Synechococcus elongatus with heterotrophic Escherichia coli K-12, Escherichia coli W, Yarrowia lipolytica, or Bacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing to S. elongatus-E. coli K-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secreting  Successful application of microbial community for bioproduction relies on the selection of appropriate heterotroph and phototroph partners. Here, the authors construct community metabolic models to guide strain selection and experimentally validate metabolic exchanges that sustain the heterotrophs in minimal media. This type of association dominates many biofilms, microbial mats, and lichens3, thriving in desiccation, nutrient starvation, and salinity or temperature extremes4. This ability to survive extreme conditions is due, in part, to division of labor and subsequent interactions between members of the community. Photoautotrophic members, classically either cyanobacteria or eukaryotic algae, convert CO2 into organic carbon for growth and maintenance of the heterotrophic partner(s). Exchange of these metabolites can sustain the heterotrophs under conditions devoid of any organic carbon source. In turn, the heterotrophs provide additional CO2, protection from environmental factors and predation, and often, a diverse array of metabolites produced by secondary metabolism5.Phototrophic microbial communities exhibit symbiosis between photoautotrophic and heterotrophic organisms supported primarily by solar energy and the fixation of carbon dioxide  have been utilized for the production of biofuels7, α-amylase, and polyhydroxyalkanoates among other compounds12. Complex communities consisting of algae and bacteria also have potential applications in waste-water treatment15, bioremediation16, and as a bloom control method for phytoplankton17. Traditionally, microbial communities have been selected in long term adaptation and optimization experiments to define optimal culture conditions18. The critical challenge in synthetic community design is to maintain syntrophic interactions between members to avoid culture collapse19. Development of SPCs for bioproduction involves four critical steps: (a) strain selection, (b) screening of cultivation conditions, (c) efficient extraction of added-value products, and (d) process control and biomass recycling. The first three steps (a–c) are important drivers for implementation of successful bioproduction processes that can be optimized and guided using metabolic modeling.To date, understanding, engineering, and determining viable cultivation conditions for natural phototrophic communities remains challenging21 and microbial communities (community-metabolic models henceforth referd to as CM-models)25. These models account for biochemical and genomic information for an individual or community at the genome-scale. Resulting models are solved using flux balance analysis and can accurately predict thousands of functional states26. Simulations performed with CM-models describe key metabolic functions of microbial communities, defining all possible interactions among partners based on genetic and/or metabolic fitness. CM-models also enable prediction of effective culture conditions for production in robust biotechnological processes22.Constraint-based metabolic modeling is a systems biology tool that provides a comprehensive metabolic understanding about individual microorganisms (metabolic models henceforth referred to as M-models)Synechococcus elongatus PCC7942, with its high growth rate and robust metabolism, is able to synthesize hydrogen, ethanol, 2,3-butanediol, sucrose, squalene, and fatty acids among other organic chemicals from light and CO227. For heterotrophic partners, we focus on established microbial cell factories, i.e., Escherichia coli, Bacillus subtilis, and the fungus Yarrowia lipolytica. CM-models for four synthetic communities based on the sucrose-secreting S. elongatus in pairwise combination with E. coli (strains K-12 and W), B. subtilis str. 168, and Y. lipolytica Po1g are deployed. Predictions about biomass and metabolites  production yields as well as flux distributions through the metabolic networks are experimentally validated using physiological and gene expression data. We explore metabolic fitness and genetic stability by evaluating compensatory mechanisms in resistance to knockouts of genes with lethal phenotypic traits mimicking enzymatic damage. We test predictions experimentally for ten knockout strains and explore potential production capabilities of organic chemicals, such as methanol, formaldehyde, ethanol, butanal, and succinate for all microbial communities.Here, we reconstruct CM-models encompassing bacteria and fungi that are important contributors to biofuel production. The cyanobacterium 23. These models can be used to elucidate, understand, and generate hypotheses about biological mechanisms shaping interactions between community members22. CM-models and M-models predict biological functions based on key substrate uptake capabilities referred to as constraints. Using the M-models of E. coli K-12 iML1515, E. coli W iJO1366W29, B. subtilis str. 168, iYO84430, and Y. lipolytica Po1g, iYali431  was added into the SMP of all models. Metabolites in the SMP of all CM-models are listed in Supplementary Data CM-models are a mathematical representation of genomic and metabolic knowledge of microorganisms within a community431 Fig. , we reco−1 h−1 by an engineered S. elongatus strain (S. elongatus cscB+)33. When this constraint was added into the M-model of S. elongatus (iJB792) the growth rate dropped about 16% from 0.0539 h−1 to 0.044 h−1, similar to experimental observations (0.045 ± 0.07 h−1). Growth rate simulations and experimental results are shown in Fig. Y. lipolytica. Since, Y. lipolytica cannot use sucrose as its sole carbon source we used an engineered strain (Y. lipolytica suc+) with sucrose uptake capabilities in all the experiments shown here34.We simulated CM-models based on experimental constraints, such as the sucrose secretion rate of 0.182 mmol gDWS. elongatus (cscB+ or WT) should be able to grow at similar growth rates with E. coli or B. subtilis as in monoculture (0.039 ± 0.01 h−1 averaged across cocultures), but should grow faster when it is cultivated with Y. lipolytica (0.062 h−1). These expectations were validated by experiments with the exception of S. elongates-Y. lipolytica communities in which S. elongatus did not see a boost in growth but instead exhibited diminished growth, despite our simulations showing active exchange of isoleucine and other amino acids.Experimental validation of the CM-models yielded interesting and varied results. Predictions using the CM-models showed that S. elongatus cscB+ with each heterotrophic partner supported growth of both community members over 72–96 h . Growth curves for all SPCs are shown in Supplementary Fig. Synthetic phototrophic communities (SPC) of 6 h Fig. . Surprish−1 Fig. . For theE. coli K-12 was capable of producing the most metabolites (111 metabolites), followed by E. coli W (106 metabolites), Y. lipolytica (60 metabolites), and B. subtilis (39 metabolites).CM-models offer a remarkably higher topological potential, achieving feasible solutions while optimizing for the synthesis of target metabolites by shifting the resources across members. CM-models contain the most comprehensive collection of metabolites that are intermediates in the synthesis of biomass or are part of the biomass compositions  of the community members. Here, we simulated production capabilities of every metabolite in the models , or succinate (0.066 ± 0.004 g gDW−1), but only SPCs containing E. coli strains were able to synthesize formaldehyde (average yield 0.017 g gDW−1) and butanal (average yield 0.012 ± 0.002 g gDW−1) , that are declared as primary feedstock for chemical synthesis by the Environmental Protection Agency of the United States of America. Currently these metabolites are manufactured primarily through either chemical synthesis or single-strain biorefineriesals Fig. , we pred−1) Fig. . MetabolE. coli K-12, the model predicted that S. elongatus cscB+ would increase the flux exchange of serine and threonine over two-fold, resulting in higher secretion of these metabolites. In turn, E. coli K-12 would provide adenine, phenylalanine, and isoleucine at higher fluxes . We predicted that the SPC containing E. coli K-12 was the most efficient in exchanging amino acids based on its total metabolite exchange flux, e.g., isoleucine, valine, glutamate, serine, and phenylalanine  were pyruvate, methanol, formate, acetate, and high concentrations (13.7 mM) of ethanol, suggesting that these are also available for metabolite exchange. These metabolites were often lower in coculture than in monocultures, providing further support of this exchange.Metabolic interactions were experimentally characterized using Nuclear Magnetic Resonance (NMR) and Gas Chromatography-Mass Spectrometry (GC-MS). Metabolic profiles for 34 metabolites were measured from biomass samples and culture medium supernatants under mono- and coculture conditions. Experimentally determined metabolic profiles validated the exchange of predicted metabolites. NMR and GC-MS analysis of intracellular Table  and extrS. elongatus cscB+ and E. coli K-12 using GC-MS. Some intracellular metabolites, including the amino acids glutamate and aspartate and the dipeptide glycylproline (precursor of serine and threonine synthesis) accumulated to higher relative levels in S. elongatus compared to E. coli. Other metabolites, such as malonate, were detected intracellularly as well. GC-MS analysis  .CM-Models not only unravel metabolic exchange but can also decipher alterations in the activity of complete pathways. Shifting from mono- to coculture conditions triggered changes across fluxes in multiple pathways of the entire network. Our predictions suggested that these metabolic changes occur in all SPCs studied here. The predicted fold-change (coculture/monoculture) of subsystem fluxes in all SPCs were remarkably correlated  Fig. . SimilarS. elongatus and E. coli W showed significantly differential expression  of genes associated with alanine, aspartate, and glutamate metabolism, arginine and proline metabolism, the pentose phosphate pathway, phototroph-specific metabolism, purine and pyrimidine metabolism, and transport reactions as predicted  across the entire metabolic network. Histidine metabolism is linked with the pentose phosphate pathway and alanine, aspartate, and glutamate metabolism, encompassing the synthesis of several glutamate precursors. Even though we could not quantify/detect intracellular accumulation of histidine experimentally, glutamine, a histidine precursor, was accumulated in both S. elongatus (9.9 mM) and E. coli K-12 (2.1 mM). This metabolite was not found in the supernatant of any mono- or cocultures. Overall, CM-models are a reliable tool to identify functional states in association with growth phenotypes, offering details of cellular communication and potential network bottlenecks limiting microbial interactions.We quantified and compared expression and fluxes by pathway, identifying potential bottlenecks that if relieved could improve biomass yields. For example, predicted flux and observed expression data associated with transporters located in the thylakoid as well as histidine metabolism in the cytoplasm were highly activated, making up around 50% of the total expression (RNA-seq reads) and flux .Tolerance to inhibitors is one of the main challenges for strain engineering of monocultures. Often growth rates and productivities are limited by the accumulation of toxic intermediates  that can harm productivity or lead to culture collapseS. elongatus cscB+, 350 genes are essential, meaning if they are knocked out S. elongatus cscB+ cannot grow in minimal media. This is in contrast to S. elongatus cscB + grown in communities where the number of lethal deletions falls to 327 or 326, depending on the cocultivation partner  mutants with variable growth phenotypes in monoculture versus coculture conditions to test experimentally. Six of those mutants were predicted to not grow in monoculture and were associated with amino acid metabolism , cofactor and prosthetic group biosynthesis (cysG and thrC), and inner membrane transport (phoE). Of these, most strains died in monoculture in minimal media as expected and only the strain lacking ilvE grew in monoculture under our cultivation conditions , oxidative phosphorylation (atpC), the pentose phosphate pathway , and inner membrane transport (cycA)  Fig. , 9. All E. coli strains missing ilvC, cysG, pheA, and thrC was rescued by S. elongatus cscB+ as predicted