Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.  •A method to study effects of diet composition on mouse liver cancer metabolism•Western diet increases glycerol and succinate production in both tumor and liver•Western diet amplifies tumor/non-tumor tissue metabolic differences•Dietary carbohydrates and fatty acids together promote tumor lipid metabolism Biological sciences; Physiology; Cellular physiology; Cancer Gene oThese observations suggest that chronic exposure to WD is associated with changes in expression of genes that mediate metabolic processes which, in addition to dietary nutrient availability, could influence both the rate and functions of tissue metabolism.With the outlook of assessing how the observed gene expression changes induced by diet may modulate the metabolism of dietary nutrients across tissues, we generated an updated mGSMM. To this end, we constructed, then combined four intermediate models (IMs) based on existing human and mouse GSMMs A.Figure https://github.com/sysbiomelab/MMRNHep.Specifically, we directly replaced proteins in HMR2 with their known mouse orthologs to yield IM1. We also reconstructed two metabolic models based on the sequence homology of mouse proteins to the protein sequences encoded by genes in HMR2 (IM2) and Recon3D (IM3), respectively. We finally considered MMR, the most advanced mouse GSMM at the time we started our study, as IM4. Only 60.5% of genes and 41.9% of metabolites overlapped between IM1-4 A, indicaHep]  refers to diets where X = WD diet component that was swapped to its respective CD value) (lipid(CD) revealed a dependence of succinate production on the increased lipid content of WD, which drove higher TCA cycle flux. The quantitative increase in glycerol production by WD was abrogated with WDcarbs(CD). Further investigation of the flux distributions with WDcarbs(CD)  E. We colarbs(CD)  showed tarbs(CD) F and 3G.arbs(CD) .,,Excess glycerol and succinate produced in WD-fed tissues could influence tissue physiology in a non-cell autonomous manner, through mechanisms that cannot be captured by our single-tissue models.WD led to a modest increase of biomass production in all csGSMMs compared to the respective CD-fed GSMMs B, demonsr models . These oWD caused a greater shift, relative to CD, in the flux distributions of al model A and 4B.al model A and 4C.To explore specific pathways that underlie differential response of tumor and peritumoral tissue models to WD driven by carbohydrates and lipids, we first identified subsystems of all reactions that carried flux in tabolism D. We thetabolism . From thEN−PT−WD , and betEN−PT−WD .Comparison of the reaction fluxes in this network revealed a distinct diet-dependent fate of dietary FA’s in the tumor and the peritumoral tissue models. On one hand, FA uptake and FA-derived acyl-CoA synthesis fluxes were higher in WD than in CD for both the T and PT models, and equal between T and PT models in each diet. However, our models predicted increased use of FA-CoA for lipid macromolecule synthesis and decreased use for β-oxidation through the mitochondrial carnitine shuttle in n tumors . Consistn tumors G.lipids(CD) or WDcarbs(CD)  B and S4CGpat/Gpam) into lipids ceased for both models in CD but was increased in in vivo . These investigations provide evidence for a combined effect of nutrient availability and gene expression in determining the characteristic metabolic profiles of tumors and response to diet modulation.,Bottom-up approaches to reconstruct GSMMs are labour-intensive and time consuming.,,To overcome these limitations, we used both HMR2 and Recon3D, and complemented classic gene orthology-based gene replacement methods with protein sequence homology-based identification of mouse orthologous genes. Used together with a previous mGSMM, MMR, this approach yielded MMRN. Compared to other mouse models at the time we initiated our study, MMRN has expanded coverage of the mouse metabolic network and overall improved attributes evaluated by MEMOTE, a framework of standardized tests that aims at consistently assessing GSMMs.,,,,,,,Application of GSMMs to specific biological questions is commonly aided by constraining metabolic fluxes with context-specific data.A complete DiCoS from WD to CD as input for the models revealed that all csGSMMs took up more nutrients from WD than from CD. WD has more FA-derived and fewer carbohydrate-derived carbons, and a higher overall carbon content per unit weight of consumed diet. However, neither the total amount nor the amounts of specific nutrient classes taken up by csGSMMs reflected the amounts of these nutrients found in the input diet. For example, all csGSMMs consumed more carbohydrates in WD than CD, although the amount of carbohydrates in WD is lower than that of CD B.,,,,Consistent with carbon-balanced models, we found increased efflux of products in WD-fed than CD-fed csGSMMs, which SyDiCoS revealed to be largely due to increased glycerol and succinate efflux. In a variant of SyDiCoS, we selectively swapped specific WD components and found that increased glycerol efflux is derived from carbohydrates whereas increased succinate efflux is attributable to lipid catabolism. We speculate that if these metabolites efflux out of cells as our models predict, they may mediate metabolic activities between cells or tissues. For example, glycerol is a precursor for carbohydrate and lipid synthesis in the normal liver and its metabolism is perturbed in metabolic disease.Together, the application of SyDiCoS in various csGSMMs revealed that, irrespective of differences in gene expression, all csGSMMs limit both how much and which nutrients they take up from the diet, and what they produce to fulfill a given objective function.,A complete WD-to-CD DiCoS caused a significant shift in the flux distributions of all csGSMMs. On the other hand, we also detected differences in flux distributions between csGSMMs on each diet, with the tumors being the most distinct. WD-to-CD DiCoS amplified these flux distribution differences. We therefore focused on comparisons between the flux distributions of tumor and peri-tumoral models because such differences are of both diagnostic as well as therapeutic interest. Identification of the pathways that account for flux differences is challenging as it requires either sophisticated network analysis algorithms or laborious manual exploration of the flux distributions.Gpat, Abdh5, Lpin and Mogat. SyDiCoS revealed that these lipid gene expression patterns sufficed to drive differential lipid metabolism between T and PT when either lipids or carbohydrates were normalized to CD levels, however, in a full swap to CD, this differential metabolism ceased. In contrast, increased serine synthesis in T relative to PT persisted in all diet inputs tested but in the absence of lipids serine did not contribute to lipid biomass more in T than in PT. Even though higher glucose uptake and flux to DHAP persisted in T with CD, it was not sufficient, alone, to sustain acylglycerol synthesis, even with increased Gpat expression, likely because fructose uptake ceased in CD.With WD, both tumor and PT models took up FAs at similar rates. However, increased expression of genes that mediate lipid anabolic pathways and decreased expression of genes in β-oxidation resulted in a higher use of these FAs for biomass production and away from mitochondrial oxidation in T compared to PT models. Our models also predict increased production of precursors for lipid biomass derived from carbohydrates through increased expression of genes in two pathways. On one hand, increased expression of serine synthesis genes provides serine for phosphatidyl-serine synthesis; on the other hand, fructose breakdown produces DHAP and G3P that are used as backbone for acylglycerol production, through a series of reactions encoded by ,,,,,Several of the pathways and nutrients we describe here have been shown to promote liver disease and cancer.,MMRN contains reactions, several of which involved in lipid metabolism, with stoichiometric inconsistencies  that may,,,The cellular composition of both the liver and tumors is heterogeneous and can change as a function of time, location within the tissue, and hormonal signals.,,,The use of mouse diet composition as input to csGSMMs also has limitations. Although some dietary nutrients may reach the liver intact in some physiological settings, others can be digested in the gut lumen or metabolized by the microbiome before they reach tissues through the circulation.dimitrios.anastasiou@crick.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Dimitrios Anastasiou  and comply with a license to DA’s lab ratified by the UK Home Office. C57BL/6J mice were housed under a light-dark cycle of 12:12h with controlled temperature (22-24°C). Two-week old male mice were injected intraperitoneally with 25mg/kg of the carcinogen diethylnitrosamine (DEN). From the time of weaning, mice were fed a western-like diet . Mice were anaesthetized with 1-2% isoflurane in O2 and prone-positioned inside a1H quadrature volume coil . The body temperature was maintained at 37 °C using a heating pad and breathing was monitored using a pressure sensor recording thorax movement . Tumor development was monitored regularly from 20 to 36 weeks in DEN-WD and DEN-CD mice. Axial and coronal MR images were acquired using breath-gated FLASH sequence with repetition time (TR) of 321ms, echo-time (TE) of 4.39ms, matrix of 256x256 and 4 averages.All At the indicated experimental time points, mice were culled, livers were rapidly excised, tumors (where existing) were separated from adjacent tissue, and tissues were snap-frozen in liquid nitrogen. Samples were stored at -80°C. Total tissue was pulverised with mortar and pestle under a liquid nitrogen atmosphere and RNA was extracted from the equivalent of 20-30mg frozen tissue in TRIzol Reagent  followed by phenol removal with chloroform. RNA was further purified with the RNeasy Mini Kit . DNase treatment was performed to remove any genomic DNA contamination. After RNA quantification and quality controls for integrity