grown in mineral medium [4Cl, 2.7 g; KH2PO4, 0.5 g; MgCl2 · 6H2O, 0.33 g; CaCl2 · 2H2O, 0.05 g; NaCl, 0.4 g; 9.5 ml Pfennig and Lippert’s mineral solution; and 8.5 ml vitamin solution [R. palustris was cultured in aerobic medium for 3 days at 30°C under dark conditions. Optical density (OD) measurements were taken at 600 nm (OD600). A solution based on OD600 readings was prepared for the Biolog plates experiments. After the inoculation, Biolog plates were incubated aerobically at 30°C in the dark (chemoheterotrophic condition).l medium  in lightsolution . R. paluR. palustris were tested for growth on 190 different carbon sources using Phenotypic Microarray (PM) plates 1 and 2A from Biolog, Inc.  following the company’s instructions. Briefly, cell suspensions from a 10-day old culture (see above) were washed and resuspended in Biolog’s IF-0a GN/GP Base (1.2X) inoculating fluid (#72268) up to an optical density of 0.025 at 600 nm ). The suspentions were further supplemented with ATCC trace minerals and vitamin supplements (ATCC-MDTMS and ATCC-MDVS). 100 uL of these washed cell suspensions were inoculated into each well of the Biolog plates PM1 and 2A (a full list of compounds including a blank well with no carbon source (negative control) can be found here). Plates were incubated at 30°C without shaking with lids coated with an aqueous solution of 20% ethanol and 0.01% Triton X-100 (Sigma) to prevent condensation [R. palustris as an OD600 increase > 0.02  over a 96 hour incubation period.Bacterial suspensions of ensation . We defiThe bacterial suspensions for the nitrogen use assays were prepared in the same way as for the carbon source but were washed in IF-0a GN/GP Base (1.2X) inoculating fluid (#72268) containing 5 mM potassium phosphate monohydrate, pH 6 (Millipore-Sigma), 2 mM sodium sulfate (Millipore-Sigma) and 20 mM sucrose as the carbon source. The rest of the steps and determination of substrate utilization as a nitrogen source were similar to the ones described above for the carbon source assay.2 exchange flux. The constraints related to mineral requirements were set according to the BOF estimations. Growth and internal fluxes simulations were performed in the COBRA Toolbox for MATLAB [R. palustris Bis A53 [2 was tested as the nitrogen source (diazotrophic capabilities) in combination with other soluble nitrogen compounds for 14 anaerobic conditions [R. palustris Bis A53 under autotrophy and heterotrophy. Reaction flux distributions were analyzed to identify how pathways participate according to the medium conditions. Specifically, pathways involved in energy production, amino acid, nucleotide and lipid metabolism, pollutants degradation (aromatic compounds), photosynthesis, polymer production (PHB), carbon fixation, and cofactor and vitamin synthesis were analyzed according to the participation of each subsystem in all the different conditions. For experimental conditions with no specific uptake rates or experiments with only qualitative results (growth or non-growth), the validation process was reduced to true positive, true negative, false positive, and false negative calls. Additionally, the model was validated through experimental data generated in this study, with a total of 190 carbon sources (Biolog plates PM1 and PM2) and 95 nitrogen sources (Biolog plates PM3). For carbon source assessment, ammonium assimilation was not fixed to a specific value (non-diazotroph conditions). The experimental results from Biolog plates were matched with data retrieved from the literature to determine and evaluate model precision during the simulations. During nitrogen condition simulations, sucrose was used as the main carbon source. Statistical parameters were calculated according to the comparison between the M-model predictions and the experimental values. All Biolog plates conditions were tested and simulated in the model under chemoheterotrophic conditions (photons and CO2 assimilation constrain set to 0). Carbon and nitrogen sources without available or well-defined metabolic pathways for assimilation and metabolic integration were not mapped into the metabolic model. Additionally, nitrogen fixation metabolism and N2 assimilation constraints were fixed to 0 since the experiments were performed under aerobic conditions. The model accuracy from the Biolog plates results was compared with the in-silico predictions of other R. palustris’ models to determine the quality of model simulations. Statistical parameters were calculated for each model to determine model precision, accuracy, sensitivity, positive and negative predictions, and MCC. These statistical parameters were calculated using the formulas employed in the confusion matrices to evaluate model performance previously described [Experimental conditions from the literature were employed to determine specific medium constraints of the M-model. For all heterotrophic growth conditions, carbon and nitrogen uptake rates were calculated depending on the values obtained from results in the literature. For phototrophic (photoautotrophic and photoheterotrophic) conditions, photon fluxes were set to a maximum uptake rate value of 1000 and limited based on the COr MATLAB ,83 usingr MATLAB . The sto Bis A53 . Mineral Bis A53 ,80,81. A Bis A53 ,17,30. Snditions . The reaescribed .R. palustris M-model, kinetic studies were employed to determine the prediction capabilities of the metabolic model. Carbon source consumption and growth rate values were obtained from the literature [iRpa940) [iRpa940 for all nine experimental conditions and the results were compared to our M-model predictions. Statistical parameters and correlation values were calculated for growth rates and substrate consumption data using experimental time points as a reference. Correlation values were estimated employing the Pearson correlation algorithm of Python based on the experimental observations and predicted values.For in-depth validation of the terature . A totaliRpa940)  was modiS1 Material(XLSX)Click here for additional data file.S2 Material(XLSX)Click here for additional data file.S3 Material(XLSX)Click here for additional data file.S4 Material(XLSX)Click here for additional data file.S5 Material(XLSX)Click here for additional data file.S6 Material(XLSX)Click here for additional data file.S7 Material(XLSX)Click here for additional data file.


