Safe, sustainable, and green production of hydrogen peroxide is an exciting proposition due to the role of hydrogen peroxide as a green oxidant and energy carrier for fuel cells. The current work reports the development of carbon dot-impregnated waterborne hyperbranched polyurethane as a heterogeneous photo-catalyst for solar-driven production of hydrogen peroxide. The results reveal that the carbon dots possess a suitable band-gap of 2.98 eV, which facilitates effective splitting of both water and ethanol under solar irradiation. Inclusion of the carbon dots within the eco-friendly polymeric material ensures their catalytic activity and also provides a facile route for easy catalyst separation, especially from a solubilizing medium. The overall process was performed in accordance with the principles of green chemistry using bio-based precursors and aqueous medium. This work highlights the potential of carbon dots as an effective photo-catalyst.The online version of this article (doi:10.1007/s40820-017-0143-7) contains supplementary material, which is available to authorized users. Polyurethane-supported carbon dots were developed as heterogeneous catalyst, and the developed catalyst was biodegradable.2O2 production from water, ethanol, and oxygen was achieved.Solar-driven H2O2) by photo-splitting of water (H2O). In the current scenario of rapid depletion of fossil fuels, sustainable production of H2O2 is very significant. H2O2 can be used as a fuel in one-compartment fuel cells with an output potential of 1.09 V, which is comparable to that of the hydrogen fuel cell with an output value of 1.23 V [2O2 serves as a clean and green oxidant with H2O as the only by-product [2O2 is highly desirable for implementation of “green chemistry” principles and can provide a promising prospect for resolving global fuel woes.Recently, greener, inexpensive, and efficient water splitting techniques have garnered significant interest in the thrust toward sustainable energy conversion processes –3. In thf 1.23 V . Further-product . Thus, g2O2 is produced by the anthraquinone method, which is highly disadvantageous from the green chemistry point of view. The drawbacks include the multi-step process, and high energy consumption, et cetera [2O2 from H2 and O2 [2O2, but have an attendant high risk because of the explosive nature of the H2/O2 mixture. Most recently, semiconductor-based nanocatalysts have been used for sustainable production of H2O2 from H2O and alcohols. Such methods involve irradiation of an oxygen (O2)-saturated alcohol or aqueous system under UV light in the presence of metallic semiconductors. State-of-the-art literature cites the use of different metal-based semi-conductor catalysts for sustainable production of H2O2 from H2O and alcohol. These include Ru-complexes, Ir-complexes, Ti nanoparticles, and Ni, et cetera [g-C3N4) for the production of H2O2 from ethanol [g-C3N4 hamper its successful use, especially for splitting H2O under solar irradiation. Thus, the development of an eco-friendly and sustainable catalyst system with the capacity to produce H2O2 is desirable to meet the current challenges.In the conventional process, Ht cetera . Catalyt2 and O2 –11. Sucht cetera –20. Howe ethanol . Howeversp2 hybridized graphitic carbon. The most fascinating feature of CDs is their capability to harvest photons in the UV/visible spectral region. Such light-harvesting ability renders CDs as a potential candidate for photo-catalytic application [Carbon dots (CDs), with unique physical, chemical, and optical properties, have attracted immense interest in the scientific community based on their utility in diverse areas, such as bio-imaging, biosensors, light-emitting diodes, and other opto-electrical devices –25. The lication , 26. Howlication . Such obIn this context, the use of a polymeric matrix as a support can facilitate the application of CDs. The polymer matrix can prevent solid-state quenching and also provide sufficient mechanical strength and catalytic stability and facilitate application with easy separation. Such polymer/CD nanocomposite systems have found use in opto-electrical applications. Photo-catalytic applications have also been reported recently . In this2O2 by using H2O, C2H5OH, and O2 as raw materials. Further, bacterial degradation of the catalyst is carried out by using Pseudomonas aeruginosa to highlight its eco-friendly behavior. Thus, the present study reports a metal-free, low-cost, easily separable, non-toxic, and biodegradable carbon dot-impregnated polymeric nanocomposite system as a heterogeneous catalyst for sustainable and green production of H2O2.Herein, a CD-impregnated WPU nanocomposite film is used as a heterogeneous photo-catalyst for solar-driven production of HMn) of 600 g mol−1 , 2,2-bis(hydroxymethyl) propionic acid , 1,4-butanediol , tannic acid , triethylamine , and tetrahydrofuran  were used for WPU preparation. Glycerol-based hyperbranched epoxy (HE) and vegetable oil-based poly(amide amine) (PAA) were used to cross-link the fabricated WPU/CD system [Colocasia esculenta were used to prepare the CDs. Moreover, ethanol , Millipore water , and oxygen  were used as starting materials in the photo-catalytic application. Potassium permanganate  was used in the estimation of hydrogen peroxide by following a titrimetric method. In the biodegradation study, the bacterial strain Pseudomonas aeruginosa (strain MTCC 7814) was used. This strain was procured from the Microbial Type Culture Collection and Gene Bank, Chandigarh, India. The bacterial culture was collected through the Department of Molecular Biology and Biotechnology, Tezpur University, India.Isophorone diisocyanate , poly(ethylene glycol) with a number average molecular weight . The CDs formed were dried under reduced pressure. The CD-impregnated WPU polymer was prepared via an ex situ technique according to a method described elsewhere [The CDs were prepared by using corms of  earlier . Howeverlsewhere . Howeverθ = 10°–70° at a scanning rate of 2°min−1). The graphitic structure of the CDs was studied using Raman spectroscopy . The shape and size of the CDs, as well as the distribution of the CDs over the WPU matrix, were visually studied by using high-resolution transmission electron microscopic (HRTEM) analysis. TEM images were obtained by using a JEOL, JEMCXII, Japan, microscope at an operating voltage of 200 kV using a Cu grid . The microscopic data were analyzed as inverse fast Fourier transform (IFFT) images by using Gatan Digital Micrograph software. A UV–visible photo-spectrometer  was used to determine the band-gap. The photoluminescence (PL) behavior was studied by using a PerkinElmer, USA, Model: LS-55 fluorescence spectrometer. The surface morphology of the biodegraded catalyst was studied by scanning electron microscopy  after platinum coating the surface.X-ray diffraction (XRD) patterns of the CDs and CD-impregnated WPU were obtained by using a Bruker AXS, Germany, Model: D8 Focus instrument 2. Extrapolation of the curve to (αhν)2 gives the value of Ebulk.Here The quantum yield of the CDs and PNCs was determined by using Eq. QCD is the quantum yield of the CDs, QR is the quantum yield of the reference compound (quinine sulfate), ICD is the intensity of the emission of the CDs, IR is the intensity of the luminescence of the reference compound (quinine sulfate), ACD is the absorbance of the CDs at the given excitation wavelength (360 nm), AR is the absorbance of the reference compound (quinine sulfate) at the given excitation wavelength (360 nm), and η is the refractive index of the solvent used (water). The quantum yield of quinine sulfate at 360 nm is 54%.Here, 2O, C2H5OH (100%), and mixtures of H2O and C2H5OH ) were used as the starting materials for the solar-driven production of H2O2. The reactants were placed into a two-necked glass reactor equipped with an O2 inlet. Initially, the mixture was saturated with molecular O2 by passing the gas for 10 min and the reactor was then sealed. The system was then irradiated under normal solar light with an intensity of 80,000–100,000 lx at ca. 25 °C with a continuous supply of O2 for different time intervals (5–50 h). The production of H2O2 was estimated via the redox titrimetric method using KMnO4 [Five different compositions, i.e., Hng KMnO4 .P. aeruginosa as the test organism [4)2SO4, 2.0 g Na2HPO4, 4.75 g KH2PO4, 1.2 g MgSO4·7H2O, 0.5 mg CaCl2·2H2O, 100 mg MnSO4·5H2O, 70 mg ZnSO4·7H2O, 10 mg H3BO3·5H2O, 100 mg CuSO4·7H2O, 1 mg FeSO4·7H2O, and 10 mg MoO3 in 1000 mL distilled water was prepared and sterilized at 120 °C under 15 lb pressure. The P. aeruginosa bacterial strain was cultured in a shaker incubator for 48 h using this medium. A 100-µL aliquot of the cultured bacteria was placed in a conical flask with 10 mL of the salt medium. UV-sterilized (wavelength 254 nm) catalyst films with a known weight were immersed in the medium and incubated at 37 °C for seven weeks. Growth of the bacteria was followed by measuring the absorbance of the medium at 600 nm at an interval of seven days. Similarly, a change in the weight of the catalyst was recorded after every seven days. SEM images of the biodegraded films were also taken after completion of the experimental period.Analysis of the biodegradability of the catalyst system was carried out by using organism . A minerC. esculenta was used as the precursor. The CDs were obtained through a hydrothermal method. It can be assumed that in the hydrothermal process, extensive carbonization and aromatization took place through condensation and polymerization of different carbohydrate molecules at elevated temperature and pressure [The synthesis of the CDs provides a green and sustainable route in which a large number of naturally available carbon resources are utilized . In the pressure , 36.The prepared CDs were characterized by TEM, XRD, and Raman spectroscopic techniques. As shown in Fig. θ = 22° . Thus, TEM, XRD, Raman, and UV–visible spectroscopic analyses confirmed formation of the CDs. The CDs were incorporated into the WPU matrix for the development of a heterogeneous catalyst system for solar-driven H2O2 production.The Raman spectrum of the CDs displayed two distinct bands, i.e., the D band near 1360 cmm−1 Fig. b. The D In this context, the critical issue is to obtain a homogeneous distribution of the CDs over the WPU matrix without agglomeration. Agglomeration of the CDs can lead to the loss of their light-absorbing capability, which in turn hampers their photo-catalytic activity. However, in the present study, we found that the CDs and WPU acted complementarily to produce a compatible and uniform composite system. The CDs, being a highly functional nanomaterial, undergo sufficient interactions  with the hyperbranched polymer matrix Fig. . Such prVisual evidence obtained from the TEM image of the catalyst system confirmed the uniformity, even at high nanomaterial loading (4 wt%) Fig. d. Comparθ = 22°, ascribed to the (002) plane of graphitic carbon  based on their photo-excited states [Before studying the photo-catalytic activity of the prepared catalyst, effort was made to evaluate different parameters, which ensured suitability of the polymer-supported CD system as a photo-catalyst for Hd states . The assd states , 38. Howd states . Hence, hν versus (αhν)2 (Tauc’s method) and was found to be 2.98 eV . Considering quinine sulfate as the reference, the quantum yield was calculated at the excitation wavelength of 360 nm and was found to be 9.5%. Values of 5.5, 6.7, 7.4, 7.8, and 7.6% were obtained for PNC1.0, PNC2.0, PNC3.0, PNC4.0, and PNC5.0, respectively. The band-gap was calculated from UV–visible spectroscopy by plotting 2O splitting requires the bottom level of the conduction band to be more negative than the redox potential of H+/H2 (0 V vs. NHE), while the top level of the valence