and purity , libraries were prepared using KAPA mRNA HyperPrep Kit (Kapa Biosystems). mRNA sequencing  was performed on an Illumina HiSeq 2500 instrument.WD and eight nonDENWD mice were individually housed in a laboratory animal monitoring system . After an acclimatization period, O2 consumption and CO2 production, food, water intake and activity were continuously monitored for each mouse for a period of ≥48h. The mean O2 consumption and CO2 production rates measured over 48h were used to constrain context-specific GSMMs – see Eight DENhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc). Reference genome alignment was performed against Genome Reference Consortium Mouse Build 38 (GRCm38) with RSEMhttps://www.ncbi.nlm.nih.gov/geo/) accession number GSE199899.Raw RNA-sequencing data were processed using an in-house analysis pipeline. Quality of raw sequencing data was checked with FastQC v0.11.7 (clusterProfiler package was used for Gene Ontology (GO) over-representation tests using the enrichGO function and Benjamini-Hochberg method was used to correct for multiple tests.,The RSEM gene count matrix was used for differential gene expression (DGE) analysis with DESeq2To generate a new mouse GSMM with increased metabolic network coverage, we used two human GSMMs, the Human Metabolic Reactions (HMR) database version 2 (HMR2)www.biomart.org/, date accessed – January 2019). HMR2 uses Ensembl gene identifiers as gene annotations and human genes in HMR2 could therefore be directly replaced with their corresponding mouse ortholog.Human-to-mouse orthologs were downloaded using the online BioMart tool .To augment this network, two networks based on sequence homology between mouse and human protein sequences in HMR2 and Recon3D were reconstructed in a two-step process using the RAVEN2.0 toolboxmergeModels function in RAVEN2.0.A metabolite identifier map between HMR2 and Recon3D was generated  and usedcheckTasks function was then used to assess whether MMRN can perform 56 common metabolic growth tasks.The resulting reconstruction, Mouse Metabolic Reaction Network (MMRN), was computationally evaluated. Firstly, duplicate reactions, genes and metabolites were removed and the biomass reaction from HMR2 was added to the reconstruction. The getElementalBalance function in RAVEN2.0 was used to calculate the mass balance of all metabolic reactions for carbon, nitrogen, oxygen, sulfur or phosphorous. Connectivity was assessed by converting each GSMM to a bipartite graph using the S-matrix and counting the frequency of components. MEMOTE (version 0.13.0) was then used to evaluate MMRN using the same parameter settings as.https://github.com/sysbiomelab/MMRNHep/tree/main/data/memote). As in,test_blocked_reactions, test_find_stoichiometrically_balanced_cycles, test_find_metabolites_not_produced_with_open_bounds, and test_find_metabolites_not_consumed_with_open_bounds.Three mGSMMs, Mouse1, MMR and iMM1865, and three hGSMMs, Human1, HMR2 and Recon3D, were used as references to compare to and benchmark MMRN. The MMRN scored well for most MEMOTE consistency attributes , but, similar to HMR2, Recon3D and MMR, MMRN failed ‘stoichiometric consistency’. We used the SInConsistentMetBool variable of the findStoichConsistentSubset function in COBRAHep]. The expression of genes was classified as high (FPKM ≥50), medium (10 ≤ FPKM >50), low (1 ≤ FPKM <10) or no expression (FPKM <1).The maximum FPKM expression values across all mouse experimental conditions were calculated for genes in MMRN. This was used with 56 common metabolic growth tasks as input for the task-driven Integrative Network Inference for Tissues, tINIT, , to generate a generic hepatic GSMM,[MMRNHep]. For this purpose, a vector b was constructed for each experimental condition using expression data and gene-reaction rules (GR-rules) of [MMRNHep] as follows. For a reaction catalyzed by a single gene the mean expression value across biological replicates was considered for bj. For reactions with multiple enzymes associated with ‘or’ relationships the sum of the mean expression values for individual genes was considered for bj. For reversible reactions the negative value of bj was also imposed as a lower bound allowing these reactions to be bi-directional. Orphan reactions, reactions without gene association, were kept unconstrained.An adapted version of the E-flux2 production for 5 mice were converted to flux values (mmol/mouse/day) using Equation 1b is the constraint value, p the density, V the average volume and M the molecular weight of either oxygen or carbon dioxide. The calculated flux values were used to set the lower bound of corresponding reactions.We used the RER measured for DEN and nonDEN mice to constrain oxygen uptake and carbon dioxide production for DEN and nonDEN GSMMs, respectively . For thib is the constraint value, P is the % w/w of a metabolite in the diet, M the molecular weight of that metabolite and W the average grams of food consumed per mouse per day (3g). The % w/w content of nutrients in CD and WD was obtained from the respective diet information sheets, AIN-93G and AIN-76A  and also listed in The content of the WD and the CD was used to constrain uptake rates (or exchange reactions) of the GSMM using Equation 2Cmoles) for each diet was calculated using Equation 3Cmoles is the moles of carbons, C the number of carbons in each metabolite and b the upper bound flux for j = 1 to n metabolites within each diet. The same equation was used to calculate the Cmoles for uptake by using the calculated uptake flux value, v, instead of b for each metabolite. Defining the physiologically correct metabolic input for flux simulations in mammalian tissue GSMMs is challenging,The calculated values (mmol/mouse/day) were used to constrain the upper bounds exchange reactions and thus reflect the maximum uptake rate of a particular metabolite available to the GSMM to perform flux balance analysis. The carbon flux  in MATLAB R2019a. The HMR2 biomass equation was used as an objective function for all GSMMs. Since the chosen objective has a remarkable influence on the resulting flux distribution when solving the linear problem, we chose to use the same objective for all GSMMs to be able to do pairwise comparisons of all of flux vectors. To compare two flux distributions, we calculated the Euclidean distance between two flux vectors using the SciPy package implemented in python3.7. To identify differential flux reactions between two FBA experiments we directly compared two flux vectors and calculated a flux ratio between two experiments.The g for 10 minat 4°C. After discarding the top lipid layer, the supernatant was centrifuged at 10000xg for 10 minat 4°C. The pellet was resuspended in washing buffer  and centrifuged again at 10000xg for 10 minat 4°C, a process that was repeated twice. The resulting mitochondrial pellet was resuspended in 0.5mL of washing buffer. Mitochondrial protein quantification was performed with Pierce BCA Protein assay.Mice were culled and the tissues of interest  were quicky excised and rinsed in cold phosphate buffer before being transferred to mitochondrial isolation buffer . Tissue was homogenized on ice with a Potter-Elvehjem homogeniser before centrifugation at 800x2PO4, 5mM MgCl2, 5mM Hepes, 50μM EDTA, pH7.2 at 37°C) supplemented with 4mM ADP and 0.5mM octanoyl-carnitine.Mitochondrial respiration driven by fatty acid oxidation was assessed with an oxygen electrode system  using 1mg of mitochondrial protein in 2mL of working buffer  to hepatic or tumoral triglyceride (TAG) pool was determined in vivo using 2H2O.,2H2O and supplied with 5% 2H2O drinking water while feeding ad libitum. 16 h later, animals were culled, tissues were collected and frozen in liquid N2. Tissues were powdered under liquid N2 atmosphere and lipids were extracted with the Folch method.De novo synthesised FAs and glycerol incorporated in TAGs were determined as the fraction of FA methyl or glyceryl moiety, respectively, in TAG that were labeled with 2H, normalized for the enrichment of 2H in H2O in the plasma of each mouse measured using 2H NMR.The contribution of 2-fold change ≥1 were considered statistically significant. Statistical analysis of all experimental procedures was performed in GraphPad Prism software v9.0. The specific statistical tests used are described in the corresponding figure legends.Statistical analysis for RNA-sequencing data was performed in R software v 4.1.2 using the DESeq2 package; genes with an FDR-adjusted p-value ≤0.05 and absolute log


Acetogens are microorganisms that can grow on CO2/H2 gas mixtures and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with other microbial species that can further process such products, can expand the variety of products to, for example, medium chain fatty acids (MCFA) and longer chain alcohols. Solventogens are microorganisms known to produce MCFA and alcohols via the acetone-butanol-ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of an acetogen and a solventogen in a consortium provides a potential platform to produce valuable chemicals from CO2. In this study, metabolic modeling was implemented to design a new co-culture of an acetogen and a solventogen to produce butyrate from CO2/H2 mixtures. The model-driven approach suggested the ability of the studied solventogenic species to grow on lactate/glycerol with acetate as co-substrate. This ability was confirmed experimentally by cultivation of Clostridium beijerinckii on these substrates in batch serum bottles and subsequently in pH-controlled bioreactors. Community modeling also suggested that a novel microbial consortium consisting of the acetogen Clostridium autoethanogenum, and the solventogen C. beijerinckii would be feasible and stable. On the basis of this prediction, a co-culture was experimentally established. C. autoethanogenum grew on CO2/H2 producing acetate and traces of ethanol. Acetate was in turn, consumed by C. beijerinckii together with lactate, producing butyrate. These results show that community modeling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.One-carbon (C1) compounds are