Leishmaniasis is a global concern especially in underdeveloped and developing subtropical and tropical regions. The extent of infectivity in host is majorly dependent on functional polarization of macrophages. Classically activated M1 macrophage can eliminate parasite through production of iNOS and alternatively activated M2 macrophages can promote parasite growth through by providing shelter and nutrients to parasite. The biological processes involved in immune signaling and metabolism of host and parasite might be responsible for deciding fate of parasite.Using systems biology approach, we constructed two mathematical models and inter-regulatory immune-metabolic networks of M1 and M2 state, through which we identified crucial components that are associated with these phenotypes. We also demonstrated how parasite may modulate M1 phenotype for its growth and proliferation and transition to M2 state. Through our previous findings as well as from recent findings we could identify SHP-1 as a key component in regulating the immune-metabolic characterization of M2 macrophage. By targeting SHP-1 at cellular level, it might be possible to modulate immuno-metabolic mechanism and thereby control parasite survival.https://github.com/cran/FactoMineR/tree/master.Mathematical modeling is implemented as a workflow and the models are deposited in BioModel database. FactoMineR is available at:   Leishmania , mucocutaneous (ML), or cutaneous leishmaniasis (CL) are the three different clinical manifestations of leishmaniasis. The World Health Organization (WHO) estimates that in 2020, VL was the primary form in more than 90% of cases that occurred in Brazil, Ethiopia, India, Kenya, Somalia, South Sudan, and Sudan. In 10 nations—Afghanistan, Algeria, Brazil, Colombia, Iraq, Pakistan, Peru, Syria Arab Republic, Tunisia, and Yemen—more than 84% of the cases recorded in 2016 were due to CL . When a sandfly harboring Leishmania protozoa takes a blood meal, it injects saliva that prevents blood clotting  and the M2 macrophage (anti-inflammatory) are the primary (Leishmania spp. They are characterized by the impaired tricarboxylic cycle (TCA cycle), production of pro-inflammatory cytokines such as Interleukin-12 (IL-12), interferon gamma (IFNℽ) and by generation of reactive oxygen species (ROS) and nitric oxide (NO) that help in efficient parasite killing. The M2 macrophages are identified by intact TCA cycle, production of anti-inflammatory cytokines such as Interleukin-4 (IL-4), Interleukin-10 (IL-10) etc., which promote tissue repair and parasite survival  convert tryptophan to kynurenine, reducing the availability of tryptophan to amastigotes. TDO levels are negatively correlated with parasite load  of parasite interacts with toll-like receptors (TLRs), TLR2 and TLR6, that activate myeloid differentiation primary response 88 (MyD88) which promotes nuclear factor kappa B (NFκB) activation and IL-12 synthesis. Crucial transcription factors, including interferon regulatory factor 1 (IRF1), c-Rel and nuclear factor of activated T cells 5 (NFAT5), are involved in the synthesis of IL-12 . IFNγ siite load . Later, Leishmania also secretes some proteases like GP63. GP63 activates host protein tyrosine phosphatase, non-receptor type 6 (SHP-1) that later inhibits NFAT5 and prevents IL-12, IFNγ, and NO production  and the other functioning as the early stage of M1 to M2 polarization in which the macrophage resident amastigotes leads to IL-10 expression and also use host immuno-metabolic pathways to produce Arg1, trypanothione, and ergosterol for their survival and proliferation (diseased state).Both models were reconstructed in Cell Designer and exported as systems biology markup Language (SBML) file. The biological reactions in the model were mathematically defined and quantified by including the kinetics rate law, parameter estimation, initial component concentration , a biochemical network simulator. It solves mathematical model ODEs and determines the flux of each reaction in the reconstructed biological network. The biological system’s outcome is determined by the rate at which components are produced, hence a reaction with a higher flux is crucial in the network  is a multivariate approach for analyzing a data table in which observations are characterized by numerous inter-correlated quantitative dependent variables. Its intent is to obtain important information from the data table and represent it as a set of new variables known as principal components. PCA also depicts the similarity pattern of the data and variables by presenting them as points on maps . We usedThe PCA input file contains information regarding each of the components present in M1 and M2, such as the name of the model in which that component is present, as well as the concentration of production/utilization.Cytoscape is an open-source software platform for building molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data. It also provides different plug-ins for the analysis of our network , maximum neighbourhood component (MNC), density of maximum neighbourhood component (DMNC), maximal clique centrality (MCC) and six centralities  based on shortest paths.P-value was adjusted at 0.01.The Cytoscape plugin program Biological Networks Gene Ontology tool  collected GO annotations for the M1 and M2 components. BiNGO retrieved the overrepresentation of GO categories in a sub-graph of a biological network, which is displayed on Cytoscape, using the input list of M1 and M2 components. The Benjamini and Hochberg correction was used to give tight control over the false discovery rate under positive regression dependence of the test statistics, and the hyper geometric test The healthy state mathematical model had four compartments including cell membrane, cytoplasm, mitochondria and nucleus. A total of 97 components and 74 reactions were used to construct the model. Simulation for 20 time units was performed, using SBML ODE Solver Library SOSlib (1.7.0) available in Cell Designer (4.4.2).In the M1 model, we observed that LPG bound to TLRs activated MyD88-mediated signaling, which in turn triggered the transcription of IL-12 through NFκB and lead to the activation of IFNγ, which further produced NO. Succinate dehydrogenase participated in the formation of ROS through ETC when the Krebs cycle was simultaneously hindered, which causes succinate accumulation. In addition, tryptophan was being catabolized in order to use it for the formation of ROS and prevent the parasite from using it as a nutrient source. Moreover, ROS also activated GSK-3b, which in turn prevented IL-10 from being transcribed. These combined processes are what allow for the elimination of parasites . We obseThe diseased state mathematical model had four components which included cell membrane, cytoplasm, and nucleus and parasite cell. Model consists of 103 components and 85 reactions. The simulation was performed for 100 time units, using SBML ODE Solver Library SOSlib (1.7.0) available in Cell Designer.In the M2 model, we found that the concentration of IL-10, ergosterol, and trypanothione had increased which showed parasite survival state . ConcurrAccording to comparative flux analysis, the reaction associated with M1 model that had high flux are cytokines producing reaction such as IL-12, IFNγ, NOS2 activation, NO production, citrulline to L-arginine conversion, succinate accumulation through impaired Krebs cycle, tryptophan catabolism, ROS production, activation of GSK-3b. The flux value of each high flux reactions is mentioned in the table below . Flux ofThe reactions associated with M2 model that had high flux were reactions that lead to production of IL10, polyamines production, trypanothione, and ergosterol. Activation of SHP-1, and AKT downstream pathway also had high flux. The flux value of each high flux reactions is mentioned in the table below . Flux ofWith only two datasets, the scree plot does not show the elbow, which means that maybe both datasets are equally important. . FurtherUnique components from M1 model which stood out from comparative analysis of both models were IFN-g (nucleus), IL12 (cytoplasm), succinate, ROS, and NO. In case of M2 model, they were SHP-1, AKT, trypanothione, and ergosterol . Matrix Two networks were constructed based on the connections present between the components of M1 and M2 models . The conThe entire network was analysed using parameters such as betweenness centrality, degree of nodes, edge betweenness, closeness centrality, clustering