for the strains ilvC and pheA . The model demonstrated good accuracy for growth/no-growth prediction of the ten assayed KOs. Matthews correlation coefficient (MCC) values were estimated for each condition, by comparing experimental and predicted outcomes (Supplementary Data E. coli K-12 monoculture (0.90), followed by S. elongatus cscB+ coculture (0.7) and E. coli K-12 coculture (0.7); the positive predicted values ranged from 0.83 to 1 for all conditions. Selected strains were also cultured in rich medium (80% Luria-Bertani (LB) + 20% BG-11). We observed that all KOs were rescued in monoculture. Interestingly, the phototrophic communities maintained syntrophic interactions despite the capability of E. coli to thrive in rich medium. Overall, the growth rates of community and monoculture increased up to 41% while cultured in rich medium  has resulted in a promising approach to overcome current limitations11. Furthermore, the use of both synthetic and natural phototrophic communities allows for the utilization of renewable resources for sustainable production39, maintaining diverse and robust microbial interactions, reducing overall cost of feedstocks and minimizing transport logistics35. We explored the potential of photosynthesis-based processes as sustainable growth. Simulations predicted different growth phenotypes depending on the heterotrophic partner . The SPCs containing E. coli improved community growth effectively by exchanging metabolites , as well as into the interactions among stages for each microbial community. The broader potential of CM-model simulations enables quantitative insights into metabolic exchange and pathway fitness. Other approaches, e.g., one developed for the diatom Phaeodactylum tricornutum and the bacterium Pseudoalteromonas haloplanktis48, using a combination of kinetic and constraint-based models, require determination of specific exchange rates  for simulation. Experimental quantification of these parameters can be challenging even for monocultures23, limiting the simulation capabilities of these multiscale models. The shared metabolite pool provides strong versatility to CM-models to determine production capabilities of each microbial community as well as to track quantitative biochemical fluxes and therefore specific resources used to produce metabolites of interest.Mathematical models of heterotrophic-autotrophic communities have been previously published, encompassing a limited number of parameters, such as inorganic carbon, nitrogen, phosphorous, and light uptake rates50, but our modeling predictions for all SPCs indicated that cocultivation can help circumvent potential inhibitory effects by redirecting flux and metabolic exchanges between members 52. Additionally, experimental results using transposon sequencing (TN-seq) applied to microbial communities have shown differences in gene essentiality between mono- and coculture conditions55. This effect, described as community dependent essentiality, resulted in similar phenotypic outcomes as generated by our metabolic modeling approach. For example, TN-seq data of E. coli collected during cocultivation with Hafnia alvei, Geotrichum candidum, and Penicillium camembertii54 showed similar gene essentiality  as our SPC of S. elongates and E. coli. Interestingly, gene essentially of the ATP synthase/thiamin triphosphate synthase complex (atpC in our SPC) was also observed in a different coculture composed of Staphylococcus aureus and Pseudomonas aeruginosa (atpD)53 hinting at commonalities in the evolution of diverse microbial communities that could be independent of the members. This intriguing hypothesis requires additional experimental confirmation.Usually, inhibitory effects can diminish the growth of microorganisms in their natural environment and in many industrial applicationsers Fig. . An expe56. However, the development of innovative modeling tools that enable quantitative identification of potential metabolic drivers among a myriad of physiological stages  results in solid advances toward a more comprehensive understanding of microbial community structure and function. Additionally, combinations of these model-driven hypotheses with omic studies will help guide the design and execution of synthetic phototrophic microbial communities.To date, a lack of detailed knowledge about microbial communities has limited our ability to predict their responses and redirect their activity for improved bioproductionS. elongatus PCC7942, iJB79232, B. subtilis, iYO84430, Y. lipolytica, iYali431, E. coli W and K-12, iECW137228 and iML151429. Additional information about the models is shown in Table 20. The COBRA Toolbox is a MATLAB  software suite for quantitative prediction of cellular and multicellular biochemical networks, implementing the most comprehensive collection modelling methods and algorithms to perform high-throughput model-driven analysis20.CM-models were reconstructed using M-models of 22. This compartment streamlines the connectivity of individual models and the extracellular space based on each microorganism’s capabilities. Metabolites suitable for sharing were manually curated based on experimental data of community members in isolation. We performed a high-throughput growth assay  using S. elongatus, testing over 180 different carbon sources  as described by Zuniga et al.20 for MATLAB. We simulated the maximal growth rate of the community members using the Flux Balance Analysis (FBA)-associated algorithm OptCom20. This algorithm maximizes for the biomass objective functions or biomass reactions, which contain all metabolites that comprise organism biomass . The growth rate of the population results from the sum of fluxes through both biomass reactions of individual models. Constraints about CO2 and light consumption rates were imported from the iJB792 model. The sucrose secretion rate of S. elongatus was constrained to 0.182 mmol gDW−1 h−1. Benchmarking of experimental and predicted results was performed by calculating modeling statistics for the community members under mono- and coculture: true positive (TP), true negative (TN), false positive (FP), false negative (FN), and the confusion matrix and various measures of quality, such as accuracy, specificity, sensitivity, positive predicted, negative predicted, and Matthews correlation coefficient (TP + TN/(TP + TN + FP + FN))60. Metabolites productivity calculations were generated by constraining each metabolite production rate (0–2 mmol gDW−1 h−1) and maximizing for the biomass growth of both community members at the same time. We considered metabolite production feasible if both community members grew; these metabolites were selected and are shown in Fig. 61. Examples of the calculations are given in Supplementary Data CM-models benchmarking was performed using the Gurobi Optimizer Version 5.6.3 solver  in the COBRA Toolbox20. Exchange rates of these metabolites were iteratively varied to zero flux or double flux. Simulations deployed growth rate sensitivity of each member in the community. Core sets of vital metabolic exchanges were defined and compared for all CM-models  of individual microorganisms when using M-models and predicted growth rate of the community member when using CM-models, Eq.  for the community member (j), Eq.  for all subsystems, conditions, and community members. The culture condition  exits at m. Predicted fluxes and gene expression data were normalized to values from −1 to 1 and compared using two sided t-test of The Statistics and Machine Learning Toolbox for MATLAB. The flux variability was determined using FVA64.We developed a method to compare expression data of individual microorganisms and communities with predicted flux distributions. Transcriptomic data for the pair els, Eq. , where iy k, Eq. . For exa(j), Eq. . SupplemSnk,j from mono- to coculture conditions defines pathway usage as syntrophically active , neutrally active , and deactivated . Syntrophically active pathways were assumed to uptake or secrete mass from and to the partner as a result of metabolic exchange. Thus, Snk,j,m1 represents each pathway and can take values from −1 to 1 depending on the total carried flux under mono- (m1) and coculture (m2) conditions, Eq.  simulations of over 12,800 genes in the metabolic networks (monoculture and community) were performed in turn, determining growth phenotypes under monoculture and community conditions. Genes predicted to be essential and non-essential were compared across conditions and growth patterns stabilizing, improving or decreasing the growth of the community members were determined.S. elongatus PCC7942 (ATCC #33912) was engineered to secrete sucrose through the expression of the sucrose/proton symporter cscB+ 33. Inoculum culture of sucrose-secreting cyanobacterium was prepared using BG-11 (Sigma–Aldrich) medium supplemented with 1 g L−1 HEPES (pH 8.9) and 100 mM NaCl at 35 °C. BG-11 medium is composed of (in 1 L): 1.5 g NaNO3, 0.04 g K2HPO4, 0.075 g MgSO4·7H2O, 0.036 g CaCl2·2H2O, 0.006 g Citric Acid·H2O, 0.006 g Ferric Ammonium Citrate, 0.001 g Na2EDTA·2H2O, 0.02 g Na2CO3, 1 mL BG-11 trace metals solution. The BG-11 trace metals solution (in 1 L) consists of: 2.86 g H3BO3, 1.81 g MnCl2·4H2O, 0.22 g ZnSO4·7H2O, 0.39 g Na2MoO4·2H2O, 0.079 g CuSO4·5H2O, 49.4 mg Co(NO3)2·6H2O. Cultures were illuminated 24 h a day while shaking at 150 rounds per minute8. All experiments were performed with six to eight replicates. Flask cocultures were completed in 25 mL volumes in baffled flasks. Cultures were plated on rich media to check for contamination. Once cyanobacteria were exposed to coculture media (CoBBG-11 or CoYBG-11) no subsequent subcultures were made to avoid evolution away from sucrose secretion. To prepare precultures for S. elongatus, S. elongatus cultures at exponentially growing phase were centrifuged and washed twice with BG-11 and suspended in a proper volume of coculture medium. To prepare precultures for heterotrophic strains , single colonies were picked into LB-Miller media and grown until turbid at 37 °C. Cells were diluted into the appropriate coculture media containing 2% sucrose to acclimate the cells to coculture media, and maintained within log-phase growth (OD600 < 0.70) before use in cocultures. The log-phase cultures were centrifuged and washed twice with BG-11 and suspended in a proper volume of coculture medium. Coculture inoculum was prepared from cultures growing in exponential phase (OD750 0.5) using ~2.5 × 107 of S. elongatus (0.1 OD750) and ~7.5 × 106 of heterotrophs (0.01 OD600). Experiments were carried out in batch cultures. Samples were obtained in exponential phase assuming pseudo steady state. The Synechococcus elongatus biomass concentration was analyzed by FACSCalibur flow cytometer  after adding BD Liquid Counting Beads . The absolute cell numbers in samples were determined by comparing cellular events to the counting beads events measured by the flow cytometer using the equation provided in the kit’s total dissolved solids document. For the heterotrophic strains, serial dilutions were plated on LB plates to determine cell numbers. Colony-forming units (CFU) were counted after overnight incubation at 37 °C. All heterotrophs were propagated in BG-11 (Sigma–Aldrich) using the same methods of Hays et al.8, while E. coli and B. subtilis 168 strains were maintained in LB-Miller at 37 °C and Y. lipolytica Po1g was maintained in YNB media at 25 °C as recommended by Yeastern Biotech Co. . Strains were streaked from frozen stocks on LB plates before growth in liquid media. Seven E. coli knockout strains  were obtained from the Keio collection65 in Pamela Silver’s Lab at Harvard University. Knockout experiments in rich media were performed in 24-well plates using 0.8 mL of LB + 0.2 mL of BG-11 + 0.4% glucose. Inoculums were prepared as in growth experiments and we kept same culture conditions. Cell numbers were determined using LB agar plates. Samples were collected at 0, 8, 24, 32, and 48 h. The experiment was performed in triplicates.S. elongatus were determined experimentally by performing growth assays on 180 carbon sources. Biolog plates PM1 and PM2 were obtained from BIOLOG Inc. and the manufacturer’s protocol was followed66. Starting culture density was OD750 = 0.3, culture media was standard BG11, incubated 25–27 °C. Biolog plates were run in duplicate. The plates were incubated in a light room and examined at 490 nm for dye absorbance alterations and at 750 nm to assess optical density. Intracellular metabolites of E. coli and S. elongatus were extracted by the methanol-chloroform-water extraction method. This yielded 8.39 mg and 7.46 mg of dry metabolite extract, respectively. One milligram of each extract was used for NMR (Varian Direct Drive (VNMRS) 600 MHz spectrometer, Agilent Technologies, Santa Clara, CA, USA) and GC-MS 67. Medium samples were dried and derivatized as well. 1D 1H NMR analysis was done with normal setting values for global metabolomics68. The remaining 7.39 mg and 6.46 mg were dissolved in 180 µL D2O with 0.5 mM NMR standard for NMR data acquisition.Exchange capabilities of 69. The reads were aligned to the S. elongatus genome in the NCBI database (Assembly No. GCF_000012525.1). Subread package-featureCounts (version 1.5.0-p1)70 was used to determine reads per each coding region. The aligned sequencing reads were used to determine RNA expression as reads per kilobase per million. The aligned reads were also used to determine differential gene expression using DESeq271.RNA extraction, library generation and sequencing were performed by harvesting and snap-freezing cells in liquid nitrogen in biological triplicates for each condition. Cell lysates were prepared by grinding the frozen cell pellets in liquid nitrogen with 400 µl of RLT buffer (Qiagen RNeasy kit). RNA was stabilized by the addition of 2 ml Trizol reagent (Thermo Fisher Scientific) to each 1 ml of lysate. Total RNA was extracted using the RNeasy kit (Qiagen). mRNA was enriched using the Dynabeads mRNA purification kit (Invitrogen). Sequencing libraries were generated using the KAPA RNA HyperPrep kit (Roche) and following the recommended protocol. The libraries were paired-end sequenced on an Illumina HiSeq TM 4000, using 100 bp cycle kits. The sequencing adaptors were trimmed using the trim_galore programFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9