band should be more positive than the redox potential of H2O/O2 (1.23 V vs. NHE). Thus, the minimum value of Ebulk for any photo-catalyst should be 1.23 eV for H2O splitting. However, in practice, for effective H2O splitting, the band-gap should be larger than 2.0 eV. This is because, in reality, energy losses may occur due to kinetic overpotentials.Generally, HEbulk was evaluated as 2.98 eV for the CDs. Hence, these species satisfy the criteria to act as a catalyst for solar-driven production of H2O2. In this context, the CDs appear to be the superior catalyst compared to g-C3N4. This may be because, g-C3N4 has a band-gap of 1.4 eV, which is good enough to split alcohols but is insufficient to split H2O under solar irradiation. The photo-catalytic production of H2O2 involves photo-irradiation of O2-saturated H2O or alcohol in the presence of a photo-catalyst, i.e., the CDs in the present case.In the present study, −)/hole (h+) pair by photo-excitation of the CDs. In the case of an alcohol, h+ oxidizes the RCH2OH to generate aldehydes and a proton, while e− promotes two-electron reduction of O2 to produce H2O2 [2O is used as the raw material, h+ oxidizes H2O to O2. Similar two-electron reduction of O2 follows thereafter to produce H2O2 [Semiconductors can act as photo-catalysts for light-induced redox processes due to their electronic structure, which is characterized by a filled valence band and an empty conduction band. Absorption of a photon with energy greater than the band-gap energy leads to the formation of an electron/hole pair. In the absence of suitable scavengers, the stored energy is dissipated within a few nanoseconds by recombination. A plausible mechanistic framework involves the formation of an electron , and C2H5OH (100%) saturated with molecular O2. In this regard, it is important to mention that many of the traditionally used semiconductors absorb only light energy or mostly in the UV region; hence, for a solar-driven process, dye sensitization is required [In the current study, we used Hd C2H5OH 00% satur2O2 at different intervals using different compositions of starting materials with PNC4.0 as the photo-catalyst. The bar diagram reveals the influence of the time and the composition of the starting materials on the production of H2O2. The formation of H2O2 increased with time for the photo-reaction. Here, it is pertinent to mention that the virtual yield of H2O2 shown in the bar diagram might be less than the actual amount formed, as some amount of H2O2 is destroyed by sunlight.Figure 2O2 with time clearly indicates the efficiency of the polymer-supported CDs as a photo-catalyst in the H2O splitting reaction. The rate of formation of H2O2 also supports this claim . Some interesting observations emerge from this study. When H2O was used by itself, less H2O2 was produced (~46 µM after 50 h of reaction) compared to that obtained with 100% C2H5OH (~82 µM). On the other hand, in the case of the C2H5OH/H2O mixtures, the H2O2 production increased with an increase in the amount of C2H5OH in the mixture . The increased H2O2 production in the presence of C2H5OH may be because of the favorable thermodynamic driving force, which is promoted by the suitable oxidation and reduction potential of the catalyst when alcohol is used (compared to water) [2O mixtures toward solar-driven water splitting [2O with C2H5OH can enhance the H2O2 yield.Nevertheless, the increased production of Hplitting . Thus, t2O2 production when different compositions of starting materials were used. When H2O was used as the starting material, 100% selectivity for the production of H2O2 was achieved. However, when C2H5OH was used, the reaction showed no selectivity. Along with H2O2, a mixture of acetaldehyde and acetic acid was also formed, in which acetaldehyde was the predominant product, as confirmed by GC analysis. With an increase in the H2O gradient in the mixtures of C2H5OH/H2O, the selectivity toward H2O2 production also increased . In other words, the production of H2O2 increased with respect to the by-products. This clearly suggests that the two-electron reduction of oxygen loses its selectivity when C2H5OH is used, whereas addition of H2O enhances the selectivity. Thus, it can be inferred that photo-reaction of C2H5OH with H2O around room temperature promotes selective H2O2 production.Figure 2O2 generation was evaluated  may hamper the light-harvesting ability of the CDs. Secondly, the distribution of the CDs within the polymer matrix is not as uniform as in the solution state. This also contributed to reduction of the photo-catalytic activity of the polymer-supported CDs compared to the pristine CDs.Here, it is pertinent to mention that CD alone can act as an effective catalyst with higher production yield . However, the ease of separation makes the polymer-supported CDs more advantageous for use as a heterogeneous catalyst than the pristine CDs. This observation can be explained based on two factors. First, the increase in the size of the CDs after incorporation into the polymer matrix Below is the link to the electronic supplementary material.


This is reflected in the few available examples of histidine producers in literature. Since knowledge about the metabolic interplay is limited, we systematically perturbed the metabolism of Corynebacterium glutamicum to gain a holistic understanding in the metabolic limitations for l-histidine production. We, therefore, constructed C. glutamicum strains in a modularized metabolic engineering approach and analyzed them with LC/MS-QToF-based systems metabolic profiling (SMP) supported by flux balance analysis (FBA).l-histidine, equimolar amounts of glycine, and possessed heavily decreased intracellular adenylate concentrations, despite a stable adenylate energy charge. FBA identified regeneration of ATP from 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) as crucial step for l-histidine production and SMP identified strong intracellular accumulation of inosine monophosphate (IMP) in the engineered strains. Energy engineering readjusted the intracellular IMP and ATP levels to wild-type niveau and reinforced the intrinsic low ATP regeneration capacity to maintain a balanced energy state of the cell. SMP further indicated limitations in the C1 supply which was overcome by expression of the glycine cleavage system from C. jeikeium. Finally, we rerouted the carbon flux towards the oxidative pentose phosphate pathway thereby further increasing product yield to 0.093 ± 0.003 mol l-histidine per mol glucose.The engineered strains produced l-histidine overproduction scenario and an insufficient supply of C1 units. To overcome these limitations, we provide a metabolic engineering strategy which constitutes a general approach to improve the production of ATP and/or C1 intensive products.By applying the modularized metabolic engineering approach combined with SMP and FBA, we identified an intrinsically low ATP regeneration capacity, which prevents to maintain a balanced energy state of the cell in an  Since analytical standards of those compounds were not commercially available or only by custom synthesis, fragmentation patterns were computationally evaluated with MassHunter Molecular Structure Correlator . By combining accurate mass and plausible fragmentation patterns IGP, adenylosuccinate, SAICAR, and FGAR could be identified.Differential metabolite analysis was performed on an Agilent 1260 bio-inert HPLC coupled to an Agilent 6540 Accurate-Mass LC–MS/MS Q-TOF system with ESI Jet Stream Technology . Two different hydrophilic interaction chromatography (HILIC) systems were used to get high metabolite coverage. The first method was ammonium acetate based  utilizing a Sequant ZIC-pHILIC column  with guard column  at 40 °C, 0.2 mL minails see . Additiop values were filtered (p < 0.05). Peaks were identified by accurate mass and with a personal compound data library, containing retention times of authentic standards. Unidentified significant features were searched against the METLIN [l-histidine biosynthesis could be identified and analyzed with the applied method: fGAR, phosphoribosyl-N-formylglycineamide; SAICAR, phosphoribosyl-aminoimidazolesuccinocarboxamide; AICAR, 1-(5-phosphoribosyl)-5-amino-4-imidazolecarboxamide; IMP, inosine monophosphate; AdSucc, adenylosuccinate; ADP, adenosine diphosphate; ATP, adenosine triphosphate; IGP, imidazole-glycerol phosphate; l-histidinol and l-histidine.System control and acquisition were performed using MassHunter Data Acquisition . As first step, an untargeted differential analysis was carried out to generate hypothesis free data. Peak picking and integration were done in MassHunter ProFinder  using “batch recursive feature extraction”. Subsequently, statistical analysis was performed in Mass Profiler Professional . Significance testing was done by one-way ANOVA and e METLIN  and Masse METLIN  database


Improving feed efficiency (FE) is a major goal of pig breeding, reducing production costs and providing sustainability to the pig industry. Reliable predictors for FE could assist pig producers. We carried out untargeted blood metabolite profiling in uncastrated males from Danbred Duroc (n = 59) and Danbred Landrace (n = 50) pigs at the beginning and end of a FE testing phase to identify biomarkers and biological processes underlying FE and related traits. By applying linear modeling and clustering analyses coupled with WGCNA framework, we identified 102 and 73 relevant metabolites in Duroc and Landrace based on two sampling time points. Among them, choline and pyridoxamine were hub metabolites in Duroc in early testing phase, while, acetoacetate, cholesterol sulfate, xanthine, and deoxyuridine were identified in the end of testing. In Landrace, cholesterol sulfate, thiamine, L-methionine, chenodeoxycholate were identified at early testing phase, while, D-glutamate, pyridoxamine, deoxycytidine, and L-2-aminoadipate were found at the end of testing. Validation of these results in larger populations could establish FE prediction using metabolomics biomarkers. We conclude that it is possible to identify a link between blood metabolite profiles and FE. These results could lead to improved nutrient utilization, reduced production costs, and increased FE. Thus, feed efficiency (FE) is the most important trait in commercial pig farming2 as increasing the amount of meat produced per feed is beneficial both economically and environmentally. Thereby, improving FE is beneficial for producers and increases the sustainability of pork meat production. Fortunately, FE is a highly heritable trait in Danish pigs (ranging from 0.34 in Duroc to 0.40 in Landrace), thus suitable for the genetic selection of pigs with high breeding values in breeding programs aimed at improving this economically important phenotype3.With the expanding human population and requirement for nutrient-rich food, there is an increasing demand for improvement of meat production, but simultaneously, to decrease the input costs in terms of feed4. FCR determines the ratio of feed intake (FI) to output and found to correlate with growth rate and body weight5. RFI calculates the difference between the actual and expected FI6 predicted based on production traits such as average daily gain (ADG)7. ADG is also considered important in commercial pig production as pigs with higher ADG can achieve a target market weight within a shorter period than those with lower ADG, thereby saving feeding costs8. Thus, selection for RFI has proved to be effective in improving the FE in pigs10. Selection for FCR will results in co-selection for other traits, such as body composition and ADG. In contrast, RFI selects for increased metabolic efficiency without the same side effects13. RFI and FCR are well correlated, with a reported correlation of over 0.7 in the literature3.Since FE