Blood circulation mainly aims at distributing the nutrients required for tissue metabolism and collecting safely the by-products of all tissues to be further metabolized or eliminated. The simultaneous study of arterial (A) and venous (V) specific metabolites therefore has appeared to be a more relevant approach to understand and study the metabolism of a given organ. We propose to implement this approach by applying a metabolomics (NMR) strategy on paired AV blood across the intestine and liver on high fat/high sugar (HFHS)-fed minipigs. Our objective was to unravel kinetically and sequentially the metabolic adaptations to early obesity/insulin resistance onset specifically on these two tissues. After two months of HFHS feeding our study of AV ratios of the metabolome highlighted three major features. First, the hepatic metabolism switched from carbohydrate to lipid utilization. Second, the energy demand of the intestine increased, resulting in an enhanced uptake of glutamine, glutamate, and the recruitment of novel energy substrates (choline and creatine). Third, the uptake of methionine and threonine was considered to be driven by an increased intestine turnover to cope with the new high-density diet. Finally, the unique combination of experimental data and modelling predictions suggested that HFHS feeding was associated with changes in tryptophan metabolism and fatty acid β-oxidation, which may play an important role in lipid hepatic accumulation and insulin sensitivity. The modifications of the metabolites in the arterial and venous circulation allow to integrate the whole-body metabolism and therefore to maintain homeostasis. Many molecules are transported in the blood, including nutrients , electrolytes and messengers . The composition of the blood passing through a tissue is therefore able to reflect its metabolic function: substances taken up by the tissue on a net basis will be in higher concentration in the arterial inflow than in the venous outflow, and vice et versa for released products. Then, for many of these substances, the circulatory system is not just a way to go in and out of an organ, but is also a complex system of cross-talk allowing a whole body integrative metabolism2. For instance, we and other have shown that untargeted metabolomics was particularly adapted to study the time-course adaptation of metabolism7. However, so far, the majority of the untargeted studies has focused on biomarkers discovery by profiling blood or urine samples8, which provides an interesting static snapshot of the whole body metabolism but offers limited mechanistic information. Indeed, little can be drawn from urine metabolomics about the metabolic activity of a particular organ. Concerning blood, venous samples, which are easily accessible and widely used in clinical diagnosis, likely reflect the metabolism of a particularly-drained organ. On the contrary, arterial blood  is able to provide a picture of available molecules for the whole body metabolism as it represents the phenotype of the fluid prior to being processed by any particular organ9. The simultaneous study of both venous and arterial samples seems therefore a particularly relevant approach to better understand the metabolism a given organ. Assessment of respiratory gases, glucose and amino acids (AA) have been traditionally explored using the arterio-venous (AV) approach since 1930’s in order to perform fundamental metabolic studies at different levels . However, such studies remained focused on a very limited and targeted set of metabolites or nutrients. Therefore, the global profile of metabolic utilization and exchange of metabolites and nutrients by tissues remains poorly known. In order to obtain an extended view of the organ metabolism beyond biomarker discovery by metabolomics, the assessment of a large number of metabolites in arterial and venous blood is needed. We therefore performed a global profiling of paired arterial and venous  blood, focusing on the splanchnic area , as the central metabolic crossroad and link between absorption and intermediary metabolism. Samples were collected using a time-course protocol in a minipig model of obesity and insulin resistance 10 in order to challenge the metabolism with an overload of energy and nutrients. Blood was simultaneously drained at the fasting state before and after 7, 14, 30 and 60 days of a HFHS diet and plasma samples were analysed by NMR-based metabolomics platform. The metabolites showing significant changes between venous and arterial samples were discussed in the context of the biochemical knowledge of these organs and based on the concept of arteriovenous metabostasis, as recently developed by Ivanisevic et al. for the study of the skeletal muscle metabolism11.Metabolomics has proved to be a valuable tool to further understand metabolic processes12. We used one of the most consolidated reconstruction of the human metabolic network, Recon2.0413. It includes more than 7700 reactions and 2600 metabolites, capturing all the reactions that can possibly occur in any human tissue or cell. Constraint-based modelling (CBM) methods then allow to predict the metabolic state of a tissue, by setting bounds (lower and upper constraints) to some specific reactions based on available experimental data14. In this study, constraints were set on exchange reaction fluxes in the models to enforce uptake or release of metabolites according to the consumption and production profiles. This modelling strategy allows contextualizing the generic model and generating time- and tissue-specific constrained models, which can specifically account for the functional metabolic network in these conditions.One challenge when interpreting metabolomics data is to explore the direct physiological roles of metabolites and their involvement in organs’ metabolic networks. Metabolic networks can provide a mechanistic context for understanding metabolic profiles and explore the metabolic pathways altered during the onset of obesity. In this study, we therefore integrated the AV metabolic profiles with Genome-Scale Metabolic Network Models (GSMNM) to model the global metabolism of liver and intestine. GSMNMs gather, in an organized and mathematical format, all the biochemical reactions (and associated metabolites) that can occur in a given organismThe study involved five female adult Yucatan mini-pigs (31.5 ± 1.4 kg). Three weeks before the experimentation, mini-pigs were surgically fitted with a catheter in the abdominal artery aorta (Art), the portal vein (PV) and the hepatic vein (HV). They were housed in subject pens (1 × 1.5 m) in a ventilated room with controlled temperature (21 °C) and regular light cycle (L12:D12). They were fed once daily with 400 g/d of a concentrate feed containing 17.5% protein, 3.2% fat, 4.3% cellulose, and 5.2% ash  and had free access to tap water. All procedures were in accordance with the guidelines formulated by the European Community for the use of experimental animals . The protocol was approved by the Ethical Committee for Animal Experimentation Auvergne, authorization 02090.01.10.After the recovery period, minipigs were fed a HFHS diet consisting in a regular pig diet enriched with fat  and sugar (10% sucrose)  for two months. Animals ingested the whole mixture in no more than 10 minutes. After an overnight fasting, blood was sampled through the three catheters simultaneously on heparinized tubes, before (d0), and 7, 14, 30, and 60 days after HFHS feeding. Blood was centrifuged at 4,500 × g for 10 min, plasma rapidly collected and stored at −80 °C until further analyses. Biochemical and phenotype measures were also performed at the different time points: the related procedures and data have been previously publishedg at 4 °C for 15 minutes, and 600 µL of supernatant were transferred to 5 mm NMR tubes. All 1H NMR spectra were obtained on a Bruker DRX-600-Avance NMR spectrometer operating at 600.13 MHz for 1H resonance frequency using an inverse detection 5 mm 1H-13C-15N cryoprobe attached to a Cryoplatform (the preamplifier unit). The 1H NMR spectra were acquired at 300 K using the Carr-Purcell-Meiboom-Gill (CPMG) spin-echo pulse sequence with presaturation, with a total spin-echo delay (2nτ) of 240 ms to attenuate broad signals from proteins and lipoproteins. A total of 256 transients were collected into 32k data points using a spectral width of 20 ppm, a relaxation delay of 2 s and an acquisition time of 1.36 s. Prior to Fourier Transformation, an exponential line broadening function of 0.3 Hz was applied to the FID. All NMR spectra were phased and baseline corrected, then data were reduced using AMIX  to integrate 0.01 ppm wide regions corresponding to the δ 8.6–0.7 ppm region. The δ 5.1–4.5 ppm region, which includes the residual water resonance, was excluded. A total of 581 NMR buckets were included in the data matrix. To account for differences in sample concentration, each integrated region was normalized to the total spectral area. For the exchange of metabolites across the organs we calculated the ratios between the artery and the veins: Art/PV for the intestine, and [(Art × 0.2) + (PV × 0.8)]/HV for the liver. An averaged relative contribution of 20% of the hepatic arterial flow (Art) to the total hepatic flow was used as an approximation based on previously reported results15. We then expressed these ratios as percentage of metabolisation from steady state, considering steady state as inflow/outflow = 1.200 µL of plasma samples were mixed with 500 µL of phosphate buffer (pH 7.0) prepared in deuterated water, and then centrifuged at 5500 2 parameter) of the adjusted model. In addition, the robustness and validity of the PLS-DA models were calculated using a permutation test (number of permutations = 200). Significant NMR variables were identified using 1D and 2D NMR spectra. Discriminated time points (i.e. with different metabolic profiles) d0, d7 and d60 were selected based on the multivariate statistical analyses. For these three time points, comparisons between venous and arterial blood across the organs were performed using t-paired Student tests and the time effect was assessed using a repeated-measures one-way ANOVA test. SIMCA-P software  was used to perform the multivariate analyses. SigmaPlot v12.3  was used to perform the univariate analyses.Multivariate analyses were used to study the effect of high fat diet along time course on the metabolome. Principal components analysis (PCA) was first performed to reveal intrinsic clusters and detect eventual outliers. Partial least squares-discriminant analysis (PLS-DA) was then used to model the relationship between group and spectral data. PLS-DA is similar to PCA but uses discriminant variables that correlate to class membership. Before analysis, orthogonal signal correction (OSC) filtering was used to remove variability not linked to the studied conditions . Filtered data were mean-centered and Pareto scaled. For all the figures, Hotelling’s T2 statistics were used to construct 95% confidence ellipses. The R2Y parameter represents the explained variance. Seven-fold cross validation was used to determine the number of latent variables to include in the PLS-DA model and to estimate the predictive ability  and for each of the metabolites measured experimentally, paired t-tests between Art and PV and between HV and (Art × 0.2 + PV × 0.8) were performed, to identify metabolites significantly released or taken up by the gut or the liver. Metabolites with p-value < 0.05 and with a metabolisation % higher than 5% were considered as taken up by the tissue, whereas metabolites with p-value < 0.05 and with a metabolisation % lower than −5% were considered as released. For the metabolisation % between −5 and +5% the metabolites were considered as not exchanged or used by the tissue. The remaining metabolites, with a % metabolisation lower than −5% or higher than +5% but with a p-value > 0.05, were