Pseudomonas aeruginosa is one of the leading causes of hospital-acquired infections. To decipher the metabolic mechanisms associated with virulence and antibiotic resistance, we have developed an updated genome-scale model (GEM) of P. aeruginosa. The model (iSD1509) is an extensively curated, three-compartment, and mass-and-charge balanced BiGG model containing 1509 genes, the largest gene content for any P. aeruginosa GEM to date. It is the most accurate with prediction accuracies as high as 92.4%  and 93.5% (substrate utilization). In iSD1509, we newly added a recently discovered pathway for ubiquinone-9 biosynthesis which is required for anaerobic growth. We used a modified iSD1509 to demonstrate the role of virulence factor (phenazines) in the pathogen survival within biofilm/oxygen-limited condition. Further, the model can mechanistically explain the overproduction of a drug susceptibility biomarker in the P. aeruginosa mutants. Finally, we use iSD1509 to demonstrate the drug potentiation by metabolite supplementation, and elucidate the mechanisms behind the phenotype, which agree with experimental results. Pseudomonas aeruginosa explains the metabolic pathways leading to drug resistance and provides a computational platform to design experiments targeting P. aeruginosa metabolism.An updated genome-scale model of  Pseudomonas aeruginosa is a Gram-negative proteobacterium that is metabolically versatile and an opportunistic human pathogen. It is a leading cause of nosocomial infections2. One of the well-known P. aeruginosa infection sites is in the lungs of cystic fibrosis (CF) patients. Such infections can lead to high morbidity and mortality2. The ability to resist multiple drugs , synthesize virulence factors , and produce biofilms3 allows P. aeruginosa to infect and colonize its host. The pathogenicity of P. aeruginosa and the host response can differ between strains1.6. GEMs can be advantageous in investigating pathogens because identifying potential intervention strategies can be challenging due to the wide range of genetic mutations and metabolic targets, and niche-specific alteration of metabolic processes8. GEMs have been utilized for systems investigation of pathogenic species such as Acinetobacter baumannii10, Klebsiella pneumoniae11, Mycobacterium tuberculosis12, Vibrio vulnificus13, and P. aeruginosa15. For the development of GEMs, first a reconstruction of the metabolic pathways of the organism of interest is required, which can then be converted to a mathematical format that can be analyzed using constraint-based modeling and flux balance analysis (FBA) approaches.Genome-scale metabolic models (GEMs) provide a reliable tool for systems study of bacteriaP. aeruginosa continue to be discovered. Mechanistic models need to be updated continually to incorporate this new knowledge and to facilitate quantitative simulations. For instance, the characterization of multiple terminal oxidases19 and phenazine-dependent redox reactions continues to expand, and were not included in previous reconstructions22.Biological functions associated with virulence and drug resistance in P. aeruginosa using PA14 as the strain of choice. It is a hypervirulent strain and belongs to the most common clonal group23. A study in 2004 first published its genome, which is highly similar to P. aeruginosa PAO1’s genome, but carries two additional pathogenicity islands that contribute significantly to the virulence of PA1425. The metabolic model (iSD1509) is developed using human-interpretable BiGG 26 identifiers cross-referencing other databases. The iSD1509 contains the largest gene content of any GEM of P. aeruginosa to date, and demonstrates high prediction accuracy. Using iSD1509, we analyzed four different metabolic traits related to pathogenic behavior and drug resistance. First, using iSD1509, we investigated a recently observed pathway for ubiquinone-9 (UQ9) production that is necessary for growth in anaerobic conditions27. Since the ability to survive in anaerobic condition is associated with pathogenicity and biofilm growth, we decided to study these phenomena in Pseudomonas29. Hence, the knowledge from literature30 was incorporated into the current model. Second, phenazine production is associated with Pseudomonas survival in biofilm environment and virulence ultimately leading to drug resistance29. We used the limed-FBA approach31 to extend iSD1509 to investigate the effect of phenazine biosynthesis on the biomass production at various levels of oxygen availability. Third, since one of the biomarkers of virulence was determined to be gluconate production32, we used the model to demonstrate its predictive ability by simulating gluconate production in Pseudomonas aeruginosa mutants to provide mechanistic explanation for this metabolic behavior. Finally, through flux sampling analysis using iSD1509, we mechanistically elucidate the role of metabolite supplementation in antibiotic susceptibility potentiation to recapture the experimental findings33.In this study, we developed a highly curated reconstruction of iSD1509 was developed using the pipeline outlined  score of iSD1509 is 88%  substrate utilization in minimal media, and (2) gene essentiality. We used the same datasets utilized for validating iPau112914. The iSD1509 model can simulate growth on LB, SCFM (Synthetic Cystic Fibrosis Medium), and minimal media containing individual substrates (87 compounds). On the minimal medium, the model predicts substrate utilization with an accuracy of 94.3%. This is a significant improvement compared to the iPau1129 model (80.5%). The predicted biomass yield on the glucose minimal medium (0.089 gDW.(mmol Glc)−1) is within the range of experimentally determined yields for P. aeruginosa −1) at temperatures of 30 °C, 38 °C, and 41 °C, respectively) under similar conditions37.Next, we validated 24, we used the data for PAO138. For the simulation, the minimal medium as described in the study38 was used with glucose flux being constrained at 9.7 mmol gDW−1 h−1. FBA predictions and MFA results were highly correlated  results with the predicted FBA values for growth on glucose minimal medium. Due to the unavailability of MFA data for PA14 and since both PA14 and PAO1 strains are highly similariSD1509 and iPau1129 , iSD1509 retained a similar accuracy (iSD1509: 90.6% vs. iPau1129: 91.1%) by achieving a >5% higher recall , we computed gene essentiality on these three conditions. For LB, SCFM, and M9 minimal medium, overall accuracies were 92.4%, 92.3%, and 90.2%, respectively. Precision ranged between 51.2% (M9) and 64.0% (LB), and recall ranged from 57.2% (LB) to 57.9% (M9 and SFCM)  between the two models.Next, we assessed the model using new datasets. For the gene essentiality data, 321 core essential genes identified in a recent transposon insertion sequence study were used40. The model was simulated on 123 minimal media containing different substrates, and model simulated growth on 43 conditions 14, and it could not simulate growth either. Two biomass constituents– ubiquinone-9 (UQ9) and thiamine diphosphate (THMPP) were not produced during anaerobic simulation. Since the biomass constituents were predetermined in the previous study14, model simulations demonstrated a knowledge gap that needed to be filled for the model to grow anaerobically. For anaerobic THMPP production, no alternative pathway could be determined in P. aeruginosa either through exhaustive search in the literature or by annotation-based methods. The bottleneck for THMPP production is iminoglycine formation. In aerobic conditions, glycine oxidase is utilized by P. aeruginosa. However, for anaerobic conditions, no alternative source could be identified. One probability is that the enzyme 2-iminoacetate synthase has not yet been identified in this organism. Nevertheless, the possibility of the presence of yet uncharacterized pathways in the organism cannot be ruled out. Hence, we removed this metabolite and adjusted the biomass reaction creating an anaerobic biomass reaction 41 to determine their feasibility.In the case of anaerobic UQ9 production, a recent study27. The flux-sum was computed for UQ9 in aerobic and anaerobic conditions (as described in the Methods section). The model predicted that the UQ9 production is consistent in wildtype and mutants in aerobic conditions whereas it is drastically lowered from wildtype to mutants during anaerobic conditions  is higher in anaerobic over aerobic conditions. Cellular yield would be predicted as intracellular concentration (pmol/L) over biomass concentration (gDW/L). However, genome-scale metabolic models do not predict intracellular concentrations4. Here, we approximated cellular yield by computing UQ9 turnover as flux sum over growth rate. The computed UQ9 turnover values using iSD1509 were 25.2 mmol/gDW (anaerobic) vs. 24.3 mmol/gDW (aerobic). The higher UQ9 turnover in anaerobic vs. aerobic conditions is consistent with the observed differences in UQ9 yield. However, as noted, since intracellular concentrations are not computed, further investigation is needed in future studies.Following the changes, we could simulate anaerobic growth in LB medium. The oxygen or nitrate uptake rates were derived using the model simulations by constraining the growth rates estimated from the experimental dataiSD1509 to investigate phenazine production in P. aeruginosa. Pseudomonas can produce and secrete phenazines , which possess redox properties and can cycle in and out of the cell. Usually, they are reduced within the cell, then they go to the extracellular space to get oxidized (by donating electrons to acceptors such as oxygen), and finally get transported back in the cell to complete a redox cycle42. This knowledge has been added to iSD1509 using the information derived from the literature review48 to use the model for the identification of potential intervention strategies that target phenazine redox cycle  do not dilute metabolites that are not part of the biomass reaction, we applied limed-FBA on iSD1509. As phenazines take part in active reactions31 in low-oxygen condition, they need to be synthesized as cell divides. With limed-FBA, phenazines are diluted and replenished to achieve mass-balance31  as the pyocyanin synthesis flux decreased from 0.000904 mmol gDW−1 h−1 to 0.000882 mmol gDW−1 h−1, respectively, changing by only 2.5%. Hence, we decided to use 0.5 mmol gDW−1 h−1 as the lower threshold for oxygen uptake rate. Using the sensitivity analysis, we also decided to use 10 mmol gDW−1 h−1 as the upper threshold for oxygen uptake rate as the pyocyanin production rate decreased by half from the maximum production value ). For each value of oxygen import flux and pyocyanin biosynthesis flux, the growth rate was computed. With this analysis, we observed that for lower oxygen import flux (less oxygen available to the cell), the effect of pyocyanin production on the growth rate is pronounced. In contrast, when more oxygen is available to the cell, pyocyanin synthesis does not considerably contribute to the biomass production , the increase in growth rate over the pyocyanin production flux range is 0.41 h−1 whereas at highest oxygen uptake rate (10 mmol gDW−1 h−1), the increase in growth rate is 0.14 h−1.For each of those flux constraints, pyocyanin synthesis flux was also constrained over a range , the model predicts that phenazines are required for biomass production  correlated to reduced antibiotic susceptibility. Further, the investigators demonstrated that only the mutant of one of the indirect targets of RpoN, 6-phosphogluconate dehydratase  could replicate the gluconate overproduction phenotype of the rpoN mutant32.The loss of function mutation in iSD1509 to recapitulate the results of the study32 on the glucose minimal (M9) medium. Of the knockouts of the twelve indirect targets of RpoN, the FBA simulations accurately predicted that only the deletion of 6PGDH ) leads to a significant increase in the gluconate production compared to the wildtype  leading to increased flux through glucose transport reaction  l