cannot be measured directly, feed conversion ratio (FCR) and residual feed intake (RFI) have been used to evaluate the animal efficiency14. However, despite these efforts, FE is a complex trait with many aspects involved and the functional molecular background is still somewhat elusive1. Among the approaches, the metabolomics profile reveals the relationship between animal genetics and physiological phenotypes15, thereby increasing the fundamental understanding of efficiency and selection. Although affected by prandial activity, many metabolic processes underlie the transport of molecules through the blood. Blood is the sole way of absorption of nutrients into the body, and the blood metabolites are useful as a prime candidate for the study of FE in livestock16. In this context, it is also generally considered than improvement of RFI is associated with improved efficiency in the utilization of feed12 and thus improved utilization of nutrients.As part of the existing genetic determinants of FE, genome-wide association studies (GWAS) and differential expression (DE) analyses have reported a large number of polymorphism and genes for RFI or FCR in pigs17. In the context of metabolomics, the clusters (modules) represent specific metabolic processes or pathways and gives a better understanding of the function, interaction, and common regulatory mechanisms. WGCNA has been widely applied in pigs and several livestock species with fruitful results21. Therefore, one of the main objectives was to identify key blood metabolites associated with FE and related traits in Danbred Duroc and Danbred Landrace . As the Durocs are more FE than the Landrace, the two breeds serve biological contrast in FE. Furthermore, selecting two diverse breeds can help generalize any results obtained versus only focusing on one breed.An effective way to get insights into the interactions at a molecular level involved in complex phenotypes can be done by applying a network-based approach like weighted gene co-expression network analysis (WGCNA)Here, we applied an untargeted metabolomics approach for a better understanding of changes at a molecular level associated with nutrient utilization. We test the hypothesis that we are able to associate metabolite concentrations in blood at an early growth stage to predict future growth and FE measurements, and that metabolites profiles in general are associated with growth and efficiency phenotypes. We applied linear regression models to select the top metabolites predictive of FE, combined the results from network-based methods, and conducted a functional enrichment and pathway analyses to provide potential easy-to-screen candidate metabolite biomarkers and metabolic processes modulating FE in pigs.The phenotypic traits summary, including feed consumed (FC), FE, daily gain (DG), and delta weight (DW), for 109 pigs from Duroc and Landrace breed is shown in Supplementary Table The number of metabolites for each of the breeds at each time point with p-value ≤ 0.05 are provided in Table With an initial dataset of 729 metabolites, only those metabolites with relative standard deviation >0.15 were used for each group based on the raw counts. This amounts to 691 and 702 metabolites in Duroc (TP1 and TP2), while 684 and 689 for Landrace (TP1 and TP2), which were subjected for further analysis. To test if the metabolite profile was associated with the most distinct factors such as age and breed, the data were visualized on the first two principal components, colored by time point and breed, as given in Fig. The significant metabolites at two time points were identified, as given in Table We also did exploratory clustering analysis was done for the metabolites found significant for RFI in Duroc 36) and Landrace 29) (both time points combined) Table . The hea9 . Next, we selected the significantly associated modules  that were labeled by color for further analysis. The expression of any FE trait, such as RFI, is dependent on the stage of maturity while for other traits, this correlation is low23. However, in our study, we observed low to medium correlation for all the traits with respect to the metabolites.Since the metabolites interact and/or are a part of the same or related metabolic pathways, a weighted gene network approach using WGCNAIn Duroc (TP1), 144, 131, 335, and 81 metabolites were clustered, respectively, in MEblue, MEbrown, MEturquoise, and MEyellow , taking all the metabolites together irrespective of the time points in each breed  softwareed Table . The uniIn Duroc, 32 metabolites were involved in 49 pathways in TP1 as compared to 35 pathways obtained by 70 unique metabolites in TP2  Table . The difTo visualize and interpret metabolomics data in the context of human metabolic networks, to trace connections between metabolites and genes, and to visualize compound networks, the unique metabolites were cross-referenced with the KEGG database. Only metabolites with specific KEGG IDs were considered for compound-gene network and pathway analysis. The hub metabolites were identified by taking the highly connected metabolites that were associated with more than one gene in the compound gene network -Lactate, D-Erythrose 4-phosphate, 3,4-Dihydroxy-L-phenylalanine, Xanthine, Deoxyuridine, phenylacetaldehyde, pyridoxine phosphate, 4-Pyridoxate, Taurolithocholate sulfate, 5-Guanidino-2-oxopentanoate were specific for Duroc while L-Methionine, D-Glutamate, Thiamine, Deoxycytidine, Chenodeoxycholate were specific for Landrace.Compound-gene network for both the breeds Fig.  along wi25. Therefore, any reliable predictors of FE that can be easily measured and used in selecting animals would be helpful for pig producers. There are many genetic/genomic studies on pig FE in Danish pig breeds26. However, this is the first study to relate FE with metabolomics to identify metabolomic markers or signatures in Danish pigs.Improving FE greatly reduces the feed expense and increases the profit for the producers. However, it difficult to measure as it involves the accurate recording of dry matter intake and other featuresIn our study, using a high throughput UPLC/MS system, we analyzed metabolite concentration in blood collected before and after the FI testing period to search for a metabolomics signature with respect to the FE and other related traits in Danish production pigs at two time points. A clear clustering of sampling time and breed, among Duroc (TP2) and Landrace (TP1) (component 1) and Duroc (TP1) and Landrace (TP2) (component 2) gathered the samples according to their breeds and time points in four different groups, and supports the hypothesis of change in the metabolite profiles of the samples according to the breeds and time points. This also shows that the metabolite concentrations are not random and do have meaningful biological information.We carried out an exploratory analysis by applying untargeted metabolomics, linear and network analyses, and pathway over-representation to unravel the effect of metabolites on FE phenotypes. A stronger association of metabolites with FE was expected at TP2; it is based on data recorded at the second sampling point. We do however believe that any metabolites found in TP1 would be more valuable for selection as this would allow for early screening of the pigs, leading to less wasted resources. As we also do find that the metabolites have a linear association between the time points, we do believe there is a potential for early screening using blood metabolites. Although the relation between the two time points and the lack of significant metabolites at TP1 may seem contradictory, it can likely be explained by several factors. The metabolite concentration in TP1 do not explain all the variation in TP2. If we combine this with the fact that FE is a multifaceted phenotype, which is not strongly controlled by a single factor, and in general is a somewhat subtle phenotype, it is easy to imagine that despite the connection between TP1 and TP2 we do not find the same results in both time points. Thus more data, and possibly a multiple-metabolite model may be needed for successful application of early screening.From the KS test, we can observe that for most traits, the p-values are not uniformly distributed, with the highest p-value being 0.19. This means that if we apply FDR correction, all traits seem to have an overall relation to our traits, meaning even the borderline significant results are likely to be showing an underlying true effect. This establishes that metabolite profiles are a relevant source of information for our phenotypes of interest. Beyond the relevance of the metabolites for our phenotypes, we also established that for a large proportion of metabolites, the concentrations are linearly related over time. This shows us that despite variation over time, metabolites profiles show a level of temporal stability and predictability in our data.From the heatmap clustering analysis of the top metabolites based on p-values in Duroc and Landrace separately for RFI, we observed that the samples clustered at two time points in both the breeds. A clear demarcation is observed while clustering the metabolites. In Duroc, the clusters identified were involved with the metabolism of phenylalanine, vitamin B6, arginine and ornithine, digestion of dietary lipids. Regarding Landrace, the clusters identified were found to be involved with biosynthesis of arginine-proline metabolism, bile secretion, and lysine degradation.17 to analyze the metabolomics data in this study. From the network analysis, we found several modules associated with FE, TDG, and RFI in both the breeds at different time points, pointing towards the common pathways influencing these traits. The change in the metabolites found at different time points supports the fact that there are changes in metabolomic levels related to FE, TDG, and RFI between Duroc and Landrace breeds. We also constructed a compound-gene network for the significant unique metabolites in Duroc and Landrace to identify the pathways after cross-referencing with the KEGG pathways specific for humans and identifying genes underlying these pathways.We applied a well-known gene co-expression network approach – WGCNABased on the over-representation pathway analysis, we identified some key pathways in two time points in each breed , which is involved in glycerophospholipid metabolism and glycine-serine-alanine-threonine metabolism. Choline was found to be a hub metabolite involved with both FE and RFI in Duroc TP1 Fig. . Choline29. Metabolic shifts in lipid and carbohydrate utilization in high FE animals were reported14. They also reported reduced hepatic usage of fatty acid in high FE animals with a molecular alteration in lipid metabolism. A complementary analysis pointed out increased circulating triglycerides accompanied by a lower concentration of saturated and polyunsaturated fatty acids in the liver of high FE pigs14.The hub metabolite pyridoxamine (C00534), was found to be significant in Duroc TP1 (RFI) and Landrace TP2 , which was identified for Vitamin B6 metabolism. Pyridoxamine phosphate plays an essential role in the interaction of amino acid, carbohydrate, fatty acid metabolism, and TCA cycle. Studies reported the relationship of B6 in tryptophan metabolism of weanling piglets but were unable to detect an effect on the oxidation of the tryptophan pathway and suggested that B6 may stimulate another pathway in tryptophan metabolism30. Oxidative stress reprograms lipid metabolism increasing the mitochondrial fatty acid oxidation31. Butanoate metabolism was also found to be enriched for differentially expressed genes in Nelore cattle muscle for RFI32. In our study, we found acetoacetate as the hub metabolite responsible for butanoate metabolism related to TDG in Duroc TP2  to be the most significant for pathways