considered as not significantly exchanged and therefore were not constrained in the modelling.16 17 to simulate metabolic fluxes through intratissular reactions and predict the changes in gut and liver metabolic state during HFHS feeding. The Recon2 model contains 7440 reactions and 2626 metabolites, including 642 exchange reactions and associated metabolites which can potentially be taken up or secreted from the model. All these exchange reactions are initially unbounded by default (with lower and upper bounds set to −500 and +500 respectively), allowing uptake and secretion of all these metabolites. By definition of the exchange reactions in the model, a negative flux through an exchange reaction corresponds to an uptake of metabolite (incoming flux into the model), whereas conversely a positive flux corresponds to a release of metabolite (outgoing flux out of the model). Acquired metabolomics data were used to set specific constraints on these exchange reactions considering the metabolites evidenced as taken up, secreted and not exchanged from metabolomics data at each time. For released metabolites, the corresponding exchange reactions were constrained to enforce minimal release , by setting the upper bound (ub) to the flux equivalent to the limit of detection (LOD) value . The concentration was defined as LOD in 1H NMR spectroscopy when the signal-to-noise ratio (S/N) of signals reached 3:1. LOD was estimated at 25 μM in NMR tube for signals corresponding to 3 protons, i.e. 87.5 uM in plasma samples. LOD was estimated at 75 μM in NMR tube for signals corresponding to one proton, i.e. 260 μM in plasma samples. LOD, were converted to equivalent fluxes using an estimated blood flow value of 100 ml/g liver/h18. Constraints were set with a 20% margin from the LOD. Three of the experimentally exchanged metabolites  could theoretically not be exchanged in the Recon2 model as they were not associated to any exchange reactions: exchange reactions and extracellular transport reactions were added for these metabolites to allow their import and export. All exchange reactions not constrained from experimental data were set to secretion only (lb = 0), except for a list of metabolites which are considered as being always present in plasma and for which uptake from plasma was allowed in the model. A constrained model was generated for each time point and each tissue, therefore leading to 6 time- and tissue-specific models: SI-d0, SI-d7, SI-d60, Liver-d0, Liver-d7 and Liver-d60. The list of constraints defined for each model in given in Suppl. Table We integrated these extratissular exchange data into the GSMNM Recon219 and to account only for tissue maintenance (deoxyribonucleotides were excluded). Coefficients were also rescaled to get fluxes in µmol/g tissue/h instead of mmol/gDW/h (using an estimate of 3.8 for the liver Wet Weight (WW)/Dry Weight (DW) ratio)20. The lower bound for the biomass reaction was constrained in order to get a minimal protein fractional synthesis rate of 5%.h−1 21 and the albumin production and release was allowed in the 3 liver-specific models.The coefficients of the biomass reactions were adapted to better fit with the composition of an hepatic cell using data from the literature  and d7  is computed as follow:The possible range of flux values through each reaction  was computed using Flux Variability Analysis (FVA)This represents the overlap between the possible flux values at d0 and d7: J distance of 0 indicates that the 2 flux intervals do not overlap at all and therefore that the reaction i has distinct flux values at d0 and d7, whereas a J distance of 1 (100%) indicates that there is a complete overlap between possible flux values at d0 and d7.24, using the metabolic pathways defined in Recon2.For each pairwise time comparison and each tissue, we performed a pathway enrichment analysis on the reactions with J distance lower than 1. This aims to assess whether the reactions with modulated flux values between 2 time points are significantly over-represented in a metabolic pathway. Pathway enrichment statistics were performed using one-tailed exact Fisher test, with a Bonferroni correction for multiple tests6. More details about the biochemical and clinical phenotyping have been previously published25.After two months of HFHS feeding minipigs developed an obesity-like phenotype, with a significant increase in body weight (from 31.5 ± 1.4 kg to 44.7 ± 1.7 kg), most likely as the consequence of fat deposition at the visceral and subcutaneous adipose tissuei.e., PV, Art and HV is shown in Fig. The trajectory of the metabolome obtained from different sampling sites, Based on the results presented in the Fig. vs. venous plasma during circulation across the gut and liver of HFHS minipigs. Based on the PLS-DA results those metabolites with significant different levels in venous vs. arterial plasma at d0, d7 or d60 are represented in Figs 11. Among the significant changing metabolites, several AA were identified displaying consistently shifted ratios in time across the gut: higher arterial vs. venous levels were observed for glutamine (20–60% of metabolisation), glutamate (5–40% of metabolisation), threonine , and methionine , whereas increased venous levels were found for glycine (no more than −20% of metabolisation at d0 and 60). Several of these AA showed also modified levels between the inflow (Art and PV) and outflow (HV) across the liver. Opposite trend compared to gut was observed for glutamate and glycine, with a release profile for glutamate (−35% metabolisation) and an uptake profile for glycine (20–40% metabolisation). Higher levels of alanine (20–45% of metabolisation), methionine , lysine (10% of metabolisation at d7 and 60), and phenylalanine (8% of metabolisation at d0) were found in the inflow plasma when compared to the outflow levels across liver.The global profiling of inflow and outflow blood samples measured simultaneously using NMR metabolomics approach highlighted the significant changes in arterial vs. venous plasma across liver, whereas no significant differences were evidenced between inflow and outflow across the gut , this is the first time that such strategy is simultaneously applied to two organs (liver and intestine) in a pathophysiological model (obesity). Applying this AV measurement strategy for in vivo exploration using the obese minipig model offers two main advantages: first, to solve the problem of the PV access in humans27; and second, to be able to perform a follow up on the onset of obesity on the same individuals. We evidenced a similar metabolic trajectory in each blood vessel metabolic profile, with a clear discrimination of three steps: d0, d7 and d14 to d60, which is consistent with our previous study of the urine metabolome trajectory6. We therefore focused on the comparison of the metabolites exchanges in healthy state (d0) and in early (d7) and late (d60) obesity stages. The metabolomics data obtained across the intestine and the liver were also analysed by constraint-based mathematical modelling, which allowed to formulate further explanations about the fate of the exchanged metabolites.The paired arteriovenous metabolomics data-driven study presented here provides a comprehensive overview of the metabolite exchange and utilization across the splanchnic area in normal and obese minipigs. While in previous studies single organs have been explored using this approach 28 28 Fig. . In our 31: during the postabsorptive period, its AA supply comes mainly from the artery and is especially fueled by glutamine32 and glutamate31, which are used for energy purposes33. In our study, these two AA were actively taken up by the intestine, which is in agreement with the extensive glutamine utilization by the pig intestine31. The precise origin of glutamine could not be determined in the present study, but it would be likely released from the skeletal muscle30, while according to our data glutamate was rather released by the liver.The intestine is a metabolically active organ34. Concerning propionate, it is also known that the liver clears the major part of this metabolite from the portal circulation35, and that it is mostly used for gluconeogenesis purposes36, which is further consistent with our results.Finally, both, acetate and propionate showed a positive A/V ratio across the liver and a negative ratio across the intestine, reflecting their intestinal production and their hepatic uptake from the portal circulation. Acetate has been shown to be used as an energy substrate by the liver through its oxidation by the tricarboxylic acid cycle (TCA), but also as a substrate for the synthesis of cholesterol and long-chain fatty acidsFrom the metabolome trajectory, we noticed that the phenotype of all vessels shifted a d7 Fig. . We ther37.In contrast to the situation observed at d0, where several metabolites involved in phospholipids metabolism  were actively taken up by the liver, their uptake was blunted after only one week of HFHS feeding Fig. . A reduc38, which at d7 was no longer taken up by the liver, but rather released. This is also consistent with a reduction in glucose catabolism, which could further explain the pyruvate accumulation and release40. Thus, the increased availability of lipids could displace the use of pyruvate as a substrate for oxidative metabolism. Pyruvate will then cumulate and be subsequently diverted into lactate, resulting in both pyruvate and lactate release from the liver. In the same line, we observed that succinate was importantly taken up by the liver. In high substrate availability contexts, such as HFHS feeding, succinate could be released from tissues41. Under these conditions, the TCA may increase the H+ gradient over the mitochondrial membrane, leading to inhibition of enzymatic steps mediated by complexes within the electron transport chain and eventually, release of succinate into the blood stream42. In mice fed on a high fat diet and supplemented with sodium succinate, a reduced lipid accumulation in the liver was observed43. We may therefore consider that the early (d7) increased succinate uptake by the liver of the HFHS-fed minipigs could be part of a strategy aiming at reducing hepatic steatosis as the consequence of the deleterious fat and sugar intake. Despite a maintained intestinal release of acetate under HFHS feeding, its uptake by the liver was blunted after d7, which is consistent with the strategy to reduce lipid production from this substrate, by redirecting the intestinal acetate to peripheral organs for further oxidation44.After one week of HFHS feeding, we observed a reduced glucose release from the liver, most likely due to a reduced utilization of gluconeogenic substrates, like lactate and AA . During the postabsorptive period, gluconeogenesis is predominantly fuelled by lactateAs a whole, in the first week of HFHS feeding, we observed several features suggesting that the minipig metabolism rapidly reprogrammed and that there might be a metabolic shift at the hepatic level toward utilizing the most abundant nutrients (i.e. lipids) while alleviating the potentially associated deleterious effects.45. The positive A/V balance of glutamine and glutamate is significantly increased from d0 to d60. Interestingly, the energy demand seems to be complemented by other metabolites poorly used at d0, like creatine, a major metabolite involved in energy metabolism46. Creatine could be then preferentially used to rapidly yield energy47, which would be consistent with the positive correlation found between the adiposity and creatine levels in HF-fed pigs48.Globally the shifts observed at d7 tend to persist at d60 as suggested by the fewer changes in metabolome observed between d7 and d60 Fig. . The maj49. Some metabolic features observed in this study are consistent with this hypothesis and could be related to biological processes aiming at developing the intestinal functionality. For