Komagataeibacter nataicola is a representative species for BC production, its intracellular metabolism leading to BC secretion is unclear. In the present study, a genome-scale metabolic network of cellulose-producing K. nataicola strain RZS01 was reconstructed to understand its metabolic behavior. This model iHZ771 comprised 771 genes, 2035 metabolites, and 2014 reactions. Constraint-based analysis was used to characterize and evaluate the critical intracellular pathways. The analysis revealed that a total of 71 and 30 genes are necessary for cellular growth in a minimal medium and complex medium, respectively. Glycerol was identified as the optimal carbon source for the highest BC production. The minimization of metabolic adjustment algorithm identified 8 genes as potential targets for over-production of BC. Overall, model iHZ771 proved to be a useful platform for understanding the physiology and BC production of K. nataicola.Bacterial cellulose (BC) is widely used in industries owing to its high purity and strength. Although  They have been isolated from a variety of natural sources, including fruits, fermented foods, plant organs, and soil4. Komagataeibacter and Acetobacter are the main genera of AAB, and have been widely used in several industrial processes, such as acetic acid production and cocoa bean fermentation5. Besides, these bacteria have been employed in chemical industries for the production of ascorbic acid and bacterial cellulose (BC)7.Acetic acid bacteria (AAB) are a group of aerobic, gram-negative bacteria belonging to class α-ProteobacteriaKomagataeibacter nataicola (formerly known as Gluconacetobacter xylinus) has been reported as a high-yield cellulose-producing strain8. The strain possesses a number of remarkable physiological properties, such as the ability to oxidize a wide range of substrates and to tolerate high concentrations of acetic acid and ethanol9. The ability to produce BC is one of the most interesting features of this strain5. Although, BC has a primary chemical composition similar to that of the cellulose derived from plants and algae, its excellent structural and mechanical characteristics make it a valuable resource for industrial applications12. Nonetheless, low fermentation yields and high cost of production are the main bottlenecks for large-scale production of BC13. To overcome these disadvantages and to improve BC production, three major strategies have been implemented: (1) identification of efficient and sustainable cellulose-producing strains14; (2) optimization of the fermentation process15; and (3) improvement of productivity of the strains through genetic engineering. Strain mutagenesis is the most common approach to increasing BC production16. Up to a 2.3-fold increase in BC production was attained by knocking out the membrane-bound glucose dehydrogenase (GDH) enzyme responsible for oxidizing glucose to gluconic acid, which is the main by-product of BC fermentation17. Poor understanding of the detailed cellular metabolism involved in the production of BC prevents full exploitation of the industrial potential of K. nataicola. Therefore, the development of BC-synthesizing microbial cell factories would be worthwhile.19. Advances in GSMMs have resulted in the identification of gene-protein-reaction associations, which have found broad applications in a number of processes, such as elucidation of physiological characteristics, organization of principles of metabolism, computational predictions for metabolic engineering, and identification of multi-species relations21. More than 100 GSMMs for organisms across all three domains of life have been reconstructed so far22. For AABs, the only GSMM reported is iXW433 belonging to Gluconobacter oxydans 621H23. This model was used to examine the production of dihydroxyacetone by G. oxydans using glycerol as a carbon source. Nevertheless, no study so far has shown a refined GSMM for K. nataicola. The present study describes for the first time reconstruction of a GSMM for K. nataicola strain RZS01. The model, iHZ771, has been used to study cellulose production characteristics of AAB. Elucidation of the pathway for BC biosynthesis in strain RZS01 was based on genome annotation and literature mining. The model was also used to propose suitable strategies for improvement of BC production.The advancement of the genome-sequencing technology stimulated research at the systemic level. As a result, genome-scale metabolic models (GSMMs) have been developed to characterize the physiological behavior and metabolic status of an organism subjected to different environmental and genetic changesiHZ771 comprised 771 genes, 2035 metabolites, and 2014 reactions located throughout the cytosol, periplasm, and extracellular compartments, covering 21.9% of the annotated open reading frames. The metabolic reactions could be classified into eight major sub-systems: carbohydrates, amino acids, energy, cofactors, lipids, glycan, nucleotides, and transport24. Among these, the largest sub-system was amino acid metabolism (18.9%), followed by transport (17.0%), and carbohydrate metabolism  is 21.9%, 27.3%, 16.0%, and 25.0% respectively. A total of 137 genes, mostly belonging to central carbon metabolism (8.2%), amino acid metabolism (24.1%), and biosynthesis of the cytoskeleton (11.3%) were common among the models. The analysis also revealed that the K. nataicola model consists of 106 unique genes catalyzing 245 reactions, some of which belong to carbohydrate metabolism responsible for BC biosynthesis from glucose 6-phosphate. The analysis of transport reactions indicated that most of the amino acids and fatty acids are transported via ATP-binding cassette transporters or via co-transport, depending on the proton gradient; this situation results in excessive consumption of energy. Of note, in K. nataicola, conversion of ethanol to acetic acid is guided by a membrane-bound alcohol dehydrogenase, membrane-bound aldehyde dehydrogenase, and ubiquinol oxidase which generate a large amount of ATP at early stages25.Three related models were compared with model ics Fig. . CoveragiHZ771, qualitative simulation of cellular growth in the presence of different carbon and nitrogen sources was carried out. Overall, 30 carbon sources  and 25 nitrogen sources  were predicted for cell growth via flux balance analysis (FBA). Comparison of experimental data and the model prediction revealed that K. nataicola can utilize 15 types of carbon sources and 25 types of nitrogen sources , 1-pyrroline dehydrogenase (EC 1.2.1.88), and 4-hydroxy-2-oxoglutaratealdolase (EC 4.1.3.16) are absent in K. nataicola. The incomplete decomposition pathway of proline shrinks the substrate utilization spectrum; glutamic acid and arginine are degraded to proline for further utilization28.To assess the accuracy of model in vivo growth data29. The experimental data served as constraints to simulate cell growth parameters, including the glucose uptake rate, gluconic acid production rate, acetic acid production rate, and bacterial cellulose production rate. As shown in Supplementary Information in silico was highly consistent with the observed experimental data. Qualitative and quantitative assessments indicated that model iHZ771 may be sufficient to accurately describe the cellular metabolism of K. nataicola.For quantitative assessment of the model’s accuracy at predicting the growth rate, we compared simulated growth phenotypes, obtained using glucose and ammonium as the sole carbon and nitrogen sources, respectively, with K. nataicola was carried out in model iHZ771 using single gene deletion. It was found that 9.2% (71 genes) and 3.9% (30 genes) of the total of 771 genes in iHZ771 are necessary for cellular growth in a glucose-containing medium and complex medium, respectively. A total of 112 reactions were predicted to be essential for cell growth. We found that 68 reactions (60.7%) overlap with the corresponding reactions encoded by essential genes in a glucose-containing medium. The distribution of essential genes and reactions across various metabolic processes was also identified  transporters driven by ATP31. Constraint-based flux analysis simulations were carried out for K. nataicola grown on glucose, fructose, or glycerol, and the maximal uptake rate of each carbon source was set at −100 mmol/[(g of dry cell weight [DCW])·h] . Nevertheless, the genes encoding enzymes of the PPP were present, suggesting that glucose is degraded via the PPP, as also reported for Acetobacter pasteurianus 386B4 and G. oxydans 621H33. Besides, a large fraction of glucose is converted into gluconic acid in a reaction catalyzed by membrane-bound glucose dehydrogenase in the periplasm, resulting in a sharp decline in pH of the medium. It was also found that glycerol switches the pathway from PPP to the tricarboxylic acid (TCA) cycle, resulting in the oxidation of triose phosphate without formation of gluconic acid. This way, carbon is not wasted on CO2 production. Fructose has different metabolic fate, and is transported into the cell through PTS. This process is followed by degradation of pyruvate to acetate, followed by transformation to acetyl-CoA, which then enters the TCA cycle for energy synthesis. Addition of ethanol is believed to generate more of reduced nicotinamide adenine dinucleotide, which provides suitable environment for BC production by lowering the redox potential34. After comparison to the predicted results, we found that glycerol yielded the highest BC production, at 5.86 g/L, which was approximately 1.82-fold and 1.49-fold greater than BC production in the glucose and fructose medium, respectively. In the presence of ethanol, BC production was improved by 5.9%.Based on reconstructed model B0W47_02175 or B0W47_13495. This action is followed by the reaction of glucose 1-phosphate with UTP catalyzed by B0W47_02365- or B0W47_02370-encoded UTP-glucose-1-phosphate uridylyltransferase (EC 2.7.7.9), leading to the formation of uridine-5′-diphosphate-α-D-glucose (UDP-glucose). This is a rate-limiting step. Finally, the crucial enzyme, bacterial cellulose synthase (EC 2.4.1.12), transfers glucosyl residues from UDP-glucose to the nascent β-D-1,4-glucan chains. For identification of gene overexpression targets to achieve enhanced production of BC, minimization of metabolic adjustment (MOMA) was carried out to re-evaluate the fluxes for an over-expression algorithm. The experimental specific production rate of the wild type was found to be 0.5 mmol/[(g DCW)·h] and was set as the lower bound for BC transport flux. Under the given constraint conditions, the specific growth rate was found to be 0.3321 h−1 by FBA. Thereafter, all the reactions that have a non-zero flux value in the FBA simulation were over-expressed computationally. At the end of this process, 8 reactions were identified as the potential over-expression targets based on the fPH value of greater than 1.0  encoded by .4.2.2 enB0W47_12635, B0W47_12640, B0W47_12645, and B0W47_12650, respectively, is responsible for the formation and translocation of glucan chains. The