The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites.Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance.Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics – chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance.The online version of this article (doi:10.1186/s12918-017-0427-z) contains supplementary material, which is available to authorized users. Streptococcus pneumoniae is a consequence of mutations in putative iron transport systems [Staphylococcus aureus [Nocardiopsis, identified through metabolome characterization, was a consequence of acquired resistance [E. coli it was reported that overflow metabolism and reactive oxygen species (ROS) formation are inherent cellular response to antibiotic lethality [Edwardisiella tarda [A post-antibiotic apocalypse portends the utility of antibiotics being massively compromised through evolution of antibiotic resistance. The genetic basis of several antibiotic resistant populations have been delineated via whole genome sequencing to the level of identifying resistance genes –4. Antib systems . Stress s aureus . The evos aureus –9. β-Lacs aureus . Derepreethality . A recenethality . Specifiethality , 13. Prola tarda –18. InveChromobacterium violaceum. Primarily a zoonotic pathogen, it is opportunistic in humans and converts the essential amino acid tryptophan to violacein, a blue-violet pigment [Chromobacterium violaceum is also reported sensitive to aminoglycosides, chloramphenicol, and tetracycline and resistant to ampicillin, penicillin, and first-generation cephalosporins [in silico resistant growth phenotype was predicted to be a function of the rigidity of flux network.In this study, we have identified benign metabolites to stimulate antibiotic action and death of streptomycin and chloramphenicol resistant pathogen  pigment . The oxi pigment –22. Chroosporins , 24. OurChromobacterium violaceum strain ATCC 12472T, wild type, (C. violaceum or WT) was obtained from the American Type Culture Collection Center (ATCC), USA. The wild type C. violaceum was routinely cultured on Luria-Broth  and maintained at 30 °C with continuous aeration in a shaker incubator set at 180 revolutions per minute (rpm). C. violaceum was tested to be susceptible to low concentrations of both antibiotics (Chloramphenicol: MIC 8 μg/mL and Streptomycin: MIC 10 μg/mL in liquid culture and 60 μg/mL in agar plates). Antibiotic resistant strains of C. violaceum were evolved separately under controlled laboratory environments using the two antibiotics, chloramphenicol (chl) and streptomycin (str) at sub-lethal concentrations (10 μg/mL) on Luria Bertani agar (LBA) plates. Clonal purification by repeated sub-culturing of the colonies obtained on LBA plates with antibiotic (10 μg/mL) resulted in single colonies. These colonies were cultured in LB with antibiotic (10 μg/mL) and further cryopreserved in 50% glycerol and all genotyping and phenotyping experiments done by thawing the frozen vials and sub-culturing in LB with antibiotic (10 μg/mL) at 30 °C with continuous aeration in a shaker incubator set at 180 rpm until mentioned otherwise  on Müller-Hinton agar plates using the manufacturer’s instructions  according to the dilution method described previously [Antibiotic susceptibilities were determined with EzyMICons Fig. . The MICeviously  , mixed with an equal volume of LB that contains different metabolites at a final concentration of 2 mg/mL, except for lactate which was at a concentration of 0.27 mg/mL. C. violaceum was cultured in 96-well flat-bottom plastic microplates at 30 °C for 30 h with or without antibiotic pressure. 30 different metabolites were tested in biological triplicates. The plates were monitored for growth, biomass and violacein using iMark™ Microplate Absorbance Reader (BIO-RAD) at 550 nm and 655 nm at regular time intervals. The amount of violacein and dry cell weight were determined using 550 nm and 655 nm readings and compared to standard calibration graphs . Nonlinear curve fitting was done using Gompertz growth equation [equation  for the TM (Life Technologies) NGS using the 314TM chip with mean read length of 180 and 188 base pairs respectively. The sequencing data analysis for ChlR and StrpR samples showed 86 and 84% of the bases read were of ≥ Q20 quality respectively. For 77.07% of ChlR and 71.3% of StrpR samples the genome base coverage was 20x and for 18.61 and 10.01% coverage was 100x. The assemblies and comparative analysis against NCBI sequence of C. violaceum (accession number NC_005085) were also performed. The genome sequence data for this publication has been deposited at Sequence Read Archive (SRA) (http://www.ncbi.nlm.nih.gov/sra) under accession number SRP072862.In order to extract genomic DNA for whole genome re-sequencing, ChlR and StrpR cultures were revived from previously cryopreserved glycerol stocks on LBA plates with respective antibiotic at 30 °C. These cultures have been previously tested for all the phenotypic traits as described in the results section. A single colony from LBA was cultured in LB broth at 30 °C, 180 rpm and mid – log phase cells were harvested for genomic DNA extraction using the DNeasy Blood and Tissue Kit  according to the manufacturer’s instructions. The quality of the genomic DNA was assessed for RNA contamination using A260/A280 ratio and visualized on agarose gel. The DNA was also quantitated using Qubit before library preparation. Genome sequences were obtained for the two evolved populations, ChlR and StrpR, using Ion Torrent PGMAll NGS identified sequence variations were confirmed by Sanger sequencing. Primers  except for RpsL (homology modeling was used) and the models generated were checked for various parameters for model assessment, such as Ramachandran Plots using PROCHECK. Finally, visualization and manipulation of the three dimensional models were performed using the software PyMOL . In addition 3DLigandSite [Ab-initio models were made using ROBETTA server  (http://gandSite  was usedC. violaceum were inoculated (10% inoculum of overnight starter culture) and incubated in a shaker incubator at 30 °C, 180 rpm for 30 h. 2 mL of cell cultures were harvested at the end of 0, 6, 12, 18, 24 and 30 h by centrifugation at 12000 g at 4 °C for removal of extracellular media. The following steps were carried out on ice. The pellets were reconstituted with ice-cold ethanol for quenching as well as maximal extraction of metabolite features as used in previous study [For performing metabolomics experiments, the three different populations of us study , 30. ForThe intracellular as well as extracellular extracts were used for violacein estimation with BioPhotometer (Eppendorf) by comparing to standard calibration graphs for quantitation purpose as previously described .m/z at resolution of 70,000 Full Width at Half Maximum (FWHM) with Automated Gain Control (AGC) target 1e6 and injection time of 120 ms. Two technical replicates, each of 5 μL sample volumes were injected during analysis in both positive and negative ion mode. A total of 7 QC samples were run at beginning, intermittently and end of the run.The intracellular extracts were dried in centrivap (Labconco) at 4 °C, followed by reconstitution in 100 μL of 10% water in acetonitrile containing 4 μM atorvastatin . 