instance, methionine and threonine were more highly taken up after two months of HFHS consumption. This is consistent with the fact that methionine participates in the epithelial cell turnover as well as in the maintenance of the antioxidant status (by keeping cysteine available) and the cell level of reduced glutathione50, while threonine is largely used for mucins synthesis in pig mucosa52, which is of outmost importance in epithelium protection and innate immune defence.HFHS consumption may induce cellular stress and inflammation at the intestinal levelet al. reported that in cafeteria-fed animals glutamate was released from the liver while urea production was reduced53, which is consistent with our previous observations6 and could reflect a nitrogen sparing mechanism already observed in other situations55. We may therefore suggest that these changes would result from a metabolic switch in which AA catabolism would be reduced in detriment of lipids utilization, highly abundant in the HFHS diet, and able to induced a reduction in the urea cycle in both rodents56 and pigs10.In parallel, we also reported that glutamate A/V across the liver was further increased at d60, suggesting its larger exportation into the peripheral circulation. Barber In order to go deeper into the exploration of the changes in splanchnic metabolism under HFHS feeding, we have submitted the AV metabolomics data to constraint-based mathematical modelling. Our results allowed gaining new insights into the intrahepatic routing of exchanged metabolites experimentally assessed by NMR and mainly predicted changes in three metabolic pathways: fatty acid oxidation, tryptophan metabolism and purine synthesis Fig. .in silico simulations predicted that the metabolic fluxes through the tryptophan pathway reactions tend to be increased towards the production of kynurenine and its downstream metabolites, some of them  being major crossroads for the production of kynurenate, xanthunerate or quinolenate57. These metabolites were shown to be elevated in obese and insulin resistant subjects, and some of the metabolites participating at these particular pathways are known to reduce insulin production and sensitivity in laboratory animals59. Further, changes in tryptophan and its related metabolites have been associated with altered indoleamine 2,3-dioxygenase-1 activities, one of the rate-limiting enzyme of tryptophan catabolism and a possible indirect indicator of immune mediated inflammation in obese and overweight conditions61.Our We also predicted that the activity of the purine synthesis pathway would be transitory increased in liver after 7 days of HFHS diet, with a potentially larger flux through a set of reactions belonging to this pathway at d7. This temporary shift might be linked to a possible higher hepatic uptake of glutamine, which is used as a co-substrate for some reactions in the pathway, at d7.25. The reactions predicted to be modulated are specifically those relying on FAD as co-factor. The fact that betaine became significantly taken up at d7 and d60 and that its metabolism also consumes FAD could therefore be linked with the reduction of the fatty acid β-oxidation pathway as a result of FAD competition between reactions.Simulations based on the metabolomics data also predicted modulations in the fatty acid oxidation pathway, with smaller fluxes through about 60 fatty acid mitochondrial β-oxidation reactions, which is in line with our previous study showing a reduced expression level of limiting enzymes of this pathwayIt should be noted, that one limit of the modelling approach is that the predictions highly depends on the metabolic network model used as a basis: in this study, because there is currently no manually curated, and therefore reliable, reconstruction for the metabolic network of the minipig, or even the pig, we chose to use one of the most up-to-date and consolidated reconstruction of the human metabolic network, Recon2.04. Also, we use the global human metabolic model rather than tissue-specific models since we wanted to be able to compare the analyses made from the metabolomics data obtained both for the liver and intestine. Despite this, it is interesting to notice that the pathways pointed out by the modelling are different from those identified when looking directly at the metabolomics data. By considering the metabolic network and all the interconnections between the metabolic reactions, we are able to predict changes in metabolic pathways that could be the indirect consequence of a differential use of external metabolites. Therefore, we provided strong cues that this modelling approach based on the metabolic network is useful to suggest new and complementary pathways that could not be evidenced by looking directly at the data and would be a helpful tool when digging further into the mechanisms of the observed changes.25. Further, our high-throughput approach allowed reflecting the metabolic activity of these organs by providing unique information that could not be obtained by a single venous sampling.Overall, our time-course study of the paired arterial and venous metabolomes across the intestine and liver of HFHS-fed minipigs provided original information about the subtle metabolic regulation taking place during the onset of obesity and insulin resistanceOur approach allowed characterizing the splanchnic area metabolism on healthy minipigs Fig. , includiSupplementary FiguresSupplementary Dataset


Quantification of substrate fluxes in brown adipocytes clarifies contributors to extracellular acidification, identifies a futile cycle of lipolysis and re-esterification, and suggests a role for glycogen during activation. Brown adipocytes are highly specialized cells with the unique metabolic ability to dissipate chemical energy in the form of heat. We determined and inferred the flux of a number of key catabolic metabolites, their changes in response to adrenergic stimulation, and the dependency on the presence of the thermogenic uncoupling protein 1 and/or oxidative phosphorylation. This study provides reference values to approximate flux rates from a limited set of measured parameters in the future and thereby allows to evaluate the plausibility of claims about the capacity of metabolic adaptations or manipulations. From the resulting model, we delineate that in brown adipocytes (1) free fatty acids are a significant contributor to extracellular acidification, (2) glycogen is the dominant glycolytic substrate source in the acute response to an adrenergic stimulus, and (3) the futile cycling of free fatty acids between lipolysis and re-esterification into triglyceride provides a mechanism for uncoupling protein 1–independent, non-shivering thermogenesis in brown adipocytes. Brown adipocytes are highly specialized cells with the unique metabolic ability to dissipate chemical energy in the form of heat, thereby providing a means of non-shivering thermogenesis. This process is mediated by uncoupling protein 1 (UCP1) located in the inner mitochondrial membrane, where it uncouples respiration from adenosine triphosphate synthesis. Upon cold exposure, sympathetically released norepinephrine induces lipolysis. Released free fatty acids (FFAs) serve as both activators and fuel of non-shivering thermogenesis  incubator for 1 h. Basal respiration was measured in untreated cells. Coupled respiration was inhibited by oligomycin treatment (5 μM). UCP1-mediated uncoupled respiration was determined after isoproterenol (0.5 μM) stimulation. Maximum respiratory capacity was assessed after carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (Sigma-Aldrich) stimulation (7 μM). Finally, mitochondrial respiration was blocked by antimycin A (Sigma-Aldrich) (5 μM), leaving only non-mitochondrial OCR to be measured. OCR, ECAR, and PPR were automatically calculated by the Seahorse XF-96 software.Oxygen consumption rate (OCR), ECAR, and proton production rate (PPR) were measured at 37°C using a XF-96 extracellular flux analyzer (Agilent Technologies). At day 7 of differentiation, the medium was replaced with prewarmed unbuffered DMEM (5.5 mM glucose) supplemented with 2, 3, or 4% essentially fatty acid–free BSA and incubated at 37°C in a non–carbon dioxide . Conditioned medium was analyzed for FFA and glycerol content by commercial kits . Measurements were performed using a microplate reader  in accordance to manufacturer instructions. Intracellular glycogen concentration was determined by means of a commercial kit system .Lactate concentration in supernatants was determined using an enzymatic assay . Perchlo1 d before the experiment insulin was removed from medium. After serum starvation (3 h), cells were stimulated with isoproterenol for 30 min. Then, the medium was changed to fresh assay medium (5.5 mM unlabeled glucose) containing stimulants and trace amounts (3 µCi) of 2-deoxy-d-[1-3H]-glucose. After three minutes, glucose uptake was terminated by washing cells with ice-cold stop solution . Cells were lysed with 0.2 M NaOH at 60°C for 1 h. Incorporated radioactivity was determined by liquid scintillation counting (Perkin Elmer).P-value generation). Original data and analyses have been deposited at Gene Expression Omnibus (GSE119873).Fully differentiated brown adipocytes were scraped into 200 µl Trisure (Bioline), RNA extracted following the manual and further purified by a column system . We constructed libraries  and sequenced each sample to a depth of 10–20 million single reads 50 bp in length . We mapped reads to the mouse genome  and assigned uniquely identified reads  to known transcripts to provide abundance values in reads per kilo bas per million mapped reads (RPKM) . Pairwaise comparison was performed with the DESeq2 1.4.5 algorithm  is metabolically produced during substrate combustion. In aqueous solution, generated CO2 gets hydrated to carbonic acid (H2CO3), which subsequently dissociates to hydrogen carbonate (HCO3−) and H+ at physiological pH. The fraction of hydrated CO2 was calculated with the help of the overall pKa of 6.093 for CO2 in aqueous solution at 37°C  and Gibbs energy (9.8 µJ/pmol) of palmitate (exported + FFAoxidized + FFAre-esterified)/3.Carbon dioxide . We found the vast majority of liberated FFAs released into the medium  production increased more than threefold . Oxygen consumption and deduced carbon dioxide  isoproterenol-induced and (2) oligomycin-sensitive changes that are (3) specific for Ucp1-KO cells. The only parameter fitting this description is the re-esterification rate , that isTaken together, we describe a model of key catabolic pathways in WT and Ucp1-KO brown adipocytes at baseline and stimulated by isoproterenol. Comparison of substrate fluxes with ECAR revealed FFAs as a significant proton source. We further identified high intracellular glycogen stores as a preferred initial source for glycolytic substrate under adrenergic stimulation. Finally, respiration of Ucp1-KO brown adipocytes was stimulated β-adrenergically to similar levels as in WT brown adipocytes. This UCP1-independent thermogenic component requires ATP and likely involves the re-esterification of FFAs to glycolytically provided G3P. A futile cycle of lipolysis and simultaneous re-esterification may provide the ATP sink required for the observed thermogenic effect.We present an interconnected, quantitative model of major catabolic pathways in cultured, primary brown adipocytes. This model of metabolic fluxes describes and predicts how an adrenergic stimulus changes key metabolic reactions in brown fat cells. Because all parameters were determined in the presence and absence of the thermogenic protein UCP1, our model differentiates both β-adrenergic and UCP1-dependent thermogenic effects. Primary data generated  with regard to glycolytic flux. Glycolytically produced lactic acid is often considered the major source of medium acidification and ECAR is commonly used as a direct and quantitative measure