tag: PA1_22910) ledd mutant was a requirement for the optimal growth. The gluconate secretion (GLCNtex) reaction flux range  in the mutant (4.71 mmol gDW−1 h−1) is fixed at a higher rate than in the wildtype (0 mmol gDW−1 h−1) suggesting that for the optimal growth of the mutant, gluconate is forced to be transported out of the cell . We proposed that the gluconate production in 33 showed that the addition of fumarate along with tobramycin leads to greater drug uptake and activity whereas glyoxylate protects the cells from tobramycin lethality. The study showed that the higher tobramycin uptake and activity in fumarate-containing medium is due to greater proton motive force (PMF) and increased respiration rate caused by higher flux through the TCA cycle, respectively. Likewise, in glyoxylate-containing medium, glyoxylate, by directly inhibiting α-ketoglutarate dehydrogenase, diverts the flux away from TCA cycle towards the glyoxylate shunt leading to reduced TCA cycle activity. We used iSD1509 to test an alternative, i.e., whether the law of optimal growth can explain drug protection by glyoxylate supplementation. Furthermore, we used iSD1509 to study the pathway utilization differences between the two metabolite supplementations.Meylan et al.iSD1509 on minimal (M9) medium containing glyoxylate or fumarate, and low amounts of citrate by optimizing for the biomass production. The medium was derived from the study39 and the flux constraints were chosen based on a separate minimal medium information provided by Papin Lab. Citrate flux was arbitrarily constrained at low flux at 0.5 mmol gDW−1 h−1. Glyoxylate was not predicted to be a growth-inducing substrate in the substrate utilization assessment step. However, Meylan et al.33 performed wet lab experiments clearly showing that glyoxylate supplementation in culture media induced phenotypic changes to P. aeruginosa, including suppression of electron transport chain activity. Such phenotypic responses likely require uptake of glyoxylate into the cell, presumably via an uncharacterized transporter . Therefore, an artificial glyoxylate uptake reaction was added to the model. We performed a flux sampling analysis by constraining the biomass flux to 90% of the estimated growth rate (by FBA) on both media assuming the cells optimize for high growth rates to counterbalance the effect of the drug killing. For proton flux calculations, we computed the flux-sum yield of periplasmic proton (as described in the Methods). Then, we compared the median fluxes of the reactions pertaining to TCA cycle and glyoxylate cycle between the two media conditions. The model predictions agreed with Meylan et al.33 that the glyoxylate shunt indeed drives the flux away from TCA cycle in glyoxylate-containing medium. Likewise, the simulations also indicate that the glyoxylate flux is diverted towards the reactions catalyzed by malic enzymes, which recycle the necessary cofactors– NADH and NADPH. Unlike the Meylan study, the model predicted higher flux activity through pyruvate dehydrogenase reaction in the glyoxylate minimal medium. Moreover, the production of oxalate and glycolate were not confirmed by the model predictions. Instead, the glyoxylate flux was diverted towards the reaction catalyzed by glyoxylate carboligase. We also observed that the oxygen uptake rate and proton flux-sum yield were higher in the fumarate- than in the glyoxylate-supplemented medium, which leads to increased drug uptake and activity in fumarate treatment33 3 and an ability to survive within the biofilm environment, eradication of Pseudomonas infections can be challenging. Since it contains extensively curated literature-derived knowledge, iSD1509 is highly predictive and can be used as a platform to design experiments and strategies to combat infections caused by P. aeruginosa.iSD1509 is a highly predictive and reliable model with accuracies of 90.2–93.4% and 93.5% for gene essentiality and substrate utilization predictions, respectively. Further, guided by the knowledge gap in our model, we identified that the biosynthesis of ubiquinone-9 is crucial for the anaerobic growth of P. aeruginosa, and added the information from recent studies30 to fill this gap allowing model to simulate growth in anaerobic conditions. The model also predicted that the survival of P. aeruginosa in low oxygen environment (such as within biofilms produced in chronic CF infections) is possible due to phenazine production and provided mechanistic insight into their biosynthesis. With the model, the role of phenazines in CF condition can be explored further. For instance, how different media condition affect phenazine production can be studied using this model56. Furthermore, using iSD1509 along with individual-based models, biofilm production and the role of phenazines in CF infections can now be investigated57.In this study, we have demonstrated that iSD1509, more computational experiments can be designed to identify other mutants that overproduce gluconate. Likewise, other possible biomarkers of drug resistance can be predicted. Furthermore, using multi-strain modeling approaches58, models of various P. aeruginosa isolates can be simulated to compare biomarker production simultaneously. Other host pathoadpative mechanisms such as pyruvate overproduction in pyruvate dehydrogenase mutant strains59 can also be investigated using iSD1509.The model successfully identified the mutant that produced higher amounts of gluconate than the wildtype among twelve candidates. Using iSD1509 to recapitulate the potentiation of an antibiotic by metabolite supplementation33. The model correctly differentiated between metabolites that increased drug lethality versus those that did not and offered mechanistic explanations for these responses. The model iSD1509 predicts that Pseudomonas possesses mechanisms to uptake glyoxylate by yet unknown systems as the simulations  were comparable to the experimental results. Namely, supplementing glyoxylate diverted flux away from the TCA cycle, lowering the respiration rate that consequently led to lower drug activity. Supplementing the medium with fumarate caused higher oxygen uptake rate due to higher TCA cycle activity which leads to higher drug activity33. Likewise, PMF (and ultimately drug uptake) was higher in the fumarate-containing medium than in the glyoxylate-containing medium. These results suggest that our model can be used to design antimicrobial strategies based on metabolic mechanisms, including metabolite supplementation, against P. aeruginosa. This genome-scale model can be integrated with machine-learning approaches for mechanistic understanding of antibiotic efficacy. This model can be applied in such pipelines as demonstrated in recent studies60.Finally, we used P. aeruginosa metabolism at various growth states including within the biofilm environment. Likewise, novel predicted targets of P. aeruginosa  can now be investigated to identify the best strategy to inhibit the growth of the pathogen. Overall, we expect the model to significantly accelerate our understanding of P. aeruginosa to combat the associated infections.In conclusion, we have developed a highly curated model that provides a computational platform to design experiments targeting 14 unless stated otherwise. Carbon source uptake rates  were constrained at 10 mmol gDW−1 h−1 whereas uptake rates for terminal acceptors (oxygen or nitrate) were constrained at 20 mmol gDW−1 h−1. Any changes to these bounds are mentioned within the text. For a more detailed method on media formulation, please see 61. In most simulations excluding anaerobic growth predictions, we used the aerobic biomass reaction as the objective function. For anaerobic growth predictions, we used the anaerobic biomass reaction. If the biomass flux was computed to <10−5 h−1, “no growth” was assigned.For the simulation of the model, we performed flux balance analysis (FBA) on conditions reflecting the media used in a particular experimental study (Supplementary Table growthrates package (v. 0.8.4) in R (v. 4.2.0). The function fit_growthmodel was applied for logistic growth model with the BFGS method for both aerobic and anaerobic conditions. The growth data used for this analysis was estimated from Vo et al.27 using Engauge Digitizer (v. 12.1)62. The model was constrained using the derived growth rates to determine the uptake fluxes for the oxygen or nitrate in respective conditions. In aerobic condition, oxygen uptake rate was constrained at 20 mmol gDW−1 h−1 and nitrate uptake rate at zero and vice-versa for anaerobic condition. Likewise, the ubiquinone-8 reactions were turned off for both condition whereas the oxygen-associated terminal oxidases were shut down for anaerobic condition only. Next, the computed uptake rates were added as lower bounds on the model. The gene knockouts were performed using delete_model_genes module in COBRApy in aerobic and anaerobic conditions. The simulations were performed by optimizing for biomass production followed by the computation of flux-sum for ubiquinone-9 for wildtype and mutants.For these simulations, first the growth rate was determined using 63 (with loopless method and fraction of optimum set to 1) were applied. All simulations were performed in M9 medium. Experimental data to validate gluconate production were obtained from Behrends et al.32, where measurements for RpoN mutant and wild-type were performed in M9 media. Gluconate production for mutants besides RpoN were measured by Behrends et al.32 in SFCM media. However, for consistency and to facilitate interpretation, we simulated all mutants in M9 medium  and found that these simulations were consistent with experiments in SFCM medium (a rich medium).For the computation of gluconate production, both FBA and flux variability analysis (FVA)To compare the wild-type and mutant fluxes, we applied a heuristic approach. First, any computed flux values were increased by 0.01 to avoid “division by zero” error as mutant reaction fluxes were divided by those of the wildtype during normalization step. For FBA simulations, the reaction fluxes of the mutant were divided by those of the wildtype. For FVA fluxes, an average flux of each reaction was calculated by taking the mean of minimum and maximum flux values for both mutant and wildtype. Then, the mutant average reaction flux values were normalized by those of the wildtype.