10 μL of each sample was pooled to prepare a technical quality control (QC) sample. Metabolic profiling of samples was carried out on High Performance Liquid Chromatography – Heated Electrospray Ion Source – High-resolution Mass spectrometry (HPLC-HESI-HRMS). The separation was achieved on Sequent (Zwitter ionic hydrophilic interaction Liquid Chromatography) ZIC-HILIC column  column using HPLC consisting of Accela quaternary gradient pump, a degasser and Accela autosampler (Thermo Fisher). The column was maintained at 45 °C using column oven (PerkinElmer). The mobile phase for elution consisted of 0.1% formic acid in deionized water (Mobile phase ‘A’) and 0.08% formic acid in acetonitrile (Mobile phase ‘B’). Gradient was set with 5% of mobile phase A , 13% A , 45% A , 90% A , 5% A . Heated electrospray ionization (HESI) source was used as an interface between Liquid Chromatography (LC) and High-resolution Mass Spectrometry (HRMS) instruments. The spray voltage of the source was set at 3.7 kV with capillary temperature 300 °C, sheath gas 45 units, auxiliary gas 10 units, heater temperature 390 °C and S-lens RF at 50 units. The data was acquired in range of 70–1050 m/z of possible adduct was used to generate the XIC. After establishing the retention time (Rt) and peak width of respective metabolites as well as internal standard (IS), Tandem Mass Spectrometry (MS/MS) was carried out in respective ion modes to confirm their identities. The MS/MS data was analyzed using fragment search tool in METLIN [http://metlin.scripps.edu) and Mass frontier 7.0. A data processing method from processing setup module of Xcalibur software was prepared to integrate and generate area under peak data. The peak areas of metabolites were normalized to the area response of uniformly spiked internal standard, atorvastatin (ATV). This ratio is representative of the intracellular metabolite concentration (abundance) after taking into consideration the dilution factor for each sample extract. Principal component analysis (PCA) was used to get an overview of the data and to reduce the high dimension of the data set. For each metabolite, we calculated the temporal variation, using the coefficient of variation (CV):For data analysis, the Qual browser module of Xcalibur (Thermo) was used for manual inspection of presence of metabolite of interest through accurate mass - extracted ion chromatogram (AM-XIC). A mass extraction window (MEW) of 20 ppm around monoisotopic n METLIN  data baswhere σ and μ are the standard deviation and mean of the measurements across the time points respectively.m/z 50–750 at resolution 35,000 FWHM. Spray voltage at 2.5 kV, capillary temperature at 250 °C, AGC target of 1e6 and 500 μs injection time were optimized before beginning analysis. Laser fluence was optimized at 70% and Pulse Dynamic Focusing (PDF) value of 100 μs was used with automated rastering motion chosen for data acquisition.It was also estimated on Thermo Q-Exactive mass spectrometer (MS) coupled with an atmospheric pressure - matrix-assisted laser desorption/ionization (AP-MALDI) source equipped with a solid state Nd:YAG laser operating at 355 nm. A mixture of 2,5-dihydroxybenzoic acid  and 2-cyano-4-hydroxycinnamic acid (CHCA) was used as a matrix for the analysis. Samples were mixed with internal standard  before spotting on MALDI target plate in 6 replicates. The instrument was operated in full MS scan mode within http://www.ldi-ms.com/services/software) for quantification [Data analysis was performed with Thermo XCalibur Qual Browser, mMass  for qualfication  with a cC. violaceum presented here is a manually curated stoichiometric network reconstruction and model that allows probing special characteristics of this bacterium. It was done using available literature data [Stoichiometric network analysis based on the constraint-based modeling framework has been proven to be a valuable tool to study cellular metabolism and phenotypic capabilities of many organisms –38. -Theure data –42 as weure data –45.in silico and the sensitivity of growth yield to various precursors with specific reference to the cofactors NADH, NADPH and ATP. Constraints based flux balance analysis (FBA), as described in the following section, was used to simulate for growth (maximize biomass objective function) and violacein production. Constraints-based methods were used to perform a comparative analysis between the susceptible and resistant populations to understand the connections between metabolism and resistance.Further a set of constraints that define the antibiotic susceptible WT and differentiated the evolved populations (ChlR and StrpR) were determined. The constraints used in different simulations included (i) Substrate (Glucose) uptake rates (ii) Growth yields (iii) Violacein secretion (iv) ATP maintenance costs associated with molar growth yields of each strain as discussed Table . The speC. violaceum and constraints-based analysis was done using Constraints Based Reconstruction and Analysis (COBRA) Toolbox 2.0.2 [https://opencobra.github.io/).Implementation of the central metabolic reconstruction for ox 2.0.2  with MATFlux-balance analysis is a method for assessing the systemic properties and cell behaviors of a metabolic genotype. In short the primal FBA problem, equation (1) describes the steady-state mass balances of the biochemical reaction network –50\documLB, and vUB are vectors of length n, and S is the m x n stoichiometric matrix.where c, v, vMathematically, the S matrix acts as a linear transformation between the vector that defines fluxes through n reactions in the biochemical network and the vector of the time derivatives of the concentrations of m metabolites involved in these reactions. The fundamentals of FBA have been widely and critically reviewed , 51–54.The function optimizeCbModel(model), in COBRA toolbox was used for maximization of pathogen growth or biomass by fixing the objective function to be the biomass equation in the model.C. violaceum. Shadow price corresponds to the sensitivity of the growth rate as an objective function (Z) in response to a change in the availability of a metabolite (i), and indicates how much an increment in that metabolite will increase or decrease the growth rate. Analogous to shadow price, reduced cost is the sensitivity of the objective function in response to change in fluxes of a particular reaction and its effect on the objective. In addition to the primal solution , the LP solver provides the corresponding dual solution i.e., shadow price and reduced cost for the FBA problem.Two sensitivity parameters – shadow prices and reduced costs  - were ai) represents the sensitivity of each precursor yield to its biosynthetic demand whereas scaled reduced costs (Wi) are used to assess the