promising feedstocks for the sustainable production of commodity chemicals. CO The most promising carbon sources were experimentally tested and validated. On the basis thereof, we constructed a community model of Clostridium autoethanogenum and C. beijerinckii, and qualitatively assessed the fermentation of CO2/H2 and the new carbon source through scenario simulations. Model predictions guided the experimental work and led to the successful establishment of this new co-culture.In this study, we followed a model-driven approach to find an alternative route for production of butyrate. We produced butyrate from COC. autoethanogenum DSM 10061, iCLAU786 , and Hydrogenase (R1563). Formate dehydrogenase (R1562) was removed since it was not found in the genome of C. acetobutylicum. Three new reactions were added in the model: Pyruvate transport (pyrt), Pyruvate exchange (EX_PYR_e), and glycerol kinase (R0426), the latter reaction was found to be present in the genome of C. acetobutylicum . Finally, we replaced the ethanol transport reaction (R1708), expressed as a proton (H+) symport reaction, by a diffusion transport reaction. The modified version of the model (iCac803) is available at: https://gitlab.com/wurssb/Modelling/coculture_cacb.The GEM of C. beijerinckii NCIMB 8052, iCM925  and ferredoxin-NADP+ reductase (FDXNRy) reactions were removed, and Na+-translocating ferredoxin:NAD+ oxidoreductase (Rnf) complex and electron-bifurcating, ferredoxin-dependent transhydrogenase (Nfn) complex, were added to the model. Transport of hydrogen reaction (Habc) was replaced by the ATPase reaction (ATPase). The EC number and genes of (S)-3-Hydroxybutanoyl-CoA:NADP+ oxidoreductase reaction (HACD1y) were modified and the EC numbers of butyryl-CoA dehydrogenase (ACOAD1) and 3-hydroxybutyryl-CoA dehydrogenase (HACD1x) as well. Finally, the reduced ferredoxin:dinitrogen oxidoreductase (ATP-hydrolyzing) reaction (DNOR) was stoichiometrically balanced. The updated version of the model (iCM943) is available in the git repository.The GEM of C. acetobutylicum and C. beijerinckii on a wide range of carbon sources. Model simulations were done using COBRApy, version 0.15.4 , was defined as the objective function for maximization. Growth was considered when the growth rate was higher than 0.0001 h-1. For each assessed carbon source, the maximum uptake (corresponding to minus the lower bound of the associated exchange reaction denoted “EX_xx”) was constrained to 20 mmol gDW-1 h-1, and the minimum uptake (corresponding to minus the upper bound of the associated exchange reaction) was constrained to 0.1 mmol gDW-1 h-1. In addition, uptake of small metabolites and ions was allowed by setting the lower bound of the corresponding exchange reaction to −1,000 as described in the corresponding script in the git repository.We systematically assessed growth capabilities on the updated versions of the GEMs of C. autoethanogenum and C. beijerinckii was obtained by combining single species models iCLAU786  for both species. Therefore, the same namespace was applied to metabolites secreted by both species. All metabolites present in both intracellular compartments and the extracellular compartment can be exchanged between species if favored by the directionality. Interchanged metabolites are assumed to be first transported into the extracellular compartment, before taken up by the other species using the corresponding exchange reaction. The co-culture GEM contains one biomass synthesis reaction per species, termed “biomass_auto” and “biomass_beije” for C. autoethanogenum and C. beijerinckii, respectively. Additionally, the model contains a community biomass synthesis reaction (“Community_biomass”), which incorporates the biomass of C. autoethanogenum and C. beijerinckii in the form of metabolite “biomass_ca” and metabolite “biomass,” respectively. The combined model incorporates a transport reaction of butyrate (“BUTex_au”) from the extracellular compartment to the intracellular compartment of C. autoethanogenum, and the reaction to produce butyraldehyde from butyrate (“buttobuta”) in C. autoethanogenum. In addition, we incorporated the transport reaction of acetone (“ACETONE_ca”) from the extracellular compartment to the intracellular compartment of C. autoethanogenum; an alcohol dehydrogenase to convert acetone into isopropanol (“ISOBIO”); a transport reaction of isopropanol from the intracellular compartment of C. autoethanogenum to the extracellular compartment (“ISOPRO_ca”), and an exchange reaction of isopropanol (“EX_IPRO_e”).A compartmentalized co-culture model of The final three-compartment co-culture model was translated into SBML level 3 version 1 (see git repository).DW−1 h−1) were substituted by environmental fluxes (mmol l−1 h−1), and thus, the biomass synthesis reaction of each species was changed accordingly accounting for the growth rate and biomass of each species (gDW l−1 h−1). The relative contribution of each species to the community biomass was calculated from the total biomass of the community and the species ratio.Co-culture model simulations were carried out using a previously described modeling framework  as the objective function and we performed FBA to assess the feasibility of each condition. For a selected number of feasible conditions, the solution space and the set of fluxes compatible with the measured constraints were sampled using the sample function in the flux_analysis submodule of COBRApy. Presented results show the average and standard deviation based on 5,000 iterations generated at each condition (git repository).In this study, we simulated hypothetical scenarios varying biomass species ratios, growth rates, and substrates environmental fluxes to explore the feasible solution space of the co-culture. We selected a community biomass of 0.22 gC. beijerinckii NCIMB 8052 and C. acetobutylicum ATCC 824 were stored as spore suspensions in 20% glycerol at −20°C. Spores of C. beijerinckii and C. acetobutylicum were heat-activated for 1 min at 95°C and 10 min at 70°C, respectively, before inoculation. C. autoethanogenum DSM 10061 was kindly provided by Professor Diana Z. Sousa from the Laboratory of Microbiology, Wageningen University and Research, Wageningen, the Netherlands, and was stored as vegetative cells suspended in 25% glycerol buffered with phosphate and reduced with Ti(III)citrate under anoxic conditions at −80°C.The laboratory strains -1 yeast extract (Duchefa Biochemie), 1.0 g l-1 KH2PO4 (Fischer Scientific), 0.61 g l-1 K2HPO4 (Sigma-Aldrich), 1 g l-1 MgSO4 · 7H2O (Roth), 2.9 g l-1 ammonium acetate (USB), 0.10 g l-1 4-aminobenzoic acid (Duchefa Biochemie), 6.6 mg l-1 Fe(II)SO4 · 7H2O (Sigma-Aldrich), and 0.5 mg l-1 Na-resazurin (Sigma-Aldrich). Acetic acid (Sigma-Aldrich), l-lactic acid , ethanol (Merck) and glycerol (Sigma-Aldrich) were added to final concentrations of 40 mM. pH was set to pH 6.1–6.2 with KOH and/or HCl. Media were made anoxic with N2(g) and autoclaved. d-Glucose was made anoxic and autoclaved separately and added to a final concentration of 40 mM. Media were inoculated with 4% (v/v) culture made from heat-activated spore suspension grown overnight at 37°C in CM2 medium supplemented with 40 g l-1d-glucose (Duchefa Biochemie). Cultures were incubated at 37°C and sampled at t0 and after 4 d. Cell density was measured as optical density at 600 nm (OD600), extracellular metabolites were analyzed with high-performance liquid chromatography (HPLC) as described in Section 2.9, and pH was measured. Acetate co-assimilation was determined by calculating the fraction of the total converted carbon coming from consumed acetate.Cultures were prepared in serum bottles containing CM2 medium consisting of the following components: 2.5 g l3PO4. Foaming was controlled with Antifoam 204 (Sigma-Aldrich). 2.9 g l-1 ammonium acetate in the CM2 medium was replaced by 2.5 g l-1 ammonium sulfate (Merck). 0.75 g l-1 anoxic and sterilized l-cysteine HCl · H2O (Merck) was added after autoclaving.pH-controlled bioreactor experiments were performed in a working volume of 2 l in Infors HT Labfors 5 bioreactors . The stirrer, set at 150 rpm, consisted of at equidistance from top to bottom a pitch-blade and two Rushton impellers. Temperature was controlled at 37°C and pH at pH 5.5 ± 0.1 using 3 M KOH and 2 M HC. beijerinckii growing on different concentrations of acetate and lactate, the adapted CM2 medium was supplemented with acetic acid and l-lactic acid prior to autoclaving. 10 ml min-1 N2(g) was flushed across the head space to keep anoxic conditions. Reactors were inoculated with 1% (v/v) C. beijerinckii culture growing overnight at 37°C in CM2 medium supplemented with 20 g l-1d-glucose and 0.75 g l-1l-cysteine HCl.H2O. Cultures were sampled at regular time intervals for analysis of cell density, extracellular metabolites, and morphology with phase-contrast microscopy. The overall stoichiometry was calculated by scaling the difference of the concentrations of the main extracellular metabolites between t0 and tend to the difference in the measured lactate concentration.In pH-controlled batch fermentations of C. autoethanogenum and C. beijerinckii, reactors were equipped with sinter spargers to flush 40 ml min-1 H2(g) and 10 ml min-1 CO2(g) through the medium. At t0, reactors were inoculated with <1% (v/v) C. autoethanogenum culture growing at 37°C in CM2 medium supplemented with 10 g l-1d-fructose  and 0.75 g l-1l-cysteine HCl.H2O. After establishment of growth and acetate production by C. autoethanogenum, reactors were inoculated with <1% (v/v) C. beijerinckii culture growing overnight at 37oC in CM2 medium supplemented with 20 g l-1d-glucose and 0.75 g l-1l-cysteine HCl.H2O. Furthermore, the continuous l-lactic acid feed was started. Cultures were sampled at regular time intervals for analysis of cell density, extracellular metabolites, and morphology with phase-contrast microscopy. Theoretical acetate production from CO2 was calculated for each time point after the start of the l-lactic acid feed as follows: The amount of lactate converted was calculated from the difference in the amount