−1 h−1 to 20 mmol gDW−1 h−1 (with step 0.01 mmol gDW−1 h−1) to compute pyocyanin synthesis flux to determine the range of oxygen uptake rates to use for future simulations , please see 41 in Python (v. 3.7.7). The parameters- pH = 7.5, ionic strength = 0.25 M, temperature = 25 °C, control of magnesium ion (pMg) = 3 were used. Physiological concentrations (aqueous reactants at 1 mM) were assumed for the calculation of Gibbs free energy of transformation (ΔG’m). Reactions with ΔG’m<0 were added to the model. For instance, reactions NADNAQ_q8, NADNAQ_q9, rxn12880_pphn, rxn13107_pphn, rxn13108_pphn were added using this method.For the addition of uncharacterized reactions derived from the literature, we first checked for mass-and-charge balance. Then, we entered the reactions in the eQuilibrator program (v. 0.4.1)31. Briefly, all the reactions were first made irreversible. Then, for the reactions consuming or producing the desired cytosolic cofactor , a small dilution constant (ϵ) was applied such thatSij ≠ 0, and ϵii for metabolite i was computed using the following formula,ϵii was made zero. Likewise, for simple transport reactions in which no chemical transformations  → pyocyanin (cytosol)) occur, their binary coefficients were assigned zeros to avoid any high-flux loops.For the simulation of dilution of cofactors , we used limed-FBA approachκ) by taking into account the changes in the amount of pyocyanin (which can lead to superoxide production) and hydrogen peroxide (a product of superoxide dismutation) produced after 24 hours of growth21. According to this method,ODs represent the ratios of absorbance measurements at 0 and 24 hours for either phenazine or hydrogen peroxide which were approximated from Das et al.21. Then, we multiplied the stoichiometric coefficients of reaction (2) by κ and those of reaction (1) by 1-κ before adding up the reactions to generate a net reaction. The value of κ was estimated to be 0.29. Therefore, the net reaction used in the model is: 0.935 o2_e + pyoh2_e → 0.58 h_e + 0.71 h2o_e + 0.58 o2_e + pyo_eWe reconstructed the superoxide leakage by creating a net reaction composed of two: (1) normal reaction, in which water is produced as a by-product of redox metabolism (0.5 o2_e + pyoh2_e → h2o_e + pyo_e), and (2) leaky reaction in which superoxide is produced as a by-product (2.0 o2_e + pyoh2_e → 2.0 h_e + 2.0 o2s_e + pyo_e). We estimated the ROS leakage stoichiometric coefficient  for metabolite i can be computed using the following formula,j is the index for a reaction in which the metabolite i participates in.For the metabolites of interest, we performed flux-sum analysisFor the computation of flux-sum yield, the flux-sum was divided by the substrate flux multiplied by the number of carbon atoms in the substrate (for proton motive force (PMF)). For computation of UQ9 turnover, the flux-sum was divided by the growth rate.65 in the COBRApy package61 by using optGpSampler66 with 10,000 samplings in both fumarate and glyoxylate minimal media. The growth rate was constrained to 90% of the rate predicted in FBA simulations in the respective media condition. By using the validate method of the optGpSampler, only valid samples were kept for further analysis. Furthermore, we used the autocorrelation plots and trace plotting for convergence analysis.We applied flux sampling approachn = 10,000 samplings) between the two different media conditions (fumarate versus glyoxylate), we performed Wilcoxon rank-sum test. In this test, the alternative hypothesis is that measurements in one sample is more likely to be larger than those in the other sample. A p-value ≤ 0.05 is considered statistically significant. Data were analyzed using scipy package (v. 1.7.3) in Python. Median of the distribution was computed using numpy package (v. 1.21.5).To compare the distributions of reaction fluxes or metabolite flux-sums .We first created a preliminary Escherseaborn package (v. 0.10.1) in Python. All the respective flux values were divided by their respective wild-type values. To avoid division by zeroes, all computed fluxes were first offset by 0.01. For color range, robust quantile computation function was applied.We produced the heatmaps using Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary DataSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


The retina is one of the most metabolically active organs in the body. Although it is an extension of the brain, the metabolic needs of the retina and metabolic exchanges between the different cell types in the retina are not the same as that of the brain. Retinal photoreceptors convert most of the glucose into lactate via aerobic glycolysis which takes place in their cytosol, yet there are immense numbers of mitochondria in photoreceptors. The present article is a focused review of the metabolic dysregulation seen in retinopathies with underlying vascular abnormalities with aberrant mitochondrial metabolism and Hypoxia-inducible factor (HIF) dependent pathogenesis. Special emphasis has been paid to metabolic exchanges between different cell types in retinopathy of prematurity (ROP), age-related macular degeneration (AMD), and diabetic retinopathy (DR). Metabolic similarities between these proliferative retinopathies have been discussed. The retina is formed by very well-organized layers of different types of cells with close metabolic symbiotic exchanges between these cell types . RetinalRetinal pigmented epithelium, retinal blood vessels, and neurons are metabolically coupled to the retinal macroglial Müller cells . Müller The retina and RPE are mutually dependent on metabolic exchanges between them. RPE cell death leads to photoreceptor cell death in the retina, and vice versa . RPE relRetinopathy of prematurity is a neurovascular retinal defect in prematurely born infants. Supplemental oxygen is needed to prevent mortality in the prematurely born infants with underdeveloped lungs . On one In utero, blood vessels develop in a relatively hypoxic environment. Blood vessel development needs a hypoxic environment, where HIF-dependent pathways such as VEGF gradients, lactate gradients derived from the retinal neurons and Müller cells, play a central role in development ,38. All 13[C]6 glucose, we demonstrated that the retinal explant from P10 age mice were unable to produce their own serine or glycine, and retinal serine at this age is derived from circulation. Inhibition of serine/one-carbon increased avascular area in the retinas treated with FG-4592 as compared to controls [To understand the metabolic basis of OIR and protection in HIF stabilized condition, we performed an untargeted metabolite profiling of polar metabolites extracted from the retina or the plasma of the P10 old noromoxic, hyperoxic, and hyperoxic mice treated with HIF stabilizer FG-4592 . We founcontrols . Paris econtrols . These fcontrols . The crecontrols . In a secontrols .2O2, have been implied to underlie the neurotoxic apoptotic activity in the OIR retina [All these recent studies indicate perturbation in amino acid metabolism, specifically serine/one-carbon metabolism, in arginine and proline, and nitrogen metabolism in the ROP or OIR. Of these metabolic pathways, arginine metabolism to ornithine by arginase has been studied earlier in a neurodegeneration context in retinal OIR models. Mitochondrial isoform of arginase, arginase 2, has been shown to be involved in apoptosis of neurons in the mouse OIR model . OrnithiR retina .We demonstrated that hyperoxia downregulates proliferation of human primary retinal endothelial cells in the culture. We have also seen upregulation of polyamine degradation enzymes SMOX and PAOX, and decreased levels of putrescine in hyperoxic treated primary endothelial cells. We demonstrated downregulation of Myc in response to hyperoxia . Myc andOther than the central carbon metabolism discussed above, polyunsaturated fatty acids, arachidonic acid, and Docosahexaenoic Acid (DHA) have been shown to be low in the blood of preterm infants who develop ROP ,56,57,58Altogether, all the studies point toward a systemic metabolic imbalance underlying ROP development, and a systemic biochemical multi-organ metabolic model is much needed to understand and find treatment for ROP. Biochemical alterations seen in OIR/ROP have been summarized in the Retinal pigmented epithelium (RPE) degeneration leads to photoreceptor death in the AMD, causing vision loss in the macular region of the retina. There are two different types of AMD: wet-AMD and dry-AMD . In the RPE acquires its nutrient supply from the blood supplied by choriocapillaris, and tight junctions between RPE cells serve as a barrier between blood supply and the retina . RPE higDiabetic retinopathy is an example of systemic pathogenesis, where uncontrolled systemic hyperglycemia underlies the damage to the retinal blood vessels. Diabetic vascular retinopathy can be divided into two sub-types based on progress in the disease development. Early stage is referred to as non-proliferative DR, which is characterized by blood vessel damage and loss. Severe microvascular loss to the blood vessels supplying nutrients to the retina leads to neuronal damage and causes local hypoxia in the retina. This local hypoxia initiates the more aggressive stage of the DR, referred to as proliferative DR, in which neovascularization takes place. Sustained hyperglycemia leads to buildup of sorbitol by the action of aldose reductase . SorbitoThere are ample metabolic studies on DR patients published lately, and it is out of the scope of this manuscript to discuss them all in this review. Only a few of the latest research articles have been discussed here. Tomita et al. compared vitreous metabolome from PDR patients to non-diabetic controls. They found increased concentrations of lactate, pyruvate, allantoin, and proline in PDR patients compared to non-diabetic controls. Pathway enrichment analysis demonstrated changes in serine, glycine, proline, and arginine pathways. They also found increased concentrations of glycine and decreased concentration of downstream product creatine . Yun et 3−, and CO2 [There are many different animal models to study DR. It can be induced by complete or partial removal of the pancreas or damage caused by certain drugs targeting pancreases. DR can also be induced by a diet high in galactose or glucose, but these diet-induced models are used less frequently. Oxygen-induced retinopathy models are also used as a proxy of advanced PDR. There are also genetic models, like Akita (Insulin mutations leading to loss of beta cells and insulin production mimicking type I diabetes), db/db (Leptin Receptor mutation leading to obesity and diabetes), Kimba (trVEGF029) and Akimba (cross between Akita and Kimba). The most used DR models are streptozotocin-induced, db/db and Akita mice. Mora-Ortis et al. performed NMR-based metabolomics of db/db mouse model tissues, including of the eye. They found increased amounts of lipids and glucose in the eyes of db/db mice. Metabolites which were found to be decreased in the eyes of db/db mice were citrulline, alanine, glutamate, GABA, glutamine, hypoxanthine, isocitrate, myo-inositol, phenylalanine, O-phosphocholine, tyrosine, and inosine . Patrick and CO2 .Retinal vasculature is very sensitive to availability of nutrients, metabolic exchanges between cell types within and outside of retina, and oxygen availability. Hypoxia stimulates proliferation and migration of endothelial cells, which is seen in all the retinal vascular metabolic disorders discussed here. All three diseases discussed here in this review have common HIF-dependent pathogenesis, where hypoxia causes neovascularization in later stages. Overall, amino acid metabolism, serine/one-carbon, nitrogen metabolism, urea cycle, and fatty acid metabolism have been seen to underlie the development of these disorders. Recent findings demonstrate the systemic nature of these diseases and dependence on systemic metabolic changes.Metabolomics in retinal research is still in a naïve stage. After the brain, the retina has the highest heterogeneity of cell types. The retina has six major classes of cells with a total of 58 cell types in the foveal region and 57 in the peripheral region . MetabolMany of these studies cited here relied on very limited metabolic pathways specifically because most of the labs measure polar metabolome, and lipidomic at the moment is not a routine method of choice in many labs. To have a better picture of the metabolic changes underlying these vascular retinopathies, one must study both the polar metabolome and the lipidome. Additionally, modalities of mass spectrometry like spatial metabolomics and imaging mass spectrometry may provide better insight into these metabolic retinopathies. Metabolome is a good starting point to find out aberrant pathways in these diseases; however, stable-isotope labeling of metabolites offers a better time-resolved metabolic change which cannot be obtained from single time-point metabolomics.In summary, metabolomics is a strong tool to not only find biomarkers, but to also understand the metabolic basis of vascular retinopathies. It is encouraging to see similarities between metabolic findings between patient data sets and corresponding animal models in the publications cited in this review. Our current knowledge is limited to known biochemicals in metabolomic libraries or databases. However, the biggest problem with metabolomics is annotation of unknown compounds, and this limits the identification of novel metabolic markers or biochemicals. Combing untargeted metabolomics with stable isotope labeling of metabolites and machine learning may help find new biomarkers and metabolic causes to these vascular retinopathies.