limiting reactions.Scaled reduced costs  and logai is the flux through a particular reaction having a wi reduced cost associated with it. Z is the objective function value, in this case being biomass. Similarly, in equation (3) dX/dM is the associated shadow price to a particular metabolite and dM is the coefficient of the metabolite in the objective function equation.In equation (2) vAnother sensitivity parameter, gamma redox, the difference between the shadow prices for the redox couplet (NADH/NAD) and (NADPH/NADP) were calculated .4\documeThis is an index of available reducing capacity available to the cell and whether it is limiting or in excess for biomass formation. A positive shadow price suggests available reducing capacity in excess of the optimal demand for growth and where it is negative, limiting for optimal growth. A positive shadow price doesn’t necessarily indicate accumulation but also suggests metabolic rewiring into overflow metabolism.FVA was utilized to investigate the resulting space of feasible flux distributions . FVA candFBA was utilized to qualitatively predict the outcomes of growth in batch culture conditions matching our experimental condition . The resC. violaceum cultures  were measured using MyQubit Amplex Red Glucose Assay Kit (Thermo Fischer Scientific) according to the manufacturer’s instructions using Qubit 2.0 Fluorometer . The assay is based on the D-glucose dependent generation of hydrogen peroxide with glucose oxidase (GO) followed by the horse radish peroxidase (HRP) catalyzed oxidation of colorless, stable Amplex Red  to fluorescent resorufin.Glucose levels of C. violaceum cultures  were measured using a commercially available kit  according to the manufacturer’s instructions  from a small inoculum onto Luria Bertani agar (LBA) plates chloramphenicol (chl) and streptomycin (str), both targeting protein translation at the ribosomal subunit level. To provide strong evolutionary pressure while maintaining a sizeable population, the concentration of antibiotic was chosen such that no more than 60% of growth was inhibited. Once defined this sub-lethal concentration (SLC), of the antibiotic (10 μg/mL) was not varied throughout the evolution as well as other experiments unless specified. The adaptive evolution continued for about 3 weeks and when the first positive trait appeared, these colonies were cultured multiple times on LB agar plates with the respective antibiotics followed by colony purification. Multiple clones that were evolved in parallel were colony purified. Broth cultures of the respective antibiotic resistant population were used for Minimum Inhibitory concentrations (MIC) calculations. One of the parallel lines of evolved clones resistant to antibiotics was further used in genotypic, phenotypic and metabolic profiling studies  does not show capacity to utilize citrate, oxalate and glyceraldehyde-3-phosphate  [pabC, encoding the pyridoxyl 5’ phosphate (PLP)–dependent enzyme 4-amino-4-deoxychorismate lyase catalyzing PABA biosynthesis [Ab initio models built using ROBETTA and ligand binding analysis using 3DLigandSite showed that the mutated KdpD and PabC proteins truncated after 682 and 226 amino acids respectively resulting in loss of critical ligand binding or sensory domains   and pabCynthesis , 69. Ab ains see .C. violaceum. Metabolite relative abundance levels in the three populations span three orders of magnitude intracellularly  Fig. . IncludiThe linearized dynamics around the steady-state level of metabolites is captured by the temporal variation Fig. . Growth C. violaceum iDB149 was developed  that represents the energy required to sustain basal cellular activities. The values interpolated/fitted from growth yield curves differed for the wild type and the resistant populations due to varying molar growth yields  was performed to understand the modulation of NADH/NAD and NADPH/NADP ratios in growth. A reaction representing the NADH/NADPH oxidase (water forming) with the right balance of protons and oxygen was added to the model. This reaction essentially acts as a drain if there is excess NADH/NADPH in the system. On probing the relation to ATPase  by a pareto front analysis  walk dictated by the drugs in the regime of strong selection and weak mutation (SSWM) [acrR, the transcription repressor of the tripartite AcrAB-TolC multidrug efflux pump and marC another multidrug efflux pump and may be potentially commutative. The continuous activity of the tripartite AcrAB-TolC multidrug efflux pump, which is proton dependent, could result in membrane potential changes due to efflux of small molecules like violacein. Antibiotics are known to activate the AcRAB-TolC pumps [The evolutionary fitness landscape of n (SSWM) , 73. SuclC pumps  and hencrpsL  that is converted subsequently to virulence factor violacein. Potentially, developing a synthetic operon to convert tryptophan  to violacein may be useful as a visual reporter/biosensor of emergent resistance in even other infectious diseases. Reports exist that chloramphenicol lower intracellular indole and accumulation of tryptophan as observed [Violacein with its differential abundances Fig.  as compabacteria . C. violobserved . Furtherin silico central metabolic network reconstruction representing C. violaceum, iDB149, we were able to understand emergent properties of redox and energy homeostasis across susceptible and resistant populations. On shadow price and reduced costs analysis of growth limiting metabolites and reactions, AKGDH and ICL were identified in ChlR and and StrpR respectively. The alpha-keto glutarate dehydrogenase (AKGDH) reaction is known to regulate oxidative phosphorylation, lysine and tryptophan synthesis. Isocitrate lyase (ICL) reaction on the other hand, has been implicated in pathogenesis and persistence in Salmonella and resistance in Mycobacterium [rpsL may need a model of regulation and metabolic to explain the big change in growth rates and yields in presence of streptomycin. Flux variability analysis showed reactions that have flux category change in resistant strains compared to WT as a function of antibiotic involved redox cofactor balancing (NADH/NAD and NADPH/NADP ratios); increased TCA cycle/Oxidation Phosphorylation in the StrpR population and increased overflow metabolism population diverting from TCA cycle in the ChlR population. The wild type growth on pyruvate, malate and succinate also indicates the increase in NADH levels as a mechanism for survival in the presence of antibiotics (data not shown). The metabolism of these substrates after their uptake, all involve the use of the cofactor couple NAD/NADH [Using the acterium . The useacterium . The fluacterium . The appNAD/NADH . The parTaken together, our data unveiled that disruption of redox homeostasis by certain benign metabolites as key to killing antibiotic resistant pathogens. To distinguish between causal and correlational factors in the evolution of antibiotic resistance more rigorous experiments and comprehensive genome-scale