of lactate fed and calculated amount of lactate remaining in the reactor. The conversion of lactate via pyruvate yields the intermediate metabolite acetyl-CoA and CO2 in a 1:1 ratio. The fraction of the amount of carbon from lactate available for the formation of products was subtracted from the amount of carbon present in the produced (iso)butyrate to obtain a theoretical amount of carbon coming from a difference source than lactate, i.e., from converted acetate. This theoretical amount of converted acetate was added to the calculated amount of acetate in the reactor to get a theoretical amount of acetate produced from CO2. Subsequently, the stoichiometry for the production of (iso)butyrate from lactate and acetate was calculated by scaling the difference of (iso)butyrate produced, theoretical acetate converted, and lactate converted between t0 and the selected time points to lactate converted.In pH-controlled fed-batch co-cultivation experiments of 2SO4 with 30 mM 4-methylpentanoic or 100 mM pentanoic acid as internal standard. This was filtered through a 0.2 μm regenerated cellulose filter followed by analysis on a Waters HPLC system with a Shodex KC-811 column at 65°C, 1 ml/min 3 mM H2SO4 mobile phase, and a refractive index and UV detector.Concentrations of acetate, acetone, butanol, (iso)butyrate, ethanol, fructose, glycerol, glucose, and lactate were analyzed with HPLC. Supernatant was mixed with an equal volume of 1 M HC. autoethanogenum and C. beijerinckii for the production of butyrate from CO2/H2. GEMs of solventogens were used to evaluate candidate species and possible carbon sources. After the experimental validation of the model predictions, the co-culture was successfully established.C. autoethanogenum, iCLAU786 was used as the original version  complex (EC 7.2.1.2) in the model of C. beijerinckii. Rnf is formed by the following gene cluster: rnfC, rnfD, rnfG, rnfE, rnfA, rnfB, . Additionally, we have identified an electron-bifurcating, ferredoxin-dependent transhydrogenase (Nfn) complex that catalyzes NADH-dependent reduced Ferredoxin:NADP+ oxidoreductase activity in C. beijerinckii. The Nfn complex has two subunits: NAD(P)-binding subunit, and a glutamate synthase subunit. These two subunits showed 56–79% identity with the Nfn subunits of C. kluyveri, C. autoethanogenum, C. difficile  is not found in the genome of C. beijerinckii, and the ferredoxin NADP+ reductase (EC 1.18.1.2) showed lower percentage identity to the FNR of C. acetobutylicum, and thus, we hypothesized that the former FNR corresponds to the NADP-binding subunit of the Nfn complex.Regarding the GEM of C. acetobutylicum . Pairwise combinations of most carbon sources that led to growth as single carbon source, also led to growth in combination with an alternative carbon source. However, C. beijerinckii  encoded by the following isoenzymes: Cbei_4072, Cbei_4903, or Cbei_2789 , generating reduced ferredoxin and CO2. Model predictions showed that reduced ferredoxin is partly spent to produce H2 and oxidized ferredoxin via ferredoxin hydrogenase , and partly spent to regenerate oxidized ferredoxin and NADH via the Rnf complex , translocating Na+/H+ . As previously mentioned, C. beijerinckii produces butyrate via acetyl-CoA -3-Hydroxybutanoyl-CoA:NAD+ oxidoreductase  or via NAD(P)-dependent acetoacetyl-CoA reductase . Then, 3-hydroxybutyryl-CoA is converted into crotonyl-CoA by 3-hydroxybutyryl-CoA dehydratase . Crotonyl-CoA is reduced via the butyryl-CoA dehydrogenase/electron-transferring flavoprotein complex (Bcd-EtfAB) producing reduced ferredoxin. Two complete clusters were identified in the genome: Cbei_0322 (Bcd), Cbei_0323 (EtfB), and Cbei_0324 (EtfA) or Cbei_2035 (Bcd), Cbei_2036 (EtfB) and Cbei_2037 (EtfA). An acyl-CoA dehydrogenase (Acd) showed 79.4% similarity with the Bcd subunit of C. acetobutylicum ATCC 824. Butyrate can be produced from butyryl-CoA via two routes in C. beijerinckii. The first route is a linear pathway in which butyryl-CoA is first converted into butyryl phosphate via butanoyl-CoA:phosphate butanoyltransferase . Butyryl phosphate is then converted into butyrate producing ATP via butyrate kinase . The second route is catalyzed by a butyryl-CoA-acetoacetate CoA-transferase , where the CoA moiety of butyryl-CoA is transferred to acetate producing acetyl-CoA and butyrate. However, model predictions showed that butyrate is mostly produced generating ATP via Ptb and Buk.Model predictions suggested that acetate is converted into acetyl phosphate (acetyl-P) investing ATP by acetate kinase , and acetyl-P is converted into acetyl-CoA C. autoethanogenum and C. beijerinckii for the fermentation of CO2/H2 and lactate in a wide range of growth rate and species ratio combinations.The GEM of the co-culture consisted of 2,005 metabolites, 2,107 reactions, and 1,659 genes. Community model simulations supported the co-existence of the co-culture of 2, H2, and lactate feeds. When the maximum uptake rate of lactate is 2.5 mmol l-1 h-1 (-1) for all species ratios, and feasible at higher growth rates (up to 0.07 h-1) when C. autoethanogenum and C. beijerinckii are similarly present in the community for CO2/H2 feed ratio of 0.5. The co-culture is infeasible in all conditions when the CO2/H2 feed ratio is 2, and only feasible when the presence of C. autoethanogenum is low and the CO2/H2 feed ratio is 1. The range of feasible solutions becomes wider when the lactate feed rate is 5 mmol l-1 h-1. When C. autoethanogenum and C. beijerinckii are equally present in the community, the co-culture can be established with all explored growth rates, except when the CO2/H2 feed ratio is 2, that is only feasible up to 0.04 h-1. Again, only at lower growth rates (<0.02 h-1), the co-culture is feasible for all tested species ratios.C. autoethanogenum or C. beijerinckii. Part of the acetate produced by C. autoethanogenum is taken up by C. beijerinckii since the steady-state production rates in the co-culture are lower than the production rates of C. autoethanogenum. The fermentation of acetate and lactate leads to the production of butyrate in C. beijerinckii. A small amount of butyrate is reassimilated by C. autoethanogenum and by C. beijerinckii and converted into butanol (not shown). Furthermore, ethanol is being produced in smaller amounts by C. autoethanogenum and C. beijerinckii. Model predictions also showed an exchange of CO2 and H2 from C. beijerinckii to C. autoethanogenum. C. beijerinckii produces CO2 and H2 that are taken up by C. autoethanogenum, since the flux through C. autoethanogenum is higher than the flux through the exchange reaction in the co-culture. Model predictions suggested that acetate consumption by C. beijerinckii varied depending on the lactate feed rate, being lower when the lactate feed rate was higher than 2.5 mmol l-1 h-1 (≈ 5 mmol l-1 h-1). A higher lactate feed rate also led to more CO2 and H2 produced by C. beijerinckii, and thus, to more gases being recirculated and consumed by C. autoethanogenum producing more acetate (git repository).Furthermore, we observed traces of formate, 2,3-butanediol, acetone, isopropanol, and butanol (see git repository).2/H2 and the co-substrate lactate by the modeled co-culture of C. autoethanogenum and C. beijerinckii was experimentally verified with two biologically independent pH-controlled fed-batch fermentations . No production of butyrate was observed in this period and microscope observations showed that the consortium consisted almost entirely of vegetative cells (data not shown). These cells could not be assigned to either species as the morphologies of After this adaptation period, a second growth phase was observed between 13 and 15 d coinciding with a larger fraction of sporulating cells in the culture and the co-production of butyrate and isobutyrate to final concentrations of 53 and 31 mM, respectively. While measured in both replicates , the proC. autoethanogenum takes-up CO2 and H2 through the Wood-Ljungdahl pathway, where H2 is used as an electron donor for CO2 reduction to acetyl-CoA .In addition, the model predicts traces of formate and lactate produced by C. beijerinckii, since there are more reducing equivalents when glucose is converted to pyruvate. Acetate would still be the main product in C. autoethanogenum and ethanol would be produced in minor amounts. Part of the acetate could be metabolized by C. beijerinckii, but also produced together with butyrate during the acidogenic phase. Once the pH drops enough, the acids could be partly reassimilated during solventogensis to produce solvents. Acetone and butyrate could be partly taken up by C. autoethanogenum producing isopropanol and more butanol. Possibly, part of the CO2 and H2 produced by C. beijerinckii would be consumed by C. autoethanogenum as described for the co-culture growing on lactate.2/H2 and lactate. We have proven the capacity of C. beijerinckii NCIMB 8052 to grow on lactate and acetate as the sole carbon and energy source. Moved by the need to upcycle sustainable feedstocks, we have used this new found capacity of C. beijerinckii to established a novel synthetic co-culture of C. autoethanogenum and C. beijerinckii for the fermentation of CO2/H2 and lactate into butyrate.GEMs are mathematical representations of the metabolism and have been successfully employed to gain insights into metabolic capabilities of single species  as well as NAD-independent lactate dehydrogenase (i-LDH) activity. d-LDH regulated the conversion of pyruvate to lactate and required fructose-1,6-biphosphate to be active  (Kumar et al., C. saccharobutylicum (Diez-Gonzalez et al., Model predictions showed a high production of butyrate, acetate, and traces of ethanol, acetone, butanol, isopropanol, 2,3-butanediol, and formate. Fed-batch experiments also showed butyrate and acetate as major fermentations products, and ethanol and butanol as minor fermentation products. Charubin and Papoutsakis  observedC. beijerinckii could grow on lactate as the sole carbon and energy source, as was recently observed (Schwalm et al., C. autoethanogenum was consumed by C. beijerinckii, as indicated