of glycolytic flux . As otheWe noticed a stark discrepancy between glucose uptake rate and glycolytic lactate production. The latter seemed to require a substantial, additional glucose source that we confirmed to be intracellular glycogen. An exceptionally high glycogen content of brown adipocytes reaching levels comparable with fed liver has been reported before . It seemIn brown adipocytes, adrenergic signaling leads to increased lipolysis and activation of UCP1. Most parameters that were assessed in our model such as glucose uptake, triglyceride degradation, or FFA and glycerol release were elevated in response to isoproterenol treatment, irrespective of the presence or absence of UCP1. Interestingly, this was also true for oxygen consumption. A β-adrenergic agonist caused a dramatic increase in ATP turnover in Ucp1-KO brown adipocytes, leading to an oligomycin sensitive respiration undistinguishable from UCP1-mediated uncoupling. Importantly, although Ucp1-KO mice are sensitive to acute cold exposure , they arIntriguingly, gene expression is virtually undistinguishable in WT and Ucp1-KO cells. All components of powerful, UCP1-independent thermogenesis by futile lipolysis and re-esterification are obviously already present in sufficient amounts in brown adipocytes. On the one hand, this renders the detection of said mechanism by transcriptomic methods difficult or impossible. On the other hand, it suggests that no additional component is required and could thereby be invoked in any cell of sufficient lipolytic/re-esterification capacity, possibly even non-adipocytes.In summary, this study provides quantitative, interconnected substrate fluxes in brown adipocytes and resolves their dependency on adrenergic stimulation, the presence of UCP1 and the ability to phosphorylate ATP. It provides reference values to approximate flux rates from a limited set of measured parameters in the future and thereby allows to evaluate the plausibility of claims about the capacity of metabolic adaptations or manipulations. We furthermore delineate from our model that in brown adipocytes (1) FFAs are a significant contributor to extracellular acidification, (2) glycogen is the dominant glycolytic substrate source in the acute response to an adrenergic stimulus, and (3) the futile cycling of FFAs between lipolysis and re-esterification into triglyceride provides a mechanism for UCP1-independent, non-shivering thermogenesis in brown adipocytes.


To understand the functioning and dynamics of microbial communities is a fundamental challenge in current biology. To tackle this challenge, the construction of computational models of interacting microbes is an indispensable tool. There is, however, a large chasm between ecologically motivated descriptions of microbial growth used in many current ecosystems simulations, and detailed metabolic pathway and genome-based descriptions developed in the context of systems and synthetic biology. Here, we seek to demonstrate how resource allocation models of microbial growth offer the potential to advance ecosystem simulations and their parametrization. In particular, recent work on quantitative resource allocation allow us to formulate mechanistic models of microbial growth that are physiologically meaningful while remaining computationally tractable. These models go beyond Michaelis–Menten and Monod-type growth models, and are capable of accounting for emergent properties that underlie the remarkable plasticity of microbial growth. We outline the utility and advantages of using biochemical resource allocation models by considering a coarse-grained model of cyanobacterial growth and demonstrate how the model allows us to address specific questions of relevance for the simulation of marine microbial ecosystems, including the physiological acclimation of protein expression to different environments, the description of co-limitation by several nutrients and the differential use of alternative nutrient sources, as well as the description of metabolic diversity based on our increasing knowledge about quantitative cell physiology. Microbes typically form complex, interacting, and dynamically changing communities, with examples ranging from marine plankton communities to the human microbiome. To understand the organizing principles and the functioning of such communities is of paramount importance for a vast number of basic and applied research questions, including questions pertinent to biotechnology, climate change and human health –4. DespiTraditionally, ecosystem dynamics and microbial communities are the realm of microbial ecology, with a long history and a wealth of results concerning the organization, stability and functioning of  ecosystems ,10. In tParallel to progress in theoretical and experimental microbial ecology, the past two decades have seen an unprecedented advance in our understanding of microbial molecular physiology—mainly driven by advances in our ability to monitor, measure and modify the inner workings of cells. Theoretical and computational descriptions of microbial metabolism, facilitated by large-scale metabolic network reconstruction and constraint-based analysis, have become established tools in molecular systems biology –17. CuraMore recently, also the quantitative growth physiology of bacteria has gained renewed attention, with numerous studies providing novel insights into the principles of microbial growth and resource allocation –27. Key The purpose of this work is therefore to outline a bridge between these recent studies on microbial resource allocation and current models of microbial ecology. We argue that biochemical resource allocation models offer significant potential to advance ecosystem simulations beyond current applications of constraint-based analysis of microbial metabolism. In particular, we seek to demonstrate that biochemical resource allocation models, as defined below, can be constructed and parametrized for large classes of microbial organisms based on available biochemical and physiological data; and are, unlike Monod-type models, capable of exhibiting emergent properties of growth, such as photoacclimation or a preferential hierarchy of nutrient sources. Our study is motivated by recent calls for a new generation of plankton models to better capture the emergent properties of marine ecosystems ,13. As wThe paper is organized as follows: in the first section, we briefly recapitulate computational models of microbial growth. In the second section, we provide an overview of metabolic network reconstruction and recent biochemical models of cellular resource allocation. In the following section, we consider a coarse-grained model of phototrophic  growth and describe its parametrization. In the subsequent section, we discuss properties of relevance for the simulation of microbial ecosystems, such as metabolic plasticity, cellular growth laws and co-limitation by several nutrients, as well as the representation of microbial diversity and the emergence of a preferred hierarchy of nutrient uptake. In the sixth and seventh sections, we briefly present a case study: the co-existence of two phytoplankton species with a gleaner–opportunist trade-off. In the final section, we provide a discussion and outlook.2.ρ denotes the concentration of cells (described here in units of cells per volume), μ denotes the specific growth rate and D is the dilution or death rate. The specific growth rate μ is a function of the respective environment, and depends on the concentrations of one or more nutrients. Typically, a limiting nutrient n with concentration [n] is considered that is supplied with the inflow of fresh medium at a concentration [nx]. The respective nutrient dynamics are then described byY denotes the yield coefficient, defined as the number of microbial cells (or units biomass) per unit of nutrient.Assuming a chemostat-like setting, the growth dynamics of a population of  cells can be described by an ordinary differential equation,μ as a function of the concentration of the limiting nutrient n. To this end, among the most widely used approaches is to make use of the hyperbolic dependency proposed by Monod in 1949  of cellular growth . The sum of all translation rates is constrained by the available ribosomal capacity and hence by the number of ribosomes.More formally, the models are based on a description of the intracellular protein allocation of growing cells. The concentration of a protein m is described by a stoichiometric matrix N and a vector ν that denotes the rates of  interconversion rates,To account for the synthesis of metabolic precursors and other cellular components, the interconversion of internal metabolites N · ν = 0.Typically, intracellular metabolism is assumed to be at steady state and the dilution terms for intracellular metabolites are neglected due to the high turnover rate of metabolites compared to their dilution rate by growth. Hence, the mass-balance constraint on intracellular reaction fluxes simplifies to kkcat, denotes the specific activity of the enzyme or protein. The maximal uptake rate νT of an external nutrient nx can be further constrained by the concentration of the respective nutrient and the amount of the respective transporter complex PT,To account for biochemical resource allocation, the rates of those reactions that are catalysed by proteins are constrained by the amount of the respective catalysing proteinset al.  denotes the amount of uptake transporter (in molecules per cell), KC denotes the half-saturation constant of the uptake complex. We note that equation (5.1) provides an upper limit only, the actual flux can be less (for example by inactivating a fraction of the uptake transporter). Likewise, additional constraints can be included, such as an upper limit on the uptake flux induced by diffusion limitations  and [n2] denote the concentrations of two potentially limiting nutrients and Km1Km2 the respective half-saturation constants, respectively. As discussed by Saito et al. [Another challenge for phenomenological growth models is to describe the dependence of growth on several potentially limiting nutrients, see Saito et al.  for a dio et al.  both funo et al. ,42.For biochemical resource allocation models the description of growth limitations as a function of two or more nutrients emerges without further assumptions about the functional form of growth equations. As expected, the coarse-grained model described above is not consistent with Liebig’s law of the minimum: growth on a single nutrient, as shown in b shows a Lineweaver–Burk plot of the growth rate as a function of nitrogen availability for different values of the external carbon concentration. Parallel lines in a Lineweaver–Burk plot correspond to non-competitive inhibition, whereas the multiplicative functional form of equation (6.2) would result in lines with an identical x-intercept. Hence, the absence of carbon acts analogous to uncompetitive inhibition, and affects both the apparent organismal half-saturation constant of growth as well as the maximal growth rate of the cell—again with important consequences when modelling, for example, growth limitations and nutrient dynamics in coupled ecosystem models. While we are not aware of dedicated growth studies to quantitatively investigate the functional dependencies of co-limitation in cyanobacteria, the plasticity of metabolism and re-allocation of proteins makes the strict multiplicative dependence of equation (6.2), as assumed in most current models, highly implausible.More importantly, however, the interdependence of growth limitations by two nutrients implies that the emergent growth curve is also not consistent with the multiplicative functional form: figure 47.Prochlorococcus strains). The model definition may account for this fact by allowing only limited concentration ranges for certain intracellular protein complexes.Recent studies have emphasized the