coefficient, and others to present a condensed and robust nature of the components with each other in both the networks.The Cytohubba was used to identify the top-ranked nodes in the entire network based on each topological analysis methods. MCC, DMNC, MNC, EPC, radiality, degree, closeness, betweenness, eccentricity, bottleneck, clustering coefficient, and stress centrality are the 12 scoring methods that were used to identify top-ranked nodes in the network .Each method yielded the top 10 nodes in the network. Based on their occurrence in each of the 12 scoring methods, the top 10 components are represented in Based on the occurrence of nodes in each of the 12 scoring methods, the top 10 components are represented in The BiNGO, version 3.0.5, plugin was used for identifying gene ontology (GO) categories that were substantially over-represented in a set of genes-metabolites or a biological network sub-graph of M1 network and M2 network. We investigated and displayed the biological functions and cellular elements of discovered components of M1 network. Arginine metabolic process, glutamine family amino acid catabolic process, nitric oxide biosynthesis process, superoxide metabolic process, response to cytokine stimulus, regulation of IL-12 production, regulation of transcription, and regulation of cellular respiration .The BiNGO, version 3.0.5, was used for GO categories for immune-metabolic biological network sub-graph of M2 network. We investigated and displayed ontologies associated with M2 components which highlighted significant involvement in cytokine signaling immune system, arginase activity, regulation of host lipid pathways, regulation of ROS, negative regulation of animo acid glutathionlylation, decreased IL-12 signaling, decreased NO production, and increased IL10 production . Gene liImmuno-metabolic response of host macrophages may decide fate of parasite infection; it may either promote parasite elimination or parasite survival. The extent of infection is dependent on multiple factors, including the species involved, host genetics, early innate immune response, adaptive immune response and crucially, the polarization of macrophages . As NO production is inhibited, L-arginine is shown to be used for the production of polyamines like spermidine in the presence of Arg1 which is synthesized through IL-4 mediated PPAR-γ activation. Spermidine is one of the precursors for trypanothione synthesis. Since NFAT5 is dephosphorylated, it may not bind to IL10 promotor, leading to activation of IL-10 synthesis.Flux analysis defines the flow of molecules at a specific time , which pThrough PCA we identified that both the dataset may be essential. From variable plot, the inverse correlation between M1 and M2 models signifies essentiality of components from both the datasets as cos2 value of both the datasets is same. We distributed the PCs based on significance of their concentration and identified 12 components for further studies. SHP-1 was the principal component from M2 dataset as it is directly activated by GP63 to inhibit NFAT5 through phosphorylating it at TYR143 residue thereby inhibiting its localization to nucleus affecting production of NO, IFNγ, and IL-12 in M1 model . GSK-3b The purpose of the network analysis was to look into the important components in terms of connectivity and its functioning present in both the network. For the M1 network, the top 10 components were oxaloacetate, L-arginine, aspartate, NFAT5, citrulline, citrate synthase, arginosuccinate, CoA, acetyl CoA, and citric acid. Oxaloacetate had highest frequency of occurrence score, which is one of the initial components for the impaired TCA cycle that later leads to the production of ROS. Other than oxaloacetate, acetyl CoA, citrate synthase, CoA, and citric acid were also part of the same impaired TCA cycle, which is important for ROS production . FurtherFor the M2 network, the top 10 components were trypanothione, trypanothione reductase, spermidine, spermine, putrescine, trypanothione synthase, HMG-CoA, NADPH, T[SH]2, and SHP-1. Trypanothione had highest frequency of occurance score that crucially helps in parasite survival by neutralizing the ROS. Trypanothione synthase, NADPH, T[SH]2, trypanothione reductase are all responsible for continuous production of trypanothione. Spermidine and spermine are polyamines required for parasite proliferation and spermidine also acts as one of the initial components for trypanothione synthesis. Furthermore, HMG-CoA is intermediate for ergosterol production that strengthens the parasite cell membrane. SHP-1 is the only host component that was enriched in Cytohubba analysis. SHP-1 functions are associated with inhibition of pro-inflammatory cytokine synthesis through transcription factor inhibition that enhances IL-10 production, which helps the parasite in its survival. Hence, from our model we identified that SHP-1 has role in promoting and inhibiting different processes, which promote parasite survival.Through our model and network analysis, we could infer that SHP-1, (a host factor) may promote parasite survival. This also suggests that parasite hijacks host machinery to regulate host associated transcription factors through chromatin remodeling. Thus, SHP-1 can be taken as a potential candidate for further studies to investigate parasite survival mechanism.As a conclusion, we assert that computational modeling and network construction of immuno-metabolic response against leishmaniasis helps us to understand the signaling mechanism responsible for parasite survival. Thus, system’s biology approach gave us the necessary lead in a specific reconstructed biological network. By regulating SHP-1 through synthetic biology approach we might be able to find a better therapeutic for treating leishmaniasis by gaining better control over immune-metabolic signaling of host.Our study has laid an insight into inflammation and macrophage associated infectious disease that is leishmaniasis. Such approach can be useful to study diseases which show dynamics in parallel with infection and inflammation, where cytokine expression and host pathogen metabolism might regulate the disease.vbad125_Supplementary_DataClick here for additional data file.


Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell’s entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation . Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms  Genome-scale metabolic networks (GSMs) are a representation of a cell’s stoichiometrically balanced reactions. Here the authors report Decrem, a GSM reconstruction method, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Such a resilience is achieved through complex coordination on a systems scale, mainly determined by local and global regulation5. First, the microbial flux state appears to evolve toward optimality under one growth condition and a minimal adjustment between  conditions6; consequently, local metabolite levels are stable in the face of environmental or genetic perturbations11 due to quick compensation from local reactions. This suggests that local rerouting of fluxes in GSMs operates efficiently, which plays a crucial role in metabolite homeostasis in maintaining global optima under one condition or across conditions6. Such quick compensations come as a result of the evolution of metabolic networks, in which reactions and metabolites preferably attach to high-efficiency biochemical reaction chains in an organism, e.g., pathways or biological processes, according to the principles of network growth and preferential attachment14. This mode of network organization leads to a topologically local coupling for metabolic flux rerouting. For example, in the TCA (tricarboxylic acid) cycle, the reaction chain D-isocitrate --> a-ketoglutarate -->… --> malate (Km = 0.029 μM and Kcat = 106.4 for IDH3 (Isocitrate dehydrogenase)) is preferred as the primary branch for oxidation of acetyl-CoA over the low-efficiency reaction chain D-isocitrate --> succinate (Km = 8 μM and Kcat = 28.5 for AceA (Isocitrate lyase)). Recent discoveries in both network properties and enzyme parameters, e.g., Km and Kcat, support such network organization16. Some reactions are more closely interconnected with high kinetic capabilities than others and form “small world” structures in GSMs19, particularly in central metabolism20. Therefore, those topologically coupled reactions constitute the biochemical properties-derived local metabolic auto-regulation and flux coordination in