Candida albicans, a commensal fungal pathogen, the Ras signaling pathway plays a significant role in the yeast‐to‐hyphal transition; specifically, two enzymes in this pathway, Adenyl Cyclase Cyr1 and GTPase activating protein Ira2, facilitate this transition, in the presence of energy sensor ATP. However, the precise mechanism by which protein interactions between Ira2 and Cyr1 and the energy sensor ATP result in the yeast‐to‐hyphal transition and create a switch‐like process are unknown. We propose a new set of biochemical reaction steps that captures all the essential interactions between Ira2, Cyr1, and ATP in the Ras pathway. With the help of chemical reaction network theory, we demonstrate that this set of biochemical reaction steps results in bistability. Further, bifurcation analysis of the differential equations based on this set of reaction steps supports the existence of a bistable switch, and this switch may act as a checkpoint mechanism for the promotion of growth‐to‐hyphal transition in C. albicans.Recent biochemical experiments have indicated that in  C. albicans implied the existence of interactions between the energy sensor ATP, adenyl cyclase CYR1, and the GTPase activating factor IRA2 in the Ras signaling pathway. Considering these interactions, we built a Ras network and the corresponding ODE model to show the existence of a bistable switch, which we attribute to the morphological transition.The cause for yeast‐hyphal transition in recent experiments on  ACadenyl cyclaseC. albicansCandida albicansCRNTchemical reaction network theoryGAPGTPase activating proteinGEFguanine nucleotide exchange factorMMMichaelis–MentenODEordinary differential equationsPDEphosphodiesterasesRDRas‐GDPRTRas‐GTPxppautX windows‐Phase‐Plane‐AUTomatic bifurcation softwareCandida albicans is an opportunistic, commensal fungal pathogen that can cause life‐threatening disease in people with compromised immune systems. They can thrive in many hosts by adapting themselves to any conditions for their survival. C. albicans can exist in three morphologies: yeast‐like, hyphae, and pseudo‐hyphae, with the last two together commonly referred to as filamentous morphology. Interestingly, C. albicans turns out to be virulent only when switching from yeast‐to‐hyphae is favorable under a given host condition. If a strain is either locked into a yeast or filamentous form, it fails to mount any infection, and both in vitro and in vivo infection models support it. It is the morphological switching rather than specific morphology per se that is important for virulence [irulence , 2, 3. TRecent biochemical experiments have implicated the Ras signaling pathway as one of the major players in effecting morphological switching , 5. In tC. albicans. They show in their experiments that depletion of ATP by its inhibitors results in less Ras‐GTP activity and, as a result, arrests yeast‐to‐hyphal formation. Further, they show that Ras activity requires Ira2 but not Cdc25. Based on these observations, they proposed that (a) During ATP depletion, Cyr1, although a downstream regulator, together with upstream regulator Ira2, downregulates Ras‐GTP, and (b) Ira2‐Cyr1‐dependent repression with intracellular ATP concentration acts as a checkpoint for the yeast to decide to switch between the reverse virulent hyphal and nonvirulent yeast forms [C. albicans. TFs also play a critical role in facilitating the yeast‐to‐hyphal transition. For example, ras1, cyr1, or pka1 mutation leads to a loss of function in biofilm formation, and overexpression of EFG1, an important TF, leads to the rescue of hyphal elongation in biofilm formation [C. albicans. Environmental cues also trigger selective signaling pathways that mediate yeast‐to‐hyphal transitions and vice versa. For example, nutrient limitation triggers the MAPK pathway, Alkaline pH induces the Rim101 pathway, and elevated temperature triggers HSP90/cAMP‐PKA pathway. All the above pathways trigger yeast‐to‐hyphal morphogenesis. Hyperosmotic stress conditions trigger the HOG pathway, while quorum sensing molecules trigger cAMP‐PKA pathways. These two pathways repress hyphal growth [Recently, Silao et al.  and Grahst forms . During st forms  showed tst forms  showed tC. albicans varies, and under favorable conditions the yeast‐to‐hyphal transition takes place.In this work we provide a new set of biochemical reaction steps to explain the occurrence of morphological switching. Based on these, a new set of reaction steps, we capture the dynamics of the Ras/Cyr1/PKA/cAMP pathway through ordinary differential equation models. The reaction steps are broadly based on the experiments of Grah et al.  that shoWe build our model of the Ras signaling pathway by only considering the mass action kinetic rate laws rather than the Michaelis–Menten (MM) type of kinetics. Since Ira2‐Cyr1 are enzyme–enzyme interactions, modeling by MM laws may be inappropriate as, it may not satisfy the requirement of substrate concentration ≫ enzymatic concentration , 10.We also apply chemical reaction network theory (CRNT) to identify whether our network can give rise to bistability without having to rely on kinetic parameters . We are The following section explains all the biochemical steps based on the network structure of the Ras/cAMP/PKA network Fig. . We alsoThe Ras network broadly consists of two modules. The dotted box in Fig. i's and kri's are ith forward and backward reactions, respectively. Ri's are the reactions of the ith biochemical step, and ri's are the corresponding rates of the reaction.We base all the reactions below on the laws of mass action kinetics. We map the biochemical steps directly from the network given in Fig. r1–r4, on the right side. Note again that we use short form RD for Ras‐GDP and RTP for Ras‐GTP. We maintain this throughout the article.R1–R4: The first step involves the conformational switch of Ras protein from an inactive Ras‐GDP (RD) to active Ras‐GTP (RTP) in response to environmental stimuli brought about by the guanyl‐nucleotide exchange factor (GEF) Cdc25. Ira2, the GTPase activating protein (GAP), carries out the reverse reaction . In reacC. albicans [C. albicans  by PKA. This degradation is independent of the enzymatic degradation carried out by high‐affinity PDE‐2 to convert cAMP to AMP. The final product AMP also undergoes first‐order degradation.The corresponding differential equations are:To determine the conservation relationship, we apply Gaussian elimination to the reaction rate stoichiometric matrix obtained from ODEs. Gaussian elimination gives linearly independent and dependent variables  from whiC. albicans experiments to analyze any further, we apply CRNT to determine the structural properties of the network. CRNT provides a connection between the network structure and dynamics, independent of the kinetic parameters. The specific dynamic we look for in this network is bistability. Bistability occurs when two stable equilibrium states, namely nodes, are separated by an unstable equilibrium state saddle. When saddle and node collide, it gives rise to saddle‐node bifurcation. Bistability can be irreversible or reversible (toggle switch). We are specifically interested in the toggle switch dynamics because the proposed cooperative interactions of Cyr1 and Ira2 may provide positive feedback that acts as a checkpoint in yeast‐to‐filamentous growth [https://cbe.osu.edu/chemical‐reaction‐network‐theory#toolbox), and this tool takes as input the elementary chemical reactions based on mass action kinetics and provides output information related to the existence of multistability. When we input the reactions R1–R29, the CRNT toolbox indicates that the network structure can exhibit multistability. Besides, we also obtain other information, like deficiency of the network based on the number of complexes (n), linkage class (l), and rank of the stoichiometric matrix (r) from the toolbox. The deficiency δ = n − l − r is > 1 for our network structure; i.e. 7.Since the kinetic parameters of the model are not known from the s growth . Craciuns growth  provide C. albicans.Using the CRNT toolbox, we also looked for redundant reactions in the minimal model that do not contribute to multistability. The minimal model based on the laws of mass action kinetics consists of reactions R1–R12. We eliminated each reaction one by one, checked for the existence of multistability, and found all the reactions to be important. Next, to the minimal model we added the reactions R13–R28, namely, CAMP regulation by PKA, as shown below in the dotted box of Fig. m as the unit for chemical species and seconds for time. We provide the .NET file to run our network using the CRNT toolbox in Appendix xppaut [To perform bifurcation analysis, We used most of the kinetic constants  obtained from the toolbox Table , and, whx xppaut .C. albicans experiments are not readily available. Time course data are also not available. We found that most of the Ras pathway models in the literature are in MM form or the combination of MM and MAK forms [Vmax and KM, but hardly any association or dissociation constants suitable for mass action kinetic models are available. Therefore, rather than arbitrarily plugging kinetic parameters in our simulation, we decided to back‐calculate the parameter values from KM and Vmax values available from the literature [We combed the literature to extract parameters for the Ras pathway. However, kinetic parameters from AK forms , 27, 28.terature . This waVmax and KM values are available for some of the reactions of our model from the database . For example, many reactions in this work follow the sequence of standard enzymatic reactions like the one given below.Vmax = k2 × ET, where ET is the total enzyme concentration. Since Vmax values are available from the database, and to get k2, we fixed the ET value in our model. Once we know Vmax and ET, we get k2 = Vmax/ET. To get other constants, k1 and k−1, we use the KM value. KM is (k2 + k−1)/k1, and since we do not know k1 in our case, we use the value obtained from the CRNT toolbox. This fixed the values of k1, k2, and KM. We finally get k−1 from (KM × k1) − k2.Briefly, we explain the way we back‐calculated to get the kinetic parameters. For the unavailable parameters, we use the values we obtained from the CRNT toolbox. In this way, out of 48 parameters we could estimate 23 parameters by back‐calculation, which are from the enzymatic reactions. We provide all the details in Appendix We start by fixing the total value of various species in the conservation relationships. Since our interest lies in determining the role of total Cyr1 (Cyr1T) and Ira2 (Ira2T) in morphological switching, we first fixed Ira2T and took Cyr1T as the bifurcation parameter to assess the steady‐state levels of RTP, ATP, and CAMP Appendix . ImportaC. albicans with farnesol, hyphal formation attenuated due to Cyr1 inhibition. Their experiments also indicated the involvement of the Ras signaling pathway in the H → Y transition with lower Cyr1 concentration  and the reverse hyphae‐to‐yeast (H → Y) transitions. The (Y → H) and (H → Y) transitions happen in a switch‐like fashion. The switch involves a threshold that controls the transition, and the threshold is taken as a checkpoint that determines whether the transition will occur or not.Before we proceed further, we would like to explain why we hypothesize a bistable dynamic for yeast‐to‐hyphae  are the same as those given earlier in model‐1 for downstream PKA derepression and negative feedback regulation.We first briefly explain the main difference between the two‐network construction. In Fig. To see model‐2's behavior, we again construct one‐ and two‐parameter bifurcation diagrams with Cyr1T as the bifurcation parameter. We use the kinetic constants from the CRNT toolbox to build the bifurcation diagrams. The bifurcation diagram is given in Fig. Finally, there are three transitions in normal to virulence transitions; yeast to pseudo‐hyphae to hyphae. Presently the signaling pathway and regulatory mechanisms are unknown about how tristability can happen. We know the existence of tristable dynamics from a gene regulatory network point of view in the white‐opaque‐gray transition , but havC. albicans in the presence of the energy sensor, ATP. Writing a stepwise elementary reaction for the protein interactions helped us to identify the cause of switching in C. albicans. The approach of going from the biological circuit → molecular mechanism → ODE model → bifurcations → biological meaning is rife with many difficulties [C. albicans. There are two modules in our model. The first module is the Ras regulation by Cyr1 and Ira2 in the presence of ATP to produce the final product, cAMP. The second module involves the downstream regulation of cAMP by PKA through negative feedback loops. Despite taking the modular route, and only the first module that creates a bistable switch, our circuit is complex with 29 biochemical steps, 10 corresponding ODEs, and conservation relationships. Although modeling by MM kinetics may have reduced the number of variables and parameters and possibly would have obtained the same result, this may be inappropriate [C. albicans.In this work, we have captured the specific important reaction steps of the Ras pathway to determine the cause of yeast‐to‐hyphal transitions in iculties . First, iculties  suggest ropriate . Moreoveropriate  for examropriate  for multropriate , it is nThe authors declare no conflict of interest.SK conceived the study, performed the experiments, analyzed the data, and wrote the article.Appendix S1. Matlab file get a conservation relationship from the stoichiometric matrix (Appendix S2), The output from the Matlab file gives the linearly dependent variables. This is cut and pasted in the Appendix S2 file in the second sheet. This gives the conservation relationship.Click here for additional data file.Appendix S2. The stoichiometry matrix for MAIN_MODEL‐1.Click here for additional data file.Appendix S3. The.NET CRNT file for Model‐1.Click here for additional data file.Appendix S4. The reaction parameter estimates are given in the file.Click here for additional data file.Appendix S5. Xppaut file used for all the simulations. This contains the estimated parameters as well as CRNT parameters to simulate Figs. 2‐5.Click here for additional data file.Appendix S6. XPPAUT file of the primary model to get all the bifurcation diagrams with only CRNT parameters.Click here for additional data file.Appendix S7. The.NET CRNT file for Model‐2.Click here for additional data file.Appendix S8. XPPAUT file to get the bifurcation diagram given in Fig‐7.Click here for additional data file. Click here for additional data file.


R2 of 0.86 between the predicted and experimental measurements over the performance of existing methods. Using this approach, we examined the metabolic networks of a Chinese centenarian cohort and identified the elevated fatty acid oxidation (FAO) as the most significant metabolic feature in these long‐lived individuals. Evidence from serum metabolomics supports this observation. Given that FAO declines with normal aging and is impaired in many age‐related diseases, our study suggests that the elevated FAO has potential to be a novel signature of healthy aging of humans.Although it is well known that metabolic control plays a crucial role in regulating the health span and life span of various organisms, little is known for the systems metabolic profile of centenarians, the paradigm of human healthy aging and longevity. Meanwhile, how to well characterize the system‐level metabolic states in an organism of interest remains to be a major challenge in systems metabolism research. To address this challenge and better understand the metabolic mechanisms of healthy aging, we developed a method of genome‐wide precision metabolic modeling (GPMM) which is able to quantitatively integrate transcriptome, proteome and kinetome data in predictive modeling of metabolic networks. Benchmarking analysis showed that GPMM successfully characterized metabolic reprogramming in the NCI‐60 cancer cell lines; it dramatically improved the performance of the modeling with an  Little is known for the systems metabolic profile of centenarians, the paradigm of human healthy aging and longevity. Here, we developed a method of genome‐wide precision metabolic modeling (GPMM) to systematically analyze human metabolic networks. We used GPMM to examine the metabolic networks of centenarian and demonstrated the elevated fatty acid oxidation as a novel signature of healthy aging of humans. CENcentenarianF1centenarian‐childrenF1SPspouses of centenarian‐childrenFAOfatty acid oxidationGPMMgenome‐wide precision metabolic modeling1Population aging is an increasingly urgent issue confronting many countries worldwide  data and kinetome information into genome‐wide precision metabolic modeling (GPMM), aiming to accurately identify metabolic changes in human health and diseases. To benchmark its performance, we applied GPMM and other methods commonly used for metabolic modeling on the same transcriptome data from the NCI‐60 cell lines  method to address the “low accuracy” challenge. The method quantitatively integrates the enzyme kinetics information from knowledge bases and the enzyme levels from transcriptome and proteome data into metabolic models Figure . SpecifiNotably, GPMM enabled several in silico analyses that were not included in the state‐of‐art COBRA methods toolbox (COBRA toolbox v3.0), and hence can be broadly applicable in metabolic engineering and therapy Figure . These i2.2R2 or R‐squared) between the genuine and noise‐induced samples should approach 1. The results showed that the R‐squared in each cell line is larger than 0.98 under either 1%, 5%, or 10% noise  into the original expression data  of each gene. Specifically, we induced 1%, 5%, and 10% noise into the gene expression data of NCI‐60 cell lines to construct noise‐induced transcriptomes. Then, we performed the metabolic modeling on the genuine and the noise‐induced expression datasets, and then compared the obtained flux results between both datasets. If a method is robust, this method should be able to tolerate a certain extent of noise on quantified gene expression; then, the correlation  than GIMME , Fastcore , rFASTCORMICS , and ecModel .To benchmark the performance of GPMM, we chose to utilize the transcriptome and metabolic flux (uptake and secretion) data of the NCI‐60 cell lines Table . Four otR2 = 0.86, p = 2.2e‐24)  Figure . One of 3 Figure . However3 Figure , althoug3 Figure e. These 2.3To shed light on the metabolic characteristics of the CENs to better understand why these individuals are able to delay or avoid many serious age‐related diseases that afflict the normal population   Figure a,b, sugg) Figure ; in the ) Figure . Similar) Figure . Consist) Figure .p = 0.032) in the CENs   and significantly lower palmitic acid levels  than F1SP. These results suggested that several fatty acid features from CENs, such as decreased free fatty acids, are likely heritable. Intriguingly, among the upregulated metabolites, the most significant ones were bile acids, a group of metabolites for fatty acid absorption  Figure . Notablyn Figure . These rn Figure . A similn Figure .3E], where Kcat is mined from enzyme databases and the concentration of the enzyme is either measured by proteomics or estimated from transcriptomics (under the steady‐state approximation). In a large benchmark study, this method (GPMM) successfully characterized metabolic reprogramming in NCI‐60 cancer cell lines  is a promising method that can capture metabolic signatures in health and diseases, but has been a long‐standing challenge to quantitatively predict molecular phenotypes  and five key mitochondrial beta‐oxidation genes   Figure a. We als) Figure b. These l‐carnitine, an essential transport of long‐chain fatty acids from the cytosol to the mitochondrial matrix, exhibited a significantly decreased level with aging , phosphatidylethanolamines (PEs), phosphatidylcholines (PCs), and long‐chain fatty acid sphingomyelin (SM), were significantly downregulated in the CENs Figure . Consistr = 0.45, p = 1.8e‐185; Figure Previous studies have shown that the offspring