representation of microbial community diversity as a fundamental requirement of model ecosystems . SeveralBeyond different acclimation states, however, cellular diversity also arises due to genetically encoded differences between organisms. Firstly, microbial organisms exhibit metabolic diversity with respect to the encoded metabolic functionality within their genomes. As shown by recent studies of the cyanobacterial pan-genome and pan-metabolism –55, genoNx considered above, there is a second source of extracellular nitrogen Ny that is permanently available in the environment . The uptake of the permanently available nitrogen source Ny and its conversion to the intracellular nitrogen precurser N is facilitated by a set of proteins that is represented in our models by a coarse-grained protein complex TY. The synthesis of the protein complex TY, however, requires more amino acids and its catalytic turnover number is lower, as compared to the complex TN. Within their genome, the cyanobacterial strains may either encode one of the two (coarse-grained) uptake protein complexes, TN or TY, or both. The respective strains are denoted as (TN)-strain, (TY)-strain and (TN + TY)-strain. The inclusion of both protein complexes within a single genome entails additional cellular costs: a larger genome corresponds to a (slightly) higher fraction of the non-protein biomass Q. In addition, further protein machinery is required to facilitate cellular decisions that control the expression of both enzyme complexes. The increased protein machinery is represented by an increased fraction of non-catalysing (quota) proteins PQ. The exemplary parametrization of the three strains is provided in the Material and methods.To illustrate how BRAMs may represent such genetic diversity, and to illustrate the functional consequences of different metabolic strategies in model simulations, we consider the uptake of two alternative sources of extracellular nitrogen. We assume that, in addition to the nitrogen source a shows the growth curves of all three strains as a function of Nx in the presence of a constant basal availability of Ny (a). Figure 5b shows the expression of the respective uptake complexes for the (TN + TY)-strain harbouring both uptake complexes. As expected, the (TY)-strain exhibits a constant growth rate, due to the constant basal availability of NY. The (TN)-strain exhibits a Monod-type dependence on the availability of Nx, as already shown in TN + TY)-strain, however, exhibits a switch between two growth regimes: for low availability of external Nx, the strain expresses the protein complex TY and uses the nitrogen source NY. In this regime, the (constant) specific growth rate is slightly below the rate observed for the (TY)-strain due to the increased burden of non-catalytic biomass. If the availability of Nx exceeds a certain threshold, the (TN + TY)-strain switches its preferred nitrogen source and expresses the protein complex TN. The growth rate then increases with increasing availability of Nx, but always remains below the growth rate of the (TN)-strain . Hence, we expect that the (TN + TY)-strain will be outcompeted in any constant environment, but will have a competitive advantage in (some) environments with variable nitrogen availability.Figure 5ty of Ny a. Figureet al. [et al. [For our purposes, the example serves to illustrate the following points: (i) biochemical resource allocation models allow us to represent genetic diversity within strains, including differences in genome size and genomes that encode several potential metabolic strategies; (ii) the associated costs of larger genomes, including the costs for additional expression of regulatory proteins can be incorporated into the parametrization of the model . The differences between both strains reflect trade-offs between the maximal specific catalytic activities and the substrate affinities in the protein complexes involved in carbon and nitrogen uptake. The parametrization of both strains is provided in Material and methods and et al. [ρG) and opportunists (ρO) are described by the following ODEsVN denotes a constant influx, and νn,O and νn,G denote the specific cellular uptake rates (as emergent properties of the respective models) of external nitrogen by the gleaners and opportunists, respectively. The population dynamics of both strains in constant and time-varying environments are shown in Following Tsakalakis et al. , we simuet al. [As shown in et al.  and demo9.As noted by Follows & Dutkiewicz , there iThe aim of our study was to heed the call of Allen & Polimene  to proviet al. [Biochemical resource allocation models can be formulated for almost all microbial strains for which a reference genome is available. Supported by recent analysis of the cyanobacterial pan-genome –55, and et al.  for a br—et al. [BRAMs can be formulated using different levels of complexity, ranging from GSRs that take into account the comprehensive set of cellular proteins and enzymes ,35, to iet al. , that fa—Model parametrization can be based on available biochemical knowledge provided in databases, such as BRENDA , as well—BRAMs allow us to represent metabolic diversity by taking distributions of parameters  into account. Biochemical resource allocation models therefore enable implementation of selection-based approaches—following the Baas-Becking paradigm ‘everything is everywhere but environment selects’ (cited after Follows & Dutkiewicz ).—The analysis of biochemical resource allocation models reveals complex metabolic behaviour, such as switches between different metabolic strategies. Most microbes are capable of more than one metabolic strategy and phenomenological Monod-type models face difficulties to describe transitions between different metabolic strategies. For biochemical resource allocation models the modes of energy generation or nutrient uptake strategies (and their respective hierarchies) emerge as part of the optimization procedure without further specification.—The latter fact also enables consideration of resource allocation models from an evolutionary perspective to explain how different metabolic strategies and strains with different genome sizes emerge and coexist. Likewise, while the trade-offs affecting individual biochemical parameters are not an outcome of the analysis, the models can provide insights into what factors shape the trade-offs in kinetic parameters within their physico-chemical limits, for example under which environmental conditions would a faster catalytic rate of an enzyme outweigh a reduced specificity?—Biochemical resource allocation models of the mathematical form discussed here only require linear optimization and hence are computationally tractable. While it is (currently) not possible to formulate kinetic models at the genome-scale, the implementation of biochemical resource allocation models is computationally feasible even for large models ,35. CoarIn this study, our aim was not a comprehensive review of computational resource allocation models, but rather to highlight the specific aspects and properties that are of particular importance in simulations of ecosystems, specifically acclimation to different growth environments, co-limitation, and the representation of metabolic diversity. In this respect, the merits of biochemical resource allocation models are as follows:et al. [Despite their merits, however, current biochemical resource allocation models are not (yet) the panacea for ecological simulations. We expect that diverse approaches are still needed, along with further improvements of biochemical resource allocation models and other whole-cell systems biology models. In particular, current biochemical resource allocation models may be extended and improved along the following lines: (i) current simulations typically focus on steady state analysis. While it has been shown that biochemical resource allocation models can be solved for time-varying environments ,35, the et al. . In partOverall, we are confident that biochemical resource allocation models, whose construction is based on reference genomes and increasingly automated , offer s10.10.1.PQ and remaining biomass Q) fulfil no explicit functional role within our model and their synthesis is enforced using fixed quotas .We consider a particular variant of biochemical resource allocation models following the methods and algorithms described in ,32,35. A10.2.et al. [et al. [PQ. The enzyme and transporter complexes catalyse the following reactions:The biochemical resource allocation model shown in et al.  , such that its abundance follows the equationProtein translation is described by the equationThe dynamics of all other protein complexes are modelled according to equation (4.1).10.3.All enzyme-catalysed reactions are constrained by the amount of the respective catalysing enzyme, according to equation (4.3). The constraints for nutrient uptake and light harvesting also depend on the availability of the respective external substrates. In particular, for the uptake of inorganic carbon,sis, see  for detanp denotes the protein size (in amino acids per molecule), γp its translation rate and γmax denotes the maximal translation rate of ribosomes. We note that all capacity constraints are implemented as linear constraints.The constraints on the ribosomal capacity are10.4.Cx, Nx, I and specific growth rate μ, the model implemented as a LP(μ). The LP problem is described by three matrices N, W≤, and =W, as well as the vector of reaction rates v = T (combining metabolic and translation rates), the vector P of (the concentrations of) macromolecules,ω that describes the sizes of the macromolecules (in units of amino acids per molecule). For each macromolecule, we define a lower bound that ensures the synthesis of quota components,For any given set of external parameters N describes the stoichiometry of the system, including metabolic and translation reactions, as well as the stoichiometry of the decay of the PSU as a result of photodamage (vd). The matrices W≤ and =W describe the relationship between reaction fluxes and P. The entries of both matrices may depend on the external parameters Cx, Nx and I.The matrix W≤ is largely diagonal, except for the constraints on the translation rate. Constraint (10.14) describes enforced reactions that are proportional to the abundance of a macromolecule (such as photoinhibition). Constraint (10.15) provides a lower bound for the abundance of each macromolecules (zero except for the quota components). Constraint (10.12) ensures positive fluxes in the LP problem. Constraint (10.16) enforces a constant cell density, the parameter d denotes the cell density (in units of amino acids per cell).The full set of constraints isμ. To obtain a solution for the maximal specific growth rate in a given environment, the global optimum of μ is found using bisection as described in [The above described LP is solved as a feasibility problem for a given ribed in .10.5.et al. [PQ and the biomass component Q is arbitrary. Turnover rates are chosen according to average values described in [et al. [σ denoting the effective absorption cross-section per photosystems, and kd the rate of photodamage.A complete list of model parameters is provided in et al. . The sizribed in . The des [et al. , with σ To simulate the growth on two alternative sources of external nitrogen, we used the set of additional parameters given in 10.6.https://github.com/surajsept/cyanoRBA).A computational model is developed using Python as a programming language. The framework uses functionality from the following packages: numpy , scipy , matplot