Manufacturing artificial living cells would open endless research possibilities in basic and applied sciences. With this motivation, many research groups are developing methodologies to construct a stable minimal cell that is capable of achieving metabolic homeostasis, reproducing, and evolving in a controlled environment. Using as a template the gene set for a minimal cell proposed previously by Gil and coworkers, we have put together a network depicting its inferred minimal metabolism needed for life. This network has been further compressed as a metabolic Directed Acyclic Graph (m-DAG) in order to better visualize its topology and to find its essential reactions . We have also compared this minimal m-DAG to those of the smallest natural genome known until now and a synthetic minimal cell created in the laboratory. The modeling of m-DAGs based on minimal metabolisms can be a first approach for the synthesis and manipulation of minimal cells.Mycoplasma mycoides JCVI-syn1.0, and of “Candidatus Nasuia deltocephalinicola”, the bacteria with the smallest natural genome known to date. The comparison of the m-DAGs derived from a theoretical, an artificial, and a natural genome denote slightly different lifestyles, with a consistent core metabolism. The MetaDAG methodology we employ uses homogeneous descriptors and identifiers from the KEGG database, so that comparisons between bacterial strains are not only easy but also suitable for many research fields. The modeling of m-DAGs based on minimal metabolisms can be the first step for the synthesis and manipulation of minimal cells.Defining the essential gene components for a system to be considered alive is a crucial step toward the synthesis of artificial life. Fifteen years ago, Gil and coworkers proposed the core of a putative minimal bacterial genome, which would provide the capability to achieve metabolic homeostasis, reproduce, and evolve to a bacterium in an ideally controlled environment. They also proposed a simplified metabolic chart capable of providing energy and basic components for a minimal living cell. For this work, we have identified the components of the minimal metabolic network based on the aforementioned studies, associated them to the KEGG database and, by applying the MetaDAG methodology, determined its Metabolic Building Blocks (MBB) and reconstructed its metabolic Directed Acyclic Graph (m-DAG). The reaction graph of this metabolic network consists of 80 compounds and 98 reactions, while its m-DAG has 36 MBBs. Additionally, we identified 12 essential reactions in the m-DAG that are critical for maintaining the connectivity of this network. In a similar manner, we reconstructed the m-DAG of JCVI-syn3.0, which is an artificially designed and manufactured viable cell whose genome arose by minimizing the one from  One of the most ambitious aspirations of modern biology is to synthesize artificial living cells. Manufacturing a cell opens endless research possibilities, both in basic and advanced sciences, and it would be critical and a turning point in fields from medicine to evolutionary biology. To reduce the levels of difficulty on this task, most efforts are focused on the synthesis of minimal cells. On the one hand, they will help by increasing our understanding of living systems; on the other hand, they can be used as capsules for the introduction of genetic material to customize cells for applied purposes . SeveralThe bottom–up approach consists of the assembly, piece by piece, of each non-living biological component  in ordehttp://www.essentialgene.org/ [The top–down approach consists of deconstructing living cells ,9. Takinene.org/ ). Comparene.org/ . In addiene.org/ ,14. The ene.org/ .There is a third approach for the construction of a minimal genome that searches for the biochemical and modular description of well-defined pathways needed to perform all essential functions . DespiteA cohesive metabolic network proposal can lead the path to the synthesis of minimal cells. A minimal cell would depend on a minimal set of anabolic pathways to convert and assemble its biomolecule building blocks with the use of the energy and nutrients available in the environment, to reach metabolic homeostasis, and to achieve cellular growth and reproduction. Nevertheless, there is scientific consensus regarding the existence of a variety of minimal metabolic schemes that are ecologically dependent and able to sustain a universal genetic machinery . The simMetabolic networks determine the physiology and biochemistry of a cell. They are made of three components: the metabolic pathways, the chemical reactions involved in the metabolism, and the regulatory interactions of these reactions. Metabolic networks tend to be highly complex, even for simple organisms. For example, if we consider the metabolism of porphyrin and chlorophyll which is present in some animals, plants, fungi, bacteria, and archaea, we get a metabolic pathway map of 135 nodes and 181 edges in the reference pathway in the KEGG database (pathway: map00860). A pathway map with so many components is very difficult to visualize, especially when we are interested in the pathway topology. To this extent, it is highly advantageous to suitably reduce the number of nodes in order to visualize the network more precisely. Alberich and coworkers (2017) designed a methodology called MetaDAG , which cFor the current work, we constructed the minimal metabolic network from the theoretical minimal gene set machinery revised in Gabaldón and coworkers (2007) , and comCa. Nasuia deltocephalinicola” str. NAS-ALF [The metabolic networks for this study were inferred from the reviewed version of the theoretical minimal genome described by Gabaldón et al. (2007) , the gen NAS-ALF  (which i NAS-ALF ), and th NAS-ALF . We firsRG = , is a directed graph with a set of nodes R that are reactions and whose edges are defined as follows: there is an edge pointing from reaction iR to reaction jR if, and only if, a metabolite produced by reaction iR is a substrate in reaction jR. The fact that it is a directed graph establishes a natural production/consumption order between two reactions—that is, what is produced by iR is then consumed by jR. Before generating the directed graph, we manually curated it to remove redundancies (enzymes encoded by orthologous genes).Using the above obtained information, which is a set of reactions for each metabolic network, we generated the corresponding reaction graph that models the relationship between reactions in terms of shared metabolites. A reaction graph, denoted by iR, jR are said to be biconnected if there is a path in each direction between them. A strongly connected component of a reaction graph is a subgraph such that every pair of reactions in it are biconnected. These strongly connected components are contracted in a single node. The reactions that are not biconnected to any other reaction become a node by themselves. Each node is called a Metabolic Building Block (MBB for short), and the MetaDAG software automatically assigns an ID to each MBB. When each MMB is contracted to a single vertex, the resulting quotient graph is a metabolic Directed Acyclic Graph (m-DAG for short). Thus, the m-DAG is defined as follows: its nodes are the MBBs obtained from the reaction graph, and there is an edge between two MBBs, MBB1 and MBB2, if there is an edge in the reaction graph from a reaction in MBB1 to a reaction in MBB2. We denote by Gm the m-DAG, thus Gm =  where N is the set of MBBs and E is the edges between them such thatIn order to analyze the reaction graph in a visually friendly manner, we used the MetaDAG methodology . In a reMBBs contracting only one reaction and whose removal disconnects the reaction graph are considered essential reactions because they are crucial to maintain the network’s connectivity.The first step toward the creation of the minimal metabolic network was to extrapolate the list of genes and enzymes belonging to the set presented by Gabaldón and coworkers (2007)  Figure  to obtaiThis methodology gave us a resulting reaction graph with some redundancies , so we manually curated this graph to include only one copy of each reaction and their corresponding metabolites needed for a functional cell. The reaction graph obtained is composed of 98 reactions and 80 metabolites . The facDespite the fact that the reaction graph of the theoretical minimal organism constructed in this work has only 98 reactions and 80 metabolites, it is difficult to visualize the detailed relationships between the reactions that make up the network’s connectivity . To solvThe m-DAG we obtained from the minimal metabolic reaction graph  has a toIn addition, essential reactions  can be easily identified using this approach , the resulting essential reactions are trustworthy in a sense that researchers might overlook an enzyme doing the same job as the one highlighted and, if they find it, it would be a new discovery not previously described for a specific metabolic pathway.In the case of a minimal metabolic network, each item included in the list of reactions and compounds is hypothetically essential for survival. When we extrapolate these results to living organisms possessing natural minimized genomes, such as pathogens or mutualist endosymbiotic bacteria, we should consider that their metabolism is a patchwork dependent on the host and, in many cases, also dependent on other bacteria with which they live in consortia. Therefore, the study of their networks’ connectivity has the potential of pointing out genes encoding critical steps that connect the different partners in a given pathway. Subsequently, the genes that encode those reactions can become targets for genetic engineering, and/or for mechanisms intended to regulate the cell metabolism; additionally, they might also have the potential to destroy the stability of the relationship, even killing the undesired organism in a parasitic relationship.Ca. Nasuia deltocephalinicola” str. NAS-ALF , while others participate closely in the same pathways , which may be useful information for genetic engineering purposes. Once again, the essential reactions are involved in the metabolism of nucleotides, phospholipids, and coenzymes, even though there are significant differences between the list of reactions included in the reconstruction of JCVI-syn3.0 and the metabolic minimal network . JCVI-syIn order to contrast the MBBs of the three m-DAGs constructed in this study, The construction of the minimal metabolic reaction graph and its consequent m-DAG presented in this work can be of great use in the field of synthetic biology. The composition of compounds and reactions that we present can easily be extrapolated to any phylogenetically diverse bacteria of interest considering that we did not focus specifically on genes. Chemistry and molecular biology technologies are also thriving. Thus, the in silico design of bacteria with the small number of metabolic genes described in this paper may be more feasible than previously thought.