activity of CS could be stimulated by its allosteric regulator, c-di-GMP, via PilZ domains35. Over-expression of CS led to enhanced BC production from 0.5 to 1.0 mmol/[(g DCW)·h]. As a mutase, PGM (EC: 5.4.2.2) catalyzes the conversion of D-glucose 6-phosphate into D-glucose 1-phosphate, which is the precursor of BC. A decrease in the growth rate by 47% was accompanied with a two-fold increase in BC production after over-expression of this gene. Bacterial cellulose biosynthesis may be regulated by energy metabolism. Both GADT (EC: 2.2.1.1) and FADT (EC: 2.2.1.2) participate in the PPP, which provides large amounts of NADPH for cellular biosynthesis and growth25. Over-expression of these genes led to 98% and 58% increases in BC production, respectively.Of the eight targets identified, four  are directly involved in the biosynthetic pathway of BC, whereas the remaining four targets  do not belong to the native BC precursor pathway. During the biosynthesis of BC, the membrane-integrated cellulose synthase  containing BcsA, BcsB, BcsC, and BcsD, encoded by 36. Nonetheless, increasing the proton extraction rate over 12 mmol/[(g DCW)·h] resulted in inhibition of cell growth and of BC accumulation. In strict aerobic bacteria, oxygen plays a crucial role in maintaining intracellular metabolism, being the primary electron acceptor37. According to simulation intended to predict the phenotype at different oxygen uptake rates, the highest BC production was observed at the oxygen uptake rate of 10 mmol/[(g DCW)·h]. For cellular growth, 32 mmol/[(g DCW)·h] was found to be the best uptake rate, which was far above that for BC accumulation. These simulation results, namely, that higher oxygen tension inhibits cellulose production, are in line with the results obtained by Hwang38. We, therefore, determined that a relatively high concentration of dissolved oxygen is beneficial for cell proliferation at the first stage. Nevertheless, to avoid the excessive oxygen exposure, a low dissolved-oxygen concentration is employed during BC production.To further investigate the cellular behavior under the influence of changes in environmental conditions, robustness analysis was conducted. Cell growth and BC accumulation were found to possess similar adaptive capacity Fig. . Simulat39. Phosphate is mainly present in plasma membrane, nucleic acids, and some coenzymes. Nonetheless, when the uptake rate of phosphate reaches 1.25 mmol/[(g DCW)·h], cell growth is retarded. This prediction is supported by one study, where a decrease in BC production was obtained after supplementation with phosphate sources40.As for other environmental disturbances, such as changes in sulphur and phosphate content, robustness analysis was carried out. Sulphur and phosphate perform key functions in the biosynthesis of the cell scaffold, where the former participates in the biosynthesis of sulphur-containing amino acids: methionine and cysteine. The simulation results indicated that a high sulphur uptake rate is beneficial for cell growth, resulting in the flux of a large amount of substrates to biomass. This finding is in agreement with other reportsiHZ771, for K. nataicola. Validation of the model revealed that it can accurately simulate phenotypic behavior under various conditions. Analysis of essentiality of genes and reactions highlighted the importance of the metabolic backbone for cell growth. Flux intensity analysis in the presence of different carbon sources revealed that glycerol is beneficial for BC production. Eight simulated targets responsible for increasing BC production were identified via the MOMA algorithm. The lack of experimental information on gene over-expression is still a setback for the validation of the model created here. Further experiments on this should help us better refine and complete the networks. Overall, the proposed model is expected to be useful for systemic analysis of AAB and should facilitate future biotechnological applications.Our study describes a GSMM, K. nataicola RZS01 was carried out as per the protocol described by Florea et al.41. Quality of the genomic DNA was assessed by agarose gel electrophoresis. Whole-genome sequencing was carried out using an Illumina technology . As a consequence, the complete genomic sequence, composed of a circular chromosome and six plasmids, was obtained. The accession number of the complete sequences of this strain from this study can be found in GenBank (http://www.ncbi.nlm.nih.gov) under the accession no. CP019875 to CP019881.Genomic-DNA isolation and purification from 42. Whole-genome sequence of K. nataicola RZS01was uploaded to the RAST server (http://rast.nmpdr.org/) to call and annotate the genes. The initial model was constructed based on the protein homology through a local sequence similarity search (BLASTp). Three existing models, iXW433, iRsp1095, and iAF1260 belonging to Gluconobacter oxydans 621 H, Rhodobacter sphaeroides 2.4.1, and Escherichia coli K-12 MG165543, respectively, served as templates. The homology search parameters for prokaryotes in BLASTp were set at an identity of ≥35%, and an e-value of ≤10−6 44. The reaction lists from BLASTp were integrated in the same format. This action was followed by manual refinement of the model with the help of public databases, such as KEGG, MetaCyc, Biopath, CELLO, and the COBRA software package. The process involved the following steps: (1) assignment of appropriate subcellular compartments; (2) determination of reaction directionality; (3) addition of metabolite transport and exchange reactions; (4) checking the consistency in terms of element and charge balance; (5) verifying connectivity of the network by the GapFind algorithm followed by filling or correcting the reactions from other organisms according to the literature.The general principles of GSMM reconstruction process have been described elsewhere45. Because the components of amino acids were not available, we utilized the genome information to estimate the amino acid composition. Lipid analysis was conducted as described previously46. As a member of family Acetobacteriaceae, strain G. oxydans 621 H was subjected to the analysis of biomass composition, specifically composition of peptidoglycan23. In all the simulations carried out in the present study, biomass composition was assumed to be constant under different environmental conditions. Growth-associated maintenance energy requirement was estimated according to the protocol described for E. coli43. Detailed information on calculation of biomass composition is provided in Supplementary Information An equation, based on experimental analysis and literature that describes the cellular composition is widely used as an objective function in the constraint-based model. The major macromolecular constituents of the cells in the exponential growth phase were quantified to generate an appropriate biomass equation. These components consisted of DNA, RNA, proteins, lipids, cell wall constituents, and small molecules. DNA and RNA were quantified using a mini kit. G + C content (61.49%) was used to determine the individual weights of nucleotides in DNA and RNA. Coomassie brilliant blue staining was carried out to determine protein content2HPO4, 1 g of KH2PO4, 0.02 g of MgCl2, 0.02 g of CaCl2, and 0.0015 g of aminobenzoic acid per litre, and the initial pH was adjusted to 6.0. The medium used for seed culture contained 20 g of glucose, 6 g of (NH4)2SO4, 1 g of KH2PO4, 0.4 g of MgSO4, 3 g of peptone, 2.25 g of yeast extract, and 0.4 g of sodium carboxymethylcellulose per litre. To prepare for the fermentation process, the strain was cultured in a seed medium for 36 h. Next, the seed cultures were centrifuged for 10 min at 8,000 × g, and then washed twice with a 0.9% NaCl solution. After that, the supernatant was decanted and resulting pellet was resuspended in 50 mL of the NaCl solution, of which 4 mL  was inoculated into 50 mL of the culture medium in 250-mL flasks. The cultures were grown at 30 °C, with shaking at 160 rpm for 5 days.Growth screens were carried out in triplicate in a 250-mL baffled-flask containing 50 mL of culture broth. For qualitative simulation of cellular growth in the presence of different carbon and nitrogen sources, a defined medium was used for analysis, which contained 20 g of a carbon source, 5 g of a nitrogen source, 3 g of NaConcentrations of glucose, organic acids and ethanol were measured by means of a high-performance liquid chromatography (HPLC) system  equipped with an HPX-87H column  and a dual λ absorbance detector. To determine DCW, the culture broth was resuspended in 0.2% (w/v) cellulase in citric acid-sodium citrate buffer (pH 4.8) and hydrolyzed for 2 h at 50 °C, then filtered through filter paper. The cells were dried overnight in an oven and weighed on filter paper, with a correction for the amount of weight lost by the filter paper that did not contain cells. The BC produced in the shake flask was collected from the medium directly and treated with 4% (w/v) NaOH at 85 °C for 2 h to remove impurities and cells attached to cellulose. Then, the BC was washed in tap water until the pH of water became neutral. The purified BC was dried to constant weight at 80 °C and weighed. Growth rates were calculated by determining the exponential growth phase region in a series of samples taken during each growth screen. For this corresponding exponential growth phase region, the ratio of an analyte to grams of dry weight (DW) of the sample was determined using a linear fit obtained by the least-squares method (‘regress’ function in MATLAB). This value (mmol/g DW) was then multiplied by the growth rate to obtain the corresponding uptake or production rate. The ratios were finally multiplied by the growth rate to obtain the uptake or production rates.K. nataicola under varying environmental conditions. The analysis was conducted by means of Cobra Toolbox 2.05 with MATLAB 2012b and Gurobi 6.5.1 optimizer. Identification of the over-expression targets was carried out using the MOMA framework for better prediction of the flux distribution. The over-expression algorithm involved five steps47: (1) BC production flux  was imposed upon the reconstructed model; (2) flux for each reaction was calculated based on the complex medium by adjusting uptake rates of specific chemical components of the medium, such as the basic elements and 20 amino acids. The maximal rates of uptake of amino acids were set to 0.1 mmol/[(g DCW)·h]. Glucose served as the sole carbon source, and its uptake rate was set to 8.45 mmol/[(g DCW)·h], as described elsewhere29; (3) amplifying flux was imposed upon individual reactions with non-zero flux (to simulate the effect of gene over-expression); (4) MOMA was performed to solve the problem of over-expression; (5) finally, identification of the over-expression targets was done and led to a phenotype fraction value, fPH, greater than unity [equation  was incorporated into then model to compare the turnover rates of metabolites in Komagataeibacter. The flux-sum of metabolite i can mathematically be formulated as equation (Sij is the stoichiometric coefficient of metabolite i involved in reaction j, and vj is flux or specific rate of metabolic reaction j. Pi denotes the set of reactions producing metabolite i, and Ci represents the set of reactions consuming metabolite i48.Constraint-based flux analysis indicates only the reaction rates in terms of fluxes, and does not provide any information about the concentration of metabolites. Therefore, the concept of ‘flux-sum’ (equation :2\documeSupplementary Information 1Supplementary Information 2Supplementary Information 3