Medicago truncatula, which has been adopted as a model organism for legumes. We employed flux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the effects of the symbiosis on metabolic fluxes and plant growth and could demonstrate how oxygen availability influences metabolic exchanges between plant and symbiont, thus elucidating potential benefits of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective.Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover  Therefore, despite its high abundance, nitrogen is often a limiting factor for growth in plants. To overcome this in agriculture, large amounts of nitrogen are applied to the soil in the form of artificial fertilisers to promote plant growth. Before the industrial revolution, farmers applied a crop rotation scheme to their fields and left the field barren or planted it with beans or peas every few years to let the soil ‘recover’. We now know that this strategy re-introduces bio-available nitrogen into the soil. This is due to ability of the planted crops (beans or peas) or plants quickly occupying barren fields, such as clovers, to form a nitrogen fixing symbiosis with rhizobia. Rhizobia carry a gene for the highly oxygen sensitive nitrogenase enzyme, which catalyses the reduction of atmospheric nitrogen to ammonium, and the plants provide the symbiont with an environment protecting it from damaging oxygen. In this way atmospheric nitrogen is introduced into the global organic nutrient cycle. Simultaneously the nitrogenase requires large amounts of energy and reductants to catalyse nitrogen fixation. This has led to the development of a tightly regulated oxygen supply for oxidative phosphorylation by the plant to the symbiont2. This symbiosis has seen the most attention and is best understood in plants of the Fabacaea (or Leguminosae) family, to which beans, peas and clovers belong7. In recent years, Medicago truncatula has become a model plant for the legume-rhizobia symbiosis8. While there is a plethora of rhizobial strains, only a limited number interact which each host10. Sinorhizobium meliloti is a well-studied symbiont of M. truncatula11 and therefore an optimal organism to model this symbiosis. Low nitrogen availability will trigger the recruitment of rhizobia to the plant roots and initiate the nodulation, in which rhizobia invade the plant root and nodules are formed. In these nodules, the rhizobia are taken up by plant cells, surrounded by a membrane and differentiate into bacteroids. Upon completing differentiation they begin fixing nitrogen, which is made available to the plant primarily in the reduced form of ammonium. In return, the plant provides organic acids as nutrients to the rhizobia12. In addition there is evidence that amino acid cycling is essential for nitrogen fixation at least in some rhizobial strains14. Obviously, the mutual dependence of the metabolism of plant and rhizobium is crucial for this symbiotic interaction. During the establishment of the nodules, the metabolic fluxes in both plant and rhizobium change drastically15.Nitrogen belongs to the elements which are absolutely crucial for life, because it is contained in all amino acids and many other essential biomolecules. Whereas nitrogen is the most abundant element in the earth’s atmosphere, most of it is present in the inert form of nitrogen gas  can be employed, the building process itself provides considerable insight into the metabolic capabilities of the investigated organism and allows to refine its genome annotation. Once a highly curated metabolic network reconstruction is established, it presents a theoretical framework allowing to query the system and understand its functional properties. The numerous possible theoretical investigations21 are for example useful to help understanding the structure and regulation of metabolic networks23, identify essential genes24, predict putative drug targets25 or support engineering of novel pathways26 producing desired compounds of technological or economic interest. In the plant sciences, genome-scale models with different degrees of accuracy and completeness exist for the model unicellular green alga Chlamydomonas reinhardtii27, the model species Arabidopsis thaliana28, rice Oryza sativa29 and others31. While initially plant models were unicellular representations of metabolism, not distinguishing between different tissues, recent efforts tend to refine these models and generate multi-tissue representations32. While the network structure and catalysed reactions are now fairly well established and can be extracted using the genome annotation, most enzyme kinetics are still unknown. This makes the use of ODE based kinetic models impossible on genome-scale networks, as thousands of parameters would have to be estimated. FBA is a modelling technique that addresses this issue by imposing a steady state assumption on the fluxes, indicating that there is no change of internal metabolite concentrations in the network33. FBA further imposes an objective that the model should optimise , which yields a unique optimal value. Thus the network can be investigated for the effects of a changing environment without the need for extensive parameter fitting and optimisation. Unfortunately, the flux distribution itself  is not necessarily unique in FBA35. This can however be addressed by using minimisation of the overall quadratic flux, which represents a solution with efficient enzyme usage, for a given optimal FBA value, at which point the flux distribution becomes unique.To explore, understand and analyse the changes in the metabolic fluxes during nodulation, computer simulations have become increasingly established in modern biology research. For this, detailed high quality metabolic network reconstructions have to be established. Whereas there exist now over a hundredM. truncatula and provide an analysis of general metabolic properties of its biochemical reaction network. For our detailed and curated reconstruction, which contains 1636 reactions in 7 compartments, we have developed an extended approach to assign metabolic reactions to sub-cellular compartments. Our approach integrates not only sequence and proteomics derived localisation information37 but also integrates known importers and exporters, thus taking advantage of an additional source of information. This information is integrated using the concept of network extension introduced in Christian et al.38. All reactions in our reconstruction were manually curated to achieve complete mass- and charge-balance as suggested in Thiele and Palsson39. This important property of a network allows for a system wide prediction of fluxes of protons and other charged particles over cellular membranes. To our knowledge, we present the first plant metabolic model, for which charge balancing is ensured on genome scale, while this was previously only achieved for small subsystems22 or core metabolism41. Charge balancing over membranes is a key prerequisite to realistically describe electron transfer chains required for ATP biosynthesis. Compartmentalisation allows for a more precise distinction between the function and necessity of isozymes which are present in multiple compartments. Omitting this information would allow the model to use reactions for which substrates are not available, because they are not transported between compartments. This would make the prediction of potentially lethal mutations less reliable. We further refined the model by integrating tissue specific information to better represent the distribution of metabolism to root and shoot of the plant.In this paper, we focus on the presentation of a highly curated genome-scale metabolic reconstruction of M. truncatula, thus providing for the first time a system wide in-depth biomass composition for this plant. To investigate the interaction between plant and microbe, this highly curated plant model was connected to a model of Sinorhizobium meliloti, derived from the MetaCyc database19, and we investigated the effect of nitrogen fixation and symbiosis on the plant model. The presented model aims at simulating the nitrogen fixing state of the symbiosis and does not reflect the process of forming the symbiosis. We further addressed the question which fluxes might be most restrictive for nitrogen fixation and investigated the effect of small alterations of oxygen supply on the symbiotic nitrogen fixation capacity. The model presented in this paper provides an important resource for researchers in plant sciences in general and for studying plant-rhizobia interactions in particular. Our simulations help to interpret and understand responses of plant metabolism, which include rearrangements of metabolic fluxes and changes in energy and reductant requirements, as a result of changes in nitrogen availability.We discuss insights gained from the reconstruction process itself and their consequences on the annotation of genes with previously unknown functions and the refinement of annotations of previously insufficiently annotated genes. We experimentally obtained tissue specific biomass data of M. truncatula (details can be found in Supplementary Table MTR_4g038440 (Entrez Gene ID: 11446905), currently annotated as a caffeic acid 3-O-methyltransferase, is very similar (87% sequence identity) to the inositol methyltransferase from Glycine max (Entrez Gene ID: 100812768). Since we could not find other candidates for this biosynthetic step, we expect that this enzyme might indeed be mis-annotated and would suggest a revision of its annotation.During the biomass analysis of Medicago truncatula encompassing 3403 genes coding for 2909 reactions in 8 compartments using 2780 metabolites. An overview of the model is provided in Fig. We created a genome scale model of Medicago truncatula using data from Benedito et al.42 employing the FASTCORE43 algorithm .This genome-scale model encompasses our knowledge of biochemical reactions catalysed by enzymes, which are encoded in the genome along with additional necessary reactions to fulfil known functions. However, it is unable to describe processes in a particular cell type or part of the organism, as no such separation is yet included. We therefore constructed a multi-tissue model of 45. The large reduction can partly be explained by many reactions in secondary metabolism which, in the current model formulation, are unable to carry flux, because specific exchange reactions have not been added, and because concentrations of those secondary metabolites are too small to be detected during biomass analysis. In total, the consistent part of the Medicago truncatula network contained only 1303 reactions, which are able to carry non-zero flux with the current configuration of exchange reactions, while all remaining reactions can be activated if additional exporters are included. The two-tissue model was able to sustain growth during day (using light as energy source and photosynthesis for carbon) and night (with starch as energy and carbon source). In addition, both ammonium and nitrate can be used as nitrogen sources to support growth under day and night conditions.The shoot tissue of this model was represented by 969 reactions associated with a total of 1824 genes acting on 860 metabolites and containing 126 internal transporters. The root tissue in turn consists of 958 reactions associated with a total of 1804 genes acting on 846 metabolites and including 122 transporters. These submodels were connected by 32 inter-tissue transporters and a combined biomass reaction, with the inter-tissue transporters derived from the literatureIn silico knockout experiments predict that during night conditions, 236 reactions are essential (including 26 transporters). Under phototrophic growth, 243 reactions are essential (25 transporters). In total, 232 reactions are essential under both conditions. Starch degrading reactions are essential only under night conditions, while those involved in the Calvin cycle and photosynthesis are essential only in light conditions. Interestingly, the additional transporter predicted as essential under dark conditions is the maltose exporter in the chloroplast envelope (mex). However, mex mutants are viable, albeit with a reduced growth and starch excess phenotype46. The