Saccharopolyspora pogona, exhibits strong insecticidal activity than spinosyn. However, the low synthesis capacity and unknown metabolic characteristics of butenyl-spinosyn in wild-type S. pogona limit its broad application and metabolic engineering. Here, we showed that S. pogona exhibited increased glucose consumption ability and growth rate compared with S. spinosa, but the production of butenyl-spinosyn was much lower than that of spinosyn. To further elucidate the metabolic mechanism of these different phenotypes, we performed a comparative proteomic and metabolomic study on S. pogona and S. spinosa to identify the change in the abundance levels of proteins and metabolites. We found that the abundance of most proteins and metabolites associated with glucose transport, fatty acid metabolism, tricarboxylic acid cycle, amino acid metabolism, energy metabolism, purine and pyrimidine metabolism, and target product biosynthesis in S. pogona was higher than that in S. spinosa. However, the overall abundance of proteins involved in butenyl-spinosyn biosynthesis was much lower than that of the high-abundance protein chaperonin GroEL, such as the enzymes related to rhamnose synthesis. We speculated that these protein and metabolite abundance changes may be directly responsible for the above phenotypic changes in S. pogona and S. spinosa, especially affecting butenyl-spinosyn biosynthesis. Further studies revealed that the over-expression of the rhamnose synthetic genes and methionine adenosyltransferase gene could effectively improve the production of butenyl-spinosyn by 2.69- and 3.03-fold, respectively, confirming the reliability of this conjecture. This work presents the first comparative proteomics and metabolomics study of S. pogona and S. spinosa, providing new insights into the novel links of phenotypic change and metabolic difference between two strains. The result will be valuable in designing strategies to promote the biosynthesis of butenyl-spinosyn by metabolic engineering.Butenyl-spinosyn, a secondary metabolite produced by  Saccharopolyspora pogona (S. pogona) and Saccharopolyspora spinosa (S. spinosa), respectively. These two metabolites share similar chemical structure and belong to a new class of highly efficient and environment-friendly antibiotic insecticides that possess broad market potential  that was responsible for the two additional carbons in the C-21 tail of the spinosyn. This suggested that the spinosyn biosynthetic gene cluster might have been derived as an in-frame deletion from butenyl-spinosyn biosynthetic gene cluster. The biosynthetic pathways of butenyl-spinosyn and spinosyn are very similar  gene has been overexpressed in S. spinosa, improving spinosyns yield by 1.28-fold compared with that of the wild-type strain  and Kyoto Encyclopedia of Genes and Gnomes (KEGG) database analysis, we focused on the changes in the abundance of proteins and metabolites associated with primary metabolic pathways and the synthetic pathway of target products. We found at least 48 differential abundant proteins that may greatly affect strain growth, glucose and phosphate utilization, and butenyl-spinosyn biosynthesis. Using these datasets, we over-expressed the rhamnose synthetic genes and methionine adenosyltransferase gene in S. pogona and greatly increased the production of the target product. The results observed in this paper not only aided in elucidating how the metabolic differences affect phenotypic changes in S. pogona and S. spinosa but also provided key target genes for promoting butenyl-spinosyn biosynthesis.In the current study, we aimed to explore the effect of metabolic differences on strain growth, glucose consumption, phosphate utilization, and antibiotic biosynthesis by comparing the phylogenetically coherent and functionally similar strains Escherichia coli strains were grown at 37°C in Luria-Bertani broth . The bacteria were cultured at 30°C for 48 h, and a 2.5 mL aliquot of the seed culture was transferred into a 300 mL baffled flask containing 50 mL of synthetic fermentation medium  and incubated at 30°C  was analyzed to determine its cell concentration during fermentation. The cells were collected every 12 h for strain growth curve measurement by UV scanning. Supernatants were collected every 12 h during fermentation to determine the glucose concentration by using glucose assay kit  until the glucose was used up. Supernatants were collected every 48 h during fermentation to determine the phosphate concentration by phosphomolybdenum blue spectrophotometry  after 4 days of culture, washed four times by resuspending the cell pellet in 20 mL of fresh PBS , and quickly frozen in liquid nitrogen. Protein extraction was performed as described previously . The lyophilized SCX fractions were re-dissolved in 2% acetonitrile and 0.1% formic acid and then loaded on a ChromXP C18  nanoLC trap column. Online trapping and the desalting were performed at 2 μL/min for 10 min with 100% solvent A. The solvents were composed of water/acetonitrile/formic acid . An elution gradient of 5–38% acetonitrile (0.1% formic acid) in 70 min gradient was then employed on an analytical column . LC-MS/MS analysis was performed using a TripleTOF 5600 System  fitted with a Nanospray III source. Data were acquired at an ion spray voltage of 2.4 kV, a curtain gas of 30 PSI, a nebulizer gas of 5 PSI, and an interface heater temperature of 150°C. MS was operated with TOF-MS scans. For information dependent acquisition (IDA), the survey scans were acquired in 250 ms, and up to 30 product ion scans (80 ms) were collected if the threshold of 260 counts was exceeded and with a + 2 to + 5 charge state. Rolling collision energy setting was applied to all precursor ions for collision-induced dissociation. Dynamic exclusion was set to 16 s.2D chromatography was conducted on an Eksigent nanoLC-UltraSaccharopolyspora strains. False discovery rate (FDR) was estimated via a reverse database strategy, and only proteins below the threshold of 1% of FDR were further considered. The differential abundant proteins (DAPs) were defined in the iTraq experiment on the basis of the following criteria: unique peptides ≥ 1, P-value < 0.05, and fold change > 1.33 or < 0.75. Only spots present in all the replicate gels were considered for subsequent analysis. The DAPs were annotated using the GO database1, and proteins were classified based on functional annotations using the GO terms for cellular components, biological processes, and molecular functions. The metabolic pathway analysis of DAPs was conducted using the KEGG database2.MS/MS data were analyzed for protein identification and quantification using the ProteinPilot software v.4.5 . The utilized protein database was the protein sequence set of all For metabolomic analysis, samples were collected at 4 days and from three biological replicates. The sample treatment methods used were as described by Wentzel et al. with some modifications . 10 mL oMetabolic profiling of samples was performed on an Agilent 1290 Infinity LC system  coupled with an AB SCIEX Triple TOF 5600 System . Chromatographic separation was conducted on ACQUITY HSS T3 1.8 μm (2.1 × 100 mm) columns for both positive and negative models. The column temperature was set at 25°C for operation. The mobile phases of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) were used in positive ionization mode, while 0.5 mM ammonium fluoride in water (C) and acetonitrile (D) were used in negative ionization mode. In the positive (negative) model, the elution gradient initially started with 1% B (D) for 1 min, linearly increased to 100% B (D) at 8 min, was maintained for 2 min, and then returned to 1% B (D) for approximately 2 min of equilibrium. The delivery flow rate was 300 μL/min, and a 2 μL aliquot of each sample was injected onto the column. TOF/MS was performed on both ionization modes. Electrospray ionization source conditions on triple TOF were set as follows: ion source gas 1, 40 psi; ion source gas 2, 60 psi; curtain gas, 30 psi; source temperature, 650°C; ion spray voltage floating, 5000 V(+) and −4500 V (−). Information-dependent acquisition, an artificial intelligence-based product ion scan mode, was used to detect and identify MS/MS spectra. The parameters were set as follows: declustering potential, 60 V (+) and −60 V (−); collision energy, 50 V (+) and −20 V (−); exclude isotopes within 4 Da, candidate ions to monitor per cycle: 10. The analysis process was conducted with the assistance of Applied Protein Technology Co., Ltd. .3. The data were subsequently processed using XCMS software for peak alignment and data filtering. For peak picking, the following parameters were used:centWave m/z = 25 ppm, peak width = c, prefilter = c. For peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5 were used. Compound identification of metabolites was performed by comparing of accuracy m/z value (<25 ppm), and MS/MS spectra with an in-house database established with available authentic standards. After normalized to total peak intensity, the processed data were uploaded into SIMCA-P  for statistical analysis. Partial-least squares discrimination analysis (PLS-DA) was conducted as a supervised method to identify the important variables with discriminative power. PLS-DA models were validated based on the multiple correlation coefficient (R2) and cross-validated R2 (Q2) in cross-validation and permutation tests by applying 200 iterations (P > 0.001). The significance of the biomarkers was evaluated by calculating the variable importance in projection (VIP) score (>1) from the OPLS-DA model. For the univariate analysis, specific biomarkers were compared between S. pogona and S. spinosa by employing Student’s T-test. A p-value less than 0.05 indicated a significantly different metabolite.The raw data generated by UPLC-Q-TOF/MS were converted into mzML format files using the Proteo Wizard MS converter tool and then subjected to data processing using XCMS online softwaregdh + kre, gtt, and epi were amplified from the genomic DNA of S. spinosa by using primer pairs P1/P2, P3/P4, and P5/P6  in accordance with manufacturer’s instructions. qRT-PCR amplification was performed using Power SYBRR Green PCR Master Mix (Applied Biosystems), as previously described (For RNA isolation for quantitative reverse transcription-PCR (qRT-PCR), all strains were cultivated in SFM at 30°C. Four-day cultures (1 mL) were collected, and the cells were harvested via centrifugation at 8,000 g for 5 min at 4°C. Total RNAs were separately isolated using TRIzol Reagent (Invitrogen). RNA concentration and purity were determined by measuring the ratio of ODescribed . The prit-test, with ∗p < 0.05, ∗∗p < 0.01, ns, no significant.All data in this study were stated as means ± Standard deviation (SD), and analysis by Student’s S. pogona and S. spinosa and then examined through HPLC and LC-MS/MS in accordance with the method described above , followed by rapid entry into the decline phase was shown by S. pogona. For S. spinosa, its growth rate was slower than that of S. pogona early in growth. However, its bacterial density decreased slowly during the recession, which the final bacterial density of S. spinosa was 1.65-fold that