of centenarians inherit part of survival advantages from their long‐lived parents, and are also used to explore the health‐protective mechanism of human aging  In GPMM method, the reduced model by maximizing the number of reactions with known Kcats could introduce potential biases, as less well studied enzyme‐related reactions and pathways are less likely to be included in the reduced model. (ii) Although the GPMM model displays much better performance in predicting metabolic flux, its ability in dealing with the low flux levels  is still limited. Fixing this limitation will be the major objective of the next version of GPMM, which would definitely be of help in improving the performance of the method in metabolic modeling. (iii) Although we used a series of analyses, including two linear models, to evaluate the potential effect of age and remove any signals associated with age, it is still possible that some age effect persists in our results, simply owing to the fact that some age signal within the F1SP population might not be associated with age in the CEN group. (iv) Although our results suggest that the CENs likely display increased lipid metabolism, which gets further support from serum metabolome, whether this signature can represent the whole body of these long‐lived individuals awaits further investigation, largely due to the fact that our transcriptome data are obtained from the peripheral white blood cells.44.14.1.1The GPMM method was designed to integrate enzyme kinetics into constraint‐based genome‐wide metabolic modeling. It integrated the knowledge‐based human metabolic reconstruction model (Recon 3D)  by the GPMM approach, we collected several relevant knowledge bases. The knowledge‐based human metabolic model was obtained from Recon 3D  and RNA‐seq data of 15 normal tissues from the Human Protein Atlas Dataset  relationships in Recon 3D, some reactions have more than one enzyme. Thus, we calculated the upper bound activities of these reactions as follows: (a) summed enzyme activity when the GPR had a Boolean logic of “OR”, and (b) minimized enzyme activity when the GPR had a Boolean logic of “AND.” Oxidative phosphorylation is essential for ATP production, and all eight essential oxidative phosphorylation reactions  require more than 20 enzymes to catalyze .Second, after setting the quantitative upper bounds of the reactions and the uptake values, we performed flux variation analysis (FVA) to obtain the maximum and minimum fluxes for each reaction. Some reactions have both the maximum and minimum fluxes of zero, thus cannot carry a flux. The individual models were then reconstructed by removing the reactions that could not carry flux in the FVA. Large‐scale FVA was performed using our recently published efficient constraint‐based metabolic modeling toolbox FastMM , and the uptake rate for each cell was obtained from the above experimental fluxes  was downloaded from the CellMiner website  cutoff = 3.0. Metabolic models were then reconstructed using the “GIMME” function in the Cobra toolbox 3.0 . The metabolic models were then reconstructed using the “fastcore” function in the Fastcore toolbox, and MCMC sampling was conducted using the “ACHRSampler” function in the Cobra toolbox 3.0 . The metabolic models were then reconstructed using rFASTCORMICS . Note: as each constructed ecModel has over 20,000 reactions and has a different model framework from the COBRA toolbox, performing MCMC sampling is difficult. Therefore, the fluxes for each ecModel were estimated using the suggested method “minProSimulation” from Human 1  to the original expression data (or the genuine transcriptome) of each gene. Specifically, 1%, 5%, and 10% noise was induced in each NCI‐60 cell line transcriptome. In these processes, the noise inducing is produced from a uniform distribution of  for 1% noise,  for 5% noise and  for 10% noise. For example, in the 5% noise translation procedure, if a gene in a cell line has a gene expression  of 1.0, the adding random number of this gene is ranged from −0.05 to 0.05, such as 0.03; then, the noised‐induced gene expression is a number ranged from 0.95 to 1.05, such as 1.03. Second, we performed the metabolic modeling on the genuine and the noise‐induced transcriptomes and compared the obtained flux results to evaluate GPMM robustness. In addition, to further test whether multiple sampling affects robustness, we also induced 5% noise to the gene expression of H460 cell line 100 times and performed metabolic modeling to determine the stability of GPMM.4.3A total of 171 individuals from longevity families, consisting of 76 centenarians (CENs), 54 centenarian‐children (F1), and 41 spouses of centenarian children (F1SP), were recruited from Hainan Province, China, as part of the study of centenarians in southern China , similar systolic blood pressure (83.2 vs. 86.1 p = 0.19), slightly lower blood glucose , lower total cholesterol , and lower low‐density lipoprotein cholesterol . These results are also consistent with our previous studies, where levels of risk factors for cardiovascular diseases, including blood glucose, triglyceride, and total cholesterol, were significantly lower in the CENs than those of the general older population from the same province, and the diagnoses of type 2 diabetes mellitus, hypertriglyceridemia, and hypertension were lower in the CENs than Chinese national levels  and then centrifuged at 1800 4.44.4.1l‐glutamine, and fatty acids, were derived from published literature (Table Gene expression (FPKM) levels in 170 individuals from the Hainan longevity cohort, including 76 CENs, 52 centenarian‐children, and 42 spouses of centenarian‐children (F1SPs), were derived from our previously published data  were older than the controls (45–75), we used two linear models to distinguish centenarian signatures and aging effects. Model l was applied to CEN and F1SP samples to determine unfiltered centenarian signature:Model 2 was applied to F1SP samples to determine aging effects:p < 0.05 and beta >0 in model 1 but not significant or beta <0 in model 2. Downregulated fluxes were defined vice versa.After the unfiltered centenarian signatures and aging effect were determined, we obtained the actual centenarian signatures by excluding the overlapping fluxes between the unfiltered centenarian signature and the age effect. Therefore, upregulated fluxes in CENs were defined as fluxes with 4.4.3ith DA score (DAi) was calculated as follows:The differential abundance (DA) score was calculated using previously published methods .To obtain the significance of DA scores, we used a “bootstrap without replacement” method to calculate 4.54.5.1Vmethanol:Vacetonitrile:Vwater = 2:2:1), with 30 μl of 1 mg/ml l‐2‐chlorophenylalanineas then added as an internal standard, followed by vortexing for 10 s and sonicating for 10 min on ice. After that, the extract was incubated for 1 h at −20°C. Following centrifugation at 16,000 g for 15 min, 350 μl of supernatant was transferred into LC–MS vials and dried with a vacuum concentrator. Finally, the vials were resuspended in 100 μl of methanol/acetonitrile/water solution, and the above process was repeated, with 60 μl of supernatant transferred into a new LC/MS vial. To assess the analytical method, 10 μl of each sample was pooled as a quality control (QC) sample, which was tested during analysis.The collected serum samples were thawed on ice. Samples (100 μl) were extracted with 750 μl of methanol/acetonitrile/water solution (4.5.24OAc and 25 mM NH4OH in water (pH = 9.75) (A) and acetonitrile (B). The optimized UPLC elution conditions were as follows: 0–2.0 min, 85.0%–75.0% B; 2.0–9.0 min, 75.0%–0% B; 9.0–14.0 min, 0% B; 14.0–15.0 min, 0–85.0% B; and 15.0–20.0 min, 85% B. The flow rate was 0.3 ml/min. Sample solution (2 μl) was injected for each run. Mass spectrometry was performed on a Triple‐TOF MS in positive and negative mode and operated in information‐dependent basis (IDA) mode. In each cycle, six precursor ions (intensity >100) were chosen for fragmentation with 35 V collision energy (CE) (15 MS/MS events with a product ion accumulation time of 50 ms each). Ion source gas 1 was 60, ion source gas 2 was 60, curtain gas was set to 30 L/h, source temperature was set to 550°C, and ion spray voltage floating (ISVF) was set to 5500 V or −4500 V in positive or negative modes, respectively.LC‐MS/MS analyses were performed with an ultra‐high‐performance liquid chromatography (UHPLC) system  equipped with a Triple‐TOF 6600 mass spectrometer (MS) . Chromatographic separation was carried out on an ACQUITY UPLC BEH Amide column . The mobile phase consisted of 25 mM NH4.5.3m/z) values, and peak intensity. The R package CAMERA was used for peak annotation after XCMS data processing. The in‐house MS2 database was applied for metabolite identification, and only the metabolites with MS2 >0.8 remained.UPLC‐MS raw data (.wiff) were converted to mzXML, with ProteoWizard Peak exaction, identification, integration, alignment, and retention time correction processed with XCMS . The preprocessing results generated a data matrix that consisted of retention time (RT), mass‐to‐charge ratio .Similar to the flux subsystem analysis in Equations  and 7)..7). The The authors declare no conflict of interests.Q.P.K. conceived and designed the study. W.X., J.H., and G.L. designed the GPMM algorithm. G.L. developed the GPMM algorithm and performed modeling analysis. F.H. collected the benchmarking datasets. K.G., Q.S., and Y.W. collected samples and isolated RNA. W.X. and W.L. performed the statistical analyses. B.L. directed the metabolomics analysis. G.L., Q.P.K., W.X., and F.H. drafted the manuscript. G.L., Q.P.K., F.H.X., and F.H. revised the manuscript. All authors read and approved the final manuscript.https://github.com/GonghuaLi/Code_for_publications/tree/master/GPMM_Centenarians; https://github.com/GonghuaLi/GPMM.This article has been awarded Open Data and Open Materials Badges. All materials and data are publicly accessible via the Open Science Framework at Fig S1‐S9Click here for additional data file.Table S1‐S7Click here for additional data file.