Metabolism is the only biological system that can be fully modelled at genome scale. As a result, metabolic models have been increasingly used to study the molecular mechanisms of various diseases. Hypoxia, a low-oxygen tension, is a well-known characteristic of many cancer cells. Pyruvate dehydrogenase (PDH) controls the flux of metabolites between glycolysis and the tricarboxylic acid cycle and is a key enzyme in metabolic reprogramming in cancer metabolism. Here, we develop and manually curate a constraint-based metabolic model to investigate the mechanism of pyruvate dehydrogenase under hypoxia. Our results characterize the activity of pyruvate dehydrogenase and its decline during hypoxia. This results in lactate accumulation, consistent with recent hypoxia studies and a well-known feature in cancer metabolism. We apply machine-learning techniques on the flux datasets to identify reactions that drive these variations. We also identify distinct features on the structure of the variables and individual metabolic components in the switch from normoxia to hypoxia. Our results provide a framework for future studies by integrating multi-omics data to predict condition-specific metabolic phenotypes under hypoxia. HypoxiaWhile metabolism has been seen merely as a marker for a long time, it is now widely accepted that it constitutes a driver of cancer; all cancer cells exhibit altered metabolism. One altered metabolic feature is an increased uptake of glucose through aerobic glycolysis, which is referred to as ‘Warburg effect’ ,4. In moPDH is an important enzyme that links and controls the flux between glycolysis and the TCA cycle. The PDH complex is a nuclear-encoded, mitochondrial matrix essential multi-enzyme component that catalyses the irreversible conversion of pyruvate to acetyl-CoA. It is a member of the α-ketoacid dehydrogenase complex family , which iMetabolism is the only biological network that can be modelled reliably at a genome scale . This enGiven the critical role of PDH in controlling glucose flux between glycolysis and the TCA cycle and its role in metabolic reprogramming in cancer cells, we model and investigate the role of PDH using a blend of constraint-based modelling and machine-learning approaches. We develop a constraint-based model of cancer metabolism and present the action of PDH, characterizing metabolic phenotypes seen during hypoxia. We employ machine-learning methods to infer the reactions that contribute to the change in the metabotype from normoxia to hypoxia. To the best of our knowledge, this is the first study to use constraint-based modelling and machine-learning techniques to understand the metabolic role of PDH under hypoxia.2.2.1.To simulate PDH complex activity during hypoxia, a model of human metabolism was constructed based on the human metabolic model iAS253 . The mod2.2.x), it does not need knowledge of metabolite concentrations and enzyme kinetics. This differentiates FBA from other modelling techniques that require kinetics parameters, usually are difficult to obtain. In FBA, the set of biochemical reactions is represented mathematically in the form of a stoichiometric matrix S; constraints are placed on individual reactions to limit the allowable flux ν each reaction can carry. A unique solution is obtained by defining the objective function through a vector c; this is a measure of how each component in the network contributes to the production of a biologically desirable phenotype.Flux balance analysis (FBA) is a widely used mathematical approach for modelling large-scale metabolic networks . BecauseFormally, we adopt the following linear program:et al. [atlab (version R2016a) using the COBRA toolbox [−1 gDW−1).For a detailed description of FBA, we refer the reader to the work of Orth et al. . To repret al.  and fulfet al. . Simulat toolbox  with the−1 gDW−1 in intervals of 10% from its level under normal conditions (19.8 μmol min−1 gDW−1). The uptake value of 10% oxygen was chosen to reflect the typical conditions used in experimental hypoxia studies [To represent hypoxia, the maximum allowable uptake flux of oxygen was reduced to 0 µmol min studies –43. For  studies . Flux baTo determine the effect of PDH on ATP production during hypoxia, the activity of PDH was reduced from its maximum flux capacity to zero. It is well established that the concentration of some metabolites is elevated during hypoxia in cancer cells. To further evaluate this, simulations were run where the uptake of amino acids were unconstrained by increasing the upper uptake bound.3.A manually curated human metabolic model was created to model and investigate the metabolic effects of PDH during hypoxia . The model is composed of 616 reactions, 765 metabolites, 90 boundary reactions and 92 transport steps . These transport steps represent the human mitochondria carrier family  and are 3.1−1 gDW−1) to hypoxia (1.9 µmol min−1 gDW−1) in intervals of 10%. The flux output of PDH gradually declined as the rate of oxygen availability was reduced from normoxia to anoxia (a). Under these simulations, lactate and succinate accumulated at a rate of 1.04 and 0.59 µmol min−1 gDW−1, respectively  diffuses to complex III where it is reoxidized; at the same time, complex III reduces cytochrome c, which in turn reduces complex IV, where oxygen is used as terminal electron acceptor and reduced to water. The energy from this chain of electron transfers is used by the respiratory complexes to transport protons across the mitochondrial membrane, so generating the proton motive force and the electrochemical potential across the mitochondrial membrane that is used by mitochondrial ATP synthase to form ATP from ADP and phosphate. Under hypoxia, this mechanism diminishes owing to the lack of oxygen availability. Therefore, the rate of succinate oxidation by OXPHOS is reduced, leading to its accumulation.Under aerobic respiration, succinate and electron-rich metabolites (such as NADH generated from the TCA cycle and fatty acid oxidation) are oxidized by complexes II and I of the electron transport chain, respectively. Oxidation of succinate (an intermediate of the TCA cycle) by complex II transfers electrons to ubiquinone, a small lipid-soluble molecule that can be reduced and oxidized, and which freely diffuses within and across the inner mitochondrial membrane. The reduced ubiquinol  ratio in the mitochondria is high owing to the reduced capacity of the electron transport chain and consequent reduction in the rate of NADH oxidation by complex I . SubsequThe production of lactate by lactate dehydrogenase (LDH), which leads to cellular acidification is a contributing factor for the malignancy of cancer cells . LDH conFurthermore, pyruvate dehydrogenase kinase 1 (PDK1) inhibits PDH during hypoxia leading to reduced mitochondrial respiration and reactive oxygen species (ROS) production . PDK1 pha).Finally, further simulations were performed to determine whether additional sources of metabolites enhanced PDH capacity. The uptake rate of amino acid metabolites in the model was unconstrained. This enhanced PDH capacity by 29%, but as the levels of oxygen fell, PDH action decreased steadily a.3.2b shows a gradual decrease in ATP production when PDH flux was decreased. More specifically, the decrease of PDH activity caused ATP production to decline only by 5% (from 138 to 131 µmol min−1 gDW−1). This suggests that the majority of the source of ATP under normoxia is driven by alternative fuel sources, including fatty acids. Under normal levels of oxygen availability, ATP production through OXPHOS was primarily generated from β-oxidation of fatty acids, which generates energy-rich NADH and acetyl-CoA. Acetyl-CoA subsequently enters the TCA cycle generating further electrons and succinate. The efficient capacity of the electron transport chain then oxidizes NADH and succinate to generate ATP as described above.To determine whether PDH affects the rate of ATP production under normoxia and hypoxia, we quantified ATP production while varying PDH in 10% decrement of its maximal flux. Figure 1c), when oxygen was further reduced, the contribution of OXPHOS to ATP production was gradually diminished. This was owing to the electron transport chain capacity to use oxygen being restricted and its inability to use fatty acid as an energy source. Reduced capacity of electron transport chain inhibits TCA cycle and NADH-producing reactions (including PDH) in the mitochondrial matrix, such as fatty acid oxidation. Therefore, under this condition, the action of PDH is reduced by 78% compared to conditions under normal oxygen supply, subsequently reducing the rate of ATP production .Under hypoxia c, when ooduction b,c.Under hypoxia, alternative carbon sources are used—amino acids, e.g. glutamine, lysine, proline and valine. The mechanism in which glutamine (as a fuel source) enters central metabolism is as follows: glutamine is converted in a two-step reaction to glutamate and then oxoglutarate by hydrolysing glutamine to glutamate and ammonia (which accumulated) by glutaminase. Glutamate was imported directly into the mitochondrial matrix and further converted to oxoglutarate and ammonia by glutamate dehydrogenase. In the TCA cycle, oxoglutarate was converted to succinyl-CoA and then succinate, subsequently used for ATP production. Consistent with this, under hypoxia, glutamine metabolism maintains ATP synthesis through OXPHOS in tumour cells –58. In a3.3Machine learning in biology can be used to reveal unique characteristics from multidimensional and high-throughput data ,61. Two a–e). The principal components always represent an orthogonal basis of the space of flux rates.We performed PCA on the aggregate flux dataset from normoxia, hypoxia and hypoxia with excess amino acid (HypoxiaAA) for all active reactions. Our goal was to identify key reactions that drive variation and investigate how these drivers change over different levels of oxygen availability a–e. The a,b). This indicates that changes in reaction fluxes are correlated under various levels of oxygen, and the changes following the uptake of additional amino acids are also correlated (a). Plotting the first two singular vectors of PCA on the fluxes shows distinct features on the structure of the variable and individual components . We found that the first component  correlates highly with variable hypoxia, while the second component correlates with the variable HypoxiaAA. The variation in each condition is driven by changes in addition to the amount of oxygen but