underlying metabolism of butanoate, tyrosine, and valine-leucine-isoleucine degradation in Duroc TP2. Since butanoate is a metabolite of gut flora and involved with energy metabolism, butanoate metabolism may be activated under the conditions of cellular stresshub Fig.  for vali36. These metabolites were also reported to be affecting the digestive efficiency in chickens37. Previous studies have shown an increase in the concentration of xanthine by increased FI39. The degradation of rumen fluid into xanthine, hypoxanthine, and uracil by the action of bacterial nucleic acids  was reported previously40. The decrease in the rumen pH in dairy cows fed with high-grain diets changes the microbiota composition due to their intolerance towards low pH41.Deoxyuridine (C00526) and xanthine (C00385) were found to be involved with purine-pyrimidine metabolism in Duroc TP2. Deoxyuridine was associated with FE, while xanthine was associated with TDG in Duroc TP2  as reported by Salleh et al.43.We identified cholesterol sulfate (C18043) associated with FE, TDG, and RFI in Duroc TP2, whereas with RFI in Landrace TP1  and were related to TDG in Duroc TP2, EDG and DG in Landrace TP2, respectively , lipid , amino acid , nucleotide metabolism , metabolism of cofactors and vitamins , and metabolism of xenobiotics, their involvement in the feeding behavior and FE traits are conceivable.NT5E associated to Deoxycytidine in Landrace TP2 was identified as a candidate gene for RFI in the QTL database46. HSD17B4, which was associated to 3,4-Dihydroxy-L-phenylalanine in Duroc TP1, was identified as a candidate gene for carcass weight, backfat at tenth rib, and drip loss in Berkshire pigs47. Previous studies show the relation of FE with pork quality. Some studies reported that animals with low RFI have less back fat50, less water holding capacity48 and impaired sensory quality50. However, in some other studies, no difference was observed in the pork quality from low RFI pigs and controls with respect to drip loss, but a correlation between RFI and sensory traits related to reduced intramuscular lipid was observed51. A candidate gene, MAOA, associated to phenylacetaldehyde, was identified in our study that has been reported previously for intramuscular fat, ADG, and loin muscle52. Previous studies in Duroc reported high genetic variability due to moderate to high heritabilities for RFI, growth and carcass traits. An increase in the loin eye area was reported with decreased RFI, backfat and intramuscular fat content in Duroc pigs53. NUDT3 was related to thiamin in Landrace TP1 and PLD2 related to choline in Duroc TP1 in our study was also found to be a candidate gene for loin muscle area and loin muscle depth in pigs55. The metabolites and the genes identified are consistent with FE related traits. Further studies are warranted to evaluate the repeatability of our results in other pig population.The genes identified from the compound-gene network were checked against the previously identified QTLs obtained from Animal Genome PigQTL database, where all previous research on QTLs is curated. Among the 198 genes identified for both Duroc and Landrace from both the time points, 9 genes were previously reported as candidate genes in the QTL database with varied traits  2016-15-0201-01123, and C-permit granted to the principal investigator/senior author (HNK).ad libitum fed and free water supply. The authors of this study were not responsible for animal husbandry, diet, and care as the testing station is a facility within the Danish breeding program, run by SEGES.The pigs used in this experiment were housed at the pig testing station “Bøgildgård” operated by SEGES within Landbrug and Fødevarer (L&F: Danish Agriculture and Food Council). Pigs were 56. All pigs consumed the same feed until the test end.Blood samples were collected at a boar testing station Bøgildgård, owned by SEGES. The pigs were purebred uncastrated males from Danbred Duroc (n = 59) and Danbred Landrace breeds (n = 50), amounting to a total of 109 pigs. The initial bodyweight of the pigs before the testing period was approximately 7 kg, followed by a 5-week acclimatization phase. The pig diet consisted of a feed mixture with the main ingredients being: 39% barley, 27% wheat, 14% soybean meal and 6% oats. For the phenotypic traits, the weight of FC in kg and FE for each pig in the testing phase was measured beginning with an initial weight of around 28 kg for each pig. Bodyweight measured at two time points, the beginning and end of the test, were available from standard test procedure of the testing station and their difference was referred to as delta weight (DW). FE was calculated as the ratio between DW and FC. The testing phase ranged from 41 to 70 days based on the viability of each pig. The DG was calculated for three time phases – birth to testing (EDG), testing start to end (TDG), and birth to testing end (DG). RFI was computed as the difference between the observed daily feed intake (DFI) and the predicted feed intake (pDFI)For the study of metabolites, approximately 5 mL of blood was collected from jugular venipuncture from each pig into tubes containing ethylenediaminetetraacetic acid (EDTA) and immediately placed on ice. Samples were collected at two time points, one at the start of this test phase (approximately 28 kg weight) and the second after 45 days referred to as TP1 and TP2 in the further study. The pigs were sampled at the same time of the day and same day of the week to insure the most comparable sampling. Pigs were in non-fasted state. For the separation of the blood plasma, samples were centrifuged at 3000 g for 10 minutes at 4 °C, and plasma was stored at −80 °C.http://www.msomics.com/; Denmark), and the analysis was carried out using a UPLC system  coupled with time of flight mass spectrometer (Xevo G2 Tof Waters). An electrospray ionization interface was used as an ionization source. The analysis was performed in negative and positive ionization mode. The UPLC was performed using a slightly modified version of the protocol described by Catalin et al. .The plasma samples extracted from each pig were subjected to metabolomics analysis. The samples were processed by MS-Omics  and 1E3 (positive mode). The chromatogram building was achieved using a minimum time of 0.05 min, a minimum height of 1E3 (positive mode) and 4E2 (negative mode), and m/z tolerance of 0.01 (5ppm). The local minimum search deconvolution algorithm was used with a baseline cutoff, the minimum peak height of 2E3 (positive mode) and 5E2 (negative mode), and peak duration range of 0.04–5.0 min (positive mode) and 0.05–5.0 min (negative mode). Chromatograms were deisotoped with m/z tolerance of 0.01 (or 5 ppm) and an RT tolerance of 0.2 minutes for positive and 0.5 minutes for negative modes, respectively. Peak alignment was performed with (m/z tolerance at 0.01 (or 5 ppm). The peak list was eventually gap-filled with the peak finder module (intensity tolerance at 50% and m/z tolerance at 0.01 (or 5 ppm).Raw files were processed using MZmine 2The identification of the metabolites was performed using both peak retention times  and accurate mass (with an acceptable deviation of 0.005 Da). As a standard quality control, samples with blank >3 were not included. The relative standard deviation between QC samples was kept less than 60, the correlation between the dilution of QC and response was >0.8.58 using exact m/z values and retention time. The metabolites that did not correspond to HMDB were left unannotated. These compounds were annotated based on a library search of the masses in the HMDB with a mass uncertainty of 0.005 Da or 5 ppm. The search in HMDB assumes that all ions originated from the [M + H]+ or [M + Na]+ (in positive ionization) or [M − H]− (in negative ionization) ions.The data were aligned and normalized using total ion intensity. The metabolites were identified by comparison with the online Human Metabolome Database (HMDB)mijk) was adjusted for fixed and random effects as follows.The metabolite data were log-normalized before fitting the linear model. For each group of metabolites, only those with relative standard deviation >0.15 were used, based on the raw counts. The log-normalized metabolite concentration  for each animal;mentclass2pt{minimAj: is the covariate for animal’s sampling age in days;a priori would thus create biases59. Thus, we included sampling age as a covariate in the final models associating corrected metabolites with our phenotypes.We did not include the sampling age with our other fixed and random effects when correcting our metabolites, as the sampling age is correlated with our phenotypes. This is because the slower-growing pigs have a higher sampling age as it takes long time for them to reach the testing phase. Adjusting for sampling age Many models were used, so instead of looking into the specific results of each metabolite in each model, we initially tested the significance of the model based on all metabolites. This was done by using the Kolmogorov-Smirnov test to compare the resulting p-value distribution with the uniform distribution for the parameter of interest in each batch. Cluster analysis and heatmap of significantly different metabolites were generated using the ‘pheatmap’ package in R (v1.0.12).17. The WGCNA methods have been successfully applied to gene expression data from microarrays60 and RNA sequencing platforms in animal sciences18. Recently this methodology was applied on genome-wide genotype data as well61. Hereby, we extended this methodology to build networks using metabolomics data. The methodology, in summary, involved the Spearman correlation between all adjusted metabolite concentrations followed by the transformation of the correlation matrix into an adjacency matrix (AM) by fitting a power coefficient beta (β). The β was chosen by testing the coefficient between 12 and 22 and selecting the one that maximizes the scale-free topology based on the scale-free R2 value >0.8. From the scaled correlation, the Topological Overlap Measure (TOM), representing the connection between metabolites was calculated. Based on the TOM and applying the dynamicTreeCut algorithm, modules of connected metabolites were generated. In each module, the eigengene values of the module metabolites were calculated.Network analysis was performed using Weighted Gene Co-expression Network Analysis (WGCNA) R package version 1.66A linear model was fitted among the eigengene values of metabolite modules and the phenotypes to assess the module-phenotype relationship. Further, we intersected the metabolites identified based on the linear association with those from the modules significantly associated with phenotypes. Metabolites with p-values ≤ 0.05 and those clustered into the modules with a phenotypic correlation ≥0.2 and p ≤ 0.1 were selected for subsequent analysis.24 to identify metabolites underlying pathways meaningful to FE related-traits. IMPaLA takes into account the pathways from 11 public databases, including Reactome62 and KEGG pathway63. Over-represented biological pathways were taken as significant with p ≤ 0.05.Over-representation analysis was performed using IMPaLA64, a Cytoscape plugin. Based on that, we identified the connections between metabolites and the putative genes underlying the pathways in a compound-gene network approach. The key metabolites that were found to be involved in the main pathways were referred to as hub metabolites. The hub metabolites (compound IDs) from each significant pathway were selected and visualized using Cytoscape. A schematic representation of the methodology is given in Supplementary Fig. The visualization of metabolomic data was done in the context of human metabolic networks using Metscape v 3.1.3Supplementary figures.Dataset 1.Dataset 2.Dataset 3.Dataset 4.Dataset 5.Dataset 6.