reason that the model predicts this transporter as essential is that, in the strict mathematical sense, no flux distribution exists which fulfils the stationarity condition for maltose. Therefore, the prediction can be understood in terms of the mathematical model formulation, and the predicted phenotype  is one of the key characteristics of the mex mutant.We investigated the model under day and night conditions to determine differences in network usage under these conditions. M. truncatula is a model organism for symbiotic nitrogen fixation, the effects of different forms of available nitrogen are of high relevance. The main sources for nitrogen are ammonium and nitrate. However, supplying both of these nitrogen sources and directly applying either flux minimisation or carbon usage minimisation would lead to the trivial result that the model selects ammonium as the source, because it is ‘cheaper’ in terms of energy and reductant  the ability to fix nitrogen by the rhizobium allows for plant growth even without any external nitrogen source present. Thus, this combined model can explain and to some extent quantify under which conditions a symbiotic relationship is advantageous.50. An initial investigation showed that the amount of oxygen available to the bacteroids is the main limiting factor for the amount of nitrogen that can be fixed. This is expected, because the nitrogenase requires large amounts of energy, which is most efficiently provided by oxidative phosphorylation. However, while a higher oxygen concentration provides more energy to the bacteroid, the nitrogenase complex becomes irreversibly inactivated if oxygen concentrations are too high51. Therefore the plant actively regulates the oxygen supply using leghemoglobin, ensuring sufficient oxygen for fixation, but minimising detrimental effects to the nitrogenase activity2. As such, it is interesting to see what effects slightly changing oxygen amounts would have on the symbiosis. While an increased potential biomass production is the most obvious effect of a better oxygen supply it is also interesting to investigate how the exchange fluxes between bacteroid and plant are affected if the amount of fixed nitrogen remains constant, but oxygen concentrations varies. For this, we fixed the growth rate of the plant to 80% of the maximal growth rate (determined above), and systematically varied the oxygen availability for the bacteroid and calculated the fluxes based on the assumption that enzymes are used in a maximally efficient way (see Methods).There are multiple propositions in the literature concerning the transport of nitrogen from the rhizobium to the plant13. Alanine is predicted to be the only nitrogen-containing export product. The amino group of glutamate is transferred by transaminases to pyruvate, yielding the export product alanine and ketoglutarate. The latter is fed into the TCA cycle, where one ATP is generated by succinyl-CoA synthase. Newly fixed nitrogen is exported as alanine, which is synthesised de novo from pyruvate by alanine dehydrogenase. Pyruvate in turn is produced by reverse action of pyruvate carboxylase. The standard Gibbs free energy of reaction of pyruvate carboxylase is ΔG0 = −1.37 kcal/mol (according to MetaCyc), but due to the comparatively large amounts of dicarboxylic acids and the high demand for ATP it appears plausible that the equilibrium can easily be shifted to pyruvate. The imported carbon source required for pyruvate synthesis depends strongly on the available oxygen. For low oxygen supply, the capacity of the respiratory chain is limited and import of malate minimises the amount of produced NADH. With increasing oxygen availability, ATP production by oxidative phosphorylation is increasingly efficient. This explains the observed switch from malate to succinate uptake, because introduction of succinate into the TCA cycle provides one additional reductant as compared to malate. Therefore succinate is only used when sufficient oxygen is available .The model predicts extensive amino acid cycling between the plant and the symbiont see Fig.  in agreede novo alanine synthesis is not required for symbiotic growth53. Our model allows a rapid reproduction of this experiment in silico. Our simulations  temperatures, while for nightly temperatures of 24 °C (which was the same as the applied temperature during the day), the ratio of respired carbon increased to 75%. Estimating from our calculations that around 20% of carbon contained in starch need to be respired to build biomass, this allows to conclude that between 25% and 55% of starch-derived carbon is respired during the night for maintenance, depending on temperature and probably other external factors. This is in good agreement with previous findings of maintenance requirements of around 40% by Williams et al.55. This comparison is similar to the approach by Cheung et al.56 to estimate the non growth-related maintenance energy. However, while they based their findings on ATP fluxes in a cell culture, we base our estimates on starch consumption in planta. The derived energy is required for processes which are not directly related to growth and not included in our model, such as degradation and repolymerisation of proteins and mRNA, as well as transport processes across intracellular membranes or phosphorylation in regulatory pathways. But why the ratio is so strongly dependent on the ambient temperature remains unclear. To further understand the detailed requirements for maintenance energy, it will be necessary to formulate mathematical models of maintenance requirements based on experimental measurements of protein and mRNA turnover and intracellular transport59. The results of such models would allow to define further constraints on genome-scale metabolic models as the one presented here, and lead to a more profound understanding of the regulation of metabolic fluxes responding to environmental changes.However, new annotations are not the primary objective for building detailed genome-scale metabolic models. More importantly, a genome-scale model provides a theoretical framework in which experimental observations can be interpreted and understood. Computational analyses allow to query the model, assess its capabilities, and enable novel interpretations of experimental observations in a theoretical context. An interesting observation is derived by our calculation of the theoretically maximal conversion rate of starch-derived carbon into biomass at night. Considering only energy and redox requirements for the formation of biomass, a remarkably high percentage of starch carbon  can be converted into biomass. These numbers show the theoretical maximal conversion rate of starch carbon into biomass carbon which will likely not reflect the real situation. However, these deliberations do allow an estimation of the non growth associated maintenance costs based on measurements of starch usage and respiration like those by Pyl 60. Thus, in an evolutionary context, our model suggests that the reason for this inhibition is an increased energetic efficiency. Simultaneously proton export is observed which is in accordance with soil acidification when ammonium fertilisers are used48. If ammonium becomes limiting, the modelled fluxes change gradually to an increased uptake of nitrate, which is accompanied by a higher energy and reductant demand that is met by increased respiration during the night. The balancing of charges, using both ammonium and nitrate, might contribute to the observation that the presence of ammonium in many plants61 inhibits nitrate uptake but does not turn it off completely, while from an energetic consideration, nitrate should not be used at all. At the same time, we have to be aware of the limitations of FBA when interpreting computational results simulating changes in environmental pH. In particular, ionic barriers commonly employed by plants cannot be modelled in FBA.Using only a model of the host of the symbiosis it is already possible to make some investigations into the symbiotic properties. The dominating factor in the symbiosis is the transfer of nitrogen between host and symbiont. We therefore first investigated the response of metabolic fluxes in the host to changing nitrogen sources. If both nitrate and ammonium are abundant, the model predicts an exclusive uptake of ammonium, because integration into amino acids requires considerably less reductants and energy when compared to nitrate. This result is in agreement with the experimental observation that nitrate uptake is inhibited when ammonium is availableBy combining the plant network with a rhizobial symbiont, we were able to illustrate the evolutionary advantages in undergoing a symbiosis and to simultaneously show that it can come at a cost when formed in a rich environment. We could demonstrate that the observations of amino acid cycling and alanine as nitrogen carrier fit into a paradigm of efficiency. Further, we were able to suggest that the reason for the use of alanine as export product lies in the ability to remove some surplus reductant from the bacteroid system, but that it is not strictly necessary for symbiotic growth. However, it is interesting that even when alanine dehydrogenase is knocked out, we observed the export of alanine (now exchanged with glutamate), which supports the hypothesis that alanine is a major export product in planta, even without being the nitrogen carrier.62. In natural environments this community may contain beneficial microbes, delivering nutrients extracted from the soil to the plant in exchange for reduced carbon, and parasites, living on dead or live plant material. In principle, our approach to combine several network models is also applicable to more complex interaction networks63. However, the bottleneck is often our limited knowledge on the exchanged metabolites between the different players. Moreover, by increasing the complexity of the network, also the model complexity and concomitantly the possible model solutions increase dramatically. We therefore view our approach, focusing on a well-investigated model system, in which only two organisms interact, as a first step towards studying more complex interaction networks.Rhizobia represent only one component of a highly complex and dynamic microbial community associated with plant rootsFor a more extensive description of the Materials and Methods, we refer the reader to the Supplemental Text Medicago truncatula genome annotation64 and was reconstructed using PathwayTools65 along with the MedicCyc database66. For compartmentalisation, data from multiple databases  and recent studies70 in M. truncatula where used for initial localisation. Further localisations were assigned using an approach by Christian et al. to make all compartments consistent38. All seeds and targets used can be found in Supplemental Dataset 74 and where they were necessary to allow compounds which could be produced in the uncompartmentalised network to be still producible in the compartmented network. Reaction directionalities were adjusted to avoid thermodynamically infeasible cycles.The plant model is based on version 3.5v5 of the 43 and gene expression data from Benedito et al.42. This was done by duplicating the model and connecting the tissues similar to an earlier approach32. For all connecting transporters see Supplemental Dataset 75 and Lötscher et al.76, respectively.The two tissue model for root and shoot was created using the FASTCORE algorithmSinorhizobium meliloti 1021 was extracted from MetaCyc. The model was curated to achieve mass and charge balance of all reactions and to allow it to produce most biomass components from an E.coli biomass definition77 in order to ensure a generally viable model. The model was connected to the plant root submodel using exchange reactions present in the literature  representing the coefficient of the i-th metabolite in the j-th reaction, and v being the vector of fluxes (i.e. reaction activities). Further we in general assume that the organisms have evolved to perform whichever task they are required with a maximal efficiency. This is represented in our simulations by minimisation of the flux through the network, which corresponds to a minimal necessity for enzyme synthesis. We commonly minimise the quadratic flux values84. In contrast to minimising the simple sum of fluxes, this yields a unique solution85:With S being the stoichiometric matrix with the position  are introduced. To test multiple conditions, we solve this system for multiple fixed values of target reactions and compare the fluxes for these ranges. Examples of these scans can be found in the Supplemental Text https://github.com/sysbiolux/MedicagoScripts. The reconstruction is also available on the github site and in Supplementary Dataset Matlab scripts that reproduce the scans in Supplemental Text et al.86 and Masakapalli et al.87. Plants were grown following the hydroponic culture protocol from the Medicago truncatula Handbook88. More details can be found in Supplemental Text 89 and90 was used. Cell wall composition was extracted from92, and93. The lipid composition was based on94. DNA GC Content was derived from95.Biomass composition was determined according to adapted protocols by Williams Supplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Text S7