also by the excess amino acids present in HypoxiaAA (a). Addition of metabolites such as glutamine and arginine increased the rate of ATP production during hypoxia, by enhancing the availability of substrates (and electrons) required for energy generation. Figure 3a and table 2b show the individuals factor map plot of the principal component scores for individual reactions on the first two principal components. Superimposed on the plot are mean scores on the components for qualitative variables that are included in the PCA. The reactions ATP synthase (reaction id: R00086MM) and HypoxiAA ATP production (reaction id: BioMass5) scored highly with the first component, therefore indicating a large impact of ATP synthase activity in determining the normoxic or hypoxic phenotype.Using PCA on our flux dataset showed that the first two eigenvectors sum to more than 98% of the variance in the dataset a,b. Thisrrelated a. Plottimponents a,c. We fomponent d, Dim1 cypoxiaAA a. Additi2 and water, yielding NADH and GTP. Electron-rich metabolites such as NADH generated from these pathways and oxidation of fatty acids, together with succinate HypoxiaAA are then oxidized by complexes I and II, respectively. As described above, the transfer of these electrons generates the electrochemical gradient across the mitochondrial membrane, which in turn is used by ATP synthase to form ATP.Our results are in keeping with the fact that ATP synthase is highly dependent on the rate of oxygen and metabolite availability for generating ATP. In fact, the mechanism by which ATP is generated is as follows and depends on molecular oxygen under normal conditions. Oxidation of glucose begins with glycolysis in the cytoplasm generating NADH, ATP and pyruvate. Pyruvate is then transported into the mitochondrial matrix and is converted to acetyl-CoA and NADH by PDH. Subsequently, acetyl-CoA enters the TCA cycle and is oxidized to CO4.The PDH complex regulates the metabolic adaptation by controlling glucose metabolism, used by cancer cells to meet the bioenergetics demands. To investigate the complexity of role of PDH in cancer metabolism, we used a small-scale constraint-based model; the choice of our model over genome-scale model was appropriate for in-depth interpretation of biological results.Our results showed PDH action diminished during hypoxia. We identified metabolic markers of cancer metabolism (lactate production and its accumulation) in agreement with experimental findings in the literature. Taken together, the combination of our model, machine learning results and known biochemistry suggests that oxygen transporters and ATP synthase and production constitute clear biomarkers of hypoxic behaviour.We envisage that our model will be useful to researchers for studying human diseases involving a hypoxia-like metabotype, including cancer. To expand and further investigate such diseases, the model can be used as a scaffold to integrate multi-omics data ,62, and


Pichia pastoris is a popular recombinant protein expression system for its accessibility of efficient gene manipulation and high protein production. Sufficient supply of precursors, energy, and redox cofactors is crucial for high recombinant protein production. In our present work, we found that the addition of glutamate improved the recombinant β-galactosidase  production by P. pastoris G1HL.13C-assisted metabolomics and 13C metabolic flux analysis was conducted based on LC–MS/MS and GC–MS data.To elucidate the impacts of glutamate on the central metabolism in detail, a combined The pool sizes of intracellular amino acids were obviously higher on glucose/glutamate (Glc/Glu). The fluxes in EMP entry reaction and in downstream TCA cycle were 50 and 67% higher on Glc/Glu than on Glc, respectively. While the fluxes in upstream TCA cycle kept almost unaltered, the fluxes in PPP oxidative branch decreased.P. pastoris G1HL. To meet the increased demands of redox cofactors and energy for higher β-galactosidase production on Glc/Glu, P. pastoris G1HL redistributes the fluxes in central metabolism through the inhibitions and/or activation of the enzymes in key nodes together with the energy and redox status.The addition of glutamate leads to a remarkable change on the central metabolism of high β-galactosidase-producing The online version of this article (doi:10.1186/s40643-016-0124-6) contains supplementary material, which is available to authorized users. P. pastoris has been widely applied in pharmaceutical and chemical industries. This can be credited to its fine characteristics including the ability to perform post-translational modification  have become reliable methods for quantifying metabolite concentrations and metabolic fluxes, due to the improved precision from the use of 13C-labeled metabolites as internal standards in metabolomics analysis and from the use of 13C isotopic abundances as extra subjects when solving metabolic equations in metabolic flux analysis  was maintained above 30%, and the pH was controlled at 5.5 by adding 28% ammonium hydroxide (as nitrogen source). The pressure was maintained at 0.05 MPa using an automatic pressure control device . The off-gas was analyzed using a process mass spectrometer . The parameters of aeration, agitation, dissolved oxygen, exhaust CO2, exhaust O2, OUR and CER, pH, pressure, temperature, and working volume of bioreactor were collected online by an in-house software BioStar. The cultivations were performed in duplicates.The batch cultivations were performed in a 5-L bioreactor  with the working volume of 3 L. The media and growth conditions were the same as those described in our previous study  measurement, 5 mL broth was filtered using a predried and weighted micro-porous filter paper . After washing 3 times, the filter paper was dried at 95 °C for 2 h. The glucose kits  were used for residual glucose concentration analysis.O-phthalaldehyde. Mobile phases  were applied at a flow rate of 2/mL/min using the Agilent Eclipe AAA 4.6 × 150 mm column. The absorbance detector was set at 338 nm. For the determination of acetic acid, an ion-exclusion column  was eluted at 50 °C with 10 mM H2SO4 at a flow rate of 0.5 mL/min and connected with an absorbance detector spectrophotometer at 210 nm.Glutamate concentration was determined by a high-performance liquid chromatography (HPLC) system. 1 mL broth was centrifuged, and the supernatant was obtained. 20 μL supernatant was added to 100 μL boric acid buffer and then derivatized with 20 μL Pichia pastoris expression kit (Invitrogen). The broth was centrifuged  and cells were collected. 1 mL cell lysis buffer and 1 mL acid-washed glass beads (D = 0.5 mm) were added. The cells were broken by incubating in an ice bath for 30 s and then shaking for 30 s (repeated 8 times). The supernatant was collected after centrifugation.The β-galactosidase assays were performed according to the protocol supplied in our previous study . The amount of enzyme that can hydrolyze 1 nmol ONPG to ONP per minute at 37 °C was defined as one unit of enzyme, i.e., 1 U. The mass of β-galactosidase was estimated upon the correlativity between the mass and activity of the pure β-galactosidase standards.For quantification of β-galactosidase, 50 μL cell extract was added into 1 mL preheated Z-buffer and the blank contained 50 μL cell lysis buffer. Then 0.2 mL 4 mg/mL ONPG was added to the samples. The reaction was conducted in water bath at 37 °C, and 0.5 mL 1 mol/L NaThe labeled substrate was fed when the residual glucose concentration was about 10 g/L at the exponential growth phase. Once the labeled substrate was added, the samples were taken with a rapid-sampling setup . Every time, about 1 mL broth was taken. During the first 5 min after labeling, ten samples were taken. Thereafter, several samples were taken until reaching the isotopically stationary state. The broth was directly injected into a tube with 15 mL pre-cooled quenching solution   at 60 °C for 1 h    with dimensions 150 mm × 2.1 mm × 1.8 mm was used for organic acids and sugar phosphates, ACQUITY UPLCR BEH Amide (Waters) 1.7 μm × 2.1 × 100 mm for nucleosides, Xselect™ HSS T3 1.8 μm × 2.1 × 100 mm Column for amino acids) coupled to a Thermo TSQ Quantum Ultra triple quadrupole instrument (Thermo Fisher Scientific) with electrospray ionization. The spray voltage, vaporizer temperature, sheath gas pressure, ion sweep gas pressure, aux gas pressure, capillary temperature used in the detection of amino acids were +3500 V, 300 °C, 15, 0, 10 psi, and 300 °C, respectively. The parameters for organic acids, nucleosides, and sugar phosphate detection were −3000 V, 200 °C, 15, 0, 10 psi, and 200 °C, respectively.The samples used for the quantification of intracellular metabolites were obtained from the 5 L bioreactor during the exponential growth phase. The extraction process has been introduced above. To accurately determine the metabolites concentration, isotope dilution mass spectrometry (IDMS) method . About 1 mL broth was obtained by the rapid-sampling setup, then quenched with 10 mL quench solution . The supernatant was discarded after centrifugation at −4 °C and 6000 rpm. The remaining processes were referred to the standard specifications. All determinations above were in triplicates.The determinations of NADH, NAD2 at a 95% confidence level. 95% confidence intervals were computed for all the estimated fluxes by evaluating the sensitivity of the minimized sum of squared residuals (SSR) to flux variations.The metabolic model used was similar to our previous study  remains almost unaltered (0.37 vs. 0.36 g DCW/g glucose on Glc and on Glc/Glu). We also found that the specific O2 uptake rate (QO2) and the yield coefficient of β-galactosidase from oxygen was 14.5 and 14% higher on Glc/Glu than on Glc, respectively. In order to elucidate the impacts of glutamate on the central metabolism including the energy and reductive cofactor metabolism in detail, it is necessary to quantify the pool sizes of intracellular metabolites and the metabolic flux distributions on these two cultivation conditions.The addition of glutamate resulted in remarkable impacts on the physiology of the high β-galactosidase-producing strain 1HL Fig. . The speP. pastoris GHL biomass grown on uniformly 13C-labeled glucose as sole carbon source are used as the internal standards.The pool sizes of amino acids, organic acids, and sugar phosphates in GHL on Glc, in G1HL on Glc, and in G1HL on Glc/Glu were quantified via IDMS method. The extracted metabolites from Intracellular amino acids are the main building-blocks for synthesis of recombinant proteins, so the pool sizes of amino acids are undoubtedly important to learn the details of cell metabolic adaptations during recombinant protein expression. Eighteen amino acids were determined with the present method Table . We obseP. pastoris, thus the addition of glutamate tend to improve the recombinant protein expression. The added glutamate may be used to provide amino group for the synthesis of other amino acids by aminotransferase reaction. From flux results  and erythrose-4-phosphate (E4P) increased. Since E4P is the main precursor of aromatic amino acids, the increase in the pool size of E4P was related to the increased need of these aromatic amino acids for high β-galactosidase production.The pool sizes of all TCA cycle intermediates were lower in G1HL than in GHL on Glc. The pool size of αKG in G1HL had the most obvious decrease in the intermediates of TCA cycle. αKG is one of the main precursors of amino acids; the high β-galactosidase expression resulted in the decrease in amino acids pool size, which in turn declined the pool size of αKG. After the addition of glutamate, the pool sizes of TCA cycle intermediates were much higher than those on Glc. The formation of αKG from glutamate is the first step to use glutamate through TCA cycle. The αKG pool size was about 25-fold higher (0.57 vs. 0.02 μmol/gDCW) on Glc/Glu than on Glc. The increased supply of αKG pool thus improved the pool sizes of other metabolites in TCA cycle.13C-MFA. The rates of substrate consumption, biomass growth, β-galactosidase production, CO2, and by-products formation were determined and the carbon recovery were confirmed for both conditions  was 50% higher on Glc/Glu than on Glc (0.54 vs. 0.36 µmol/gDCW/s). This up-regulation was related to the increased ATP demand for higher β-galactosidase expression . The oxidative branch of PPP is the main pathway for cytosolic NADPH production. The flux through this pathway is generally related to the biosynthetic demand for NADPH  remained unaltered, and the fluxes in downstream TCA reactions (from αKG to OAA) were 67% higher on Glc/Glu than on Glc. About 30% glutamate assimilated was streamed into the TCA cycle, while the rest was used as building-block precursors to the biosynthesis of amino acids and β-galactosidase. The almost unchanged fluxes in upstream TCA reactions showed the robustness of fluxes in these reactions. Sonenshein and Toya ; Toya etRedox cofactors (NADPH and NADH) and energy (ATP) play pivotal roles  , indicating that the cells on Glc/Glu were in a less reducing condition.NADH, as an important redox cofactor for cell growth and recombinant protein expression, plays a key role in the maintenance of redox balance and energy generation  was 1.48 mol ATP/molar O Verduyn . We founP. pastoris cell adjusts the flux distribution, which consequently enables a high yield of ATP from glucose , the increased ADP pool size and the lower NADH/NAD+ ratio can enhance the enzyme activity, so the OGDC flux increased 67%. Procopio has studied the effect of amino acid supplementation on the transcription of Saccharomyces pastorianus and found the up-regulation of the pyruvate decarboxylases  is activated by the lower ratio of ATP/AMP (Takama and Nosoh P. pastoris G1HL. We found that the addition of glutamate improved β-galactosidase production and reduced cell growth and carbon source utilization efficiency. 13C-assisted metabolomics analysis showed that the pool sizes of intracellular amino acids were obviously higher on Glc/Glu than on Glc. The isotopically instationary 13C-metabolic flux analysis illustrated that the central carbon fluxes were redistributed in response to the addition of glutamate. The fluxes in EMP and in downstream TCA cycle were 50 and 67% higher on Glc/Glu than on Glc, respectively. While the fluxes in upstream TCA cycle kept almost unaltered, the fluxes in PPP oxidative branch decreased. The impacts of glutamate on the central metabolism of P. pastoris G1HL was inferred to be modulated through the increase and/or decrease of the metabolite concentrations in key nodes together with the energy and redox status in order to meet the increased demands of redox cofactors and energy for higher β-galactosidase production on Glc/Glu. These results enrich the understanding of the impacts of glutamate addition on central metabolism of P. pastoris G1HL.The study aimed at revealing the impacts of glutamate on the central metabolism of the high β-galactosidase-producing strain


In their natural environment, bacteria face a wide range of environmental conditions that change over time and that impose continuous rearrangements at all the cellular levels . When facing a nutritionally rich environment, for example, microbes first use the preferred compound(s) and only later start metabolizing the other one(s). A systemic re-organization of the overall microbial metabolic network in response to a variation in the composition/concentration of the surrounding nutrients has been suggested, although the range and the entity of such modifications in organisms other than a few model microbes has been scarcely described up to now.Pseudoalteromonas haloplanktis TAC125. As each of these phases is characterized by a specific set of amino acids to be used as carbon and energy source our modelling framework describes the major consequences of nutrients switching at the system level. The model predicts that a deep metabolic reprogramming might be required to achieve optimal biomass production in different stages of growth (different medium composition), with at least half of the cellular metabolic network involved (more than 50% of the metabolic genes). Additionally, we show that our modelling framework is able to capture metabolic functional association and/or common regulatory features of the genes embedded in our reconstruction (e.g. the presence of common regulatory motifs).We used multi-step constraint-based metabolic modelling to simulate the growth in a complex medium over several time steps of the Antarctic model organism Finally, to explore the possibility of a sub-optimal biomass objective function  we have implemented a MOMA-based approach  and compared the outcomes with those obtained with Flux Balance Analysis (FBA). Growth simulations under this scenario revealed the deep impact of choosing among alternative objective functions on the resulting predictions of fluxes distribution.PhTAC125 metabolic re-wiring as a consequence of carbon source switching in a nutritionally complex medium. Our analyses suggest the presence of a potential efficient metabolic reprogramming machinery to continuously and promptly adapt to this nutritionally changing environment, consistent with adaptation to fast growth in a fairly, but probably inconstant and highly competitive, environment. Also, we show i) how functional partnership and co-regulation features can be predicted by integrating multi-step constraint-based metabolic modelling with fed-batch growth data and ii) that performing simulations under a sub-optimal objective function may lead to different flux distributions in respect to canonical FBA.Here we provide a time-resolved, systems-level scheme of The online version of this article (doi:10.1186/s12864-016-3311-0) contains supplementary material, which is available to authorized users. In their natural environment, bacteria are confronted with a wide range of environmental conditions that change over time. It is generally observed that, when facing a nutritionally rich environment, bacteria first use the “preferred” compound(s)  and only later start metabolizing the other one(s). The decrease in concentration of these compounds corresponds with the start of usage of the others. In this respect, Monod discovered the phenomenon of “diauxie”, i.e. the microbial capability of utilizing the various nutrients regardless of their abundance but regulating their uptake through the modulation of the overall enzymatic state , 2. ThisE. coli, S. coelicolor) are still unknown, despite they might provide interesting hints in the understanding of non-trivial metabolic phenotypes .. In this context, constraints-based approaches, in particular flux balance analysis (FBA), have been shown to be predictive of growth phenotypes , 501 reactions are predicted to carry flux for each of the analysed phases  used as C and energy sources.First, we evaluated the predicted flux for each reaction in the model, across all the time points. Overall, we found that 710 reactions did not carry flux in our model in any of the growth phases; these reactions are probably not essential to sustain int Fig.  and wereses Fig. . Interesses Fig. , revealiPhTAC125 in a complex medium, the shift among the different growth phases is presumably characterized by a relatively high number of reactions that are predicted (on the basis of our simulation and the biomass-based objective function) to change their flux  [PhTAC125 coding sequences (55% of the metabolic genes embedded in the metabolic reconstruction).Gene-protein-reaction (GPR) rules of our model indicate that this latter set of reactions is encoded, on average, by 438 genes. Interestingly this number resembles that found when modelling the metabolic switch of r genes)  and reprPhTAC125 with more than half of flux-carrying reactions influenced by a change in the utilized C-source or in its uptake rate. More in detail, we found that, on average, 276 reactions are predicted to decrease their flux throughout the growth period (s.d. = 74.8), whereas 69 (s.d. = 30.1) are predicted to increase it. However, the number of reactions whose flux decreases might be biased due to a general decrease, across the time steps, of the growth rate. More specifically, this systemic bias is related to the amino acid uptake rates, which have been derived from the physiological data by Wilmes et al. [According to our simulations, switching among the available carbon sources in a complex medium may have an impact on the overall metabolic network of s et al. , whose cPhTAC125 metabolic pathways are impacted by this switching of nutrients in the same manner, according to our simulations. Figure PhTAC125 . FinallPhTAC125 metabolic network, in each of the predicted growth phases  to perform a comprehensive exploration of alternate optimal routes in PhTAC125 stops using Ser, Asn and Asp as C and N sources, relying only on glutamate for sustaining growth. The importance of glutamate was previously observed from fed-batch cultivation experiments as it was the most strongly metabolised amino acid in all growth experiments [−1) among all the 20 amino acids when growth in twenty minimal growth media (embedding each amino acid the sole C and N source and an arbitrary uptake rate of 1 mmol * g(−1) * h(−1)) was simulated (data not shown).Overall, according to our model, the central phases of the growth curve are characterized by the largest set of reactions changing their flux  is predicted to be a key reaction in this phase as it allows the conversion of Glutamate to 2-Oxoglutarate to be used as energy source feeding the TCA cycle , leading to the production of 2-oxobutanoate (and succinate) from O-Succinyl-L-homoserine.More in general, our modelling framework predicts a deep re-programming at the whole metabolic network level following this shift, with 394 reactions changing their predicted flux; more in detail, 264 reactions are predicted to decrease their flux , whereas 57 are predicted to carry an increased flux Fig. . The remPhTAC125 starts using Ala, Leu, Gly and Tyr (together with Glu) , Wilmes et al. observed that