GSMs. The local metabolic coordination-guided regulation is manifested in three aspects. First, the changes due to local internal perturbations (gene deletions) are quickly compensated by neighboring reactions5. Second, functionally related genes are organized as operons coregulated in microorganisms25. Third, failure to recognize the coupled reactions leads to poor performance in perturbation effect estimation by steady-state linear flux optimization, e.g., flux balance analysis (FBA).Cellular life maintains itself and replicates through the entire set of biochemical reactions in genome-scale metabolic networks (GSMs) operating in a well-coordinated manner30, which accounts for the majority (about 70%) of the changes in gene expression between growth conditions29. The gene expression profiles are primarily regulated by the global growth state via the sequestration or release of transcription factors (TFs) with the variation in the concentration of growth indicator metabolites29, as shown in the activities of over 200 TFs showing strong correlations with few cognate metabolites following the transition from starvation to growth in E. coli9. For example, cyclic AMP, fructose-1,6-bisphosphate, and fructose-1-phosphate bind to TFs  and thereby mediate most of the specific transcriptional regulation. However, there is a low correlation between biochemical reaction rate and its enzyme expression level. For example, in central metabolism, only a few enzymes change proportionally to flux changes in the reactions in the tricarboxylic acid cycle (TCA Cycle)4. Consequently, the triangular regulatory relationship of metabolites->TFs->transcription cannot sufficiently reflect the reaction flux variation33, and this insufficiency has hampered the integration of transcriptional regulation with the current stoichiometric matrix-based GSM models31. Few studies attempting to integrate the kinetic parameters are Michaelis-Menten equation-based genome-scale multi-omics data fitting35, but do not consider the global transcriptional regulation of metabolism. In practice, it is challenging and expensive to obtain complete enzyme kinetic parameters  from paired metabolomics and proteomics data to build a reaction kinetics-constrained metabolic flux prediction model in GSMs.Besides local fast-acting flux adjustment, global metabolic homeostasis is achieved through transcriptional regulationE. coli, S. cerevisiae, and B. subtilis. The flux distributions predicted using Decrem are highly consistent with experimentally measured fluxes in multiple strains (wildtype and mutants). Then, the growth state-regulated fundamental enzyme kinetics are identified to create kinetic Decrem to model the global dynamic transcriptional regulation of metabolic networks in response to environmental perturbations. Unlike the previous kinetic models, which often focus each essential metabolite/flux on its corresponding enzyme kinetics, we focus our attention on the enzymes directly regulated by growth state (biomass composition) because they represent the more significant transcriptional regulation than the other reactions. A specific advantage of the kinetic Decrem model is that only several growth state-related metabolites suffice to achieve transcriptional regulation and thus reduce the requirements for necessary kinetic parameters and paired multi-omics data. The accurate growth rates predicted by the kinetic Decrem model in E. coli genome-scale knockout strains revealed that intracellular perturbations are mainly ‘buffered’ by highly coupled reactions, which reveals the coordination between crucial precursors of central metabolism and cell growth. Overall, we recognize metabolic regulation as the local topological coordination and global growth-related key transcriptional regulation, which demonstrates that Decrem can integrate metabolic regulation into current GSM models.In this work, we present a GSM model, Decrem, to quantitatively characterize the local topological cooperation regulation and the global transcriptional regulation. For this purpose, we integrate local flux coordination and transcriptional regulation of global growth state-mediated key metabolic reactions into Decrem to approximate flux distribution Fig. . We firs13C isotope fluxes in E. coli central metabolism  and TCA (tricarboxylic acid) cycle, respectively, as opposed to 0.505, 0.267, and 0.421 for each uncoupled reaction set  of identified coupled reactions are smaller (by 56.5%) than those (0.036 mM) in the uncoupled reaction chains  of gene levels among the element reactions from the same SLBs are higher than those (mean r = 0.28) from different SLBs in central metabolism  with reallocated stoichiometric coefficients (see Methods). To explore the variable range of fluxes in Decrem against the original GSMs, flux variability analysis (FVA) is conducted in 42 in model organisms of E. coli (iAF1260 and iML1515), S. cerevisiae (iMM904), and B. subtilis (iYO844). These four models contain 766, 1104, 558, and 332 LBRs in the corresponding decoupled Decrem models, respectively (Supplementary Data We first apply Decrem for metabolic flux prediction with an FBA strategy (see Methods), in comparison with three other methods which are carried on the canonical GSMs: FBA, pFBA (parsimonious FBA), and RELATCHE. coli iAF1260 model under MOPS medium supplemented with glucose or xylose and under aerobic or anaerobic respiration43  fluxes  and iMM904 (1226 metabolites and 1577 reactions). Thirty-eight mutants with experimental 13C-MFA fluxes, growth rates, nutrient uptake properties, and several extracellular exchange fluxes46 are used  determined by Decrem for iDN750 are 0.72 and 0.763 in the 38 mutant strains, compared to 0.615 and 0.733, 0.607 and 0.681, 0.556 and 0.707, 0.462 and 0.595 for pFBA, FBA, RELATCH, and REPPS, respectively . For the complete Yeast iMM904, the mean Spearman and Pearson’s correlation coefficients are 0.782 and 0.936, 0.765 and 0.747, 0.656 and 0.915, 0.75 and 0.925, 0.645 and 0.841 for Decrem, pFBA, FBA, RELATCH, and REPPS, respectively, which are significantly different . The differential accuracy of flux predictions between the iDN750 and iMM904 models shows that Decrem performed better than other methods, particularly on metabolic models that are incomplete   are highly coupled reactions, compared to an average of 35% (558 of 1577) for iDN750  p = 5.427e−04); this increased reaction coupling may optimize the solution space of the metabolic model and change the optimal reaction paths in iMM904. In addition, we explore the prediction difference of mutant strains across specific pathways by MSE for all the methods used and find that Decrem presents a significantly smaller MSE than other methods  in two GSMs of 04) Fig. . FurtherS. cerevisiae using the iMM904 model. PCA of the predicted fluxes among the mutant strains from all six methods shows that the top two principal components (PCs) can explain more than 99% of the variance of all predicted fluxes. However, the two top PCs predicted by Decrem are each highly correlated (r > 0.7) with experimentally measured growth rates. At the same time, the other methods have, at most, one PC that merely shows moderate correlation (r ~ 0.5) : 0.9 (Decrem) and 0.9 (pDecrem) vs. 0.74, 0.731, 0.731, 0.841, 0.9, and 0.9 for the other methods , and NAD+-related mutants in TCA: 43  to estimate the growth rate in the 38 mutant strains of on r ~ 0. 