of S. pogona  to analyze the key factors affecting the phenotypic changes. Proteins isolated from three independent samples within the same phase were mixed and fragmented using trypsin. The resulting mass spectrum data were searched against the Saccharopolyspora proteome database. A total of 5,207 proteins were identified, 2,894 of which were identified in all of the samples, which were common proteins identified by S. pogona and S. spinosa, representing approximately 29.80 and 32.15% of the predicted proteome for S. pogona and S. spinosa, respectively  (To further understand the causes of the phenotypic differences between roteome) , which wS. pogona and S. spinosa from the biological system level, we estimated the abundance value of all proteins identified in this study, and the protein abundance scales are listed in Dataset S1. Some similarities and differences existed in the most highly expressed proteins between the two strains  can catalyze L-methionine conversion into S-adenosyl-L-methionine, which is an important source for butenyl-spinosyn and spinosyn methylation (S. spinosa was remarkably higher than that of S. pogona (2.41-fold), which may ultimately affect the biosynthesis of target products. These high-abundance proteins play a role in key metabolic pathways, such as glycolysis, tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and RNA degradation, as well as basic cell function and maintenance.To evaluate the possible function of these proteins, we carried out GO and KEGG functional annotation analysis . The ana strains . The mosh strain . These phylation . Howeverp < 0.05) for further investigation. Abundances of all the metabolites are shown in S. pogona was mostly higher than that of the S. spinosa, such as phosphorylcholine (4.57), 1-oleoyl-sn-glycero-3-phosphocholine (6.77), glycerophosphocholine (3.42), phosphatidylethanolamine (1.83), etc. (Data Set S3). These detection results effectively reflected the difference in metabolic pathways between the S. pogona and S. spinosa.In order to analyze whether the changes of metabolite abundance related to strain growth development and target products biosynthesis were consistent with the changes of proteins, detection of intracellular metabolome was carried out by UHPLCQ-TOF/MS . In thisptsI), phosphocarrier protein Hpr (ptsH), PTS glucose transporter IIA/IIBC (PTS IIA/IIBC), multiple sugar transport system ATP-binding protein (msmX), and glucokinase (ppgK) were identified in S. pogona and S. spinosa during glucose fermentation. The abundance of HPr and multiple sugar transport system ATP-binding protein in S. pogona was higher than that of S. spinosa, indicating that both strains have developed ways of glucose transport by PTS and the ABC transport system. Furthermore, the ability of S. pogona to transport and phosphorylate glucose was stronger than that of S. spinosa, which was kept in line with the rapid glucose consumption rate and the improvement of cell growth. In addition, the expression abundance of the proteins related to the transport of fructose, mannose and other sugars in S. pogona was also higher than that of S. spinosa, indicating that the potential ability of S. pogona to transport other sugars was probably stronger than that of S. spinosa (Data Set S1).Central carbon metabolism is one of the most basic cellular pathways that occur in all living organisms, and metabolite interconversion at the phosphoenolpyruvate–pyruvate–oxaloacetate node involves a set of reactions that connect the major pathways of carbon metabolism . These rS. pogona and S. spinosa could catabolize glucose into acetyl-CoA via the complete glycolysis and PP pathways, and all of the proteins involved in these two pathways could be detected. Throughout the glycolysis process, the abundance of 6-phosphofructokinase (pfkA) in S. pogona was lower than that of S. spinosa; this enzyme is involved in the conversion of fructose-6-phosphate to fructose-1,6-bisphosphate and is a rate-limiting enzyme for glycolysis (glpX) was higher than that of S. spinosa; this enzyme is involved in the conversion of fructose-1,6-bisphosphate to fructose-6-phosphate and reduce substrate metabolism flow to glycolysis  and enolase (eno)  and pyruvate carboxylase (pyc), was higher than that of S. spinosa, indicating that S. pogona has a strong ability to metabolize pyruvate. The PP pathway is the second important pathway for glucose metabolism, and its initial substrate is glucose 6-phosphate. In fast-growing cells, the PP pathway plays crucial roles in anabolic reactions, converting glucose 6-phosphate into glyceraldehyde 3-phosphate and ribose 5-phosphate or producing NADPH for the synthesis of acetyl-CoA, nucleotides, and other metabolites  and 6-phosphogluconate dehydrogenase (zwf3) are the rate-limiting enzymes of this stage (tktA2) and transaldolase  play important roles in the above reactions  and 2-dehydro-3-deoxy-D-gluconate aldolase (kdgA) in the proteomic data of S. pogona and S. spinosa, which were characteristic enzymes of the ED pathway that takes only four steps to convert glucose to pyruvate  pathway, Entner-Doudoroff (ED) pathway, and pentose phosphate (PP) pathway . The glyycolysis . The abuycolysis . Meanwhise (eno) , were alabolites . The patis stage . Howevereactions . Surprispyruvate . MoreoverbsK) was also identified in our proteomic data. Ribokinase is one of the principal enzymes in carbohydrate metabolism, catalyzing D-ribose and 2-deoxy-D-ribose to produce ribose-5-phosphate and 2-deoxy-D-ribose-5-phosphate, respectively, eventually generating glyceraldehyde-3-phosphate into glycolysis; this pathway may be used as an alternative route in central carbon metabolism  in S. pogona was higher than that of S. spinosa; this enzyme is involved in the conversion of glucose-6-phosphate to glucose-1-phosphate, and its catalyzed product, glucose 1-phosphate, also has a high abundance in S. pogona. gtt), dTDP-glucose-4,6-dehydratase (gdh), dTDP-4-keto-6-deoxy-D-glucose epimerase (epi) and dTDP-4-dehydrorhamnose reductase (kre), and can also synthesize UDP-D-glucuronate successively with the participation of UTP-glucose-1-phosphate uridylyltransferase  and UDP-N-acetyl-D-glucosamine dehydrogenase (rkpK), in which dTDP-L-rhamnose is an important precursor of target products and cell wall synthesis, while UDP-D-glucuronate is an important substrate for amino sugar and nucleotide sugar metabolism. In the above biological processes, the expression abundance of dTDP-glucose-4,6-dehydratase, dTDP-4-keto-6-deoxy-D-glucose epimerase and UDP-N-acetyl-D-glucosamine dehydrogenase in S. pogona was higher than that in S. spinosa. In addition, UDP-glucose and UDP-D-glucuronate also have a high detection abundance in S. pogona, indicating that S. pogona has a strong ability to use glucose for anabolism during the analysis period. We also found that enzymes and intermediate metabolites associated with the metabolism of mannose, galactose, and glycerol 3-phosphate had high abundance in S. pogona, such as mannose-6-phosphate isomerase (manA), UDP-glucose 4-epimerase , dihydroxyacetone kinase (dak2), D-mannose 6-phosphate, galactose 1-phosphate, UDP-galactose and glycerol 3-phosphate, which effectively reflected the difference on metabolic capacity between the two strains.In addition to catabolism through glycolysis and PP pathway, glucose-6-phosphate can also be converted into glucose-1-phosphate for anabolism, including polyketide sugar unit biosynthesis, N-glycan biosynthesis and peptidoglycan biosynthesis . In thisfadD), acyl-CoA dehydrogenase (acdA-3), enoyl-CoA hydratase (fadB), and acetyl-CoA acyltransferase  in S. pogona were higher than that in S. spinosa. Meanwhile, hexadecanoyl-CoA, tetradecanoyl-CoA and decanoyl-CoA had a higher abundance in S. pogona, which was consistent with the abundance variation of acyl-CoA synthetase and acetyl-CoA acyltransferase in S. pogona. Since acyl-CoA synthetase is the core enzyme for fatty acid degradation metabolism, this result suggests the strong ability of S. pogona to metabolize exogenous fatty acids by β-oxidation. The acetyl-CoA produced from fatty acid β-oxidation and pyruvate metabolism will enter the TCA cycle to produce energy and several metabolic precursors for cell growth and the synthesis of other metabolites. Citrate synthase (gltA), isocitrate dehydrogenase (icd-2), and 2-oxoglutarate dehydrogenase (sucB) are the rate-limiting enzymes of TCA cycle and were detected in our proteomic data. The abundance of these three rate-limiting enzymes in S. pogona was higher than that in S. spinosa. The same finding was observed for other enzymes involved in the TCA cycle, such as aconitate hydratase (acn), succinyl-CoA synthetase (sucC), and succinate dehydrogenase . In addition, we also found that 2-oxoglutarate has a high abundance in S. pogona. This result suggests the strong ability of S. pogona to produce energy and metabolic precursors by using acetyl-CoA, although the concentration of fumarate hydratase (fumB) and its catalytic product malate is low. S. pogona showed stronger metabolic capacity than S. spinosa in fatty acid metabolism and TCA cycle because it rapidly absorbs and utilizes nutrients in the environment early in growth. It suggests that the metabolic flux flowing through the TCA cycle in S. pogona is increased compared with the S. spinosa, which may cause the acetyl-CoA produced by the glycolysis, fatty acid degradation and other metabolic pathways to flow more into the TCA cycle, and ultimately limits the biosynthesis of butenyl-spinosyn. These results can be used to explain why S. pogona exhibited a rapid growth rate but low butenyl-spinosyn production.Fatty acid degradation metabolism and TCA cycle are important metabolic pathways for the synthesis of acetyl-CoA and other intermediate precursors, respectively . BacteriS. pogona overall was stronger than that of S. spinosa, indicating that S. pogona demonstrated a stronger synthetic ability to convert amino acids into acetyl-CoA and other key precursors compared with S. spinosa. For example, the abundance of enzymes  involved in the leucine to acetyl-CoA, valine to (R)-methylmalonyl-CoA, and isoleucine to (S)-methylmalonyl-CoA pathways in S. pogona was remarkably higher than that in S. spinosa  and methionine adenosyltransferase (metK), two key enzymes involved in the S-adenosine methionine synthesis , succinate dehydrogenase (sdhA1/B/B1/C), polyphosphate kinase (ppk), inorganic pyrophosphatase (ppa), and ATP synthase (atpA/C/H), was higher than that in S. spinosa and showed an increased energy production capacity. NAD + /NADH ratio and total NAD(H) play important roles for whole-cell biochemical redox transformations. As shown in the previously described central carbon metabolism process, four reactions that generate reducing power (NADH and FADH2) occur in the TCA cycle: isocitrate to 2-oxo-glutarate (NADH), 2-oxo-glutarate to succinyl-CoA (NADH), succinate to fumarate (FADH2), and malate to oxaloacetate (NADH)  involved in the conversion of isocitrate to 2-oxo-glutarate, 2-oxo-glutarate to succinyl-CoA, and succinate to fumarate in S. pogona was remarkably higher than that in S. spinosa, implying the strong ability of S. pogona to