C. pasteurianum fermentation of glycerol has been shown to be significantly influenced electrochemically, the underlying mechanisms are not clear. To this end, we developed a system for the electrochemical control of ORP in continuous culture to quantitatively study the effects of ORP alteration on C. pasteurianum by metabolic flux analysis (MFA), targeted metabolomics, sensitivity and regulation analysis.Electro-fermentation (EF) is an emerging tool for bioprocess intensification. Benefits are especially expected for bioprocesses in which the cells are enabled to exchange electrons with electrode surfaces directly. It has also been demonstrated that the use of electrical energy in BES can increase bioprocess performance by indirect secondary effects. In this case, the electricity is used to alter process parameters and indirectly activate desired pathways. In many bioprocesses, oxidation-reduction potential (ORP) is a crucial process parameter. While −1. An overall increase of 57% in the molar yield for 1,3-propanediol was observed by an ORP increase from −462 to −250 mV. MFA suggests that C. pasteurianum possesses and uses cellular energy generation mechanisms in addition to substrate-level phosphorylation. The sensitivity analysis showed that ORP exerted its strongest impact on the reaction of pyruvate-ferredoxin-oxidoreductase. The regulation analysis revealed that this influence is mainly of a direct nature. Hence, the observed metabolic shifts are primarily caused by direct inhibition of the enzyme upon electrochemical production of oxygen. A similar effect was observed for the enzyme pyruvate-formate-lyase at elevated ORP levels.In the ORP range of −462 mV to −250 mV, the developed algorithm enabled a stable anodic electrochemical control of ORP at desired set-points and a fixed dilution rate of 0.1 hC. pasteurianum and increase product yield for 1,3-propanediol in continuous culture. The approach might also be useful for application with further anaerobic or anoxic bioprocesses. However, to maximize the technique's efficiency, it is essential to understand the chemistry behind the ORP change and how the microbial system responds to it by transmitted or direct effects.The results show that electrochemical ORP alteration is a suitable tool to steer the metabolism of The online version contains supplementary material available at 10.1186/s12934-022-01902-5. In this context, an especially high potential is expected for bioprocesses in which the cells are enabled to directly harvest electricity-derived electrons in bioelectrochemical systems (BES) . But expterature , is one terature , 5 but aterature , 7.2 or other volatile components.Different strategies for alteration and control of ORP have been developed and applied . Figure Clostridium pasteurianum is a promising candidate for industrial production of the valuable chemicals 1,3-propanediol (PDO) and n-butanol [C. pasteurianum cannot be considered electroactive but that its metabolism is still affected by the application of electrical energy [A third option to control ORP in fermentation processes is using electrical energy in BES Fig. c. In lit-butanol , 28. Prel energy , 30. Dep−1 in the BES. This remained the case even when the current  was increased up to −1.0 A. Also, even when the system was operated overnight with the constant application of only low currents (cathodic or anodic were tested both), washout of the cells was observed. Here, it is assumed that the electrochemical adsorption of charged trace elements may have prevented the cells from achieving the required growth rate at the set dilution rate.For the continuous cultivations, cells were initially grown to steady state without power application. Then, the first attempts to control ORP used a constant cathodic current of −100 mA with a simple on/off controller. Here, the first finding was that the ORP could, in contrast to the results obtained in fed-batch fermentations , not be −1 (+−10 mV). Overall, four different ORP levels at dilution rates of 0.1 h−1 were achieved and evaluated, while the first steady state was without the application of electrical energy. The online ORP values for the tested conditions, including the final controller output values, are shown in Fig. In the next step, to avoid cell washout in subsequent experiments, a pulse control algorithm was implemented for the ORP control. The controller was a simple manually tuned PI-controller (implemented via Labview), using the ORP online value as the input variable and the applied current as output. The pulsing time was 100 ms, followed by 100 ms without the application of electricity. Here, it turned out that the initial duration of 100 ms could not increase the ORP sufficiently, even at the used potentiostat's maximal current (1.0 A). In this case, when no increase of ORP was observed after 3 h, the pulsing time was manually increased stepwise by 100 ms. The period without electricity was kept constant at 100 ms. This strategy made it possible to maintain the ORP at the desired set-point at a dilution rate of 0.1 hC. pasteurianum converts less substrate at a more positive ORP, and the cell concentration decreases drastically. This might essentially be contributed to oxygen production by the electrode when running in anodic mode to control the ORP. Already at −416 mV 4% oxygen were detected in the off-gas. At −337 mV it were 6%. At −250 mV, the ' gas production could not be measured since the off-gas stream was lower than the minimum required of the used MS (5 mL min−1). Here, estimated and balanced CO2 and H2 production rates for calculating carbon and electron recoveries were obtained from the MFA. In general, oxygen negatively influences clostridia's growth [Table s growth , and oxy−1 (at −462 mV) to a maximum of 234 mmol mol−1 (−250 mV), equalling an improvement of 57%. Also, lactate yield and concentration increased significantly with a more positive ORP, while butanol yields decreased. The carbon and electron recoveries for the obtained data are generally unsatisfactory. Even when no electricity was used (at −462 mV), the carbon recovery yielded only 89%. Here, one explanation might be that the low growth rate stressed the cells and triggered the formation of product(s) not tracked by the analytical method. However, no unknown peak could be found in the HPLC chromatograms.Table C. pasteurianum, ATP generation by substrate-level phosphorylation, and depletion by biomass formation were added to the network. The yield of biomass per mol ATP was taken from the literature [h, which should yield values of < 7.815  for the network, which has a degree of redundancy of three. No specific rate could be identified that contained gross systematic errors. Nonetheless, it could be concluded that intracellular protons cannot be sufficiently balanced by the assumed reactions at the measured rates. The main sink for protons in the network is the formation of H2 from reduced ferredoxin and protons. The primary source is the conversion of glycerol to pyruvate and acid production. In the following, possible reasons for the issue of proton deficiency are elucidated and treated in more detail.While the product yields and final concentrations of the conducted experiments are important parameters to assess the overall process performance, these values can only hint at how the changed process conditions affected the cells' different pathways. Here, the data generated by MFA and the subsequent regulation analysis are more helpful. Also, by the mathematical representation of the assumed reaction network and the implementation of measured rates, MFA allows the statistical testing of consistency and the detection of gross measurement errors. The basis for the biochemical network used for MFA was developed within the framework of a previous publication . ATP wasterature . AdditioIn literature, it has been taken for granted that organic acids are mainly excreted passively in their membrane-permeable, protonated, and non-charged form, which is pH-dependent. This is also reflected in the transport reactions of most metabolic models. Nonetheless, this long-standing paradigm is currently under debate since it has been shown to contradict numerous experimental findings. In particular, it has been shown that most metabolic models underestimate the diversity of metabolite secretion and that simulation results do not agree with results of empirical exometabolomic studies . FurtherC. pasteurianum the intracellular pH is expected to be more alkaline than the extracellular environment. Based on the data of Riebeling et al. [C. pasteurianum can excrete both forms of metabolites, protonated but also deprotonated. The metabolic model was adjusted accordingly: the degree of deprotonation was calculated with respect to the approximated intracellular pH of 6.5 and the pka values of the corresponding acid. Furthermore, each acid's protonated/deprotonated forms are only represented by one metabolite in the equations, which are both expected to be excreted by the cells. This enables the cells to balance significant parts of the proton deficiency created by the hydrogenases with the help of acid production. Nonetheless, the MFA results with the updated network, which includes the secretion of deprotonated acids, still did not deliver statistically satisfying results. The proton deficiency was still too significant.In this context, for g et al. , an intr1F0 ATP synthase peptide of C. pasteurianum is well characterized [C. pasteurianum is more alkaline than its environment [C. pasteurianum, a reaction equation for the ATPase was added to the metabolic model and MFA was conducted again. This time, when allowing proton influx and ATP generation by the ATPase, the statistical tests for data consistency with respect to the assumed metabolic model were satisfying for all tested conditions. The MFA results obtained by the final model are displayed in Fig. Subsequently, an ATPase was considered a further option for proton balancing by enabling proton flow into the cells. The Fcterized , but onlcterized , 39. Togironment , this leironment . To examATP/H+  = 0.25 mol mol−1) by the ATPase are only estimates from the literature. This leads to the situation that in one scenario at -250 mV, the reaction, which represents ATP requirements for maintenance (r15), slightly yielded a negative value in the calculations . Hence, most likely, the actual ATP demand for growth is different from the value used in the model and varies among the tested scenarios. Moreover, it might also be that the ATPase efficiency was underestimated or that additional ATP-generating mechanisms existed in C. pasteurianum. Therefore, further experimental validation of the ATPase efficiency and improved metabolic models with more detailed biomass formulation equations are required in this relation. Nonetheless, the suggested mechanism of ATP generation, which is indirectly driven by the hydrogenase-established proton deficiency, explains how C. pasteurianum can generate sufficient ATP, even when only small amounts of acids are formed.In this context, it must be pointed out that the intracellular pH, ATP demand for cell growth, and the amount of stoichiometrically produced ATP per proton  and r7 (ethanol formation) increased while r5 (PFOR) and r9 (crotonyl-CoA formation) declined. Reactions downstream of crotonyl-CoA (r10 to r13), formate and acetate formations (r6 and r8), and reactions upwards of pyruvate (r1 to r3) show a mixed picture. Interestingly, the substrate uptake rate (r1) appeared almost unaffected by the change in ORP levels.First, the deviation index was calculated for intracellular reactions 1) from − 462 mV to − 416 mV, reaction block J0 shows a slightly positive response, as indicated by the partial integrated response . The system is displayed in Fig. J4, J6, G3P and acetyl-CoA. While the activation of J4 is mainly of direct nature, J6 profits, as already described, most from the increase