models may be needed. Reversing the phenotype by perturbing metabolism through modulating micro-environments could be one way of subverting the onset of the post antibiotic era. Determining the genetic and metabolic basis of ribosome targeting antibiotic’s resistance helps address the ‘fourth dimension’ of how heterogeneous networks in cells evolve simultaneously in space and time. This could lead to scalable pipelines integrating growth/metabolite/MIC profiling and constraints-based flux balance models for clinical isolates, ultimately leading to personalized treatment and individualized therapy.Additional file 1:Supplementary Tables S1 to S6 and Figures S1 to S5. (PDF 2833 kb)Additional file 2:Growth profiles for WT, ChlR and StrpR using 30 different C/N substrates when antibiotic was added from zero hour timepoint showing WT with no antibiotic in media , ChlR with chloramphenicol added to the media , StrpR with streptomycin added to the media . Plots made using GraphPad Prism v6.01 and n = 3. (TIF 1004 kb)Additional file 3:Growth profiles for WT, ChlR and StrpR using 30 different C/N substrates for first six hours when no antibiotic was added  showing WT (blue) with no antibiotic in first 6 h, ChlR , StrpR  with no antibiotic in media. Plots made using GraphPad Prism v6.01 and n = 3. (TIF 778 kb)Additional file 4:Growth profiles for WT, ChlR and StrpR using 30 different C/N substrates post addition of antibiotic at 6 h (t6) showing WT with chloramphenicol and streptomycin, , ChlR with chloramphenicol added to the media , StrpR with with streptomycin added to the media . Plots made using GraphPad Prism v6.01 and n = 3. (TIF 1060 kb)Additional file 5:C. violaceum core metabolic model. The file includes metabolites, reactions, and other simulations used to understand the in silico behaviour of redox state of the resistant populations ChlR and StrpR in comparison to WT in the subsequent tabs. (XLS 384 kb)This file contains information about the Additional file 6:This file contains information about 59 metabolites analyzed using HPLC-HESI-HRMS. The file includes information including the retention time information, QC data peak area, normalized peak area among others. (XLS 292 kb)


Blood transfusions are an important part of modern medicine, delivering approximately 85 million blood units to patients annually. Recently, the field of transfusion medicine has started to benefit from the “omic” data revolution and corresponding systems biology analytics. The red blood cell is the simplest human cell, making it an accessible starting point for the application of systems biology approaches.In this review, we discuss how the use of systems biology has led to significant contributions in transfusion medicine, including the identification of three distinct metabolic states that define the baseline decay process of red blood cells during storage. We then describe how a series of perturbations to the standard storage conditions characterized the underlying metabolic phenotypes. Finally, we show how the analysis of high-dimensional data led to the identification of predictive biomarkers.The transfusion medicine community is in the early stages of a paradigm shift, moving away from the measurement of a handful of chosen variables to embracing systems biology and a cell-scale point of view. The human red blood cell (RBC) has long been a starting point for the application of systems biology. The RBC is an ideal model cell because of its relative simplicity and its intrinsic accessibility, resulting in a vast amount of available data. Further, it is of great importance for our understanding of human health and physiology—RBCs account for over 84% of the native cells in the human body by number, making them the most numerous cell type by a large margin . Thus, mRecently, omics technologies have been applied to study RBCs under cold storage for use in transfusion medicine . The traAs omics data characterizing RSL are being generated, the field of transfusion medicine provides opportunities for systems biologists . In partThere have been a multitude of examples of how a systems biology approach can be applied to study a variety of organisms and biological questions of interest –29. The Time-resolved metabolomics data have yielded important insights into metabolic physiology, especially when the resolution of the time course captures the time scale of key metabolic changes. For RBCs under storage conditions, these key metabolic changes occur on time scales on the order of days, not weeks . In the experiments discussed below, data is collected every three to four days, providing a more complete characterization of these temporal dynamics. Exploration of various perturbations to the standard storage conditions will help elucidate RBC metabolic properties. Such perturbation experiments might be informed by previous experiments or by computational models .The second ingredient is the application of multivariate data analysis to the large data sets generated. Multivariate statistical analyses can reveal the overall structure of the data sets and trends within the data. In particular, methods like principal component analysis (PCA) , partialThe third ingredient is a computational, mechanistic metabolic network model capable of integrating disparate data types. Such models incorporate the results of statistical analyses to generate biological insights and testable hypotheses. A metabolic network specific to the RBC has been generated by mapping multiple proteomic data sets onto the reconstruction of the global human metabolic network , 37. ThiSeveral years ago, the use of systems biology principles in the transfusion medicine field were proposed as a way to extend the lifetime of stored RBC units . Since tThe first major step in a systems biology characterization of the storage process was to understand the baseline RBC metabolic behavior during storage . RBC uniInitial global characterization of the data was achieved using PCA of the raw metabolomics data, revealing two distinct metabolic shifts that occur during the 42 day shelf-life of a stored RBC unit. These shifts in metabolic state—occurring at days 10 and 17—showed that RBCs do not undergo a simple linear decay process but instead go through a defined series of three metabolic states each with distinct characteristics. A similar three-phase metabolic decay profile was being observed in parallel in independent studies and in different storage media  and has The previously reported high concentration of 2,3-DPG that depletes over time was observed, as well as the initial increase and subsequent decrease in ATP levels after the first shift at day 10. It was observed that the “metabolic inflection points”  approximately coincide with the depletion of extracellular adenine and onset of accumulation of hypoxanthine and xanthine in the storage medium. One notable novel observation from the quantitative metabolomics data was the existence of a large intracellular malate pool (greater than 1 mM). The discovery