produce reducing power. We hypothesized that the increased NADH level induces the elevated expression of oxidative phosphorylation-related enzymes in S. pogona, which was consistent with the quantitative proteomic analysis.Most of the intermediate precursors and reducing power generated by central carbon and amino acid metabolism follow the oxidative phosphorylation pathway for ATP synthesis . We detee (NADH) . The abufdhA), formate dehydrogenase (fdhF), and sulfate adenylyltransferase (cysD), which are involved in methane metabolism and sulfur metabolism, were identified, indicating that in addition to using organic carbon to generate energy, utilizing inorganic carbon (methane) and inorganic sulfur to produce energy in S. pogona and S. spinosa is highly possible.Unexpectedly, methane and sulfur metabolism are the other energy metabolism mechanisms found in our proteomic data based on KEGG pathway analysis  Hakobya. The enzde novo synthesis (primary) and the other is remediation synthesis (S. pogona. IMP dehydrogenase (guaB3) catalyzes the redox reaction between inosine monophosphate (IMP) and xanthosine 5′-phosphate (XMP), carbamoyl-phosphate synthase (carA/carB) catalyzes the synthesis of carbamoyl-phosphate, which belongs to the rate-limiting enzyme of purine and pyrimidine nucleotide metabolism respectively , ribokinase and transketolase, had a higher expression abundance in S. pogona, making its ribose 5-phosphate abundance much higher than that of S. spinosa, so as to meet the needs of its vigorous nucleotide metabolism.Nucleotide metabolism plays an important role in the synthesis of genetic materials, energy supply, metabolic regulation, as a messenger molecule and the formation of coenzymes, including purine nucleotide metabolism and pyrimidine nucleotide metabolism . The synynthesis . Figure ectively . The twobusJ, L), rhamnose attachment and methylation , forosamine biosynthesis , and rhamnose biosynthesis   . Howevergtt, gdh, epi, and kre) and metK involved in the biosynthesis of rhamnose and S-adenosyl methionine was low compared to other high-abundance proteins (metK in S. pogona, respectively, and then evaluated the resulting mutants SPOG-RM and SPOG-ME for discernable phenotypes (metK in SPOG-ME. The qRT-PCR results showed that the transcript levels of gdh, kre, epi, and gtt in SPOG-RM were 14. 0-, 11. 9-, 26. 8-, and 54.1-fold higher than those in S. pogona, respectively, and the transcript level of metK in SPOG-ME was 7.3-fold higher than that in S. pogona (Rhamnose and S-adenosyl methionine are the precursors of butenyl-spinosyn biosynthesis. Meanwhile, the former is also participated in cell wall synthesis, and the latter is a direct donor of methyl groups. However, the proteomic results in the present study showed that the expression abundance of rhamnose synthetic genes (proteins . Therefoenotypes –8. The benotypes . We also. pogona . The finS. pogona and S. spinosa. The results may contribute to our understanding of the biosynthetic mechanism of butenyl-spinosyn and spinosyn. Strain OD600 value, glucose consumption, phosphate utilization, and target product yield analysis indicated that S. pogona exhibited faster growth rate and glucose and phosphate utilization rate but lower target product biosynthetic ability compared with S. spinosa. Comparative proteomic and metabolomic analysis revealed the difference on central carbon metabolism, amino acid metabolism, energy metabolism, purine and pyrimidine metabolism, and the biosynthetic gene cluster expression of the target products between S. pogona and S. spinosa. These results suggest that S. pogona demonstrates high-efficiency glucose transport ability via the PTS and ABC transporter system, allowing its faster glucose utilization rate compared with that of S. spinosa. S. pogona showed strong ability for fatty acid metabolism, TCA cycle, amino acid metabolism, energy metabolism, purine and pyrimidine metabolism, allowing its vigorous basic metabolism to promote its rapid growth. However, this metabolic feature also led to insufficient precursors and energy supply of S. pogona during the butenyl-spinosyn biosynthesis, which not only caused its premature entry to the stationary and decline growth periods but also reduced its butenyl-spinosyn biosynthetic ability. The ability of S. pogona to participate in the synthesis of precursors, such as rhamnose and S-adenosyl methionine, was weaker than that of S. spinosa, thus affecting the precursor supply of butenyl-spinosyn biosynthesis. Proteomic data also showed that the overall expression level of the butenyl-spinosyn synthetic gene cluster was very low, which may greatly affect biosynthetic ability. The over-expression experiment of rhamnose synthetic genes and metK confirmed the reliability of this conjecture. We propose that the metabolic pathways associated with the supplies of rhamnose and S-adenosyl methionine, which were selected based on the results of quantitative proteomic and metabolomic data, require further research on their regulatory mechanism on butenyl-spinosyn biosynthesis. Studies on the genetic modification of their corresponding genes are useful to understand the key nodes and regulatory networks of primary metabolism and butenyl-spinosyn and spinosyn biosynthesis.This study was the first to conduct a comparative analysis of All datasets generated for this study are included in the article/S. pogona and S. spinosa iTraq-based quantitative proteomic sample preparation and bioinformatics analysis. JR, HH, ZL, and ZX conducted mutants construction. JR, XD, HH, and ZY employed butenyl-spinosyn and spinosyn HPLC and MS analysis. HH, SH, TK, YH, and WH performed mutant strain physiological and biochemical assay. JR and LX wrote the manuscript. All authors discussed the results and approved the final manuscript.LX conceived the project. LX and JR generated the concepts and designed the research. JR, HH, SY, JT, and SH performed The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health.Video abstractThe online version contains supplementary material available at 10.1186/s40168-020-00955-1. The gut microbiota is the community of microorganisms residing in the gut and include commensal, symbiotic, and pathogenic bacteria. Under normal circumstances, the gut microbiota and its host are in symbiosis . DisruptMetabolic networks can be used to predict the effect of alterations in the metabolic network in silico. A constraint-based reconstruction and analysis (COBRA) approach is often used to simulate the operation of a metabolic network under different external nutrient conditions. A COBRA method suitable for investigating the metabolism of the microbiota is flux balance analysis (FBA) . A flux (S) represents a stoichiometric matrix, and (v) represents the flux distribution. The formula describing a biomass function contains all the metabolites in the system that are required to build a new cell. In other words, a biomass function simulates cell growth. Importantly, since Eq. FBA works at steady-state, i.e., the amount of the metabolite produced is equal to the amount of the metabolite consumed. Accordingly, the set of linear equations is formulated as:mentclasspt{minima(Z) is the solution of the OF, (C) is the vector of weights, indicating how much each reaction contributes to the OF and (T) represents the matrix transpose. For example, when a single flux is maximized or minimized, C is a vector of 0s with a single 1 [Similarly, the OF is formulated as follows:single 1 , 15. Howsingle 1 , 17.Bifidobacterium adolescentis shows a reduction in the production of formate, ethanol and acetate as well as a reduction in biomass formation if lactate production is manually increased. The OF in this example is maximizing biomass production , also known as genomic-scale metabolic reconstructions (GENREs), consist of all the metabolic capacities of a bacterium , and areordingly . The AGOordingly . For exaern diet  to the dern diet , 48–50.Citrobacter sedlakii, a non-pathogenic bacterium found in human stool. The authors investigated the metabolic output of C. sedlakii by taking samples at different growth stages and analyzing the extracts using two liquid chromatography mass spectrometry (LCMS) approaches, reverse phase (RP) and hydrophilic interaction liquid chromatography (HILIC). The obtained data was compared to a predicted list of metabolites generated via FBA using a tool called MS_FBA. The comparison showed that the metabolic output of the GEM of C. sedlakii did not cover all the metabolites measured with LCMS, highlighting the need to determine the accuracy of bacterial GEMs and more precise genome annotation [Nevertheless, community efforts are being taken to standardize GEMs and assure the quality of the models . To optinotation . Lastly,notation . To answnotation  using thnotation . In addinotation . CollectTo understand the effect of the microbiota on the host, microbial metabolic networks can be extrapolated to include metabolic networks of the host. This enables predicting the effects of gut microbiota on the host and suggesting possible interventions to promote host health. Diener et al. constructed microbial metabolic networks from the metagenome data of a cohort of Swedish people with and without diabetes mellitus and used FBA to analyze SCFA production. In this study, 16S abundance data was integrated with metabolic fluxes to construct personalized predictions of metabolic output and interventions such as dietary changes and medical treatment. The authors used GEMs from the AGORA database, however the AGORA database did not cover all the species found in the Swedish cohort. Instead, the authors performed FBA on the genus and species level, whereby AGORA models were pooled together into higher phylogenetic ranks. This resulted in networks containing between 12 and 30 GEMs at the genus level and 23 and 81 GEMs at the species level. The GEMs were placed in a shared metabolic environment for each sample in the cohort representing the gut lumen. The relative biomass for each GEMs was estimated from the relative read distribution in each sequenced sample. The authors found a minimal overlap of resource utilization between microbes in different niches, suggesting an upper bound on alpha diversity in the gut. Next to ecological insights, the authors concluded from their model that SCFA production is highly specific per individual. Nonetheless production of butyrate and propionate was reduced in diabetic subjects compared to healthy controls and the overall SCFA production profile could be restored upon metformin treatment . The autLactobacillus plantarum with metabolic reactions commonly found in the gut such as butyrate and propionate fermentation. Next, the authors placed multiple expanded GEMs of L. plantarum randomly in a tube-like grid environment and used the SOA to simulate the network in 80,000 time steps. For every cell in the grid the OF is maximizing the rate of ATP production, which is an alternative to biomass formation. Periodically, glucose was added to the system at the proximal side of the tube and concentrations of metabolites in each compartment were shifted to the right until they leave the system at the distal side of the