by the model, and possibly that some acetate could also be produced by C. beijerinckii.Model predictions indicated that We should note that the deployed modeling approach predicts steady-state production or consumption rates, and thus, we cannot compare the results quantitatively with bioreactor data, which consist of concentrations over time. Instead, our study should be seen as an exploratory study assessing the feasibility of the co-culture. Future optimization of this co-culture could integrate current experimental data and relative abundance of species into dynamic modeling approaches to gain better insights into the concentration profiles over time. These results show that community modeling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of important chemicals from renewable resources. It thereby expands the space of options to possibly accelerate the transition to a biobased economy.C. beijerinckii to co-metabolize acetate and lactate for the production of butyrate. This ability was experimentally verified in batch serum bottles and pH-controlled batch bioreactor fermentations. A community model of C. autoethanogenum and C. beijerinckii was then constructed to assess the feasibility of the co-culture to produce butyrate from CO2/H2 and lactate. Community modeling predicted the feasibility of the co-culture in several conditions and the interactions between species, especially, the exchange of acetate. Following model predictions, the co-culture of C. autoethanogenum and C. beijerinckii was established in pH-controlled fed-batch fermentations. The main products were acetate, butyrate and the newly identified metabolite, isobutyrate. Our study shows the strength of a model-driven approach to explore the high metabolic flexibility of clostridial species for the production of chemicals from renewable sources.Genome-scale metabolic modeling helped identifying the ability of The original contributions presented in the study are included in the article/SB-V and NN conceived and designed the study and drafted the manuscript. SB-V constructed the models and performed model simulations and data analysis. NN performed the experiments and data analysis. MS-D, AML-C, JH, SB, PS, and VM conceived, designed, and supervised the research. MS-D, VM, JH, and AML-C acquired project funding. All authors reviewed and edited the study. All authors read and approved the content of the submitted version.


The Pathway Tools (PTools) software provides a suite of capabilities for storing and analyzing integrated collections of genomic and metabolic information in the form of organism-specific Pathway/Genome Databases (PGDBs). A microbial community is represented in PTools by generating a PGDB from each metagenome-assembled genome (MAG). PTools computes a metabolic reconstruction for each organism, and predicts its operons. The properties of individual MAGs can be investigated using the many search and visualization operations within PTools. PTools also enables the user to investigate the properties of the microbial community by issuing searches across the full community, and by performing comparative operations across genome and pathway information. The software can generate a metabolic network diagram for the community, and it can overlay community omics datasets on that network diagram. PTools also provides a tool for searching for metabolic transformation routes across an organism community. The Pathway Tools (PTools) software The software has been extended in recent years to support functional analysis of microbiomes  that proQuestions that can be addressed using the software include the following: What metabolic reactions and pathways are present in a metagenome, or in each organism in a community? How do their metabolic capabilities complement one another? What pathways are unique to a given community member? What metabolic transformations can be accomplished by the community, for example, via what metabolic route might the community convert a starting metabolite into an ending metabolite? The PTools software does perform metabolic modeling of individual microbes and of microbial communities via flux-balance analysis The first step in a typical workflow is to import a set of metagenome-assembled genomes (MAGs) into PTools. Each MAG is converted to a PTools Pathway/Genome Database. A number of PTools computational inference tools are next applied to each MAG to infer its metabolic reactions and pathways, its transport reactions, and its operons. The community members are now captured within a set of PGDBs that comprehensively encode their genomes and metabolic networks.Next the user can apply a set of search and comparative analysis tools to assess and compare the functional capabilities of community members. For example, the user can search across all community PGDBs for the presence of a gene, a metabolite, or a pathway; the software can produce comparisons of the entire metabolic networks of the community.If meta-transcriptomics and/or meta-metabolomics data are available for the community, then PTools provides an analysis tool for visualizing such data on a multi-organism metabolic map diagram.PTools provides a community route-search tool that requires as user inputs a set of PGDBs as well as a starting metabolite and ending metabolite. The tool generates minimal-cost metabolic routes (linear reaction paths) from the starting to the ending metabolite that show how the community might accomplish that transformation.The remainder of the article describes these tools in more detail and illustrates their use on the Altered Schaedler Flora (ASF), a community of eight microorganisms from the mouse gut microbiome To import a microbial community into PTools, the metagenomic sequencing data must have been binned by a separate program into separate groups, one for each detected member of the community. Each such MAG consists of a collection of sequenced contigs covering a subset of the genome of each organism. The contigs must be annotated by a tool such as MetaPathways The resulting sequence data, gene locations, and protein functions can be provided as inputs to PTools in either GFF3 format or GenBank format, preferably as one file per MAG. The files can be provided within a directory structure containing one directory per genome that is processed by invoking the PathoLogic component of PTools from the command line, as described in the Pathway Tools User’s Guide 1. The input files are parsed.2. The input sequence, gene locations, and annotations are converted to PGDB format. PGDBs are encoded using the Ocelot object-oriented database system. A database object is created for each replicon, each gene, and each gene product described by the input files.3. The reactome of the organism is predicted from the annotated gene functions using a previously published algorithm 4. The metabolic pathways of the organism are predicted from the predicted reactome 5. The Transport Inference Parser 6. The PTools operon predictor 7. PathoLogic executes an automatic layout algorithm that creates an organism-specific metabolic network diagram for the organism based on its complement of pathways, metabolic reactions, and transport reactions PathoLogic applies a series of processing steps to each input MAG to obtain a comprehensive PGDB for each organism. Those steps are as follows.BioCyc.org website (which is powered by PTools). Enter “ASF” into the BioCyc organism selection tool to search for these databases.The result of this process is a community of PGDBs—one for each binned organism—describing its genome, proteome, reactome, metabolic pathways, and operons. For example, we have created PGDBs for each of the eight members of the ASF, all of which are available through the We are not aware of other metagenome-analysis software that performs operon prediction or transport-reaction prediction. A number of other software tools MAPLE A suite of search tools enables scientists to perform basic searches across a set of microbiome-derived PGDBs, such as to determine which organisms in the community contain a given gene, protein, metabolite, or pathway. Such searches enable a researcher to quickly determine the functional roles played by different organisms in the community. In addition, more advanced searches are supported to find the organisms in the community containing genes, proteins, metabolites, or pathways matching specified conditions.These searches are available in both the web and desktop modes of PTools, with somewhat different user interfaces available in the two modes. In web mode, the multi-organism search tools are present under the Tools BioCyc.org for all pathways whose name contains “tryptophan.” Pathway searches can also search by ontology , pathway length, substrate(s) contained within the pathways, evidence code, and publication.For example, Gene/protein searches can search by sequence length, molecular weight, genome map position, pI, evidence code, cellular location, Gene Ontology (GO) term, publication, and by protein features.Metabolite searches can search by ontology, monoisotopic mass, molecular weight, chemical formula, SMILES We are not aware of other tools that provide these types of multi-MAG search capabilities.BioCyc.org under Tools • Organism comparison• Table 1: Database Summary Statistics • Table 3: Breakdown of SMM Reactions by Top-Level EC Category  present summaries of pathway abundances across different metagenome samples. In contrast, PTools reports differences in pathway compositions of different MAGs; we are not aware of other tools that perform such comparisons.In a PGDB for a single organism, the PTools-generated cellular overview diagram provides a visual summary of all the metabolic and transport capabilities of the organism. A rectangular outer border represents the cell membrane. For Gram-negative bacteria, this consists of a double membrane with an intervening periplasmic space. Transporters and other membrane proteins are drawn on the appropriate membrane. Within the interior, representing the cytosol, metabolic pathways are shown to the left, and a grid