of these metabolic phases and their characterization was only possible through the use of a systems-level perspective.13C 1,2,3-glucose tracing to elucidate this mechanism [The multi-variate data analysis results provided by this baseline study of RBC metabolic decay have proven to be a useful starting point for mechanistic model-based analysis. Since the baseline metabolomics data are absolutely quantified, they can be integrated into mechanistic, cell-scale models capable of making quantitative predictions , 47. Theechanism .The quantitative model also made predictions about the metabolic fate of citrate, a compound added to the storage medium as an anticoagulant during blood collection. These results were validated  and thenHaving characterized the baseline metabolic behavior of stored RBCs, the next step in the systems biology characterization was to determine whether the storage conditions could be perturbed in such a way that this three-phase metabolic decay process was affected. Four perturbations representing pressing questions were identified and examined Fig. : (1) doeWith the baseline behavior in SAGM media characterized, it was important to determine whether RBCs stored in other additive solutions exhibited similar metabolic behavior . RBC uniSeveral changes in basic metabolic behavior were observed across the four additive solutions. Notably, citrate uptake and metabolism was increased in AS-3 and PAGGSM compared to that of SAGM and AS-1. Corresponding changes in intracellular malate concentrations suggest that citrate uptake impacts malate utilization. Labeled citrate added to the bag prior to storage in SAGM showed that citrate is taken up and converted to intracellular malate, contributing to the large pool previously observed in the baseline characterization. This behavior has been shown to occur in other media  and was The baseline data showed that fructose and mannose found in the plasma collected with the RBCs from the donor are rapidly metabolized and depleted during the first metabolic phase. Mannose and fructose have been shown to be metabolized through different pathways than glucose in RBCs , 58, thuThese experiments suggested that the metabolism of mannose and fructose at 4°C reflects their metabolism at 37°C, but this is a potentially misleading result due to the presence of glucose . The timing of the metabolic inflection points was altered slightly with the supplemented sugars, with the observed changes primarily centered in glycolysis. The hypothesized protective effect of fructose was not observed. The additives failed to maintain ATP and 2,3-DPG levels under the tested experimental conditions, although this was likely due to the presence of glucose.13C labeling results from this perturbation study support the hypothesis that mannose is taken up by GLUT1. More importantly, these results imply that mannose is preferentially taken up and oxidized over glucose. Although some differences in the metabolic state were observed with the addition of mannose and fructose, there is no clear advantage to these supplementations. Ultimately, a better characterization of the metabolism of these sugars could be obtained by replacing glucose with mannose and/or fructose (instead of supplementing) and examining the resulting metabolomics measurements.While fructose is known to be taken up through the GLUT5 transporter , the exaFollowing the identification of the three-phase metabolic decay observed in SAGM , it was It was observed that the RBCs consumed approximately 1.5 mg/L adenine per day over the first eight days of storage, almost depleting the total adenine concentration in the bag toward the end of the first metabolic phase. During this first phase, adenine was converted into inosine and IMP but not ATP. By day 18 of storage (the end of the second phase), the extracellular adenine was completely depleted.Having detailed adenine metabolism under standard storage conditions, its initial concentration was doubled to see if its depletion was the cause for a metabolic shift. The identical consumption rate of adenine was observed until day 18, at which point adenine was no longer taken up by the RBCs. In other words, it appears that the perfect amount of adenine is found in SAGM media; adding any more would result in adenine sitting in the extracellular media without being consumed by the RBCs. One possible explanation for this result is that this behavior was previously characterized during the development of SAGM media but never published (and has now been re-discovered years later). One notable observation was that the higher levels of adenine resulted in a buildup of 5-methylthioadenosine. The conclusion of this study was that adenine is not responsible for the observed metabolic shifts: there is another internal process that leads to the cessation of adenine uptake.ex vivo (storage) and in vivo (body) temperatures—and metabolically profiled [The previous studies all examined perturbations to the storage media itself. One important aspect of the storage conditions, however, is the low temperature at which RBC units are stored (4°C) and how this low-temperature environment affects RBC metabolism. In order to investigate these effects, RBC units were stored at 4°C, 13°C, 22°C, and 37°C—temperatures that span the profiled . Bags weThis study investigated whether the three-phase decay pattern was observed at all temperatures. PCA was performed on the metabolomics measurements, observing that these trends were preserved but accelerated with increasing temperature. The temperature dependences for individual metabolites and reaction fluxes were calculated, finding that the response of these individual network components did not scale uniformly with increasing temperature. In particular, the behavior of the ions was quite different at low temperatures due to the known inactivation of the sodium/potassium pump , 68. TogThese results open the door for another interesting possibility: can the temperature dependences calculated here be used to run high throughput screens of additional perturbation experiments at higher temperatures? Our results suggest that an RBC unit stored at a temperature of only 13°C would only need to be stored for approximately 14 days to observe the equivalent 42 day storage behaviors observed at 4°C. Since the global network-level changes were consistent at higher temperatures, any screens in which the three-phase decay pattern was disrupted could then be investigated in detail under the proper conditions. It is important to note, however, that there is still further evaluation required; the temperature-driven effects on ion homeostasis—which activates ion-dependent cascades —wouOne obstacle to the routine use of metabolomics data is the high cost of generating them. With the increasing amount of metabolomics data already available for RBCs under storage conditions  and relaPaglia et al.  set out +/NADH and NADP +/NADPH) and is released from RBCs after approximately ten days of storage. The