43 Fig. . Interesxes Fig. , precisesed Fig. 46. The o35, which presents a major obstacle to the multi-omics integration of metabolism. We present here that the biomass/growth rate, rather than regulator metabolites, plays a dominant role in the activity for most of gene expression in central metabolism. This insight provides a practical strategy to quantify the cooperation relationship between the biomass/growth state-regulated metabolic genes and their kinetic flux.Cellular metabolism is rather dynamic but transcriptional regulation is insufficient to explain flux change50 and metabolic gene expression in central metabolism on a multi-omics dataset of 24 single-gene knockout strains of E. coli49. They include the expression of 85 metabolic genes in central metabolism, the concentrations of over 100 metabolites and 51 13C-MFA fluxes for each strain49. In total, 45 selected regulator metabolites (and biomass constituents) are classified into two groups according to their concentrations: the biomass-constituent group (BG) and the precursor or regulator metabolite group (PG)  Fig. . Most BGPG) Fig. . This dir > 0.84 and the correlation of first PC > 0.38 according to PLSR; Supplementary Note 7b,p-value of 3.1E−3 for the 23 identified growth-associated metabolites is observed against the randomly selected metabolites  and validate this mechanism by conducting a partial least squares regression (PLSR) to quantitatively fit the linear global regulation of the observed expression profiles of the 85 genes to the concentrations of the potential regulatory metabolites of either group (see Methods). By taking stringent combined thresholds , which agrees with the target pathway (reactions) of global cell state regulator: cAMP-Crp33. These results suggest that the expression of the genes in growth-associated pathways could be represented as a linear combination of the concentrations of biomass composition.The identified 32 genes are primarily located in the PPP and pyruvate metabolism in KEGG pathways Fig. , which a33 which directly coordinate amino acid and carbohydrate uptake31. Five reactions, Hexokinase, Acetate kinase, Pyruvate dehydrogenase, Fumarate reductase, and Alcohol dehydrogenase, are identified as global PTS regulated. We then validate whether the five reactions are correlated with each specific growth rate using 13C-fluxes of a multi-strain dataset  based only on the concentration of corresponding biomass composition (BG) and metabolites  for the reactions of glucose transport, pyruvate kinase, and acetate kinase, respectively   for each of 1030 mutants to approximate the mutant gene-specific metabolic state, e.g., the branch points of glycolysis and PPP, the flux allocation downstream of pyruvate metabolism, and the growth-associated secretion   flux distributions. For that, we calculate the distribution of pathway-specific fluxes across mutant strains, defined as the accumulated flux (AF) t-test, p = 3.04E−20); 1.56 vs. 1.07 for PC2 . This is consistent with the high robustness of the central metabolism56 (primarily consisting of LBRs). Furthermore, we suspect that the growth effects of deleted genes encoding enzymes for the reactions within the SLBs would be more pronounced than the effect of genes encoding enzymes for uncoupled reactions. Indeed, this is confirmed by the analysis of the reaction type-based growth rates—the complex LBRs (the number of element reactions of associated linear basis vector > 1), the simple LBR (the number of element reactions of associated linear basis vector = 1) and the uncoupled reactions    Fig. . Finallyly) Fig. . TherefoE. coli. Therefore, it is an effective model for accurately depicting metabolic responses and exploring the self-adapting regulation mechanism of cellular perturbation.We reconstructed a GSM model, Decrem, by identifying and incorporating local topologically decoupled reactions using SLB decomposition and by incorporating metabolic global regulation by metabolites into GSMs, which approximates the kinetic fluxes of cell state-regulated key reactions to constrain the feasible region of optimal flux distribution. Decrem effectively reduces the requirements for multi-omics data for genome-scale metabolic kinetic models. Compared to existing methods, Decrem demonstrates superior performance in predicting metabolic fluxes in three model organisms and growth rates in genome-scale knockout strains of 57. A topological orthogonality principle has been successfully used to design bioengineering strains with minimal interaction between desired product-associated pathways and metabolic components related to biomass synthesis58. In addition, several specific topological constraint treatments, such as removing the thermodynamically infeasible loops and decoupling two desired phenotypes, have been applied to GSMs to improve their metabolic production in recent studies60. But Decrem is the first genome-scale topologically decoupled metabolic model for general applications, which clearly demonstrates how the topological preference of a metabolic network can guide the metabolic flux distribution.By applying SLB decomposition, the (coupled) element reactions within identified SLBs display high coexpression among multiple growth conditions, indicating coordinated activation of topologically highly coupled reactions. Interestingly, similar approaches have been applied in identifying the non-redundant local functional units of metabolism, i.e., the minimal metabolic pathway or flux tope62. However, the construction of the GSM kinetic model is obstructed by the limited knowledge about kinetic parameters, e.g., Km, Kcat, and the scarcity of metabolic regulators and paired genome-scale multi-omics data63. To date, the largest metabolic kinetic model, k-ecoli457 of E. coli, contains only 457 reactions, 337 metabolites, and 295 substrate-level regulatory interactions according to the computationally predicted kinetic parameters34. Alternatively, by integrating the metabolite-TF regulatory regime, Decrem integrated with global regulation kinetics can predict growth rates and the corresponding fluxes. An advantage of our kinetic Decrem is that it only needs experimental concentrations of identified essential metabolites, which serve as the key indicators of metabolic states and directly regulate enzyme activities or gene expression. Although some metabolites  have long been known to regulate enzyme activities as detailed in biochemistry textbooks, not many are known on the whole genome scale, even in model organisms. On the one hand, the regulatory metabolites of specific pathways in central metabolism prefer to frequently interact with the catalytic enzymes by activating or inactivating the functional domains to synchronously adjust the fluxes64. On the other hand, several studies have revealed that ~70% of the total variance in the promoter activity of central metabolic genes of E. coli can be explained by growth rate-derived global transcriptional regulation across multiple mutant strains65. These findings suggest a potential relationship between the concentration of biomass-constituent metabolites and the expression of metabolic genes in the regulons of TFs. This relationship is verified by a recent study in which the identified metabolite concentrations are predicted by quantitative proteomics data45. Overall, both the topology of metabolic networks and regulatory metabolites are utilized to identify coactivated or key regulated reactions, which produces a minimal set of regulatory constraints to develop the genome-scale kinetic models.To explore effective metabolic dynamic robustness or adaption to internal and external perturbations, metabolic kinetic models have attracted great attention by combining fluxomics, metabolomics, and transcriptomics into a unified frameworkYeast metabolic models. A possible explanation for this observation is that the optimal flux distribution of a metabolic network is strongly determined by its topology, and the rewired or perturbed network structure will have different feasible regions. Moreover, Decrem could be applied to elucidate important regulatory branch points and the self-adaption of regulation mechanisms for the knockout strains, which is helpful for accurately predicting the potential target genes/reactions for designing bioengineering strains.Compared to other methods tested on three model microorganisms, Decrem is not only excellently performing in recovering the real flux distributions with high accuracy in a wide range of strains but also presents excellent predictions and explanations of the observed growth rate. This demonstrates the strong capability of Decrem to approximate the real intracellular state and to be used for designing high-yield mutant strains in bioengineering and synthetic biology. Decrem shows that there is a strong influence of metabolic network topology in the prediction of flux distributions; this phenomenon is found both in the reconstructed SBRs of Decrem and in the two versions of 13C-MFA flux paired with metabolite concentration data to construct reasonable kinetic models. However, the well-constructed kinetic models are convenient for transfer to any other applications. Overall, Decrem, a local topology and global regulatory network-reinforced metabolic analysis model, can accurately predict phenotypes and uncover the complex regulation of cell metabolism.The main limitation of Decrem is that it requires a medium-scale set matched The original metabolic network consists of coupled coactivated reaction cycles  and simple linear chain