lu) Fig. . Accordilu) Fig. , with 26lu) Fig. ., which is then oxidatively deaminated by glutamate dehydrogenase to yield α-ketoglutarate. However, the presence of Glu in the medium would not favour this solution as the major fraction of Glu can be obtained without additional energy expenses. Alternatively, 2-Oxoglutarate can be obtained from the conversion of Glu (the only carbon source in P4) to Tyr (required for sustaining growth in P4). In our model this reaction (R00734) is predicted to be “on” during P5 thus allowing the synthesis of 2-oxoglutarate  are predicted to carry zero flux following this transition. Despite the entire pathway is predicted to carry no flux, AICAR (1-(5'-Phosphoribosyl)-5-amino-4-imidazolecarboxamide), one of the intermediates of histidine biosynthesis and a crucial precursor in purine metabolism, might still be synthesized through reaction R04559 (adenylosuccinate lyase). This compound, thanks to the flux predicted to be carried in this phase by reaction R01049, leads to the synthesis of 5-Phospho-alpha-D-ribose 1-diphosphate from D-Ribose 5-phosphate.Furthermore, the switch to the utilization of His as the sole C-source is predicted to be responsible for the stop of the catabolic routes of the previously degraded amino acids  and, consequently, in the stop of the production of important cellular intermediates . According to our simulation, this causes the re-activation of key metabolic reactions that cannot rely on many degradation intermediates as in the previous phase. These include those belonging to the TCA cycle  and purine metabolism .Finally, the reactions involved in the conversion (degradation) of His into Glu  are predicted to be turned on following this final transition, allowing the production of glutamate and, from these, all the major pathways found to carry flux also in phase P4 Fig. .It is worth noticing that all the simulations described up to now were conducted under the FBA canonical assumption of biomass optimality, i.e. assuming that all metabolic fluxes in the cell are geared towards the production of biomass in each moment of the growth curve. However, situations in which cells invest substantial resources in alternative metabolic processes at the expense of optimal growth rates have been analysed and described quite extensively. According to this scenario, cells may invest substantial (energy) resources in a specific metabolic process at the expense of optimal growth, this being reflected by sub-optimal flux distributions , 29, 30.This situation might be observed, for example, when microbes allocate energetic resources for anticipating changing environmental conditions at the expense of optimal growth . Also, wPhTAC125 biomass objective function could not be fully optimized but, instead, in a near-optimal or sub-optimal state. Since FBA classically assumes the optimization of the biomass production flux to compute the most likely fluxes distribution inside the cell, it cannot per se provide hints concerning alternative  fluxes distribution. For this reason, we have used MOMA [To explore this alternative scenario, we have accounted for the possibility that the sed MOMA  to studyPhTAC125 growth and on the set of active pathways.Applying this modelling strategy to our study case revealed that the choice of the optimization criterion  has a great influence on the predicted fluxes distribution, both in terms of the fraction of the entire network required to sustain First, a higher number of active reactions compared to the original FBA predictions were predicted for each of the growth phases, when the implemented nutritional-MOMA approach was used Fig. .Fig. 3CoThe fact that nutritional-MOMA always predicts a higher number of active reactions might be explained by considering that to optimize the model using FBA identifies the flux distribution maximizing the objective function (biomass optimization), regardless of the metabolic states in the previous time steps. Conversely, nutritional-MOMA will seek for the solution (fluxes distribution) that is closer to the one of each previous time step . Therefore, the results of MOMA will likely include a higher number of reactions in the model to produce biomass, since it will minimize the changes (i.e. the active reactions) with respect to the previous time steps, while activating novel reactions to cope with the changes in the nutrient composition.PhTAC125 metabolic network. Thus, for each metabolic pathway in the model, we computed the fraction of reactions predicted to be active by nutritional-MOMA, FBA and both methods  reactions between the two approaches were involved in specific pathways or, rather, spanned over a larger part of the entire ods Fig. . Resultsods Fig. . Notablyods Fig. . The lasods Fig. .Fig. 4FuPhTAC125 in each time point (i.e. using each particular nutrients set) and, in other words, to infer how far from the actual fluxes distribution our in silico predictions are.To summarize, using our MOMA-based approach we identified a higher number of reactions predicted to be carrying flux in respect to the FBA-based optimization. The nutritional-MOMA approach predicted larger active metabolic networks throughout the analysed phases possessing, on average, about 150 flux-carrying reactions more than the one simulated with FBA. Furthermore, despite most of the active reactions identified through FBA were also identified by the nutritional-MOMA approach, in some cases these predictions differed significantly from a functional viewpoint  share conserved upstream motifs .We analysed these clusters to answer two biological questions, namely whether the embedded genes i) are bona fide functional patterns. Accordingly, this result corroborates the capability of our modelling framework to identify functional metabolic modules and the functional associations of their encoding genes in PhTAC125. Interestingly, in some cases apparently un-related genes  seem to be embedded in the clusters. Despite these instances may represent erroneous predictions of our model, they might also suggest the presence of still undetected functional associations and/or the use of common pools of chemical intermediates by the corresponding reactions (see below).To address the first point, we exploited the combined evidence score provided by the STRING database. As shown in Fig. in silico analyses.Cluster 23. This cluster is composed of seven reactions encoded by 19 genes. Three of these reactions are predicted to be catalysed by enzymes encoded by multiple genes, i.e. cytochrome-c oxidase encoded by PSHAa2869-71, Ferrocytochrome-coxygen oxidoreductase encoded by PSHAa1842-45 and ATP synthase encoded by PSHAa3007-15. According to the GPR rules of our model, the simultaneous presence of all the corresponding genes is required to carry flux of each of these reactions. The functional association network obtained probing the STRING database with all the genes of Cluster 23 is reported in Fig. ndk gene (encoding a nucleoside diphosphate kinase) is also found connected to one of the sub-cluster Fig. Cluster 11. We found eight reactions (encoded by 10 genes) embedded in this cluster ). The STRING-based evidence network in Fig. acsA) is also embedded in this cluster, despite it has not connections with arginine and proline metabolism-related genes. However, its presence might reflect the necessity to synthesize acetyl-CoA from acetate during the first four growth phases identified, i.e. in the absence of ketogenic amino acids. Once PhTAC125 starts metabolising the second set of amino acids, acetyl-CoA can be obtained from the degradation of specific amino acids  and thus the reaction encoded by acsA (PSHAa0698) is predicted to stop carrying flux.We here present a description of the functional relationships retrieved for genes belonging embedded in three of these clusters Fig. . A detaiad hoc computational pipeline (implementing tools from the MEME suite) to analyse the upstream regions of the genes (or arrays of genes in case of operons) embedded in each cluster. This resulted in the identification of motifs that are shared in the upstream regions of all the genes for each cluster. Upstream regions were identified using DOOR (Database of prOkariotic OpeRons) [In the previous section we reported a list of gene sets which are, presumably, functional partners and whose activity is concerted during the different metabolic switches. If this holds, it is reasonable to hypothesize that some of these clusters might embed co-regulated genes that, in turn, may share conserved upstream motifs . Thus, to address this point we applied an OpeRons) . These hOpeRons) , PRODORIOpeRons)  and RegTOpeRons) ), to ideThe whole set of data related to the identified motifs and their annotations , for each database, is provided as Additional file Cluster 2. This cluster, as described previously, is involved in the metabolism of Arg and Pro. A search on the RegPrecise regulon database [argD which, according to the RegPrecise database is not part of the arginine regulon in PhTAC125, and the other genes involved in the biosynthesis of Pro. However, both the computational approach described above and a manual search for putative transcription factor binding sites upstream of these genes detected the already known conserved motif shared by the other arg genes of the regulon , encoding the succinate dehydrogenases system, as well as other enzymes involved in energy conversion, such as other dehydrogenases  and other enzymes with a role in nucleotide modifications . Our computational pipeline predicted two different regulator genes associated with this cluster, i.e. ccpA and psrA , while the remaining two encode a galactokinase  and a galactose-1-epimerase . All these genes are involved in the metabolism of nucleotides, since folate is necessary to synthesize TDP sugars from UDP, while the other genes encode enzymes that are involved in the synthesis of UDP. The analysis of the upstream sequence of these genes revealed a conserved motif similar to the GalR binding site, thus suggesting that these genes are being co-regulated  to the exhaustion of most of the first metabolized C sources , a time step in which only Glu is used as the sole C source, and the final part of the growth curve in which PhTAC125 stops using Glu and relies on a completely different set of nutrients. Our model highlights the occurrence of such an adaption and the need for reprogramming a large set of reactions to maintain an efficient metabolic network. A similar scenario is observed at the end of the growth, when His is used as the sole C source. This transition is the most demanding, requiring a change in the predicted flux of almost 450 reactions.Our modelling framework identified the central phase of the growth curve as a probable key reprogramming point, with more than 400 reactions predicted to adjust their flux in these time points. This corresponds  that are required to sustain life across all the time points examined. Interestingly, this set of reactions resembles, in percentage, the size of the minimal metabolic network predicted