T. patula by metabolomics and network pharmacology. The study demonstrated that the flavonoids and polysaccharides of T. patula could alleviate prostatitis by improving the level of DHT, reducing the secretion of PSA and TNF-α. Besides, both could enhance Na+/K+-ATPase activity, decrease the O2 consumption, CO2 production, heat production, energy expenditure of rats and promote respiratory exchange ratio of rats. Up to 28 potential biomarkers and 8 key metabolic pathways related to the treatment of CNP were elucidated by the metabolomics analysis, including phenylalanine metabolism, taurine and hypotaurine metabolism, tryptophan metabolism etc. Network pharmacology prediction also reflected the potential mechanism was associated with tryptophan metabolism and energy pathway. Generally, the potential anti-CNP mechanism of flavonoids and polysaccharides of T. patula might be through reducing the expression of inflammation factors, adjusting the level of hormone and regulating the amino acid metabolism, energy metabolism and glucose and lipid metabolism.The major objective of this study was to investigate the anti-chronic nonbacterial prostatitis (CNP) mechanism of  T. patula is a perennial herb belonging to the family of Asteraceae, commonly known as French marigold, and it is also a traditional drug in Argentina, where it is used for the diuretic agent associated with prostatitis treatments , and the closer to 0, the more significant the enrichment is. Potential targets of active compounds and relevant target genes of prostatitis were shown in 2 and VCO2 in this study might be caused by tachypnea, so as to promote the degree of H and EE and decrease RER. However, the flavonoids constituent and polysaccharides constituent of T. patula could reverse those. In addition, the increasing of Na+/K+-ATPase’s activities in prostate and liver also reflected that the flavonoids and polysaccharides of T. patula could regulate energy metabolism.As is well known, the main manifestations of prostatitis exhibited a pain in the pelvic region and dysfunction of urination. The main manifestations of pain exhibited anxiety, tachypnea, hypertension, hyperhidrosis and etc. It is interesting to note that the increasing of VOT. patula had therapeutic efficacy. Metabonomics analysis discovered that phenylalanine, tyrosine and tryptophan biosynthesis and phenylalanine metabolism in the rat prostatitis model showed abnormal metabolism. Compared with SOG, the levels of l-tyrosine, phenylacetaldehyde, phenylpyruvic acid, l-phenylalanine, pantetheine, l-cysteine, pantothenic acid, serotonin, l-kynurenine, 4-(2-Aminophenyl)-2,4-dioxobutanoic acid, 3-indoleacetic acid, d-xylulose, l-histidine, epinephrine, maleylacetoacetic acid, taurine, glycine, uric acid and adenine in the urine of model rats were reduced to varying degrees, and the contents of uracil, tryptophan, indoleacrylic acid, d-glucuronic acid, 2-oxoglutarate, l-malic acid, citrate, dopamine and l-threonine in the urine of model rats were increased to different degrees. After the treatment of polysaccharide and flavonoids of T. patula, the concentration of l-tyrosine, l-phenylalanine, pantetheine, l-cysteine, pantothenic acid, 4-(2-Aminophenyl)-2,4-dioxobutanoic acid, 3-indoleacetic acid, l-histidine, maleylacetoacetic acid, taurine, glycine, uric acid, adenine, tryptophan, indoleacrylic acid, d-glucuronic acid, 2-oxoglutarate, l-malic acid, citrate and l-threonine in urine were adjusted backwards to normal in certain degrees.Urine samples were analyzed by UPLC/ESI-TOF-MS and multivariate statistical analysis. The results showed that the area of dynamic metabolic profiles after polysaccharide and flavonoids treatment was close to the sham-operated group, demonstrating that polysaccharide and flavonoids of T. patula, in other words, they may help in repairing these metabolites.These above differential metabolite identification results referred the pathway related to phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, pantothenate and CoA biosynthesis, tryptophan metabolism, pentose and glucuronate interconversions, aminoacyl-tRNA biosynthesis and taurine and hypotaurine metabolism. These disturbed metabolic pathways can be partially reversed by the polysaccharides and the total flavonoids of T. patula anti-prostatitis. The analysis results showed that the compounds in T. patula acts on multiple targets to anti-prostatitis. By integrating the metabolomics pathway and the target pathways predicted by network pharmacology, we found that they are similar, which could explain the accuracy of the predicted target. Research indicated that prostatitis is associated closely with hormone and inflammatory level. Uric acid has the antioxidant capacity and can prevent the emergence of an inflammatory environment  = 556.2771) and negative ion mode ([M − H] = 554.2615) to ensure accuracy during the MS analysis.Metabolic was analyzed and identified by Micromass Q-TOF microm/z and corresponding peak intensities. Software specific parameter settings are shown in Testing and matching original peak through Markerlynx (SCN803) modules in Masslynx V4.1 workstation, and the peak intensity was normalized and denoised to extract data matrix consisting of metabolite retention time, Data preprocessed analysis by using the extended statistics module in Masslynx V4.1 Workstations for compound statistics. All the data containing the retention time, peak intensity and exact mass were imported in the Masslynx V4.1 Workstations for multiple statistical analyses. Both principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) often could be taken, because of their ability to deal with highly multivariate, noisy, collinear and possibly incomplete data. PCA, unsupervised pattern recognition method, used to discern the presence of inherent similarities in spectral profiles initially. In the PCA plot, the longer distance the two samples’ scores are, the greater the difference between them.m/z data for the structural identification of biomarkers were issued by the UPLC-MS analysis platform of the Masslynx V4.1 Workstations. The variable importance in projection value  was obtained by PLS-DA, and the metabolites with VIP > 1 were selected to measure the significance of each metabolite in separating model from controls by one-way ANOVA with SPSS 17.0. Metabolites with VIP >1 and p < 0.05 were identified as differential metabolites and compound validation to verify the accuracy and eliminate unreliable differential metabolites.The retention time, precise molecular mass and m/z of these differential metabolites with the theoretical m/z based on the elemental composition of the standard products through the Molecular Weight Calculator module in Masslynx.Targeted Metabolomics: potential markers were identified by matching the retention time and + or [M − H]− ions, through the molecular ion peak to find the possible elemental composition of the metabolite; (2) Analysis of elemental composition by MS and MS/MS spectra to find the fragment ion peaks of metabolites, according to the structure which metabolites might exist and meet the fragmentation peaks of mass spectrometry characteristics to infer the possible structures of the different metabolites and verified by an online database such as the MS/MS Spectrum Match module in the METLIN database (http://metlin.scripps.edu/). (3) Some of the metabolites did not split into debris due to the complexity of urine samples, the structure of metabolites were reverse analyzed of metabolically related networks and inferred through the possible mechanisms of prostatitis and according to the metabolic correlation networks which were identified by targeted metabolomics databases such as KEGG (http://www.genome.jp/kegg/), HMDB (http://www.hmdb.org/). Non-targeted metabolomics: as a result of the relevant information on compounds structure types, elemental composition, and other information in urine samples were unknown, the identification of differential metabolites in this study mainly included the following methods. (1) Determination of differential metabolites [M + H]Metabolic pathways were obtained by analyzing and enriching differential metabolites that had been screened by MetaboAnalyst 3.0. Potential markers identified were compared with the accurate mass charge ratio in some databases, including HMDB, KEGG and Metlin database to discover related pathways. Pathway impact-alter was used to determine the statistical significance of the pathways.p < 0.05 was considered statistically significant. Prior to multivariate analysis, the resultant data matrices from the two analytical techniques were mean-centered and pareto-scaled.The PCA was used to uncover undiscovered trends in the treated groups. Statistically significant differences in mean values were tested by using one-way ANOVA, and T. patula and found that flavonoids is the main active component. Here, to certify the result of metabolism, the network-pharmacology was operating to illustrate the predictive action mechanism. The information of the active compound in T. patula was shown in T. patula anti-prostatitis by screening out the common target genes of T. patula active compounds and prostatitis.In the early stages, our laboratory was separated and identified the compound in http://www.swisstargetprediction.ch/) and stinted homo sapiens as organism. According to the CTD (https://ctdbase.org/) and Genecard (https://www.genecards.org/) databases to find relevant target genes of prostatitis, due to the large number of related genes and the complexed network, so selected gene with relevance score >30 to predict. The network was constructed using Cytoscape 3.7.1 software [T. patula—target map and prostatitis disease—target map. The common targets gene of T. patula active compounds and prostatitis was screened out by the merge function of Cytoscape, and the T. patula active compounds–target–prostatitis disease network map was constructed. Then the analysis of gene function was performed on the merge gene utilizing FunRich software [https://david.ncifcrf.gov/tools.jsp) was combined with KOPAS 3.0 (http://kobas.cbi.pku.edu.cn/anno_iden.php) to enrich the KEGG and GO pathways in the gene pathway. After the pathway enrichment, the broad pathways were excluded and the pathway information was obtained. Then Omicshare cloud platform (http://www.omicshare.com/forum/) was used to map the enriched pathways.Potential targets of active compounds were predicted by SwissTargetPrediction server (software  to obtaisoftware ,22. The