in J0 (system influx) and J7 (conversion of acetyl-CoA to butyryl-CoA), which remained almost unchanged by the parameter change from −462 mV to −416 mV. Also, the increase in the rate of the PFL (J4) has a positive effect on J6. G3P concentration does almost exclusively profit from the slightly increased system influx. The concentration of acetyl-CoA is positively affected by reaction block J7, which did not show an increased flux compared to the reference state. Accordingly, fluxes J5 and J6 did not increase sufficiently to compensate for the local increase in acetyl-CoA concentration caused by the rise in J4. For the first ORP change from −462 mV to −416 mV, the highest negative responses are seen for J2, J9 and pyruvate. While the negative effect on lactate production (J2) can entirely be explained by the increase of flux for PFL (J4), the negative response of J9 appears to be of direct nature. The negative effect of the effector change on pyruvate can also mainly be explained by an increase in J4.The data enable interesting yet highly complex insights into the response of the microbial system to the ORP changes. For the first transition  in Fig. J6 show the highest negative response. The response is transmitted directly but also through J3 (PFOR), which is reduced, and J5 (ethanol formation), which is increased. In this context, it is noteworthy that the decrease in the flux of J3 that exerts a strong influence on several system components does almost exclusive occur from direct response. Only a small proportion (11%) can be explained by a reduced system influx after the transition. The highest overall positive response  increases mainly because of the negative response of J3 (PFOR) and J9 is activated directly and positively affected by less butanol production (J8). The observed increase in pyruvate and G3P concentrations  in Fig. J2 (lactate formation) and J5 (ethanol formation) show an overall positive response. This time, the positive response of J2 is again mainly transmitted by a further decrease in J3. J5 profits from a direct activation and also from a substantial flux reduction in reaction block J7. The highest negative responses were calculated for J4 (PFL), J6 (acetate formation) and J9 (butyrate formation). PFL is, identical to PFOR during this final ORP change, solely affected directly by the change of the external effector. In general, PFOR is engaged in transmitting the negative effects of the external effector on all reactions and intermediates downstream of it. Also, the decreasing value of J3 is the main reason for the observed concentration increase of pyruvate and the increase in lactate formation (J2).For the third ORP change  in Fig. C. pasteurianum and results in an increased substrate influx towards pyruvate. Here, the reaction of PFOR remains almost unaltered and the PFL captures the additional flux towards pyruvate. To maintain redox homeostasis, the cells produce more ethanol. The following ORP change (from −416 mV to −337 mV) results in a drastic inhibition of PFOR, presumably caused by a higher oxygen activity, driven by the higher amounts of applied currents. Here, the cells again utilize the PFL reaction to compensate the lower PFOR activity. In addition, upstream of pyruvate more lactate is produced as a response. At the highest ORP level (−250 mV) and the highest activity of oxygen in the system, not only PFOR but also PFL are directly inhibited. Both enzymes carry iron sulphur clusters and are inhibited by molecular oxygen [Overall, in combination with the MFA results, the regulation analysis draws a cohesive picture of the metabolic response to the ORP alterations. Although the applied method is hampered by the derivation of the elasticities from kinetic data from literature and the assumption of a simple one substrate Michaelis–Menten kinetic, it still enables a reliable semi-quantitative elucidation of the triggered metabolic phenomena and how they were achieved from a systems level perspective. As represented by the analysis of the first change in ORP parameter (−462 mV to −416 mV), a moderate increase of ORP stimulates the metabolism of r oxygen , 45. HowC. pasteurianum. An increase in PDO yield of 57% was observed by a stepwise increase of ORP from −462 mv to −250 mV at a dilution rate of 0.1 h−1. The ORP increase was driven by the electrochemical production of oxygen, which on the one hand, directly negatively influenced the conversion of pyruvate to acetyl-CoA, and on the other hand, positively influenced product yields of compounds upstream of pyruvate. Therefore, the developed technique might also be of interest to other anaerobic or microaerobic processes. However, since the artificial ORP increase also resulted in lower biomass and product concentrations, it is essential for future application of this approach to find the right balance between product yield and volumetric productivity. Furthermore, to optimize the process specifically for C. pasteurianum, more detailed data about the in vivo inhibition kinetics of PFOR and PFL are required. Also, it would be beneficial to get improved data on the related control patterns by not deriving the elasticities from kinetic data from literature and the metabolite concentrations but directly experimentally. This could be achieved by experiments in which parallel short-term perturbations  and a modified medium from Biebl [−1 yeast extract. The second pre-culture was then used for inoculation of the main culture. The main culture medium and also the feeding solution contained Biebl medium, but yeast extract was replaced with 25 µg L−1 biotin. The initial concentration of glycerol in the main culture was 25 g L−1. The feed medium contained 36 g L−1 of glycerol. FeSO4·7 H2O, biotin and L-cysteine were prepared separately and added by sterile filtration into the main culture medium and the feeding solution. The feed medium was autoclaved in 10 L tanks and purged with nitrogen while passively cooling down to room temperature to establish anaerobic conditions. Anaerobic conditions were verified by the addition of the dye resazurin. The fermentation and feed medium also contained 1 g L−1 of D-xylose as an internal standard for calculating the cell-specific concentration of intracellular compounds obtained from automated fast-filtration. Cultures and feeding solutions were regularly examined under a light microscope to check for contamination.An electrocompetent mutant of the wild-type strain z et al.  and is nom Biebl . Strain om Biebl . First, 2, CO2, N2, O2) and volume stream were measured by a Balzer Omnistart GSD 300 mass spectrometer coupled to highly accurate EL-FLOW mass flow meters from Bronkhorst (Netherlands). Feeding was started after an initial batch phase of 17 h. It was assumed that steady-state conditions were achieved after a minimum of five hydraulic retention times, which was verified by an unchanging off-gas signal. Then, four samples for the quantification of extracellular metabolite concentrations and biomass were taken in 15-min intervals for each tested condition. After the fourth sampling, fast-filtration for gaining of the metabolomic data was conducted. Different ORP set-points were tested in the continuous cultivation. The control algorithms for ORP control by applying electric current had to be developed and further optimized during the experiments. Hence, the ORP control is considered part of the results section. The OPR control algorithm was implemented by a PID-function via the software Labview from National Instruments (US). The control algorithm used the online ORP value as input parameters to control a Gamry Interface 1000 potentiostat. Applied current and duration of electric pulses were defined as output parameters.The fermentations for the continuous electricity-aided cultivations were conducted in a glass bioreactor from Bioengineering coupled to a self-developed fast-filtration system . Here, t600. Cell dry weight (cx) was determined gravimetrically in triplicates from cell pellets of 2 mL culture broth, dried at 70 °C for 24 h. The pellets were obtained by 5 min of centrifugation at 13,000 rpm.Cell growth was tracked by measuring the OD−1 with 5 mM H2SO4 as the mobile phase. For this purpose, cells were centrifuged at 13,000 rpm for 5 min and the supernatant was filtered through a 0.2 µm PVDF filter. The ethanol concentrations were additionally verified using a commercially available enzymatic "Ethanol-Kit K-ETOH" from the company Megazyme (Ireland).Glycerol, 1,3-propanediol (PDO), lactate, formate, acetate, ethanol, butyrate, pyruvate, succinate, and n-butanol were quantified by an HPLC system from Kontron (UK). Separation took place on Aminex HPX-87H column at 60 °C with a flow rate of 0.6 mL minFor metabolomic investigations, an automated-fast filtration system was used, which was described in detail by da Luz et al. . The exacs) and products (cp) were used to calculate the cell-specific rates for substrate uptake (qs) and product formation (qp), according to the following equations:−1 and −1. The cell-specific rates were then used for the calculation of carbon and reducing equivalent balances. Carbon (CR) and macroscopic electron (NADHR) recoveries were obtained from the following equations [Concentration data obtained from the extracellular steady-state measurements of substrate  were corrected for absorption in the liquid phase, according to Zeng [2 in the off-gas stream was also corrected for electrochemically produced gasses. The Adenylate Energy Charge (AEC) was obtained from the measured intracellular molar concentrations of AMP (cAMP), ADP (cADP) and ATP (cATP) by the following equation [The off-gas rates for CO to Zeng . The amoequation :5\documeOne-way analysis of variance (ANOVA) was conducted for testing the statistical significance of the obtained metabolomic data, followed by a posthoc Tukey's test. The analysis was performed with the Software Origin Version 2020 . Four data points were available for each metabolite from one steady-state condition. The significance level (α) was chosen to be 5%.CellNetAnalyzer  at steady state were used for each condition. For off-gas values and the rate for biomass formation reaction, a standard deviation of 5% was assumed. The final metabolic network used for MFA has a degree of redundancy of three. This allowed for testing the measured rates and scenarios for consistency. In one case (−250 mV) the gas production rate was too low (< 5 mL min−1) to be measured by the off-gas MS. Here, the degree of redundancy was only one and the cell-specific production rates for H2 and CO2, required for the calculation of CR and RH, were estimated by MFA.For convenience and simple modification plus testing of different biochemical networks, MFA was conducted with the Matlab plugin  2017.3; ). The fi 2017.3; . For rat2/H2O couple (1.026 V vs. Ag/AgCl), F the Faraday constant, R the ideal gas constant, T the temperature and To quantitatively describe the response of metabolic systems to large (finite) changes in external factors, Small and Kacser  introducequation :9\documeIn relation to MCA, a regulation analysis provides a quantitative description of how a parameter causes changes in the fluxes and metabolite levels of metabolic systems and how they transmit through the system . To perfSymbolic MCA represents an extension to the classical metabolic control analysis and enables the analytical calculation of algebraic control coefficient expressions . Based olated by :12\documwith the elements:ficients :15\documThese partial integrated responses state, to which extend the process Additional file 1: Table S1.1. Metabolic model used for metabolic flux analysis for the continuous and ORP controlled fermentations of Clostridium pasteurianum .Additional file 2: Table S2.1. Estimation of elasticities from kinetic literature data and measured metabolites in C. pasteurianum. Table S2.2. Calculated flux and concentration control coefficients of C. pasteurianum.