tube. Together, this represents metabolic gradients that occur in the gut. The authors then included growth, removal, movement and evolution of single organisms in the network and showed that diversification can be an emergent property of cross feeding among microbial communities. When diarrhea was simulated by increasing the flux speed through the system, the microbial diversity was destroyed [Currently, several tools are available for modeling bacterial communities, such as OptCom, BacArena, MiMoSa, COMETS, FLYCOP and MICOM , 61–65. estroyed , which iestroyed . Chan etestroyed . These eestroyed . HoweverAnother problem in combining bacterial GEMs and host GEMs is formulating an appropriate OF. In the majority of metabolic networks, the total biomass growth, or metabolic output is optimized. In the gut, the host benefits the most from a balance between the metabolic output of all bacteria, which requires a balance among the distribution of the different microbial species. To address this problem OptCom was developed. This approach uses two layers of OFs. The first layer maximizes the biomass formation of each individual species. The second layer maximizes the growth of the whole community, resulting in a more realistic growth distribution in a bacterial community . The extAkkermansia muciniphila, and Bilophila wadsworthia in PD patients, and identified a new research target in PD research with the use of metabolic networks [. confirmed these results by using the same approach as Baldini et al. [B. wadsworthia and A. muciniphila [, which will improve the process of choosing the best treatment, when prescribing medication.While the composition of the gut microbiota fluctuates over time due to, among others, the diet, the overall composition is more or less stable  and is anetworks . Hertel i et al.  to consti et al. . Simulatiniphila . Both spiniphila . The aboiniphila . Membersiniphila , 93. On iniphila , causes iniphila . The aboClostridium difficle infection [Currently, dysbiosis can be treated in several ways such as a fecal matter transplantation (FMT), antibiotic use and using pro-, pre-, or psychobiotics. FMT has been used successfully in recurrent nfection , but notnfection . Interesnfection . This suBifidobacterium species are widely used as probiotics [Bifidobacteria in the AGORA database are not condition-specific and do not give the metabolic behavior in detail. For example the GEMs of Bifidobacteria in the AGORA database do not show growth on starch, whereas most Bifidobacteria can metabolize starch [Bifidobacteria including probiotic strains used in commercialized products. The authors compared the metabolic capabilities of the GEMs under 30 different environmental conditions, with maximizing the biomass function as the OF. Based on metabolic capabilities in the different environments the Bifidobacteria could be divided into three groups. Based on the metabolic capabilities of the GEMs the authors hypothesized that the protective effect of the probiotic candidate Bifidobacterium thermophilum RBL67 on Salmonella and Listeria species comes from the production of SCFAs. Furthermore the authors hypothesize that Bifidobacterium gallicum DSM20093 and Bifidobacterium kashiwanohense DSM21854 can help relieve constipation via production of acetate [Another possible treatment for dysbiosis is the use of probiotics. Probiotics are defined as live microorganisms that, when administered in adequate amounts, confer a health benefit on the host . Numerouobiotics . As mente starch . Therefo acetate , showingMetabolic networks and FBA are promising tools to elucidate the precise interactions among bacteria, between bacteria and the host, and can be used to test the effectiveness of probiotic treatment before clinical trials Furthermore, metabolic networks can help point at knowledge gaps. For example, if there is a discrepancy between the outcome of model simulations, and experimental work, a more thorough understanding of the metabolic capabilities of the organism of interest is needed, which leads to new hypotheses and more experiments. Next, metabolic networks can elucidate causality in the microbiota research, because unlike in experimental work, numerous alterations can be implemented in metabolic networks . CurrentInteractions within the complex microbiota community and between the microbiota and the host are challenging to elucidate but understanding these interactions is vital for successful intervention in the microbiota. This review shows that the use of computational methods based on flux balance analysis provides novel understanding of the microbial interactions, helps formulating new testable hypotheses, and enables the investigation of the effectiveness of probiotic administration in a personalized manner.


Here, we use a constraints-based integrated approach to delineate the interplay between metabolism and epigenetics, hardwired in the genome, to shape temozolomide (TMZ) resistance. Differential metabolism was identified in response to TMZ at varying concentrations in both the resistant neurospheroidal (NSP) and the susceptible (U87MG) glioblastoma cell-lines. The genetic basis of this metabolic adaptation was characterized by whole exome sequencing that identified mutations in signaling pathway regulators of growth and energy metabolism. Remarkably, our integrated approach identified rewiring in glycolysis, TCA cycle, malate aspartate shunt, and oxidative phosphorylation pathways. The differential killing of TMZ resistant NSP by Rotenone at low concentrations with an IC Genome scale measurements allow cataloging drug and therapeutic candidates7. However, their integration with constraints-based metabolic modeling presents a paradigm shift in understanding homeostasis, disease, and treatment strategies8. Cancer cells are punctuated by their characteristic ability to proliferate and for unchecked proliferation in part due to mutation and in part due to disruption of homeostatic control coordinated by metabolic networks14 and unchecked signaling pathways16. In this work, we showcase unique therapeutic windows that exploit metabolic pathway vulnerabilities during cell growth to tackle chemotherapeutic resistance.The grand challenge of designing new therapies is the cellular complexity of a multi-hit, multifactorial disease like cancer and the rapid emergence of chemo-resistance19. Temozolomide (TMZ), an alkylating agent is the most effective chemotherapeutic agent against GBM. However, TMZ resistance is increasing creating an urgent need to identify new therapeutic strategies23. Our previous study24 differentially analyzed growth limiting metabolites and nutrient preferences for respiration in TMZ resistant neurospheroidal cells (NSP) isolated from an authenticated cell-line population of U87MG cells. In this study, we have identified the Complex I inhibitor, rotenone as an alternate drug to induce killing of the drug resistant NSP. Our work highlights the use of constraints-based modeling integrated with exome, limited transcriptome and metabolome data to identify vulnerabilities of a chemo-resistant glioblastoma cell to identify differential response of an Electron Transfer Chain (ETC) inhibitor rotenone. We pioneer a scalable systems biology workflow from isolation of resistant heterogenous populations to molecular profiling/measurements integrated with metabolic modeling to rationally identify reprogramed pathways resulting in identifying potential alternate drugs.Glioblastoma Multiforme (GBM), is an aggressive brain cancer with inherent heterogeneity14 and efforts towards anabolic macromolecule biosynthesis. The drug dose dependent 3-fold higher lactate-to-pyruvate ratios in U87MG also indicated differential cytoplasmic NADH/ NAD+ ratios and altered redox state  ratios were observed with increasing TMZ concentration and thus was a potential index of higher methylation and drug action resulting in lowered cell growth Fig. , and 6. els Fig.  were druels Fig. . Higher els Fig. . Figure ons Fig.  to identTM phenotype microarray testing also identified differential coupling between growth and respiration on 57% of the C/N sources tested including glutamine in NSP24  and Complex III CYC1). ATPase (ATP4A) harbored unique mutations Fig.  in NSP a. ATPase 28. Such a disruption could result in an increase in AMP to ATP ratio, thus activating AMPK. Mutations in Adenylate kinase ADK could modify the ratios of ATP, AMP, and ADP resulting in impact of p53 and AMPK signaling. A differential upregulation or expression of genes from these pathways24 is an indication of the known crosstalk between AMPK and the cellular survival axis of PI3K/Akt/mTOR pathwayThere is a mutation in Cytochrome C-1 (Cyc1 gene) one of the 11 genes in the mitochondrial respiratory chain (MRC) complex III (Supplementary File OAZ1 the controller of the enzyme Ornithine Decarboxylase (ODC1) regulating polyamine synthesis and associated cell growth was mutated in U87MG alone  . The model was validated by estimating uptake rates of primary nutrients with more than 90% accuracy , Nitrogen metabolism , and energy metabolism (oxidative phosphorylation and ETC)  Fig. . The int50 values were identified as lower by an order of magnitude calculates as 5 nM for NSP as compared to 1.8 mM for U87MG , towards a behavior and metabolism closer to the normal cell function indicated by increased oxidative phosphorylation and the balance between glycolysis and mitochondrial oxidative phosphorylation is essential. Such integrated systems level approaches, are essential to unraveling the tacit connections between epigenetics, metabolism and genotyping and are scalable to the clinic to fill a critical need for predictive models in individualized therapy. Regardless of the requirement of rigorous characterization in animal models and clinically derived cell lines to extend the promise of this study, we foresee the capability of these approaches to expedite choices for personalized medicine.TM, ThermoFisher Scientific) and 1% non-essential amino acids (Sigma-Aldrich) was used additionally for growth. Cell lines were maintained at 37 °C in a humidified atmosphere of 5% CO2/95% air. For the separation of NSP, the sub-population sorting assay was performed for Fluorescence-activated cell sorting (FACS) with cells at 70–80% confluency using BD FACSAria III (BD biosciences Pvt. Ltd) and separated the populations using Hoechst 33342 staining procedure24. The detailed protocol is available in our previous study24. After separation using FACS, NSPs were initially maintained in neurobasal medium  supplemented with B27 supplement , 0.2 μg per mL of epidermal growth factor, EGF (ThermoFisher Scientific) and 0.2 μg per mL of basic fibroblast growth factor, bFGF (ThermoFisher Scientific). Further sub-culturing and passaging of NSP was carried out using a similar medium as U87MG to avoid any contribution from different micro-environments in delineating heterogeneity of molecular signatures. NSP were cultured as free-floating spheres in appropriate low attachment T-75 flasks or 6 well or 24 well plates  for this study.U87MG cell line  was cultured in DMEM  with Glucose (1 mg per mL) and L-glutamine (0.584 mg per mL). 