reactions . We developed a GSM model, Decrem, to capture the contribution of the coactivated and coupled reactions while preserving the consistency of the linear components. The detailed framework for our model is described below  consisting of highly coupled reaction cycles. Here, the substructures are denoted as Step 1: Identifying reaction cycle-based coupled substructures. We first defined the bipartite graph representation of the metabolic network as WCut) associated with Ck.The generalized weighted cut  can be solved with linear regression. Subsequently, the optimal model is expanded to any other application.step 3 of the section “Topology-decoupled reconstruction of the metabolic model”. Among them, i ranges from 1 to j ranges from 1 to k ranges from 1 to Step 3: The linearized kinetic optimization of Decrem. Finally, the parameterized kinetic model is utilized to describe the growth-associated key-regulated reactions in central metabolism. The genome-scale flux distribution is predicted by the kinetic regulated flux-constrained Decrem method, i.e.,E. coli strain BW25113 was grown in MOPS minimal medium  with glucose at 2 g/L with shaking at 120 rpm at 37 °C. Aliquots of cells were collected at four growth states (timepoints): the beginning of the lag phase, the transition from lag to log phase, the mid-log phase, and the early stationary phase. Aliquots of cells were collected at each timepoint/growth state for RNA-seq and metabolomic profiling. Three replicates per growth condition and time point. No statistic methods used to predetermine sample size, no sample size calculation was performed, the sample was choosing by the growth state of E. coli.Strain and culturing. 70 with a quality score of 26. The read counts for each gene were analyzed using RSEM71. These raw data are deposited onto the NCBI Short Read Archive (SRA) database with Project accession PRJNA910919.For RNA-seq, total RNA was extracted using the Qiagen RNeasy Mini kit  following the manufacturer’s instructions and sequenced on the Illumina Hi-seq 2500 platform. Raw reads were quality controlled using FASTQC and trimmed using Trimmomatic 0.3972. Specifically, the extraction solvent was eisopropanol/acetonitrile/water at the volume ratio 3:3:2 and cooled to −20 °C prior to extraction. 1 ml of cold solvent per 20 mg of cells was added, vortexed for 10 s, and shaken at 4 °C for 5 min to extract metabolites and simultaneously precipitate proteins. Extracts were centrifuged for 20 min at −4 °C at 17,000 × g to remove the cell debris. Centrifuged extracts were analyzed by LC-MS/MS, with an Agilent 6495 triple quadrupole mass spectrometer (Agilent Technologies). Data were acquired using the following chromatographic parameters. Column: Restek corporation Rtx-5Sil MS . Mobile phase: Helium; Column temperature: 50–330 °C. Flow-rate: 1 mL min-1; Injection volume: 0.5 μL. Injection: 25 splitless times into a multi-baffled glass liner; Injection temperature: 50 °C ramped to 250 °C by 12 °C s−1; Oven temperature program: 50 °C for 1 min, then ramped at 20 °C min-1 to 330 °C, held constant for 5 min.Frozen cells were broken on dry ice with the bead beater and kept in liquid nitrogen between homogenization and extractionp < 0.05, data will be normalized to the average mTIC of each group. If averages between treatment groups or cohorts are not different or treatment relations to groups are kept blinded, data will be normalized to the total average mTIC. Both the processed and raw data files are uploaded to the database Metabolomics Workbench with StudyID ST002419.Raw data files are preprocessed directly after data acquisition and stored as ChromaTOF-specific.peg files, as generic.txt result files and additionally as generic ANDI MS.cdf files. ChromaTOF vs. 2.32 is used for data preprocessing without smoothing, 3 s peak width, baseline subtraction just above the noise level, and automatic mass spectral deconvolution and peak detection at signal/noise levels of 5:1 throughout the chromatogram. Apex masses are reported for use in the BinBase algorithm. Result.txt files are exported to a data server with absolute spectra intensities and further processed by a filtering algorithm implemented in the metabolomics BinBase database. Raw results data need to be normalized to reduce the impact of between-series drifts of instrument sensitivity, caused by machine maintenance, aging and tuning parameters. There are many different types of normalizations in the scientific literature. We did a variant of a ‘vector normalization’ in which we calculated the sum of all peak heights for each sample’s identified metabolites (but not the unknowns!). We call such peak-sums “mTIC” in analogy to the term TIC used in mass spectrometry , but with the notification “mTIC” to indicate that we only use genuine metabolites (identified compounds) in order to avoid using potential non-biological artifacts for the biological normalizations, such as column bleed, plasticizers or other contaminants. Subsequently, we determined if the mTIC averages are significantly different between treatment groups or cohorts. If these averages are different by https://academic.oup.com/bioinformatics/article/33/6/893/2725488?searchresult=1#supplementary-data, and the RELATCH can be found in https://genomebiology.biomedcentral.com/articles/10.1186/gb-2012-13-9-r78#MOESM12 ).We compared the performance of our Decrem with other five methods that are utilized to flux prediction and analysis: FBA, pFBA, FVA, REPPS and RELATCH, The cobra 2.0.5 package is utilized to implement the FBA, pFBA and FVA analysis, REPPS package is download on the address:t-test or one-way ANOVA for parametric data and Wilcoxon rank-sum test or one-sided t-test for non-parametric data. The statistical analyses were performed using MATLAB R2020a. All the experiments can be reproduced by using the data and code that we uploaded to the public repository.In the experiments, we used the complete samples from the datasets, without using any statistical methods to select or remove samples. Statistical significance was evaluated using Student’s Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesPeer Review FileDataset 1Dataset 2Dataset 3Dataset 4Dataset 5Dataset 6Dataset 7Dataset 8Source Data


Lemna gibba cultures to different nutritional conditions.Duckweeds are small, rapidly growing aquatic flowering plants. Due to their ability for biomass production at high rates they represent promising candidates for biofuel feedstocks. Duckweeds are also excellent model organisms because they can be maintained in well-defined liquid media, usually reproduce asexually, and because genomic resources are becoming increasingly available. To demonstrate the utility of duckweed for integrated metabolic studies, we examined the metabolic adaptation of growing Lemna gibba based on its draft genome. Lemna gibba fronds were grown with nitrate or glutamine as nitrogen source. The two conditions were compared by quantification of growth kinetics, metabolite levels, transcript abundance, as well as by 13C-metabolic flux analysis. While growing with glutamine, the fronds grew 1.4 times faster and accumulated more protein and less cell wall components compared to plants grown on nitrate. Characterization of photomixotrophic growth by 13C-metabolic flux analysis showed that, under both metabolic growth conditions, the Calvin-Benson-Bassham cycle and the oxidative pentose-phosphate pathway are highly active, creating a futile cycle with net ATP consumption. Depending on the nitrogen source, substantial reorganization of fluxes around the tricarboxylic acid cycle took place, leading to differential formation of the biosynthetic precursors of the Asp and Gln families of proteinogenic amino acids. Despite the substantial reorganization of fluxes around the tricarboxylic acid cycle, flux changes could largely not be associated with changes in transcripts.To establish a framework for quantitative metabolic research in duckweeds we derived a central carbon metabolism network model of Lemna gibba is an excellent system for quantitative metabolic studies in plants. Our study showed that Lemna gibba adjusts to different nitrogen sources by reorganizing central metabolism. The observed disconnect between gene expression regulation and metabolism underscores the importance of metabolic flux analysis as a tool in such studies.Through integrated analysis of growth rate, biomass composition, metabolite levels, and metabolic flux, we show that The