The epidemic increase of non-alcoholic fatty liver diseases (NAFLD) requires a deeper understanding of the regulatory circuits controlling the response of liver metabolism to nutritional challenges, medical drugs, and genetic enzyme variants. As in vivo studies of human liver metabolism are encumbered with serious ethical and technical issues, we developed a comprehensive biochemistry-based kinetic model of the central liver metabolism including the regulation of enzyme activities by their reactants, allosteric effectors, and hormone-dependent phosphorylation. The utility of the model for basic research and applications in medicine and pharmacology is illustrated by simulating diurnal variations of the metabolic state of the liver at various perturbations caused by nutritional challenges , drugs , and inherited enzyme disorders . Using proteomics data to scale maximal enzyme activities, the model is used to highlight differences in the metabolic functions of normal hepatocytes and malignant liver cells (adenoma and hepatocellular carcinoma). In silico models of cells can provide insight into the causes and effects of disease states and reduce the need for in vivo studies. Here, the authors present a kinetic model of hepatocyte metabolism including energy, carbohydrate, lipid and nitrogen metabolism and hormonal and allosteric regulation of enzymatic activity. With regard to metabolic systems, reaching this goal requires the establishment of mathematical models enabling the computation of metabolite concentrations and fluxes at given external conditions (nutrients and hormones), gene expression level of metabolic enzymes, and the systems history .Inferring the response of a biological system to external or internal perturbations from the properties and interactions of its constituting molecules is a central goal of systems biology2. This example underlines the importance of biochemistry-based kinetic models that incorporate such important regulatory features of enzymes.Chemical reactions and mass transport are the basic processes in a metabolic network. They are catalyzed by specific enzymes and transport proteins that are regulated in multiple ways by their immediate ligands, allosteric effectors, hormone-dependent reversible phosphorylation, and variable gene expression. Often, a single specific regulatory enzyme feature is key for the regulation of a complete metabolic pathway. For example, the strongest regulator of the mitochondrial fatty acid transporter, carnitine palmitoyltransferase (CPT), is the competitive inhibitor malonyl-CoA. Decrease of malonyl-CoA concentration during the overnight fast is lifesaving because activation of CPT enables the enhanced oxidation of fatty acids to acetyl CoA and hence the formation of glucose-sparing ketone bodies in the liver3. Aiming at the in vivo assessment of liver metabolism, we developed a kinetic multi-pathway model of hepatocytes with hitherto unprecedented scope and level of detail. The model includes the regulation of enzyme activities by allosteric effectors, hormone-dependent reversible phosphorylation, and variable protein abundances. For each enzyme, rate equations have been developed that take into account the enzyme’s kinetic and regulatory features as revealed and quantified by means of in vitro assays. In the following, we give an overview of the model while referring the interested reader to the extensive Supplementary material containing all technical details. We focus in the main text on simulations of the dynamic metabolic output of the liver at different plasma profiles of metabolites and hormones. Using quantitative proteomics data for the scaling of maximal enzyme activities, the model opens the goal for a quantitative functional interpretation of gene expression changes. We applied this approach to reveal the patient-specific metabolic profile of adenoma and HCC. In summary, our model provides a powerful tool for computational studies of liver metabolism in health and disease.The strong medical interest in a better understanding of the molecular processes underlying the regulation of liver metabolism arises from the fact that an ongoing metabolic imbalance of the organ, e.g., due to excessive intake of drugs, alcohol or fructose, may result in an abnormal accumulation of lipids (steatosis) thereby increasing the risk of developing serious liver diseases such as hepatitis, cirrhosis, and cancer4. The rate laws for enzymes and membrane transporters were either taken from the literature or constructed on the basis of published experimental data. The mathematical form of the kinetic rate laws for enzymes and membrane transporters was dictated by the reaction mechanism and taken from enzymological in vitro studies, preferably for the liver of the rat or, and if not available, in the order mice → human → bovine → dog. The same ranking of species was applied for the retrieval of numerical values for the kinetic parameters. For every kinetic parameter we cite one experimental reference. If several numerical values for the same kinetic parameter and the same species were reported, we used one representative value that fits with the majority of the reported values and that—whenever possible—was taken from an enzyme assay that reported consistent values for other kinetic parameters. Mathematical terms in the rate law related to allosteric enzyme effectors, which are not included in the model were neglected. Their average contribution is indirectly contained in the fitted Vmax values. More than 90% of the parametric model input holds for the rat liver. Supplementary Note The metabolic part of the kinetic model comprises the major cellular metabolic pathways of cellular carbohydrate, lipid, and amino acid metabolism of hepatocytes see Fig. . The mod5. The signaling part of the model comprises the insulin and glucagon dependent regulation of key regulatory enzymes by reversible phosphorylation. The rate laws for these enzymes take into account that the phosphorylated and de-phosphorylated states of the enzyme possess differing maximal activities and kinetic properties. As in our previous work6, we used phenomenological mathematical functions to relate the enzyme’s phosphorylation state to the plasma concentrations of glucose  by hormone-sensitive reversible enzyme phosphorylation represents an important regulatory principle to control the direction of the net fluxThe model boundaries are given by the metabolite and hormone concentrations in the extracellular space. The flux through reactions not considered in the model were put to zero, i.e., carbon influx into and efflux from the modeled metabolic subsystem is exclusively mediated by the exchange fluxes through the plasma membrane.Vmax values, which may vary owing to variable gene expression, the numerical values for all other model parameters were taken from reported kinetic studies of the isolated enzyme. The Vmax values (Supplementary Table Except for the 2) and selected model variables. Under normoxic conditions, ATP is almost exclusively generated by oxidative phosphorylation. If the external O2 level drops below critical levels of about 15 mmHg, the cellular O2 level becomes too low to saturate complex IV of the respiratory chain, which decreases the flow of electrons, the proton-motive force, and thus the rate of oxidative phosphorylation. The fall of ATP blocks ATP-dependent reaction steps in anabolic metabolic pathways, such as gluconeogenesis and urea synthesis. Therefore, these two cardinal anabolic functions of the liver become severely restricted.An illustrative example of a calibration simulation is shown in Fig. 2 levels and reduced rates of hepatic glucose and urea production. The experimentally observed deviation in the cytochrome c reduction state from the expected thermodynamic equilibrium at low O2 levels points to electron acceptors outside the respiratory chain not considered by the model7.Inspection of the transition from the normoxic to the hypoxic state elicited by a sudden drop of oxygen allows to trace back the causal chain of molecular events underlying the relationship between falling OVmax values of all enzymes were randomly varied by ±10% in correspondence with reported average inter-individual variation of 19% of the liver proteome8.The metabolic state of the liver is mainly dictated by the plasma concentrations of metabolites and hormones, which are continuously changing depending on various factors such as the individual time regime of eating and fasting, the amount and composition of the food, physical activity or presence of systemic diseases like diabetes. Our model offers the possibility to simulate these scenarios in a quantitative manner by using the plasma profile of metabolites and hormones as model input. Fig. 9. During the night, i.e., without intake of carbohydrates but ongoing glucose consumption (predominantly by the brain), the liver stabilizes the plasma glucose level by the export of glucose. Glucose release is accompanied by a significant drop of the hepatic glycogen reserve by about 80% and an increase of the rate of gluconeogenesis  is low if the glucose and insulin plasma levels are high and vice versa12.During the day and free access to nutrients, the liver takes up glucose Fig. , and stoids Fig. 11. Owing13. Contrary to the fast glycogen stores, hepatic triglyceride stores serve as long-term fuel reserve and are almost unaffected during a normal diurnal feeding cycle  varies between 13:1 (at 10 h) and 2:1 (at 15 h), reflecting the adaptation in fuel preference over the day. Counterintuitively, the energetically most comfortable situation with a ratio ATP:ADP ≈ 6 is reached when the uptake of fatty acids and their relative share in oxidative ATP production is highest corresponding to times when the energetic demand for VLDL synthesis and gluconeogenesis peaks. Importantly, the 24 h concentration changes of all internal metabolites remained within experimentally overserved concentration ranges . As reference state we have chosen the steady-state that is adopted if the concentrations of all external metabolites are put to their 24 h mean values. All sensitivity measures are given in the Supplementary Data N”, which determine the deviation of the rate law from a hyperbolic shape . The large group of parameters with EC = 1 is constituted by the Vmax values, which commonly occur as pre-factor of the rate law. The balanced share of the category “KM” in the fraction of positive and negative ECs is due to the fact that increasing the Km value of a reaction substrate lowers the affinity and thus the reaction rate whereas increasing the Km value of a product (of a reversible reaction) has the opposite effect.The distribution of π-elasticity coefficients (EC), quantifying the regulatory importance of an enzyme, reveals a balance between activating and inhibitory regulatory effects Fig. . InspectR* to 0.1, 0.5, and 1.0, respectively. 181 (=17.4%), 79 (=7.6%), and 33 (=3.1%) parameters affected at least one metabolic function  may differ from the infinitesimal ones owing to the non-linearity of rate laws. We calculated response coefficients for a finite parameter change by 50% and repeated the analysis shown in Fig. Finite response coefficients  or inhibition by medical drugs. To check the functional consequences of such changes of enzyme abundances, we performed a dynamic metabolic control analysis (MCA), which differs from conventional MCA in that changes of the time course of model variables rather than changes of steady-state values are being calculated in response to a small perturbation of model parameters. To this end, we diminished the maximal activity of each of the 221 enzymes by 10% and computed the mean effect of this partial inhibition on the 24 h profile of 24 metabolic functions. This analysis revealed large differences in the capacity of individual enzymes to control metabolic functions by changes of their protein abundance see Fig. . About 3+ to NADH. It is commonly argued that inhibition of fatty acid β-oxidation due to the lowered NAD+/NADH ratio and the elevated de novo synthesis of fatty acids from massively formed acetate are the key factors contributing to the development of a fatty liver . We used our model to check the soundness of this reasoning by simulating the effect of a single bolus of ethanol added to the standard plasma profile  and acetaldehyde dehydrogenase (ALDH) converting ethanol to acetate via the intermediate acetaldehyde pathway #16, Fig. 6, Fig. 1see Fig.  that wassee Fig. . Howeveryde Fig.  can acti20 and excessive triglyceride accumulation21. These adverse effects can be attributed to the VPA-dependent inhibition of two key enzymatic steps. Being chemically an analog of a medium-chain fatty acid, VPA is activated to valproyl-CoA thus sequestering CoA and acting as competitive inhibitor of the carnitin-palmitoyl-transferase 1 (CPT1)22. VPA also inhibits the N-acetyl-glutamate synthetase (AGS) and thus the formation of acetyl glutamate, a strong activator of the urea cycle23. The interactions of VPA with CPT1 and AGS have been kinetically characterized and thus could be included in the model in detail. Fig. 24.An important application of the model consists in the quantitative investigation of metabolic changes elicited by medical drugs or toxins acting on specific metabolic enzymes. We have chosen the anticonvulsive drug valproic acid (VPA) as example. Clinical experience with VPA therapy has shown a number of fatal cases of hyperammonemiaThe model offers the possibility to assess the severity of a hereditary metabolic liver disease just knowing the altered kinetic properties of the defective enzyme. In this example, we focus on galactosemia, a rare genetic metabolic disorder that is caused by a deficiency of one of the three enzymes GALK, GALT, and GALE  is the toxic metabolite responsible for the galactosemia phenotype.The clinical manifestations of each enzyme deficiency differ markedly. Patients with GALK deficiency  may present cataracts only. In contrast, GALT deficiency  is potentially