to be active in E. coli   and thus are expected to cause a re-wiring of an important fraction of the network.To maintain such a constant trend, however, a deep reprogramming of the whole cellular metabolism appears to be necessary during the entire growth period, as up to more than 400 biochemical reaction, display at least one change in the predicted carried flux. According to our simulations, these changes do not only involve peripheral metabolic pathways (e.g. amino acid catabolism) but also a number of central pathways, e.g. TCA cycle, glycolysis and PP pathway. TCA cycle, for example, displays an increasing trend in the number of flux-carrying reactions parallel to the exhaustion (in the medium) of the amino acids whose degradation provide key TCA cycle intermediates. This is in line with the assumption that fluxes distribution within the cell are influenced by the entry-point of the a given C-source into the metabolic network . Indeed,PhTAC125 metabolic genes and, as in the case of the Arg regulon, to expand the current knowledge on commonly regulated genes. Furthermore, we have assembled a dataset of putatively co-regulated genes that can be used for further manual curation and/or subject to experimental validation.This, in turn, suggests the presence of an efficient metabolic reprogramming machinery (that includes the regulation of the expression of the corresponding genes) to continuously and promptly adapt to this nutritionally changing environment and/or to the exhaustion of the preferred carbon source(s). Modelling and dividing the growth curve in discrete time points has allowed us to infer common trends in the predicted flux patterns of the reactions of the model. We have shown that such coupled patterns likely correspond to reactions and enzymes that work in a concerted fashion and that, in some cases, represent functional partners and/or be encoded by co-regulated genes. The modelling framework we have set up here has allowed gaining insights on the (co-)regulation of PhTAC125 cells. As it is currently not possible to unambiguously discern among these two alternative solutions provided by FBA and nutritional-MOMA, further experimental evidence (e.g. time-resolved transcriptomics) of PhTAC125 cells grown in complex medium will allow deriving a clearer picture of those pathways that are really active during the growth and, consequently, which of the two approaches outperforms the other.Despite FBA-based predictions led to the identification of biologically consistent trends, the nutritional-MOMA approach we have implemented here suggests that care needs to be taken when choosing the objective function for constraint-based metabolic modelling. Indeed, when we accounted for the possibility that cells could be in a near-optimal or sub-optimal state  the two modelling frameworks predicted quite a different topological and functional reprogramming of


Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey’s Regression Equation Specification Error Test (RESET), the F-test, and the Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates; (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates; (3) the F-test could efficiently detect missing reactions; and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect, and resolve model misspecifications in the overdetermined MFA. The ability of biological systems to produce highly complex molecules at high enantiomeric excess has pushed metabolic engineering that relies on directed alterations of the cell’s biochemical reactions through recombinant DNA technology to the center stage of biotechnology ,2. The u13C isotopic labeling experiments has emerged and matured over the past two decades. The experimental, analytical, and computational procedures for 13C-based MFA have now been standardized [A simple strategy of MFA, from here on referred to as the overdetermined MFA, uses a reduced stoichiometric model of the cell’s metabolism such that the estimation of the metabolic fluxes is mathematically well-posed, i.e., the flux estimation involves an (over)determined system. For larger and more realistic metabolic models, the flux estimation in the MFA often becomes underdetermined as the number of unknown fluxes exceeds the number of balance equations. There has been a flurry of activity in the development of MFA methods for large metabolic models based on linear programming  , which gdardized .13C isotopic labeling experiments, the overdetermined MFA continues to be used in practice, thanks to its simple formulation and numerical implementation [t-test) [Due to the experimental and computational complexity in the application of MFA using genome-scale metabolic models and entation ,7. A comentation . In consentation ,10. The entation  or by prentation . Finallyt-tests [The test for goodness of fit or statistical significance of regression in the overdetermined MFA could fail because of several reasons, including (1) incorrect assumptions abuot the characteristics of data noise  and (2) model specification errors ,15. In st-tests . In this work, we adapted statistical analysis and tools for model misspecifications commonly used in the field of linear least square regression to address the issue of missing reactions in the overdetermined MFA. We posited that the simplification of the stoichiometric model, either manually or using a numerical algorithm , to genec is the vector of m metabolite concentrations, v denotes for the vector of n metabolic fluxes, and S denotes the S and the flux vector v into: The MFA is based on the mole balance equation for the intracellular metabolites under a pseudo steady state assumption, as follows:y denotes the vector of measured response variables, X denotes the (non-random) design matrix containing the values of explanatory variables,The estimation of the internal metabolic fluxes By invoking the first order necessary condition for optimality  and the least square regression problem in Equation 4), we could set , we coult-test, and simulated flux values to show that model errors could lead to gross deviations in flux estimates that are not statistically significant [Due to the rank condition on nificant . In the u denotes the vector of measurement errors for the true model. Without a loss of generality, in order to illustrate the impact of omitting reactions, we could consider the OLS estimate of the internal fluxes u has zero mean, the flux estimate In the following, we considered the problem of missing or omitted reactions in the stoichiometric matrix. We assume that the metabolic network model in the MFA given in Equation (1) is incomplete and that the true mole balance is governed by:S contains reactions that are non-existent in the actual system, would not introduce any bias to the OLS estimate of the fluxes. However, having additional reactions would artificially increase the variance and mean square error of the flux estimate [The derivation above shows how the omission of or failure to include reactions in the stoichiometric model could cause a bias in the flux estimate in the overdetermined MFA. In addition to the flux bias, missing reactions would also result in a lower variance, i.e., the variance of the OLS estimate of ion (11) . Under cion (11) . On the estimate .Xβ is a good description of the response variable y. While tests for checking the validity of constant variance or the normality of the error variables in the least square regression also exist, we do not deal with these so-called misspecifications of higher moments. Interested readers are referred to the article by Long and Trivedi [p order RESET test is based on the following linear least square problem:p powers of 0: y, K is the dimension of the unknown parameterp and There exist several tests to detect model misspecification in a linear least square regression. In this study, we focus on tests of the misspecification of regression mean; whether  Trivedi . The fir Trivedi , which dZ denotes the design matrix of the candidate missing variables; namely, the stoichiometric matrix of the possible missing reactions in the overdetermined MFA. The next two misspecification tests apply to the scenario in which the stoichiometry of the candidate missing reactions are known. Given the current extensive knowledge on metabolic reactions, including the complete maps of metabolic networks for many major organisms and the availability of extensive and curated biochemical reaction databases  , MetaCyc0: o is the number of possible missing variables SF−tZ in Equation (18), denoted by denoted by X. In addition, the LM test also incorporates a heteroscedasticity-consistent (HC) covariance matrix to accommodate the situation when the variance of the measurement error is not constant. Following the procedure formulated in Long and Trivedi [1 is the (column) vector of 1s with the same row dimension as the matrix X. The test statistic is given by:0: o degrees of freedom. The rejection of the null hypothesis due to a  significant correlation between the residual The third and last misspecification test in this work is derived from the Lagrange multiplier (LM) test, as proposed by Davidson and Mackinnon . The pre Trivedi , we firsEv, the stoichiometric matrices ES and IS, and the possible missing reaction stoichiometric matrix AS, we formulate the linear least square regression problem with Given the exchange fluxes Z constructed from every k-tuple combination of the columns (reactions) of AS. Compute k-tuple combination(s) satisfying AS to IS.Identify the AS to IS, that is, until the remaining set of k-tuple reaction combinations satisfying Repeat steps 2 to 3 until no more reactions can be moved from The rejection of the null hypothesis in the tests above does not immediately point to the identity of the missing or omitted reactions. Given the set of possible missing reactions, we could nevertheless apply the F-test or LM test to determine whether the addition of a reaction or a set of reactions among the possible missing reactions would significantly improve the linear regression. Reactions that are positively identified by the tests could therefore be added into the stoichiometric matrix. The procedure above can be repeated until no more reactions can be added. More precisely, we propose the following iterative procedure for correcting misspecifications in the stoichiometric matrix for the overdetermined MFA using the F-test:k-tuple combination of reactions. In the case study, we performed the procedure, first using k = 1 (single reactions) and using k = 1 followed by k = 2 (pairs of reactions). If desired, the LM test could also be used in place of the F-test.The procedure above is written generally for any IS has a dimension of 49 metabolites and 47 internal fluxes with a full column rank. IS in the MFA. The complete list of reactions, the measured uptake flux values, and the standard deviations reported by Sokolenko et al. [In the demonstration of the specification bias caused by missing reactions and the iterative procedure for resolving the stoichiometric matrix misspecification, we employed a