online version contains supplementary material available at 10.1186/s12870-023-04480-9. Lemnaceae, commonly known as duckweeds, are small, fast growing aquatic flowering plants. Prior to the emergence of Arabidopsis as a model organism, duckweeds were an important model system for plant biology -glucose and [U-13C6]-glucose in a molar ratio 80:10:10 . Mass isotopomer composition was determined by gas chromatography/mass spectrometry (GC/MS) in 16 analytes mostly derived from hydrolysis of biomass polymers  cycle seems to be very active as well. Based on the measured 13C enrichments in biomass  a carbon flux metabolic network of L. gibba central metabolism was defined based on genomic information and based on a generic plant carbon flux network . An initial draft model of central carbon metabolism was constructed based on 21830 proteins predicted from a draft genome of L. gibba 7742a as part of the Lemna Genome Sequencing Project -glucose and [U-13C6]-glucose  in a molar ratio 80:10:10. In another labeling experiment where 13C enrichment was determined in total biomass carbon, 40% of medium glucose was [U-13C6]-glucose (at 99%).Axenic cultures of  DWC130)  and were DWC130)  with 5 ghttps://office.microsoft.com/excel). The growth rate was calculated by the following equation:t is culture time (h), A0 and At are the frond surface area at culture time 0 and t, respectively. The doubling time of duckweeds growth is Growth of cultures was quantified based on increase in frond surface area, using Scanalyzer BS . The images were analyzed using LemnaLauncher image analysis software  to obtain the total fronds surface area. Curve fits shown in Fig. 13C6 glucose (30mM), U13C6 sorbitol (3mM) and D5-Gln (4.5mM). Then 2 mL CHCl3 was added, and the two phases were kept at 4°C for 20 min. After centrifuge at 3750 rpm for 20 min the upper phase was analyzed by direct infusion into a 4500 QTRAP mass spectrometry system  in MRM mode. The mass spectrometry settings are listed in Additional File To measure the uptake rates for medium glucose and Gln, growth experiments were repeated with unlabeled glucose. The initial and final medium glucose and Gln concentrations were measured to calculate glucose and Gln depletion and uptake rate. Glucose and Gln concentration were measured by mass spectrometry with internal isotope standards. To correct the water evaporation during the culture, a known amount of sorbitol was added to the medium after the culture. Briefly, after culture, 1 mL of sorbitol (0.7M) was added to a culture flask with originally 100 mL medium and mixed well. 100 µL medium and 1 mL water were then added to 100 µL of an aqueous solution of US0 and S being the µmol amount of substrate in the culture vessel at the beginning and end of culture, respectively, and D0 and D the dw of fronds at the beginning and end of culture, respectively.For exponentially growing cultures a substrate uptake flux can be calculated by multiplying the reciprocal of a yield coefficient with the specific growth rate. Accordingly, we define specific substrate uptake rates by:2 is accessible to growing fronds as another carbon source. The CO2 uptake rate was determined based on the measured glucose and Gln uptake rates (above) and the fractional enrichment of 13C in biomass. For this purpose, fronds were grown with 40% of medium glucose replaced by [U-13C6]-glucose enriched at 99% 13C (average 13C-enrichment 40.3%). Dry biomass of harvested fronds was analyzed by elemental analysis – isotope-ratio mass spectrometry, resulting in measurements for 13C enrichment . The analysis was performed at the University of California Davis Stable Isotope Facility (UCD SIF) using a PDZ Europa ANCA-GSL elemental analyzer interfaced to a PDZ Europa 20–20 isotope ratio mass spectrometer. Calculation of the CO2 uptake (vCO2_up) was done by the following relation. f13C,BM depends on all carbon-molar uptake fluxes and the 13C-enrichment in each of the carbon sources (40.3% in case of glucose):vGLC_up and vGln_up are the uptake rates for glucose and Gln, respectively. Knowing f13C,BM, vGLC_up and vGln_up, vCO2_up can be calculated after rearranging Eq. Besides the medium supplied organic substrates glucose and Gln, atmospheric COvCO2_up is the uptake rate of unlabeled atmospheric CO2, which is not identical to the net CO2 balance since cellular CO2 can also be released. To estimate the statistical uncertainty in vCO2_up, Eq. f13C,BM, vGLC_up and vGln_up according to their mean and standard deviations [Note that viations .3 to result in a biphasic solvent system  [3 phase), free metabolites (methanol/water phase) and cell pellets  were obtained by weighing dry weight of each phase. The total nitrogen amount in cell pellets was measured by CHN elemental analysis . The nitrogen was then converted to protein weight based on the amino acid composition of duckweed protein [Cultures were kept in tissue culture flasks with vented seal caps. Cultures grew just until the fronds covered the entire culture flask surface area to ensure they were under exponential growth stage. Fronds were harvested by filtration, extracted and dry weight fractions of cell pellet, lipids and free metabolites content were determined as described before in detail . Briefly, v/v/v) . The bio protein .Starch content was measured by modified protocol . Briefly−1 and from 170°C to 210°C at 5°C⋅min−1. To quantify the concentration of fatty acids, 100 µg heptadecanoic acid (17:0) was added prior to transmethylation to each sample as an internal standard. Quantification was done by peak area comparison with the internal standard using MSD Chemstation Data Analysis software to integrate GC–MS peak areas .The lipid fraction was derivatized by boron trichloride methanol to fatty acid methyl esters (FAMEs) . Fatty a3/methanol/H2O fractionation of frond biomass was analyzed by Hydrophilic interaction chromatography (HILIC), using an aminopropyl column  according to a protocol by Bajad et al. (2006) [The free metabolites fraction from the biphasic CHCl. (2006) . A liquiFluxes from central metabolism into cellular biomass components (biomass fluxes) were calculated based on the biomass proportions of lipid, protein, starch, the cell wall fraction and 30 most abundant free metabolites. The weight of the polymers was divided according to their respective monomer composition. For this, the protein composition was estimated by averaging 10 literature reports on the amino acid composition in duckweed . The wei13C labeling pattern of various metabolites was measured by GC–MS  with select ion monitor (SIM) mode as previously reported [−1 with an HP-5MS column . Amino acids were derivatized by N-methyl-N-(tert-butyldimethylsilyl) trifluoroacetamide (MTBSTFA) [−1; to 300°C at 10°C⋅min−1; final time, 5 min. Glucose from digested starch was converted to methyl ester alditol acetates (MAAs) as previously reported [−1; final time, 2 min. In order to analyze sucrose the free metabolites fraction was derivatized by methoxyamine-HCl and followed by N-Methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) [−1; final time, 10 min. Fatty acid methyl esters (FAMEs) were further reduced to saturated fatty acid methyl esters by PtO2 under H2 pressure and only the carboxyl group carbon (e.g. C1 and C2) labeling information were measured. To suppress the contaminant fragments around base peak of fatty acid methyl esters (m/z 74), the ionization energy for quadrupole mass analyzer was reduced from the standard 70eV to 15eV [−1; final time, 15 min. Glycerol was converted to trifluoroacetic acid ester by trifluoroacetic acid anhydride (TFAA) as previous report [−1; to 250°C at 20°C⋅min−1; final time 1 min. Altogether, 29 selected fragments in 17 analytes were monitored using the selected ion monitoring mode of the mass spectrometer. Correction for naturally occurring isotopes  in the derivative side chains was applied as reported before [The reported . All anareported . The inj (MSTFA) . Injectos report , the injd before .Lemna gibba draft genome v0.5.1 [Fronds grown on inorganic nitrogen or on organic nitrogen medium were harvested and immediately frozen in liquid nitrogen. For each condition three biological replicates were analyzed. Total RNA was extracted by grinding 200 mg frozen tissue to a fine powder in liquid nitrogen with a mortar and pestle, followed by extraction with TRIzol Reagent  and ethanol precipitation. Total RNA was resuspended and treated with RQ1 RNase-free DNase  on an RNA Clean & Concentrator-5 column . The purity and concentration of RNA were determined by a NanoDrop Nd-1000 spectrophotometer  and a Qubit fluorometer . Polyadenylated RNA was enriched from total RNA with the Dynabeads mRNA Purification Kit . Indexed, directional RNA-seq libraries were prepared with ScriptSeq v2 reagents  from 40–50 ng of polyA-selected RNA according to the manufacturer’s instructions. Libraries were sequenced on an Illumina HiSeq 2000 instrument  generating 101 bp paired-end reads. Reads from each sample were mapped against the 21,830 predicted transcripts of the e v0.5.1  using Boe v0.5.1 .p-value ≤ 5% (Benjamini–Hochberg adjustment for multiple testing) and a more than four-fold change in gene expression between conditions.Differential expression analysis for the two conditions  was performed using the DESeq package , 83. ThiLemna gibba genes were identified by BLAST search against the predicted transcripts. The primers for real-time PCR were designed and synthesized by Integrated DNA Technologies . The sequences of primers for reference and target gene are listed in  as described in the manufacturer's user manual. First-strand cDNA was synthesized by using cDNA synthesis kits from Fermentas, . Two reference housekeeping genes from Arabidopsis  were chosen from literature , 84 and ftp://ftp.uniprot.org) [–20 were kept, which associates 12,673 of the 20801 L. gibba protein sequences with 8,597 unique Uniprot sequence identifiers .Analysis of Gene Ontology (GO) terms which are enrichment in differentially expressed genes was performed with the online bioinformatics resources given by the Database for Annotation, Visualization and Integrated Discovery (DAVID) (v6.8) , 85. To rot.org)  using Prrot.org) . Top hitLemna gibba v0.5.1 draft genome [L. gibba gene loci were given as input, returning a total of 6875 proteins with a predicted enzyme function. 4436 of these are enzyme functions acting on small molecules. A network of central carbon metabolism in Lemna gibba to be used for 13C-metabolic flux analysis was then defined by mapping the E2P2-predicted reactions onto a carbon flux network originally developed for Brassica napus [13C-MFA network for B. napus have been discussed elsewhere [B. napus model can be understood here as a generic scaffold for the central metabolism of higher plants. A manual curation process was applied to improve the reaction-gene associations. If multiple isoforms of an enzyme were identified by E2P2, the likely subcellular localization could often be determined based on finding the closest Arabidopsis homologs in the reference model by bidirectional BLAST searches (L. gibba protein sequences against Arabidopsis TAIR10 predicted proteins and vice versa). In many cases the subcellular localization of L. gibba protein sequences was predicted using the online tool TargetP [L. gibba and Spirodela polyrhiza we could identify gene homologs for all four subclasses, except genes encoding the xylulose 5-phosphate translocator (XPT). Based on phylogenetic analysis of the phosphate translocator family across about 100 sequenced plant genomes it was found that a loss of the XPT occurred within subphyla of the monocots [L. gibba genome, our analysis of the genetic data left open whether enzyme isoforms exist for both the cytosol and the plastid. Despite this, the model was designed with complete cycles in both compartments in order to allow the OPPP to work in both compartments in absence of XPT. In light of these uncertainties in network definition, the interpretation of fluxes through OPPP and glycolysis at subcellular resolution must be done with caution. Nevertheless, we kept the compartmentalized scaffold. An aggregated uncompartmentalized view can always be generated and inspected. Any flux solution with statistical measures of uncertainty attached can easily be projected onto a non-compartmentalized or otherwise lumped network version, based on linear combinations of fluxes . Flux ratios can be generated in the same way. When using a Monte Carlo approach to generate the statistics (repeated iterative flux parameter fitting with noise-added measurement data), the aggregation of the best fitting flux solution and the flux solution vectors representing the statistical uncertainty is done in the same way. This means that besides the summation of corresponding flux values, no special statistical procedure needs to be developed to obtain flux statistics for the aggregated network.A metabolic network was derived from a t genome , 14 by ut genome , an enzyt genome . 21830 pca napus . Plant clsewhere . Most of TargetP . In addi TargetP  and phyl TargetP . Searchimonocots . It thermonocots , 95. If Spirodela polyrhiza that under photomixotrophy, when organic carbon substrates are available and may be catabolized, RuBisCO oxygenation and operation of the photorespiratory pathway are most likely largely suppressed due to elevated intracellular CO2 levels [Regarding the activities of ribulose-1,5-bisphosphate carboxylase/oxygenase , the ratio of carboxylation to oxygenation is largely dependent on the concentrations of both substrates present at the site of RuBisCO activity. Under normal atmospheric conditions, about 25% of the reaction catalyzed by RuBisCO is oxygenation . It has 13C-MFA, i.e., isotope tracer-based metabolic flux analysis under metabolic and isotopic steady state [13C-Metabolic Flux Analysis [13C-Metabolic flux analysis was mostly performed with the software tool 13CFLUX2 [2 value was calculated based on the general model information. Degree of freedom is the number of mass isotopomer measurements minus the number of measurement groups, plus the number of measured flux rates minus the number of free net and exchange fluxes in the model . This pool can exchange CO2 with atmospheric CO2 by independent uptake and release reactions while it can also exchange with the common cellular CO2 pool that is used by carboxylase and decarboxylase reactions other than RuBisCO  and the commercial software package ZEN [Living plants of kage ZEN . The help < 0.05.Comparisons between two metabolic growth conditions were performed using one-way ANOVA with 3 or 4 biological replicates per condition . Unless stated otherwise, mean values were deemed to be statistically significantly different for Additional file 1: Table S1. Increase in fronds area (cm2) during culture time (hours). Means and standard deviations represented three independent replicates. Table S2. Metabolite analysis by Liquid Chromatography / Mass Spectrometry. See also Table S13. Table S3. Biomass composition data (weight % in dry weight).  Means and standard deviations represented three independent replicates. Table S4. Measurements of 13C-enrichment in metabolites by GC/MS. Table S5. Measurement of 13C-enrichment in total frond biomass by Isotope-Ratio Mass Spectrometry. L. gibba fronds were grown with medium glucose consisting of 40% U13C6 glucose (99% 13C) and 60% unlabeled glucose . Table S6. Definition of carbon flux model with indication of change in gene expression and change in flux. Table S7. Metabolite names used in the flux model. Table S8. Assessment of step wise model improvement. The four model configurations  correspond panels A, B and C in Fig. S1, respectively. Table S9. Best fit flux results for inorganic nitrogen condition (INS) and organic nitrogen condition (ONS). Table S10. Flux values with statistical confidence measures. Table S11. Differential expression analysis data . Table S12. List of Uniprot accession numbers associated with Lemna gibba protein IDs . Table S13. GO term enrichment analysis for significantly differently expressed genes of Lemna gibba. Table S14. Mass spectrometer parameter settings for analysis of medium substrate levels. Table S15. Mass spectrometer parameter settings for metabolites measurements. Liquid chromatography coupled to mass spectrometer. Table S16. Primer sequences for qRT-PCR.Additional file 2: Fig. S1. Schematics for different model configurations for which the best fit between 13C-MFA simulation and experimental data was determined. Fig. S2. Central metabolism network of growing Lemna gibba fronds. Fig. S3. Central metabolism Flux map of growing Lemna gibba fronds. Fig. S4. Statistical uncertainty in the flux values for the oxidative steps of the Oxidative Pentose Phosphate Pathway (OPPP). Fig. S5. Comparison the relative gene expression level by DEseq and quantitative real-time reverse transcription PCR (qRT-PCR).Additional file 3. Additional file 4.