lethal and demonstrates long-term, organ-specific complications26.Fig. 27 but the functional implications of these deviations remain often elusive. The model offers the opportunity to unravel the functional implications of changes in the protein abundance of metabolic enzymes in patient-specific liver tumors. To this end, we used the ratio of protein abundances of enzymes measured in the tumor cell and the normal hepatocyte by quantitative proteomics .The gene expression profile of a tumor typically deviates strongly from that of the corresponding normal tissueOur work was inspired by both medical and methodological challenges. The bio-medical challenge was to establish a modeling platform that enables in silico studies of the response of the liver metabolism to variations of the external and internal conditions.In order to illustrate the utility of the model for basic research and a variety of medical applications, we simulated temporal variations in the metabolic state of the liver in response to diurnal changes in the plasma profile of metabolites and hormones at normal conditions, nutritional challenges, drugs with metabolic side effects, enzymopathies, and altered gene expression profiles in liver tumors. The value of such model simulations consists in the ability to monitor simultaneously a large array of metabolites and fluxes in vivo. This is an exquisite situation, which can hardly be achieved in experiments because of ethical, technical, and economical restrictions. Hypotheses on molecular mechanisms underlying the system’s behavior are commonly based on a restricted set of experimental observations of system variables and parameters. The presented model provides a means to check the feasibility of such hypothesis by filling the observational gaps by computation. The simulation of metabolic effects elicited by an alcohol bolus may serve as example for such model-based hypothesis testing. Our simulations demonstrate the capability of liver metabolism to rapidly return to the normal state even after pronounced ethanol challenges, thus excluding alcohol-induced steatosis to result merely from metabolic dysregulation.6. We used this approach to study the functional implications of altered enzyme levels in liver tumors. Our preliminary results suggest the existence of patient-specific metabolic profiles, which eventually may help to optimize individualized drug therapies. This computational approach may serve as an example for a proteome-based metabolic profiling of other liver diseases, such as liver steatosis, liver fibrosis, or liver cirrhosis.Applying the model to physiological or pathological states of the liver deviating from the reference state for a longer time period, adaptive alterations of enzyme abundances have to be taken into account. Hitherto, mechanistic models of the cellular protein turnover including the regulation of gene expression and proteolysis are not available yet. As an appropriate substitute, one may take advantage of the ongoing progress in quantitative proteomics by using experimentally determined changes of protein abundances to scale the maximal activities of enzymes and membrane transporters29. The applicability of this approach is restricted to the calculation of stationary flux distributions, which are obtained by optimizing a plausible objective function that relates basic cellular functions as, for example, the production rate of biomass or energy, to the fluxes through the constituting biochemical reactions. Additionally, upper flux constraints have to be imposed to assure a finite solution of the optimization problem. See ref.30 for a review of the genome-wide metabolic flux distributions for various cell types based on constraint-based methods. For a more detailed review on current activities aiming at the development of mathematical whole-cell models we refer the reader to the report of the 2015 Whole-Cell Modeling Summer School31.The methodological challenge was to provide a paradigm for the feasibility of a large-scale kinetic modeling approach that takes into account the biochemistry of the system. Most common in the modeling of large metabolic networks is the application of constraint-based optimization methods, also known as flux-balance analysis (FBA)32. But naturally, this comes at the price of limited predictive power and ability to incorporate biochemical information. FBA models lack time responses and metabolite concentrations, while simplified rate laws lack the possibility to incorporate changes in kinetic parameters (as in inherited genetic diseases), enzyme isoforms , and allosteric regulation.Another modeling approach to large systems consists in the constructions of phenomenological kinetic models, where the need for detailed enzyme characterization is bypassed by using simplified rate laws, e.g., of the mass-action type or lin-log type. Such rate laws have fewer free scalable parameters that can be conveniently estimated by the ensemble modeling methods33. Hitherto, the development and validation of such biochemistry-based kinetic models has been limited to small metabolic subsystems comprising 10–30 reactions . Frequently quoted arguments for such a long standstill in the development of larger kinetic models are the lack of kinetic information on metabolic enzymes for most cell types and the general perception that data from enzymatic assays do not reflect the in vivo situation. The latter argument must be doubted by noting that all well-validated and often cited fit-for-purpose kinetic models of small metabolic systems—from the Heinrich-Rapoport models of red cell metabolism36 to the model of the mammalian methionine cycle37—are ultimately based on in vitro enzyme parameters. There is simply no better information source for enzyme regulation than in vitro assays. Taking into account species-specific differences in kinetic constants and critically checking the plausibility of reported values by biochemical arguments  may considerably reduce the degree of uncertainty in the choice of kinetic parameters. At the end, the decisive argument for the validity of the chosen numerical values of enzymatic parameters is the correct functioning of the network under a multitude of physiological conditions.The limitations of the above modeling techniques can be overcome by kinetic models resting at the principles of chemical kinetics and thermodynamics and including all (for the purpose of the model) relevant molecular details of enzyme regulationFinally, we want to emphasize that the development of a kinetic metabolic model of this complexity and detail was possible because metabolism is the by far best investigated cellular subsystem and, therefore, better accessible to mechanistic mathematical modeling than signaling or gene-regulatory networks. Our research was inspired by the vision that in the end all this information on individual enzymes can be brought together in large dynamic network models.Vmax values of all enzymes. For each of the 585 model simulations, we forced the concentration values of 170 internal metabolites Mi  to remain within the concentration ranges i) punishing calculated metabolite concentration falling out of the expected range:We used a constraint optimization procedure to estimate numerical values for the α), With this setting, the constrained optimization problem can be converted into an unconstrained optimization problem:For the sensitivity and control analysis of the model we used the following measures:v of the isolated enzyme, i.e., at fixed valuesp,(i) The π-elasticity coefficient, which is defined as partial derivative of the rate Yi  caused by a change of model parameter pj,λ-fold change of the initial parameter value, pj → λpj.(ii) The response coefficient, which is defined as change of the steady-state value of an arbitrary model variable In the control theory of metabolism it is common to study the response of the system to infinitesimal parameter changes,We will refer to Yref and Ypert denote the time-dependent changes of model variable Y in the absence (ref) and presence (pert) of a perturbation and T is the time interval of interest.(iii) For the sensitivity analysis of non-stationary states, we defined the time-averaged response coefficientAn executable SBML file of the model is available from the authors on request.All data and public data sources used for the development, calibration, and exemplary model simulations are contained in the supplementary information.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4


Metabolic networks reflect the relationships between metabolites (biomolecules) and the enzymes (proteins), and are of particular interest since they describe all chemical reactions of an organism. The metabolic networks are constructed from the genome sequence of an organism, and the graphs can be used to study fluxes through the reactions, or to relate the graph structure to environmental characteristics and phenotypes. About ten years ago, Takemoto et al. (2007) stated that the structure of prokaryotic metabolic networks represented as undirected graphs, is correlated to their living environment. Although metabolic networks are naturally directed graphs, they are still usually analysed as undirected graphs.We implemented a pipeline to reconstruct metabolic networks from genome data and confirmed some of the results of Takemoto et al. (2007) with today data using up-to-date databases. However, Takemoto et al. (2007) used only a fraction of all available enzymes from the genome and taking into account all the enzymes we fail to reproduce the main results. Therefore, we introduce three robust measures on directed representations of graphs, which lead to similar results regardless of the method of network reconstruction. We show that the size of the largest strongly connected component, the flow hierarchy and the Laplacian spectrum are strongly correlated to the environmental conditions.We found a significant negative correlation between the size of the largest strongly connected component (a cycle) and the optimal growth temperature of the considered prokaryotes. This relationship holds true for the spectrum, high temperature being associated with lower eigenvalues. The hierarchy flow shows a negative correlation with optimal growth temperature. This suggests that the dynamical properties of the network are dependant on environmental factors. All living organisms rely on chemical reactions to exist, and the set of these life-sustaining chemical transformations is defined as metabolism. Because these reactions are mostly catalysed—accelerated—by enzymes, the transformation of organic molecules (substrates) into other chemicals (products) can directly be mapped by the enzyme set.The development of metabolic databases such as KEGG  linking Flow is an inherent concept of metabolic reactions, going from substrates to products which then become substrates for other reactions. Directed graphs are therefore a natural way to model enzymes and chemical reactions . The metWe focus on a metabolite-centered representation where the nodes of a graph are metabolites, and they are connected if an enzymatic reaction converting one metabolite into another exists. It is then simple to create metabolic networks that describe all chemical reactions of one or multiple organism(s) as a graph. Figure Recent studies take two main directions in the analysis of metabolic networks. The first one heavily relies on graph structural measures such as degree distribution, clustering coefficient, path length, and centrality –7. The sIt was reported by several studies e.g., –12) that that12])Ideally, metabolic networks require complex representations such as hypergraphs, since reactions in metabolic networks convert multiple reaction inputs into multiple outputs using other components , 14. HowA number of graph theory approaches were proposed to study relations between the structure of metabolic networks and the environment. So, Borenstein et al.  stated tThe network topology determines network functions , and theMetabolic networks are known to be extremely heterogeneous, and two networks of two different organisms are quite different . At the We reconstruct metabolic networks for species considered by  in additWe build and explore undirected and directed metabolic graphs; including all KEGG enzymes for the species or only those found in the so-called KEGG pathways.We propose to apply robust measures on directed complex graphs, namely largest strongly connected component, flux hierarchy and Laplacian spectrum, and we relate these measures to the environmental conditions.In our experiments, we have confirmed the results of , and we Our main motivation is to explore the structure of directed metabolic graphs of bacteria, and to relate it to phenotypes. In our experiments, we consider prokaryotic optimal growth temperature as a phenotype. In this article, our contribution is: In the study conducted by , undirecSince more than ten years have gone by, the databases have evolved significantly. We focused on directed graphs, and we reconstructed the metabolic networks without using pathways directly (see Methods). We obtained graphs for 100 out of the 113 species, filtering out nodes in such a way that we had metabolic reactions from known pathways only. We also added 128 additional species.There are two main differences between our experiments and the ones from . We use We apply a linear regression to estimate relation between the number of nodes in a metabolic network and the optimal growth temperature and deduce Pearson’s correlation. We consider a correlation between the number of nodes and the optimal growth temperature, a correlation between edge density and optimal growth temperature, a correlation between degree exponent and optimal growth temperature, and a correlation of average clustering coefficient and optimal growth temperature. All these measures are applied to undirected substrate graphs without ubiquitous metabolites, to be as close to  as possiIn general, we discovered the same tendencies as .Here we