metabolic network model previously created for the flux analysis of Chinese hamster ovary (CHO) batch cultivation data ,27. The o et al.  are giveIS of various sizes (m = 50 to 200 metabolites and n = 55 to 220 reactions) and with different numbers of exchange fluxes (between 25 and 100). More precisely, for each data vector y, we employed the RMBNToolbox (MATLAB) [IS with the desired dimensions and a full column rank, wherein each species participated in at least one reaction. For the specified number of exchange fluxes Em, we assigned the first Em metabolites (rows) of IS as the species whose exchange fluxes were measured, while the remaining species only existed intracellularly (without exchange fluxes). More specifically, we used the matrix IS into:Em rows of the measured exchange fluxes and Iv using a linear combination of the kernel of Iv in Step (1) according to Equation (22) and contaminated the exchange flux vector with a vector of independent realizations of Gaussian distributed random numbers with zero mean and for different coefficients of variation (CoV of 1 to 20%). Note that since the noise standard deviation scales with the noise-free y  from the matrix For a large-scale evaluation of the model misspecification tests, we generated in silico data (MATLAB)  to generThe performance of each misspecification test was judged based on the rate of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) results. For the Ramsey RESET test, the true positives and false negatives were determined by applying the test to the generated data using the stoichiometric matrix y as described above and applied the tests using the stoichiometric matrix Z came from the set of actual reactions that were removed from the matrix Z containing a distinct set of linearly independent reactions of the same size as the set of the omitted reactions. While the arrangement for determining the FP and TN rates does not reflect the null hypotheses in the F- and LM tests, it reflects better the typical scenario in practice (where the true model is unknown). The rates of TP, FN, FP, and TN were computed from 1000 repeated runs of the above tests. Finally, in all of these tests, we ensured that the rank condition for the OLS estimation was always satisfied.In order to determine the TP and FN rates of the F-test and LM test, we generated in silico data In this case study, we considered the CHO metabolic model in ,GLS see . Figure gression . p values from the ANOVA are given in p value of 0.05 as the threshold to reject the GLS regression; any p values higher than the threshold indicate a poor regression outcome. viations . For eacp < 0.05. On average, the median, mean, and maximum specification biases in the flux estimates were higher for the removal of reactions that caused a poor regression (p > 0.05). The two highest p values expectedly came from the removal of reactions with the two largest fluxes, and each expectedly had large specification biases. There were nevertheless exceptions where a poor regression resulted from removing a reaction with a moderately low flux value . On the other hand, many of the cases with a significant regression (p < 0.05) were associated with high maximum specification biases. In fact, several of the cases among the lowest p values  had a mean bias of >30% and a maximum bias of above 800%. Equally important, the removal of several reactions with a low flux magnitude led to large mean and maximum flux biases (mean bias >150%), as highlighted in The individual removal of roughly 3/4 of the reactions (26 out of 36 reactions) still produced a significant regression with IS. As outlined in Materials and Methods, we determined the rates of TP, TN, FP, and FN using randomly generated pairs of data IS with missing reactions. For the F- and LM tests, we used the information on the actual missing reactions, as well as a distinct set of reactions, as the design matrix of the missing variables Z. The results using the baseline MFA problems with 100 metabolites (m), 60 unknown internal reactions  in IS was closer to the true system and could more accurately capture the flux balance. Therefore, for the F-test to correctly detect a misspecification of IS, the missing reactions would need to cause a significant deterioration in the data fitting, a scenario that was less likely to occur as the number of missing reactions became lower. Meanwhile, the FP rates were not a strong function of the noise level. The FP rates improved with a lower number of missing reactions, albeit only slightly. In general, the results in m = 200), the rate of correctly detecting a misspecification with two missing reactions was slightly lower than 60%. Fortunately, the TP rates for detecting five or more missing reactions were still high (>88%), and the FP rates depended weakly on the size of the networks and the number of missing reactions and remained relatively low, between 10% to 15%, in most of the cases in our study  . Since tThe LM test performed better than the RESET test but produced lower TP rates than the F-test, particularly at the highest number of missing reactions  k = 1 and (2) k = 1 followed by k = 2. In the last case study, we evaluated the performance of the proposed iterative procedure to resolve stoichiometric matrix misspecifications in the overdetermined MFA . Here, we returned to the flux analysis of the CHO metabolic network in mate see  and contAS that were not positively identified by the iterative procedure to be included in the stoichiometric matrix. As an indication of good performance, the number of omitted reactions (extra reactions) that remained should be low (high). The results in k = 1 was able to correctly detect and incorporate almost all of the omitted reactions, while keeping the incorrect inclusion of extra reactions low. As expected, performing an additional run with k = 2 after finishing the procedure with k = 1 led to a higher incorporation rate of the omitted reactions, but such a strategy came at the cost of a higher rate of incorrect addition of the extra reactions. Due to the small size of the CHO model and the number of missing reactions considered, a higher k  led to the incorporation of all omitted and extra reactions . In this work, we adapted statistical analysis and tests for model misspecifications in linear least square regressions. To the best of our knowledge, such analysis and tests have not yet been applied to the flux estimation problem of the overdetermined MFA. Here, we first derived a simple formula to evaluate the specification bias in the flux estimate due to missing reactions in the stoichiometric model. Using a stoichiometric model of the CHO metabolism, we showed that the significance of the regression is not a sufficient indicator of a low flux bias. In particular, the omission of reaction(s) that results in a high bias (error) in the flux estimate could still produce a statistically acceptable regression. Furthermore, we demonstrated that the removal of reactions with a low flux value could also cause disproportionately large specification biases. In practice, the significance of the regression and the prior information on the magnitude of reaction fluxes are often used for the curation of a reduced order stoichiometric model for the overdetermined MFA. Our findings from the first case study clearly motivated computing the potential flux specification bias during the removal of reaction(s). k reactions from the set of candidate missing reactions that would  significantly improve the linear regression. The combinations that passed a certain p value threshold are then incorporated in the stoichiometric model. The application of this iterative procedure to the CHO stoichiometric model indicated that the simple implementation using k = 1 gave the most robust performance. The computational cost of performing the iterative procedure scales linearly with the number of candidate reactions when using k = 1 or combinatorically when using k > 1. Fortunately, since the F-test involves only computationally efficient matrix inversions and matrix-vector multiplications, each iteration of this procedure takes little time to finish. For example, each iteration in Case Study III, with eight missing reactions, using k = 1 finished in roughly Among the statistical tests that we evaluated for detecting stoichiometric matrix misspecifications, the results from random metabolic networks clearly demonstrated the superiority of the F-test. The F-test could provide high TP rates and low FP rates for detecting missing reactions for most problem sizes (except when there were only very few missing reactions). Based on these findings, we proposed an iterative procedure using the F-test to detect and correct missing reactions. In each iteration, we used the F-test to identify a combination of Finally, there exist several obvious limitations in the statistical tests evaluated in this work. First, these tests rely on the assumption of normality for the noise. The validity of this assumption could be checked by standard normality tests such as the Lilliefors or Shapiro-Wilk tests  or usingIn this work, we addressed the misspecification of the stoichiometric matrix in the overdetermined MFA, particularly the omission of reactions. For this purpose, we adapted statistical analysis and tools from linear least square regression to quantify, detect, and resolve the issue of missing reactions. In particular, we derived a simple formula to evaluate the flux bias caused by missing reactions in the overdetermined MFA. We further assessed the performance of several model misspecification tests, namely Ramsey’s RESET test, the F-test, and the Lagrange multiplier test, in detecting missing reactions for overdetermined stoichiometric models. Finally, we proposed an iterative procedure for resolving the issue of missing reactions based on applying the F-test to the candidate missing reactions one at a time and incorporating reactions that pass the significance test. The application of these techniques to the CHO metabolic model and random metabolic networks provided several important conclusions. First of all, the significance of the regression, a common metric for assessing the data-model consistency in the overdetermined MFA, does not guarantee a low bias in the flux estimates. In addition, a high flux bias could result from the removal of a reaction with a low flux magnitude that would be typically deemed unimportant in the construction of the stoichiometric matrix. Therefore, the potential flux bias due to any removal of reaction(s) should be computed during the construction of the stoichiometric model. When the stoichiometry of the candidate missing reactions is known, the F-test provides a robust means, with a high TP rate (nearly 100% for many cases) and a relatively low FP rate (<15%), to detect model misspecifications in the overdetermined MFA. Upon a positive detection of model misspecification in the overdetermined MFA, the proposed iterative procedure in this study gives an effective and robust systematic approach to resolve this issue.