containing all reactions not assigned to any pathway appears to the right. Within the pathway section, pathways are organized according to the MetaCyc pathway ontology, with biosynthetic pathways to the left, energy metabolism pathways in the middle, and catabolic pathways to the right. These sections are further subdivided into functionally-based blocks. For example, within the biosynthetic section are separate blocks for Carbohydrate Biosynthesis and Secondary Metabolite Biosynthesis. Pathways generally flow downwards, and connections between pathways are mostly omitted. As the user zooms in on the diagram, more detail is shown. At the highest level of detail, pathway, metabolite, enzyme and gene names all become visible. Users can overlay omics data for an organism onto the cellular overview diagram to visualize experimental results in a metabolic context vs. control).For a community of organisms, the user can create a community overview diagram (within the desktop version of PTools only) that condenses and combines the overview diagrams from multiple organisms into a grid, forming a single large diagram. While initially shown at a low level of detail, users can interrogate the diagram via mouse-overs, zoom in to show more detail, or apply a range of highlight operations. Meta-transcriptomics or meta-metabolomics data can then be mapped onto this community overview diagram to visualize how experimental conditions affect the metabolism of the entire community. Omics data are supplied as a set of tab-delimited files, one per organism in the community, each with the first column containing gene or metabolite identifiers, and a single numeric data column , which can contain either absolute data  or relative data  mice are known to exhibit an altered microbiota composition Ligilactobacillus murinus  is generally increased (red/orange reactions), with the increases concentrated in certain pathway classes; the metabolism of two other organisms, Eubacterium plexicaudatum and Schaedlerella arabinosiphila, generally decreases (blue/purple). Mousing over any reaction will show a tooltip that includes the omics data values for all genes associated with that reaction. The user can zoom in on the diagram for a more detailed view of regions of interest.In addition to visually drawing attention to particular metabolic reactions and pathways that undergo significant change, organism-wide effects also become apparent. For example, in this dataset we immediately notice that the metabolism of one organism, We are not aware of other tools that can display metabolic network diagrams from multiple organisms simultaneously and paint these diagrams with meta-omics data.Single-organism metabolic route search enables the discovery of the most optimal series of reactions , that will transform a starting compound into a goal compound, within the organism’s reaction network. Optimal means that the reaction series has the lowest cost. The cost of a route is computed by a weighted combination of atom conservation, route length in terms of sequential reactions, and other parameters. To compute the number of conserved atoms, our RouteSearch algorithm BioCyc.org, invoke Tools The Multi-Organism RouteSearch (MORS) algorithm The MORS algorithm requires an additional input beyond the inputs to RouteSearch, namely the set of PGDBs to be searched. The reaction network searched by MORS will be the union of all reactions from that organism set. Additionally, the user may alter a new MORS parameter, the cost for “organism switching.” A switch occurs when the two organism sets of two consecutive reactions in a route have no overlap. In other words, if the first reaction is known to occur in one set of organisms and the second reaction is occurring in a different organism set, but there is no organism that contains both reactions simultaneously, then the route must switch organisms by transferring the compound connecting both reactions from one organism to another (by unspecified transport mechanisms). Each discovered route is displayed horizontally across the web page, with the start compound on the left and the goal compound on the right. An organism switch is depicted in a route by a red vertical line. A SmartTable of the route can be generated, which lists the organism sets that provide the enzymes that catalyze each reaction along the route.BioCyc.org to examine how dietary L-tyrosine is transformed into toxic 4-methylphenyl sulfate, which is a protein fermentation product that has been modified in the liver and is implicated in kidney disease. As it is known that this toxin originates from L-tyrosine Homo sapiens. The total count of organisms was 553. The resulting top three routes are shown in Homo sapiens. However, the reaction immediately before the switch occurs in 26 organisms in both routes. The third route found a different choice for the first reaction, which incurs the cost of an additional organism switch.As an example, let us use We are not aware of other tools that can perform multi-organism metabolic route search.


Saccharopolyspora erythraea is considered to be an effective host for erythromycin. However, little is known about the regulation in terms of its metabolism. To develop an accurate model-driven strategy for the efficient production of erythromycin, a genome-scale metabolic model (iJL1426) was reconstructed for the industrial strain. The final model included 1426 genes, 1858 reactions, and 1687 metabolites. The accurate rates of the growth predictions for the 27 carbon and 31 nitrogen sources available were 92.6% and 100%, respectively. Moreover, the simulation results were consistent with the physiological observation and 13C metabolic flux analysis obtained from the experimental data. Furthermore, by comparing the single knockout targets with earlier published results, four genes coincided within the range of successful knockouts. Finally, iJL1426 was used to guide the optimal addition strategy of n-propanol during industrial erythromycin fermentation to demonstrate its ability. The experimental results showed that the highest erythromycin titer was 1442.8 μg/mL at an n-propanol supplementation rate of 0.05 g/L/h, which was 45.0% higher than that without n-propanol supplementation, and the erythromycin-specific synthesis rate was also increased by 30.3%. Therefore, iJL1426 will lead to a better understanding of the metabolic capabilities and, thus, is helpful in a systematic metabolic engineering approach. Saccharopolyspora erythraea (S. erythraea) is recognized as the primary strain of erythromycin production under the industrial-scale system. Lately, with the application of several techniques, such as random mutations, metabolic engineering modifications, and optimizations of the process conditions and media, the productivity of S. erythraea in the fermentation process has been significantly improved  should be added on both sides of the reaction to ensure balance; second, the stoichiometric coefficients of the metabolic reaction itself are not balanced. In such cases, it may be necessary to add protons [H] or water to the reaction and then perform stoichiometric coefficients’ equilibrium to satisfy the reaction balance. Therefore, each element and charge should be balanced on both sides of the reaction.Additionally, the elemental and charge conservation in the metabolic reaction was checked by the relevant scripts in MATLAB, which were available on the GitHub repository –(3) [S refers to a stoichiometric matrix representing the matrix composed of the stoichiometric coefficients of metabolites in the metabolic reactions; v represents the vector composed of the metabolic reactions’ fluxes; minv and maxv are the minimum and maximum constraints, which are defined as the maximum enzymatic reaction rate and the reversibility of metabolic reactions, respectively; and Tc refers to a weight vector, indicating the contribution of each reaction (such as the biomass reaction at the simulated maximum growth rate) to the objective function. Glucose was chosen as the sole carbon source in the simulations. Other extracellular metabolites, such as H2O, O2, CO2, PO43−, NH4+, and SO42−, were set to be transported freely across the membrane intracellularly and extracellularly. All simulations were performed on the MATLAB 2019b platform.The constraint-based FBA was performed using the COBRA toolbox and Gurobi (1)–(3) ,37:(1)ma13C metabolic flux analysis results were obtained from previous publications and experiments in this study [−5 h−1 was used to determine whether the cell could grow or not.The results of the model simulation and several experiments were utilized to fully assess the predictive accuracy of iJL1426. The physiological data and is study ,15. BrieMoreover, the accuracy and reliability of the model were visually assessed by comparing the experimental values of the cellular-specific growth rate and the erythromycin-specific synthesis rate with the model simulation data. When simulating the specific growth rate, it was necessary to ensure that the substrates and other parameters required for the growth were consistent with those under experimental conditions, such as water, hydrogen ion, sulfate ion, ammonium ion, phosphate ion, specific oxygen consumption rate, and specific carbon dioxide synthesis rate. When simulating the specific synthesis rate of erythromycin, in addition to constraining the required essential nutrients and specific oxygen consumption rate and specific carbon dioxide synthesis rate, it was also vital to set the specific growth rate to be consistent with the experimental conditions; that is, to simulate the specific synthesis rate of erythromycin at the same specific growth rate.singleGeneDeletion function based on the COBRA toolbox [The essential and dispensable genes were predicted using the  toolbox ,28. The  toolbox .S. erythraea E3, called iJL1426. The novel model contains 1426 genes, 1687 metabolites, and 1858 reactions. The model provides the most comprehensive knowledge base on the biochemistry of S. erythraea to date, with a particular emphasis on n-propanol metabolism. By manually organizing the model on a large scale, we greatly improved the coverage and scope of metabolism based on previously published GSMMs. With the reformulated biomass composition, the model was able to qualitatively reproduce growth phenotypes on multiple experimentally tested nutrient sources and accurately predict and identify some engineering targets for improved erythromycin productivity. In addition, the effect of n-propanol on the erythromycin-specific synthesis rate was analyzed under the guidance of the reconstructed GSMM to optimize the existing n-propanol supplementation process for the genetically engineered strain E3-ΔsucC. We strongly believe that the reconstructed model will form a solid framework to explore and understand the metabolic properties of S. erythraea E3 and help generate metabolic engineering and culture strategies in vitro to improve erythromycin productivity. Furthermore, the model contains a quality control and assurance scaffold for future reconstruction of strain-specific S. erythraea GSMs important for biotechnology and industrial applications.In this study, we developed and constructed a high-quality GSM of the