toxic effects of hypoxanthine and xanthine are well known [Glucose, lactate, 5-oxoproline, and adenine represent the primary metabolic inputs and outputs can effectively serve as “clocks” for storage time. Further, the large malate pool is related to a major component in the citrate buffer used during processing. The potentially more interesting biomarkers are nicotinamide, hypoxanthine, and xanthine that are directly indicative of the metabolic state. Nicotinamide is one of the components of major cofactors  and signaling pathways will provide challenges beyond those faced in RBC physiology.The human RBC is not only the ideal target for systems analysis, but it also represents a system of high interest for studying human physiology and is central to transfusion medicine. The RBC is the cell type most amenable to systems analysis through the integration of multiple omic data types into a mechanistic model. These data sets can be gathered to reflect various criteria such as gender, age, and ethnic diversity. Ultimately, there is great promise for the use of systems biology approaches to design experiments informed by a mechanistic understanding of RBC physiology . The fie


In Escherichia coli, changing the cofactor specificity of ICDH from NADP+ to NAD+ (cofactor swapping) decreases the growth rate on acetate. However, the metabolic basis of this phenotype has not been analyzed. In this work, we used constraint-based modeling to investigate the effect of the cofactor swapping of ICDH in terms of energy production, response of alternative sources of NADPH, and partitioning of fluxes between ICDH and isocitrate lyase (ICL) -a crucial bifurcation when the bacterium grows on acetate-. We generated E. coli strains expressing NAD+-specific ICDH instead of the native enzyme, and bearing the deletion of the NADPH-producing transhydrogenase PntAB. We measured their growth rate and acetate uptake rate, modeled the distribution of metabolic fluxes by Flux Balance Analysis (FBA), and quantified the specific activities of NADPH-producing dehydrogenases in central pathways. The cofactor swapping of ICDH led to one-third decrease in biomass yield, irrespective of the presence of PntAB. According to our simulations, the diminution in growth rate observed upon cofactor swapping could be explained by one-half decrease in the total production of NADPH and a lower availability of carbon for biosynthesis because of a change in the partition at the isocitrate bifurcation. Together with an increased total ATP production, this scenario resulted in a 10-fold increment in the flux of ATP not used for growing purposes. PntAB was identified as the primary NADPH balancing response, with the dehydrogenases of the oxidative branch of the Pentose Phosphate Pathway and the malic enzyme playing a role in its absence. We propose that in the context of E. coli growing on acetate, the NADP+-specificity of ICDH is a trait that impacts not only NADPH production, but also the efficient allocation of carbon and energy.It has been proposed that NADP Escherichia coli (E. coli), the isocitrate dehydrogenase (ICDH) from the TriCarboxylic Acids (TCA) cycle is an NADP+-specific enzyme that provides most of NADPH when the bacterium uses acetate as the sole source of carbon and energy [+-specific in microorganisms that cannot grow on acetate [Heterotrophic bacteria oxidize available carbon sources generating NADPH and NADH through the dehydrogenases of the central metabolic pathways. Dehydrogenases usually present a strong segregation in their cofactor specificity . In Eschd energy . Convers acetate .E. coli expresses two of these enzymes: the membrane-bound PntAB and the cytosolic UdhA. They rebalance the insufficiency or excess of catabolic production of NADPH, by producing (PntAB) or consuming (UdhA) this cofactor, respectively [E. coli grows on acetate, the high carbon flux through ICDH leads to an overproduction of NADPH [A balance between the production and consumption of NADH and NADPH is needed to keep the cellular homeostasis. Not surprisingly, different mechanisms have been described to enable this balance, such as the presence of transhydrogenases . E. coliectively , 5. Whenof NADPH , which i+ has evolved as an adaptation to grow on acetate [+-dependent ICDH, Zhu et al. [E. coli on acetate. The negative effect was more pronounced in the absence of PntAB and the malic enzyme . Conversely, the cofactor swapping of ICDH increased the growth rate in the absence of UdhA. Unfortunately, growth rate does not provide by itself a picture of the effect of the metabolic perturbation on the energetic efficiency of converting an energy-poor substrate such as acetate into biomass. In regard to specific responses of rebalancing NADPH deficiency, Wang et al. [+-specific malic enzyme in extracts was almost 3.9-fold enhanced upon ICDH cofactor swapping. They highlighted the importance of this enzyme in providing NADPH for the growth of E. coli on acetate. In the case of PntAB, its contribution level has not been clarified with respect to the metabolic response of E. coli after cofactor swapping of ICDH.It has been proposed that the specificity of ICDH for NADP acetate . By usinu et al.  demonstrg et al.  observedE. coli grows on acetate, a fraction of the isocitrate is diverted towards isocitrate lyase (ICL), bypassing ICDH. As this shunt avoids the oxidative decarboxylation reactions of the TCA cycle, it replenishes intermediates that are used for the biosynthesis of cellular constituents [E. coli mutants lacking ICL activity lose the capacity to grow on acetate [+-specific ICDH and they are unable to grow on acetate [E. coli growing on acetate, the flux ratio at the isocitrate bifurcation must be finely regulated to obtain both a proper separation of the carbon available for biosynthesis from that lost as CO2 and a suitable production of NADPH. This regulation is achieved through phosphorylation of ICDH, which renders the enzyme inactive, decreasing the flow of isocitrate through the TCA cycle and so forcing it through ICL instead [et al. [et al. [When tituents . In fact acetate . Interes acetate . Then, f instead , 11. LaP [et al.  analyzed [et al.  analyzedE. coli is the analysis of its metabolic fluxes. In fact, characterizing the metabolic flux response after replacing the native dehydrogenase has been the focus of other studies of cofactor swapping [E. coli, including metabolic changes such as the production of energy, the response of alternative sources of NADPH, and the partitioning of fluxes at the isocitrate bifurcation. In the present study, we used this approach to analyze the metabolic impact of changing the cofactor specificity of ICDH in the presence and absence of PntAB. We first characterized the exponential phase of aerobic growth on acetate of wild type and mutant strains bearing these modifications. Using the rates of growth and acetate uptake as constraints, we modeled optimal and sub-optimal distributions of metabolic fluxes by FBA and Markov chain Monte Carlo (MCMC) sampling, respectively. Finally, we measured the specific activity of key dehydrogenases in cell extracts of the cultured strains to evaluate possible responses to ICDH cofactor swapping at this level.One crucial aspect to understand the effect of changing the cofactor specificity of ICDH in swapping , 14–16. swapping , 18. Henicd gene has the FRT-kanR-FRT cassette just downstream of the previously described encoding sequence of an NAD+-dependent E. coli ICDH (icdNAD gene) [NAD-FRT-kanR-FRT) was amplified from the plasmid pUC57-icdNAD-FRT-kanR-FRT with ad hoc primers containing nucleotide sequences matching the chromosome 30 bp upstream and 30 bp downstream of the 1251 bp native sequence of ICDH. Likewise, the deletion of the pntAB operon was performed by amplifying the FRT-kanR-FRT cassette from the plasmid pKD13 with ad hoc primers for the homologous recombination.Each genetic modification was performed using a previously described homologous recombination method, which employs a PCR fragment to replace a native gene in the chromosome . The linAD gene) . This fr600 0.4–0.5 at 30°C and the expression of the Red recombinase system was induced with the addition of 10 mM arabinose for 1 h before transformation. The colonies obtained from the transformation using each fragment correspond to icdNAD-kanR and ΔpntAB-kanR phenotypes, respectively. Chromosomal fragments inserted in the kanamycin-resistant colonies were moved into fresh wild type isolates of E. coli using a previously described backcross method [icdNADΔpntAB) was obtained by using the icdNAD strain as the fresh isolate of E. coli for the backcross procedure and the ΔpntAB-kanR strain as donor. To detect the strains of interest, colony PCR for both icd and pntAB regions was used. In addition, sequencing of these regions was performed to confirm the modifications.Each fragment was purified and introduced into freshly prepared electrocompetent cells from the parental strain MG1655, transformed previously with plasmid pKD46 . To accos method , 21. Thead hoc antibiotics. Wild type E. coli MG1655 and mutants derived thereof were used in all physiological experiments. All physiological experiments were performed by growing the cells in full aerobic conditions, in 500 mL flasks containing 100 mL M9 minimal medium supplemented with 3.0 g × L-1 of sodium acetate as the sole carbon source at 37°C on a gyratory shaker at 200 rpm. M9 minimal medium was prepared according to a previous report [During genetic manipulations the strains were grown in liquid or solid LB rich medium, supplemented with s report . All exp600. To determine the biomass yield and calculate the acetate uptake rate, 6 samples were taken throughout the exponential phase, filtered using a 0.22 μm filter and immediately chilled at -80°C until analysis.To determine the growth rate, samples were taken every 60 min to measure the OD2SO4 and was run at a flow rate of 0.6 mL x min-1. The OD600 to dry weight conversion coefficient (ƒ) was determined gravimetrically after centrifuging at 3,215 x g samples of 15 mL obtained from cultures of wild type E. coli (taken throughout the culture), washing with distilled water, and drying at 100°C to a constant weight. Thus, the value of ƒ used in this work was 0.40 ± 0.01 gDW × L-1 × OD-1.The concentration of acetate in the filtrates was determined by HPLC (Waters 1525) using an Aminex HPX-87H ion exchange carbohydrate-organic acid column (Bio-Rad) at 65°C and a UV detector . The degassed mobile phase contained 5 mM HE. coli iJO1366 was used as the wild type default Systems Biology Markup Language model [Z) is identified. This linear programming (LP) problem is formulated as:The comprehensive genome-scale model of ge model . Modelinc is the objective vector containing the reaction to be maximized or minimized, v is the vector of fluxes, S is the stoichiometric matrix, and vi-lb and vi-ub are the lower and upper bounds of the velocity of the i-th reaction, respectively. The formalism of FBA was previously described in detail [Subject to:n detail .+ and NADPH of the reaction ICDHyr were interchanged with those of NAD+ and NADH, respectively. Additionally, the pyruvate:ferredoxin oxidoreductase (POR5) was made irreversible by setting to zero its lower bound, similarly to a previous method [Additional constraints to model the effects of genetic modifications were added. The lower and upper bounds of reactions catalyzed by deleted genes were changed to zero. To model the effects of changing the cofactor specificity of ICDH, the stoichiometric coefficients of NADPs method . All thes method  in MATLA+-dependent ICDH, after constraining the ATPM flux (reaction that drains ATP from the model) to its optimal value, representing the maximal ATP yield for the modeling condition. There on, a robustness analysis was performed after constraining the production of isocitrate to its optimal value. In this case, we observed the response of biomass yield (y-axis) as a function of the partition of flux towards ICL (x-axis). All these analyses were implemented as described in [On one hand, a robustness analysis was performed to assess the potential of the strains to produce ATP at different growth rates. On the other hand, a phenotype phase plane analysis was done to explore the behavior of biomass yield (z-axis) as a function of the production of isocitrate (x-axis) and the partition of flux between ICL and ICDH (y-axis). This analysis was taken on the wild type strain and the mutant bearing the NADribed in .i. maximization of the ATP yield along with minimization of the sum of fluxes ; and iii. elimination of loops . This method is essentially iterative, and was performed as follows:A procedure was designed to calculate a unique optimal flux distribution after constraining the models with the rates of growth and acetate uptake, both measured experimentally. The obtained distribution is intended to comply the following criteria: z et al.  the meta-1 × h-1, respectively).The constraints of ATPM were removed .The reaction ATPM was set up as the objective function again.The enumeration of the first 400 optima of maximal ATP yield was performed using MILP .vi) from a total of n reactions: A flux distribution from the 400 optima was selected by identifying the one which minimizes the sum of squares of velocities (-1 × h-1), the lower and upper bounds were both set to the fluxes obtained in the previous step. Thus, after performing the remaining steps, it should be obtained a flux distribution with lower values of flux for those reactions that exceeded the threshold.For those reactions whose velocity was below a threshold  of the i-th metabolite was calculated using the following formula:S is the stoichiometric coefficient of the i-th metabolite in the j-th reaction, and vj is the velocity of that reaction. In this case, the minimization of the number of loops was a useful strategy to avoid biologically unfeasible values.At steady state, the net flux of consumption or production , 100 mM NaCl, 2 mM