compare the influence of taking all KEGG enzymes for a species with enzymes in known KEGG pathways only. Our method to build the metabolic graphs is different from  in that On Fig. We wish to analyse the impact of the additional reactions. We observe a complete loss of the tendencies, what is shown in blue on Fig. We found a significant positive correlation between the proportion of enzymes that are not in a pathway and optimal growth temperature , meaning that more new enzymes—edges—are added for thermophiles than non-thermophiles, adding more edges and therefore likely causing the correlation inversions.When removing up to 40% of random nodes in the graphs with all enzymes, the trends stay significantly correlated. This means that the pathway enzymes are specific and greatly modify the graph structure.These differences could be due to a bias in KEGG pathways for hyperthermophiles, that could have less annotated and curated pathways than its more well-studied counterparts, and thus more enzymes not associated to pathways. Another hypothesis is that this difference could be explained by noise, since the number of nodes for hyperthermophiles is the smallest.We focus on directed metabolic networks, and we are interested in finding relevant measures on directed graphs that can explain correlation with environmental variables such as optimal growth temperature. We propose robust measures to analyse substrate graphs.We considered two measures to study cycles in networks, the size of the largest strongly connected component which corresponds to the biggest cycle in a graph, and the flow hierarchy (see Methods). The flow hierarchy is defined as the number of nodes in a component that is not a part of the largest strongly connected component. So, the two explored measures are closely related.As shown in Fig. We tested another directed network structural property to study connectivity. A temperature class network yields different graph structural properties related to connectivity, but the underlying description of these graphs is a compound flow. These properties are associated to the ’speed’ of reactions and can be assessed using the spectrum of the network .A of dimensions 3194×3194. We compute the Laplacian matrix n vertices, the eigenvalues are 0, 1 (with multiplicity n−2) and 2, and for the cycle on n vertices the eigenvalues are k<n.More precisely, for each species’ graph we computed the adjacency matrix containing all recorded compounds among all species yielding a matrix We computed this spectrum for each species, and estimated the average within the same temperature group ,The said targets.On the other hand, eigenvalues of value 1 with eigenvectors summing to 0 correspond to a particular network pattern : Source nodes are nodes without predecessors (in-degree of 0).We have observed that prokaryotic metabolic network properties can correlate with environmental phenotypes, namely with the optimal growth temperature in our study.First of all, we confirmed the results of  that datThese results hold for a particular subset of known metabolic data of the species. We consider important to take all available data (enzymes) into account. However, in the case where all enzymes associated to the species in KEGG are taken into account, these results do not hold anymore: they change sign of the correlation. The origins of these inversions are still unclear, but we believe that it could be due to biases in KEGG for non-mesophilic species .We believe a directed network representation is more appropriate to model metabolism, so we looked for directed topological properties that were robust for the different reconstruction protocols. We tested directed graph structural properties related to cycles (largest strongly connected component and flow hierarchy) and to connectivity and flow (Laplacian spectrum).We found that when there are less nodes involved in the largest strongly connected component (cycle), it is linked to higher optimal growth temperatures, and there are more nodes outside of the cycle that are still part of the weakly connected component, which is the measure of hierarchy flow. When more nodes are involved in the largest strongly connected component, it is linked to lower optimal growth temperatures, and there are less nodes outside the cycle that are still part of the weakly connected component. This is valid no matter if all enzymes are considered or only pathway enzymes.We explored the most common nodes of the largest strongly connected components, and we found several metabolites such as L-glutamate and L-glutamine (found in 213 out of 228 species), pyruvate (found in 213 from 228 species), phosphoenolpyruvate (209/228), carbamate (208/228) and some others which are molecules involved in the most basic cell metabolism, and that may imply the primordial and basic functions of these metabolic cycles.Amino acid substitutions are reported to be more deleterious for thermophiles than non-thermophiles , implyinOn another hand, we see that Laplacian eigenvalues are higher for prokaryotes that preferentially grow in colder environments, showing more particular patterns of connectivity and flow in their networks.Other directed graph topological properties were tested, with some having significant correlations with optimal growth temperature for both reconstructions, such as the fraction of nodes with an in-degree of 0  or the fraction with an out-degree of 0 , having both positive correlations with temperature (data not shown), or also the number of some of the triads among the 16 possible triads in a directed network also show significant correlations, positive and negative (data not shown). All of this shows the clear link between the immediate environment and the metabolism of a given species, and can be looked into in different contexts and environments.To integrate directed graphs and bring it a step further, an interesting future research avenue would be to study the differences, or complementarities between community graphs and single organism graphs, as well as differences in their largest strongly connected components and other directed structural properties.An important point has to however be made on the direction of chemical reactions. In this work, we fixed the directions of reactions as found in the KEGG database. However, there might be some chemical reactions happening in the opposite direction than the one fixed in KEGG. We believe that it may be interesting to infer directionality of reactions, since homeostasis is extremely important for organisms, and it is regulated, e.g., by enzymes. There is a need to study this problem, for example through thermodynamics, and it would be promising to study the flux of our graphs, in particular from observational data to investigate the dynamics of the biomass.We have reproduced the results of , and we We have noticed that when we include all KEGG enzymes we could find for a species into metabolic networks, and not only the enzymes from KEGG pathways, the results do not hold anymore.We propose three directed graph measures, namely, the size of the largest strongly connected component, the flow hierarchy, and the Laplacian spectrum. We have shown that these measures are robust for all considered graphs, and they correlate respectively negatively, positively and negatively to the optimal growth temperature. In all our experiments, we have observed strong links between environmental phenotypes and graph structure.We have also developed a pipeline to reconstruct metabolic networks taking into account all enzymes. We compared the results of our pipeline to the state-of-the-art results of , and we We are currently investigating how robust the metabolic networks are against structural modifications. Finding causal directions from purely observational data is another open challenge.oC, thermophilic species are ones with the optimal temperature between 50oC and 70oC, mesophilic species live in the range between 20oC and 45oC, and psychrophilic species prefer an environment between −20oC and 10oC.Our data set contains 228 prokaryotic species where 100 species are from the databased used by . We deciThere exist a number of ways to produce metabolic networks from chemical reactions. The nodes of such a metabolic reconstruction can be metabolites , or enzymes. We have built directed and undirected substrate graphs which are metabolite-centered graphs where each substrate is a node and is linked to each product of a metabolic reaction for a given species. Therefore, edges are enzymatic reactions linking substrates to products. Figure To build the metabolic graphs, we downloaded Ensembl or GenBank cDNA FASTA files for the 228 species. Figure 2O, ATP, ADP, NAD+, NADH, NADPH, CO2, ammonia, sulfate, thioredoxin, phosphate, pyrophosphate (PPi), and H+. We also consider NADP+ to be a ubiquitous metabolite. All these metabolites do not appear in our graphs.A common practice in metabolic network reconstruction is to exclude ubiquitous metabolites to make the network more relevant biologically, and because of the great impact on network structure. There is no strict consensus on ubiquitous metabolites, however, the metabolites used as carriers for transferring electrons and common functional groups are regarded as ubiquitous metabolites . SimilarNote that in  they dirIndeed, our main objective is to assess bacterial metabolic systems without any a priori knowledge, and therefore keep as much information as possible, which is why we keep all enzymes that can be deduced from the genome. This means that we have kept most inorganic compounds and generic reactions. We therefore may have less metabolic information regarding some nodes when considering generic reactions, but also more information as more data from the database is considered and as all substrates and products are included .For the directed reconstructed graphs, the default direction of the KEGG reaction was used, which is the direction of the catalytic reaction (substrates and products are specified). It is the direction in which the flow of biomass is expected.Our networks were reconstructed on April 2019, a description of the species and the networks can be found in Additional file ∘C.For the species also considered by , we got Here we use the definition provided in  for the E is the total number of edges, and N is the total number of nodes.where P(k) of our graph follows a power law kγ- . The number of connections k of a node is called degree of a node, and the degree distribution is the degrees of nodes over the whole graph. An estimate via maximum likelihood of the degree exponent γ is as follows: We follow the definition given by . We assuN is the number of nodes in the network, ki is the degree of node i and kmin is the smallest degree in the metabolic network. We do not take into account nodes with null degrees for this measure.where i in an undirected graph G is defined as: Here we have used an approximation of the average clustering coefficient. The local clustering coefficient of a node Mi is the number of triangles formed by a node and two of its neighbours, and Mpossible is the number of all possible triangles that could be formed with this node’s neighbourhood. The average clustering coefficient corresponds to an average value of local clustering coefficients over all nodes. The approximation we have applied is the one proposed by [n times (we have taken n=1000).where posed by  where thThe average clustering coefficient M is the number of triangles found, and n is the number of trials.where v to node w, and a path from w to v in a given graph G [Desulfurococcus amylolyticus 1221n is shown on Fig. The largest strongly connected component corresponds to the largest partition of path equivalent nodes in a directed graph. Path equivalence is the property of having a path from node  graph G . Therefov and w are connected via an undirected path. We have defined the concept of flow hierarchy as the number of nodes that do not participate in the largest strongly connected component. Hierarchy flow can therefore be deduced from the subtraction of the strongly connected component from the weakly connected component. To be precise, we take the largest strongly connected component and the weakly connected component containing it, and we then divide the remainder nodes by the number of nodes in the graph . This procedure is drafted on Fig. Desulfurococcus amylolyticus 1221n in Fig. A weakly connected component is also a property of directed graphs. It is defined as a group of nodes where each node A for each species indexed by these vertices Axy=1 if a directed edge exists from x to y. By construction, A is not usually symmetric. We compute the Laplacian matrix Lxy=−Axy if x≠y. We computed the spectrum —the list of eigenvalues— of We collected the set of all compounds described in the different graphs to create a standardized adjacency matrix Additional file 1 This file contains our prokaryotes and structural properties information in an XLSX format. It contains the species name, KEGG species code, optimal growth temperature, temperature class, number of nodes for all enzyme graphs, number of nodes for pathway graphs, the number of edges for all enzyme graph, number of edges for the pathway graph, the average clustering coefficient for the all enzyme graphs, the average clustering coefficient for the pathway graphs, the degree exponent for the all enzyme graphs, the degree exponent for the pathway graphs, the edge density for the all enzyme graphs, the edge density for the pathway graphs, the node normalised flow hierarchy for the all enzyme graphs, the node normalised flow hierarchy for the pathway graphs, the node normalised size of the largest strongly connected component for the all enzyme graphs, the node normalised size of the largest strongly connected component for the pathway graphs.Additional file 2 This file contains the average Laplacian spectrum per temperature class, sorted in decreasing order. The Laplacian matrix size is the number of nodes in the union of nodes for all our networks.