Microbial pan-genomes are shaped by a complex combination of stochastic and deterministic forces. Even closely related genomes exhibit extensive variation in their gene content. Understanding what drives this variation requires exploring the interactions of gene products with each other and with the organism’s external environment. However, to date, conceptual models of pan-genome dynamics often represent genes as independent units and provide limited information about their mechanistic interactions.We simulated the stochastic process of gene-loss using the pooled genome-scale metabolic reaction networks of 46 taxonomically diverse bacterial and archaeal families as proxies for their pan-genomes. The frequency by which reactions are retained in functional networks when stochastic gene loss is simulated in diverse environments allowed us to disentangle the metabolic reactions whose presence depends on the metabolite composition of the external environment (constrained by “nutrition”) from those that are independent of the environment (constrained by “nature”). By comparing the frequency of reactions from the first group with their observed frequencies in bacterial and archaeal families, we predicted the metabolic niches that shaped the genomic composition of these lineages. Moreover, we found that the lineages that were shaped by a more diverse metabolic niche also occur in more diverse biomes as assessed by global environmental sequencing datasets.We introduce a computational framework for analyzing and interpreting pan-reactomes that provides novel insights into the ecological and evolutionary drivers of pan-genome dynamics.The online version contains supplementary material available at 10.1186/s12862-022-02052-3. In the evolution of microbial genomes, genes are gained and lost by mutations, insertions, deletions, duplications, and horizontal gene transfers (HGTs) –4. As a Recent studies have attempted to transfer the concepts from population genetics to interpret the frequency distribution of genes in microbial pan-genomes , 10, 11.An important factor to consider when developing models for the distribution of genes in pan-genomes is that individual genes are not equally accessible to all genomes. In general, the probability that a recipient microbial genome will be capable of integrating foreign DNA increases exponentially with an increase in the similarity between the donor DNA and the recipient chromosome . As a reAnother important factor to consider is that the acquisition of new genes by genomes is counterbalanced by the frequent loss of genes , 19–21. The frequency distributions of the genes within pan-genomes fit mathematical functions with regular and universal shapes , 27–29. The commonly used neutral and non-neutral models of pan-genome evolution are simplified ‘bag-of-genes’ models that do not explicitly consider gene functions and their interactions , 32, 34.A promising approach to integrating the functions and interactions of genes into models of genome evolution is to use the genome-encoded metabolic reactions, the reactome, as a proxy for the gene content of genomes. Reactions from the reactome can be integrated into a functional network ) that represents the genotype-to-phenotype map –38. The Here we used pan-reactomes as models to simulate and understand patterns in pan-genomes. To incorporate realistic features of pan-genome evolution, we used pan-reactomes as proxies for pools of genes that are accessible to related strains by HGT. We then modeled alternative routes of gene loss by sampling minimal functional reaction sets in diverse environment compositions. These minimal functional reaction sets have similar properties as the previously defined elementary flux modes (EFMs) used to identify functional pathways in reactomes , thus, wAeromonadaceae family that was built based on the metabolic reactions encoded in the genomes of 135 strains that belong to different Aeromonadaceae species  and the structure of the metabolic network . Using the toy model as an example, in Fig. To explore the space of possible reactomes within a group of organisms that comprise a pan-reactome, we modelled functional reactomes under the hypothesis that evolution tends to lose non-essential reactions. A single panEFM consists of a group of reactions that together are functional in a defined environment, but the removal of any reaction makes the network non-functional. An example of a panEFM is shown in Fig. Due to the combinatorial explosion involved in extracting all possible panEFMs from large pan-reactomes, for bacterial and archaeal families we used a random sampling approach to approximate the space of all possible panEFMs. We first determined that fewer than 200 panEFMs sampled in different random environments were sufficient for convergence to an average reaction frequency distribution with 99% reproducibility and a mean-squared error approaching zero  that ranges from zero  to one . First, we calculated the absolute difference between the predicted reaction frequency in one specific virtual environment and its mean frequency across all 1000 random virtual environments . This association, illustrated in Fig. Aeromonadeceae pan-reactome by predicting the metabolic niche of reactomes whose evolution was simulated in a defined environment using a Moran-like process of gain and loss of genes [Aeromonadaceae pan-reactome  model on the reaction frequencies in the collection of panEFMs to predict their metabolite usage profiles across the growth-supporting virtual environments (see sections “Reaction frequencies” and “Elastic net” in Methods). We confirmed the accuracy of the EN model for the toy model and the of genes   also behave as observed values  of the stochastic distribution covered by this evolutionary landscape  pools are not strictly confined to a family and horizontal gene transfer between different families may alleviate the separation between family-level pan-reactomes, although we note that all reactions observed within the sequenced strains of a family were already included in the pan-reactome definition.To get a better idea of how the reaction frequencies of panEFMS sampled in random virtual environments compare to the natural reaction frequencies, we compared their distributions using kernel density plots , with some reactions exhibiting a significant environment-driven variation in frequency, quantified by the EDS and illustrated by the points that fall outside of dense diagonal line in Additional file l-isoleucine, l-valine, and l-leucine) and are universally shared across bacterial reactomes [l-valine or the synthesis of 2-Dehydropantoate, a precursor for pantothenate and subsequently CoA. Pantothenate and CoA are connected to the biosynthesis of several amino acids, which explains why reactions upstream of their synthesis would be essential or not depending on the availability of these amino acids in the external environment.Similar to what we observed for the toy model and the eactomes . These rAeromonadaceae pan-reactome  from 46 taxonomic families were selected from the PATRIC database   for each strain using the model SEED pipeline  implemenThe functionality of GSMMs was assessed by flux balance analysis (FBA) , optimizPan-reactomes were generated by merging the reactomes of all the strains from a given prokaryote family. Each reaction was added once. Additionally, we added exchange reactions for the compounds that have transporters in any of the reactomes  or anaerobic (with zero oxygen), selected with a probability of 0.5. We generated 1000 random samples of the environment ball and used the resulting concentrations as the growth environments . For obtaining relative uniform concentrations, we sampled a Dirichlet distribution with dimensions equal to the number of compounds (290) with uniform parameters. Samples from this distribution add to one and there is an equal probability of observing any relative concentration of any of the compounds. The resulting relative concentrations were adjusted to a constant uptake rate of water in mmol gDWThe toy model was generated with the reactions in Additional file Aeromonadaceae pan-reactomes with a Moran-like process [6), the different types of reactomes that persisted were selected as the evolved reactome types.We evolved populations of reactomes derived from the toy and the  process . For thiTo generate random samples from the environment-specific collection of panEFMs, we first constrained the pan-reactome to a given environment . The environment-driven scores for reactions and metabolites were defined as the standard deviation of these residuals, divided by their maximum value.The metabolic niches of the panEFMs of bacterial and archaeal families were obtained by enumerating which of the possible external compounds were imported into the metabolic network when optimizing for biomass production. For each panEFM we first obtained the