10% fetal bovine serum  at ~10,000 cells per well. The growth profile was studied in a 24 well plate (Nunc tissue culture-treated) for ease of harvesting. Both U87MG and NSP cells were harvested every 24 h and counted using a hemocytometer based on the trypan blue dye exclusion assay. Before counting, the NSP population was also disaggregated by trypsinization. For dose-response experiments, cells were plated in replicates at ~20,000 cells per well in 24-well plates  in full growth medium for 24 h and then treated them with different doses of TMZ . Three biological replicates were performed with three technical replicates in each biological replicate on a 24-well plate . Growth and temozolomide response curves were graphed with the number of cells on the Y axis and time on the X axis. The data was fitted using a logistic Gompertz function using GraphPad Prism software and the growth parameters were calculated.Growth of the cells (U87MG and NSP) were studied by monitoring their proliferation via cell count over a period of 168 h (7 days). The initial seeding set had a starting population  from each experiment setup  were harvested over a period of five days and used for the metabolic profiling to understand nutrient uptake and release kinetics. A sample pooling strategy33 was applied to reduce the number of samples and for high-throughput quantification. A viable cell count was performed to count the number of cells in each sample from growth curves. After cell count, the samples were centrifuged at 2795 × g (4 °C). The supernatant was used for extracellular analysis and the pellet was used for intracellular analysis.Extracellular samples. Each replicate sample was prepared and stored at −80 °C; thawed on an ice bath to aliquot 100 µL of sample for extraction. The aliquot was transferred into a fresh 1.5 mL centrifuge tube. Four hundred microliter of chilled methanol (previously stored in −80 °C) was added. The solution was thoroughly mixed for 2 min followed by centrifugation for 15 min at 2795 × g (4 °C). The tubes were carefully removed, 300 µL of supernatant was withdrawn and transferred into a fresh tube (Dilution level: 5×).Dilutions. A two-step serial dilution of supernatant was performed using 50% acetonitrile in water. In the first step, 50 µL of sample was thoroughly mixed with 450 µL of diluent (Dilution level: 50×). This solution was further diluted by mixing 100 µL of sample solution with 400 µL of diluent (Dilution level: 250×). Before injection, 100 µL of sample solution was mixed with equal volume internal standard solution containing 4.4 µM verapamil in 50% acetonitrile in water with 0.2% formic acid.Intracellular samples. The cell pellet was washed with PBS  (pH–7.4) by centrifugation at 2795 × g (4 °C). This step was repeated twice to remove any media residues. In the subsequent steps, HPLC grade methanol  software platform, whereas HESI source parameters were set using Tune module . Samples were stored in temperature controlled Accela autosampler maintained at 4 °C during LC-HRMS analysis. A reverse-phase C18 hypersil gold column (10 cm × 2.1 mm × 3.0 μm) was used for chromatography. The mobile phase consisted of 0.1% formic acid in deionized water (Mobile phase ‘A’) and 0.1% formic acid in acetonitrile (Mobile phase ‘B’). The elution gradient was set as 70% of mobile phase A (0.0–2.5 min), 10% A (3.5–5.5 min), 70% A (5.5–8.0 min) with a constant flow rate at 1000 μL per min. Five microliter of samples was injected for analysis using the auto-sampler unit. The data was acquired in both positive and negative ion mode in two separate batches. Metabolomics data analysis was carried out by the Qual and Quan browser modules of Xcalibur (Thermo Fisher Scientific Pvt. Ltd.). The same protocol was followed in our previous study24.http://biit.cs.ut.ee/clustvis/). Consumption and release concentrations were calculated by subtracting the concentration at 0 and 96 h. These values were used as inputs for generating heat maps and for PCA. Heat maps and clustering was performed with the criteria of unit variance scaling (applied to rows) where all 35 metabolites were the rows in the heat maps. Both rows and columns were clustered using correlation distance and average linkage with the tightest cluster first in tree ordering. PCA was carried out by using SVD with the imputation algorithm specified in the ClustVis tool. In some specific cases (BIOLOGTM experiments), the Euclidean algorithm was used for clustering to interpret the data. Variable Importance in Projection (VIP) scores and PLS-DA were calculated using MetaboAnalyst, a web-based metabolomic analysis tool37.The concentrations for all 35 metabolites identified from LC-MS/MS analysis were further clustered using ClustVis web tool  was used to remove any RNA contamination from the extracted DNA by incubating for 5 min at room temperature. ~15 µg of genomic DNA was extracted from each sample. This extracted DNA was used for the exome sequencing. Exome workflow of Ion ProtonTM systems  was used to obtain the exome sequences of U87MG and NSP cells. Ion TargetSeqTM Exome kit and Ion ProtonTM sequencer was used for acquiring the exome data that was further processed through TorrentSuite and Ion Reporter software to identify the variants and for the coverage analysis . A total of 4 × 10https://portals.broadinstitute.org/oncotator/). Oncotator is a web-based application commonly used for annotating human genomic point mutations including Single Nucleotide Polymorphisms (SNPs) and Insertions and Deletions (INDELS). This includes genomic annotations , protein annotations , and common SNP annotations from dbSNP database. The input file contained the details of the position in chromosomes, reference and its corresponding variants identified that was uploaded in the web tool for analysis. The output file had the results with details of the gene name, gene IDs, HUGO symbol, variant classifications , gene description, protein/amino acid change and its biological functions. These details helped further analysis and interpretation  consist of panels of PMM screening assays—(i) Energy metabolism pathways; (ii) Ion and hormone effects on cells and (iii) Sensitivity to anti-cancer agents. In PM-M1 to PM-M4, the metabolic pathway activities were assayed by using the cell suspension  prepared in an inoculating fluid (IF-M1 or IF-M2) that lacks carbon and energy sources (provided with the BIOLOGTM plates). Biolog Redox Dye Mix MA or Biolog Redox Dye Mix MB, was added to all wells. This measurement employs a tetrazolium dye that can be reduced to a purple formazan that can be measured at 590 nm with a microplate reader. The redox energy produced when a cell metabolizes a substrate is used to convert the color from yellow to purple formazan. The rate of formazan production changes linearly with time and corresponds to the number of viable cells. iMark™ Microplate Absorbance Reader (Bio-Rad), with a wavelength range of 400–750 nm, was used in our study to measure the absorbance. Cell suspension  of cell growth, followed by adding the dye and reading for 24 h of study .Biolog Phenotype MicroArrays™ (PM-M1 to PM-M14) from Biolog, Inc. USA  were used to estimate IC50 dose value. The concentrations of rotenone used in the IC50 estimation for U87MG cells were 0.5 mM, 0.1 mM, 1 mM, 2 mM, 3 mM, 4 mM, and 5 mM. The concentrations of rotenone used in the IC50 estimation for NSP cells were 5 nM, 10 nM, 20 nM, 50 nM, 100 nM, 0.5 mM, and 1 mM. The results were graphed and drug dose response parameters were calculated using Graph Pad Prism.For dose response experiments, four replicates at 20,000 cells per well were plated in 96 well Nunc32 consisting of 386 reactions  and v represent feasible flux range in each reaction from an unaltered model system, identified using flux variability analysis (FVA). For estimation of fluxes, these models were constrained using exchange rate for twenty-one metabolites including glucose, lactate and amino acids. (Supplementary File 32 and on oxygen uptake rate (OUR) based on estimations following flux balance estimations for maximal OUR feasible by the models. These contextualized models were evaluated following their in silico phenotypic predictions using constraints-based methods such as, flux balance analysis and uniform random flux sampling of flux solution space. We also added an in silico pseudo-hypoxia reaction (NAD [c] + H2[c] → NADH [c] + H[c]) involving the conversion of cytosolic NADH to NAD+ according to the previous study of testing the pseudo-hypoxia in cancer cells39, to estimate the levels of NADH and NAD+ in the cell-specific contextualized models.In Eq. vobj is an optimal solution for (Eq. 1) or alternate sub-optimal network states (0 ≤ 1) for objective function. The non-uniqueness of the FBA solution allows calculation of a range of flux that is feasible for each reaction, thus defining the rigidity and plasticity of the network.Plurality of solutions exists for the FBA problem, since the cell can choose multiple flux distributions to result in a unique objective function. FVA identifies the set of feasible fluxes at the optimal objective. The method calculates the minimum and maximum allowable fluxes through each reaction using a double optimization linear programming approach for each reaction of interest. The FVA problem, an extension of the FBA, is set up asmentclass2pt{minim38. This can be achieved by choosing a random point uniformly along each edge of parallelepiped following Monte Carlo sampling. Equation Similar to FVA, properties of metabolic flux states can be deciphered by random sampling of feasible flux space within the enclosing parallelepiped solution spaceRn is a random number chosen between 0 and 1 while αi,min and αi,max defines the flux range of feasible flux state along each reaction vector identified using FVA. These points can then be further compared to the set of constraints imposed on a constrained based metabolic model, in order to verify whether the random point falls in solution space.In Eq. 38.Solution sampling in this manner not only offers insights about plasticity of the metabolic network but offers latent information about metabolic flux states such as, coregulated list of trans-acting metabolic reactions. In addition, information about rewiring of the metabolic network imposed by system specific constraints can also be elucidated by inspecting the population distribution of random sampling for each reactionFlux sampling for U87MG and NSP cell line models was carried out using a Markov Chain Monte Carlo method of Artificial Centering Hit-and-Run (ACHR) sampler from COBRA toolbox. The initial point for the sampler was chosen amongst 1000 warmup points identified by combining random and orthogonal points. A total of 50,000 randomly distributed sampling points were computed with 1000 iterations between each stored point. Distribution of individual reaction flux values across the sampling population was represented as a histogram of feasible flux value and associated frequency in the convex polytope of solution space.Further information on experimental design is available in the Supplementary FileSupplementary File 2Supplementary File 3Supplementary File 4Supplementary File 5Reporting Summary