Chlorella vulgaris and the bacterium Pseudomonas aeruginosa. Metabolic Pathway Analysis (MPA), is applied to understand the metabolic capabilities of the co-culture and to elucidate the best conditions in removing nutrients. Theoretical yields for phosphorus removal under photoheterotrophic conditions are calculated, determining their values as 0.042 mmol of POC. vulgaris, 19.43 mmol of phosphorus (Pi) per g DW of C. vulgaris and 4.90 mmol of phosphorus (Pi) per g DW of P. aeruginosa. Similarly, according to the genome-scale biochemical reaction network the theoretical yields for nitrogen removal are 10.3 mmol of NHP. aeruginosa and 7.19 mmol of NOC. vulgaris. Thus, this research proves the metabolic capacity of these microorganisms in removing nutrients and their theoretical yields are calculated.Anthropogenic activities have increased the amount of urban wastewater discharged into natural aquatic reservoirs containing a high amount of nutrients such as phosphorus (Pi and PO Chlorella accumulates nitrogen and phosphorus to reach concentrations ranging between 5.0 to 10.1% and 0.5 to 1.3%, respectively [Diverse human activities have increased the amount of urban wastewater effluents discharged in natural aquatic reservoirs, containing a high amount of nutrients such as NH biomass . The pheectively . In addiectively .C. vulgaris and P. putida consortium inherently has shown to be a good nutrient and organic contaminants removal option in synthetic municipal wastewater compared to axenic cultures [Chlorella with an effective microorganism (EM-1) co-culture [C. vulgaris with Azospirillum brasilense in cellular immobilization increases the removal of ammonia and phosphorus [Chlorella with other bacteria removed up to 80% of total nitrogen present in animal feed wastewater production. However, it has been reported in experimental studies that pure cultures have no effect whatsoever in to removing either nitrogen or phosphorus in industrial wastewater [Pseudomonas is a common bacteria present in wastewater mentioned in a broad variety of studies [Pseudomonas. To the knowledge of the authors, there is a lack of studies in regard to the interaction between C. vulgaris & P. aeruginosa, that are focused on the metabolisms, the upper and lower bounds for nutrients removal according to the biochemical network and the possible metabolic phenotypes.Nevertheless, pure microalgae cultures are only possibly maintained in the lab. In environmental conditions, microalgae always coexist with endogenous bacteria . Hence,  factors ,9. Howev factors . The C. cultures ,12. Lana-culture . Moreoveosphorus . Co-cultstewater . These s studies ,16. HoweE. coli), using Flux Balance Analysis (FBA) to obtain the optimal carbon-flux distribution. Another technique used to analyze genome-scale metabolic networks is the Metabolic Pathway Analysis (MPA), it focuses in finding the phenotypic capabilities by numerically analyzing a set of systematically independent and unique Extreme Pathways (ExPas) [C. vulgaris and P. aeruginosa, it is possible to estimate the phenotypic potential under different schemes. Our research group has been working in the evaluation of nutrient removal by using different microorganisms and the consortium between them [Currently, the genomic information for specific microorganisms is available from biological databases that are collected from high-throughput technologies. Those databases describe the metabolisms and a broad variety of components such as genes, proteins and other metabolites. Therefore, genome-scale biochemical reaction networks for microorganisms are possible to reconstruct and analyze using metabolic engineering tools . Varma e (ExPas) . The ExP (ExPas) ,20. ExPa (ExPas) ,19. By ceen them . This paS. The output fluxes are: biomass from each microorganism, polyhydroxyalkanoates, maltose, carbon dioxide , external glucose (GLUext), sulfate ) is required by both microorganisms. First, it could enter as an external flux into endogenous phosphorus  and phosphate (POg (2013) , where tC. vulgaris. The major uptake corresponds to an ExPA of pure C. vulgaris culture within a photoheterotrophic scheme. It requires 50.79 mmol of glucose per g DW of C. vulgaris and 1252.06 mmol of photons per g DW of C. vulgaris to be held.The set of 2572 ExPas were analyzed to consider the removal of phosphorus by assimilation into biomass. The theoretical yields for phosphorus uptake were calculated as a function of biomass of each microorganism. The highest yield obtained from over 2572 analized yields was 180.23 mmol PiP. aeruginosa metabolism  enters as part of the oxidative phosphorylation of ATP synthesis. Simultaneously, it is incorporated in 28 biochemical reactions that feature the metabolisms of glycolysis, Krebs cycle and synthesis of acetic acid ), is assimilated by C. vulgaris (POC. vulgaris requires 0.04 m mol of POThe phosphate specie (POoduction .•) and is active in 90.43% of the total ExPas , which are part of the protein synthesis ( flux reaction 148) and nucleic acids (fluxes reactions 140 and 141). The removal of external ammonia (NH•) by bacteria is represented in •). It is present int 33.33% of the total ExPas. The best ExPa predicts a maximum uptake of 10.30 mmol of NHP. aeruginosa either under a photoautotrophic or photoheterotrophic scheme.Nitrogen is represented in two nitrogenous species within the metabolic network; nitrate , was considered to reduce the amount of ExPas. In this case, carbon degradation is represented as external glucose (GLUext), in the metabolic model. The introduction of this criterion reduced from 768 to 336 feasible extremes pathways. Thus, the best ExPas for nutrient removal by the co-culture is depicted as a schematic diagram in C. vulgaris and 13.85 mmol of glucose per g DW of P. aeruginosa. Even so, most of the organic source is directed towards the bacterium at glycolysis metabolism. This is in agreement with experimental reports where the growth of bacterium is related to glucose uptake at the expense of microalga development [C. vulgaris and P. aeruginosa. C. vulgaris, 0.04 mmol of phosphate per g DW of C. vulgaris and 7.19 mmol of nitrate per g of DW of C. vulgaris. For the bacterium the nutrient removals yields were 4.90 mmol of phosphorus per g DW of P. aeruginosa and 10.30 mmol of ammonia per g DW of P. aeruginosa were obtained. These results indicate a more efficient uptake of inorganic phosphorous from microalga than from bacterium in the consortium, because of the alga requirements during photosynthetic metabolism according to The source of organic carbon in elopment . While tC. vulgaris and P. aeruginosa is to assemble the stoichiometric reactions based on the genome information. Different metabolic databases exist featuring the biochemical reactions with specific genes for each microorganism. In this research, the reconstruction of the metabolic networks was made using the databases BRENDA (BRaunschweig ENzyme DAtabase), NCBI  and MetaCyc & KEGG, .Chla and Chlb), Calvin-Benson cycle, starch metabolism, glycolysis/gluconeogenesis and finally, the basic metabolism for biomass formation such as TCA cycle, fatty acids synthesis, triglycerides synthesis, oxidative phosphorylation, pentose phosphate pathway, protein synthesis (18 amino acids), nucleic acids synthesis, carbohydrate synthesis, glycerophospholipids and maintenance. Such metabolisms are represented by the first 147 biochemical reactions  [C. vulgaris and P. aeruginosa). Metabolites such as CoA, NAD, NADP, FAD, ADP, and HIn addition to the information provided by the above mentioned databases, the elementary composition of el 1108) . These r and ATP . All stoS with m) represent the mass balance for each metabolite (X), and n is the number of internal and exchange fluxes , are calculated with respect to DW of biomass of each microorganisms under a photoheterotrophic scheme, which can be related to a practical tertiary wastewater treatment. These theoretical yields are important because they could help the design of biological systems knowing the theoretical requirements of oxygen and carbon dioxide to achieve the maximum nutrient uptake. It is important to mention that nutrient uptake yields reported correspond to that ExPA where there is biomass formation for both microorganisms, meaning that the uptake of these nutrients was only by assimilating them into biomass, which means an interaction of symbiosis between these microorganisms and not a competition between them. The ExPa results indicate that there is no formation of other byproducts containing nitrogen or phosphorus under this particular scheme, all nutrient uptake is directed toward microalgae and bacterial growth.A genome-scale metabolic network was rebuilt for the co-culture of