environment-specific FBA solution. We then assessed the fluxes in the exchange reactions for this solution. Negative fluxes correspond to the metabolites that are effectively required to produce biomass. One metabolic niche corresponds to the set of metabolites whose transporters exhibited a negative flux in the FBA solution of a panEFM. Metabolic niches were summarized by the metabolite frequencies obtained from the 1000 random samples of panEFMs that were obtained for each environment. We thus obtained a metabolic niche for each random environment by enumerating how often each metabolite was used after sampling panEFMs from pan-reactomes.We restricted our analysis to reactions that had gene evidence (not gap-filled) and that could be active in a model. To define if a reaction could be active, we used flux variability analysis and excluded reactions that exhibited a flux variability of zero. We refer to the “natural reaction frequencies” as the frequencies that were observed from the reaction composition of reactomes reconstructed from the genomes of a prokaryote family, while “frequencies in panEFMs” refer to the frequency that reactions were found in the random samples of panEFMs.An elastic net model was trained to predict metabolite usage from reaction frequencies . We used the matrices described above as training sets with five-fold cross-validation. The natural reaction frequencies were used to predict evolutionary environments. To train and fit the model we used the Python 3.7 package scikit-learn version 0.22.2.For each family, we calculated its niche breadth on the scale from specialist to generalist based on its presence in a large number of publicly available environmental sequencing datasets .In short, we selected taxonomically annotated environmental sequencing projects from the MGnify dataset . We seleA family was considered present in a sample if its relative abundance was ≥ 1/10,000. Niche breadth was defined as the mean pairwise distance between all the samples in which a family is found, where the mean pairwise distance is defined as ½ − (Spearman's rank correlation on family level/2). Since this measure is solely based on the taxonomic content of a sample, it is independent of manually added metadata such as the biome from which it originates. A family with a low score is primarily found in samples with similar taxonomic profiles and we thus consider it a specialist, and a family with a high score is found in more dissimilar samples and is thus a generalist.Additional file 1: Figure S1. Convergence of the reaction frequencies of panEFMs sampled across 1000 virtual environments (Table S2) to an average. The frequency of reactions was obtained from two random-independent (non-overlapping) groups of panEFMs defined across the envAdditional file 2: Figure S2. Distribution of pan-reactome reaction frequencies and panEFMs reaction frequencies. The x-axis contains the frequency of reactions in sampled panEFMs across random environments, while the y-axis contains the natural frequency coded by the genomes of the taxonomic families.Additional file 3: Figure S3. Distribution of the environment-driven reaction score (EDS) and panEFMs reaction frequencies.Additional file 4: Figure S4. Comparison of panEFMs across environments. Scatter plot of the average reaction frequency of panEFMs defined across random virtual environments and within each environment.Additional file 5: Figure S5. Size distribution of panEFMs sampled across random virtual environments.Additional file 6: Table S1. Reactions in the toy modelAdditional file 7: Table S2. Bacterial and archaeal strains used in this study. The Aeromonadaceae strains are highlighted.Additional file 8: Table S3. Environment compounds used in this study. Concentrations of 1000 random environments that constitute the environment ball (see Methods). These concentrations are set as upper bounds to the metabolic models of the pan-reactomes.Additional file 9: Table S4. Environment-driven reaction scores (EDS) for all 46 prokaryote families.Additional file 10: Table S5. Elastic net predictions of the metabolite usage by the pan-reactomes of 46 prokaryote familiesAdditional file 11: Table S6. Correlation of variables related to panEFM, pan-reactomes, and metagenomes


HMGCR knockdown could not block the growth of HNC cells. However, the two-target combinations of  and  could achieve cell mortality and improve metabolic deviation grades over 22% without reducing the cell viability grade.Computer-aided methods can be used to screen potential candidate targets and to reduce the time and cost of drug development. In most of these methods, synthetic lethality is used as a therapeutic criterion to identify drug targets. However, these methods do not consider the side effects during the identification stage. This study developed a fuzzy multi-objective optimization for identifying anti-cancer targets that not only evaluated cancer cell mortality, but also minimized side effects due to treatment. We identified potential anti-cancer enzymes and antimetabolites for the treatment of head and neck cancer (HNC). The identified one- and two-target enzymes were primarily involved in six major pathways, namely, purine and pyrimidine metabolism and the pentose phosphate pathway. Most of the identified targets can be regulated by approved drugs; thus, these drugs are potential candidates for drug repurposing as a treatment for HNC. Furthermore, we identified antimetabolites involved in pathways similar to those identified using a gene-centric approach. Moreover,  HNC iThe efficient identification of drug targets and their side effects is a key challenge in drug discovery and development –15. DrugConstraint-based modelling approaches entail the use of context-specific genome-scale metabolic networks to identify anti-cancer targets –30,32–38. 2. 2.1https://www.gams.com/) to discover anti-cancer targets with few side effects.We combined a human metabolic network (Recon3D)  with RNA2r and 3r are catalysed by enzyme 2E or 3E (a); thus, the regulation of both enzymes is identical. We deleted the redundant enzymes in the GSMMs to represent the reduced GPR association (b), thereby avoiding surplus computations in the evolutionary optimization procedures.Genome-scale reconstructions provide a mechanistic link between genotype and phenotype. Stoichiometric models represent the material balance of metabolites in cells. GPR associations are typically implemented as Boolean rules, which link metabolic reactions in the stoichiometric models to gene-encoded enzymes in cells. A GPR model generally comprises one-to-one enzymes that catalyse a reaction, and promiscuous enzymes that modulate multiple reactions. Therefore, a reaction can be catalysed by one-to-one enzymes or by isozymes. A simple enzyme is encoded by a single gene, whereas a complex enzyme is encoded by at least two genes. In this study, a simplified network  was usedE2 or E3 a; thus, ociation b, thereb. 2.2During discovery and development, drugs generally fail in the clinical trials for two reasons: they are less efficacious or are demonstrated to have serious side effects during initial trials. This study presents an anti-cancer target discovery (ACTD) platform for identifying potential candidate targets with few side effects. The platform integrates RNA-seq expressions for cancer cells and their healthy counterparts into the IACT framework ,40 to yiHere operator ; it is uThe pioneering fuzzy optimization is introduced by Bellman & Zadeh  and revevf/b is the forward–backward flux vector of reactions; NCA and NHT are stoichiometric matrices for tissue-specific CA and HT cells, respectively, and are reconstructed from those presented in the previous section; jth forward–backward flux, respectively; and ΩTR is the set of reactions for CA and HT models. The weighting factors Regarding the inner optimization problem, we used constraint-based models of CA and HT cells to calculate the metabolic distributions of treated CA cells and perturbed HT cells, respectively. We reconstructed the tissue-specific GSMMs by using the RNA-seq expression levels of the CA and HT cells to classify metabolic reactions into four groups: high, medium, negative and other confidence, respectively. The weighting factors for the four reaction classes were used to solve the UFD problem to derive a uniform distribution. The inner optimization problem is described as follows:ith forward–backward flux obtained from XA and HT templates; IZΩ is the set of reactions regulated by isozymes determined using the GPR associations, and δ is the modulation parameter determined by a nested hybrid differential evolution (NHDE) algorithm  iron-sulfur protein 7, mitochondrialPAICSmultifunctional protein ADE2PCpyruvate carboxylase, mitochondrialPFASphosphoribosylformylglycinamidine synthasePGK1phosphoglycerate kinase 1PGS1CDP-diacylglycerol-glycerol-3-phosphate 3-phosphatidyltransferase, mitochondrialPPATamidophosphoribosyltransferasePRDX6peroxiredoxin-6RPIAribose-5-phosphate isomeraseSLC25A10solute carrier family 25 member 10SLC2A13proton myo-inositol cotransporterSLC5A3sodium/myo-inositol cotransporterSLC7A6Y+L amino acid transporter 2SPTLC3serine palmitoyltransferase 3TATtyrosine aminotransferaseTYMSthymidylate synthaseUMPSuridine 5'-monophosphate synthasesymbolmetabolite25aics[c](S)-2-[5-amino-1-(5-phospho-D-ribosyl) imidazole-4-carboxamido] succinateair[c]5-amino-1-(5-phospho-D-ribosyl) imidazoleasp_L[m]L-aspartatec6crn[c]hexanoyl carnitinecbasp[c]N-carbamoyl-L-aspartatecbp[c]carbamoyl phosphateCE0785[m]cis,cis-myristo-5,8-dienoyl coenzyme Acs_d_deg5[l]chondroitin sulfate D (GlcNac6S-Glca2S), degradation product 5dadp[c]deoxyadenosine diphosphatedag_hs[c]diglyceridedhor_S[c](S)-dihydroorotatedudp[c]deoxyuridine-5'-diphosphatedutp[c]deoxyuridine-5'-triphosphategar[c]N1-(5-phospho-D-ribosyl) glycinamideimp[c]inosine-5'-monophosphateorot[c]orotateorot5p[c]orotidine 5'-phosphatephpyr[m]keto-phenylpyruvateprpp[c]5-phospho-alpha-D-ribose 1-diphosphater5p[c]alpha-D-ribose 5-phosphatesphgn[c]sphinganinetrdrd[c]reduced thioredoxinump[c]uridine-5'-monophosphate