MgCl2, 1 mM EDTA, 2 mM dithiothreitol (DTT), and 20% glycerol. For G6PDH, 6PGDH and ICDH activities, cells were resuspended in 500 μL 50 mM Tris-HCl (pH 8.0), 100 mM NaCl and 5 mM MgCl2. After sonication for 1 min in an ice bath, the cell debris was removed by centrifugation at 14,000 rpm for 30 min at 4°C. Each enzymatic activity was determined following absorbance at 340 nm in buffer 50 mM Tris-HCl (pH 8.0), 100 mM NaCl, 5 mM MgCl2, using the corresponding substrates . One unit of specific activity (U × mg-1) was defined as 1 μmol NAD(P)+ formed per min per mg of protein.Each strain at the exponential phase was collected by centrifugation at 5,000 rpm for 10 min. In the case of the NADPOne-way ANOVA and Tukey’s HSD test were performed to analyze all possible pairwise comparisons of each physiological parameter of the strains. A web-application was used to generate automatically a table containing superscript letters that indicate significant differences .E. coli the metabolic impact of changing the cofactor produced by ICDH from NADPH to NADH in the presence and absence of the alternative NADPH source PntAB, we generated the strains icdNAD, ΔpntAB, and the double mutant icdNAD ΔpntAB. The genetic modifications were confirmed by sequencing the corresponding loci. The physiological parameters growth rate (μ), acetate uptake rate (Qac) and biomass yield (YX/S) were obtained for these strains at the exponential phase of growth in batch cultures, with acetate as the sole carbon source. When analyzing the HPLC profiles, we did not detect the accumulation of possible by-products. In order to evaluate in X/S. These values decreased by a third, irrespective of the presence of PntAB. The lower efficiency in using acetate for biomass production helps to explain why the icdNAD strain competes worse than wild type on this carbon source, as reported by Zhu et al. [X/S when comparing strains with the same cofactor specificity of ICDH. Accordingly, Sauer et al. [-1) for wild type and ΔpntAB strains as we observed in batch cultures on acetate  performing carbon fixation from CO2 (reverse direction of the reaction). When the reaction POR5 was made irreversible by setting up to zero its lower bound, we observed zero production of biomass when there is no flux through ICL . Even more, upon cofactor swapping almost half of the production was allocated to non-growth associated maintenance, suggesting the idea that the presence of an NADP+-dependent ICDH allows a more efficient use of energy for biomass production when E. coli grows on acetate.After choosing the maximal production of energy as the optimization criterion to model the metabolic fluxes, which corresponds to maximizing the ATPM flux, we evaluated this reaction at different growth rates for each strain. escribed . Moreoved ΔpntAB . Since tnd Jones  for wild+-dependent ICDH, whereas the flux through PntAB occurred in the icdNAD strain, but not in the wild type. Accordingly, FVA showed that the maximal flux through UdhA was zero for icdNAD strains, while a non-zero flux through PntAB was observed only for the icdNAD strain . Noteworthy, it has been reported that the last enzyme plays an important role in the production of NADPH in eukaryotic cells [icdNAD strain, and the dehydrogenases of the oxPPP in the double mutant.When considering the joint contribution of the main sources, we observed that the total production rate of NADPH was similar in the strains bearing the NADP strains . A diminic cells . As ment+-dependent malic enzyme the range of possible fluxes is similar for every strain, although the most probable fluxes seemed to be insensitive to the cofactor swapping of ICDH. PntAB covers a wide range of possible fluxes, but the mode of the histogram increased with the change in the reduced cofactor produced by ICDH. In the case of ICDH, histograms showed that the cofactor swapping displaced the mode of the histograms towards considerably higher values, implying an increment in the fraction of acetate converted to CO2. In the case of the double mutant, the system required more carbon converted to intermediates in order to generate NADPH through oxPPP, hence the mode was less increased.Considering that FBA retrieves only the optimal flux distribution, we also explored sub-optimal distributions of fluxes by using Markov chain Monte Carlo sampling . For theicdNAD strains showed a significant increment in the total production of NADH and a slight increase of flux through α-ketoglutarate dehydrogenase (AKGDH) and malate dehydrogenase (MDH). Considering the higher ATP yield in the icdNAD strains, due to their low growth rate and the trade-off between biomass and energy production [E. coli grows at low rates on glucose [Regarding the joint contribution of the main NADH sources, similar trends were observed: strains with identical ICDH did not show differences due to the absence of PntAB, and some of the sources were common for all strains . After coduction  Fig 1A)icdNAD st glucose .2, and hence that available to perform biosynthesis. Thus, we examined how the optimal partition changed with the physiological parameters determined for each strain. Although for all the strains the flux through ICDH was higher than through ICL, this difference is augmented in the icdNAD strains. ICDH / VICL), based on FVA results , resulting in the establishment of a new optimum value at 24% of partition towards ICL , on the specific rate of growth on acetate as sole carbon source. When the reverse direction of the reaction catalyzed by pyruvate:ferredoxin oxidoreductase (POR5) was allowed (without constraint), growth was possible with no flux through the glyoxylate bypass (biomass yield was not zero). However, after correction of reversibility of POR5 (with constraint), avoiding carbon fixation in the reverse direction, there was no growth as experimentally expected .(TIFF)Click here for additional data file.S2 FigicdNAD strain as constraints. A percentage of 0% of cofactor swapping means that the enzyme uses 0% of NAD+ and 100% of NADP+, whereas a complete shifting of cofactor specificity represents a use of 100% of NAD+ and 0% of NADP+.Evaluation of the effect that could have a non-complete cofactor swapping of ICDH over the objective function  and the flux through this dehydrogenase. A scanning from 0 to 100% of cofactor swapping was done by using physiological parameters of the (TIFF)Click here for additional data file.S3 Fig+-dependent malic enzyme (x-axis) and the flux through oxPPP (y-axis). The behavior of each strain is shown, arranged by presence of icdNADP or icdNAD gene (columns) and by presence or absence of pntAB operon (rows). FBA-calculated optimal values are shown as (☆). Experimental data is shown for the wild type strain: (+) Gerosa et al. (2015), (◇) Haverkorn van Rijsewijk (2012), (▽) Taymaz-Nikerel et al. (2010), (○) Zhao & Shimizu (2003), (⬜) Holms (1996) and (△) Walsh & Koshland Jr. .Phase plane analysis of biomass yield as a function of the flux through the NADP(TIFF)Click here for additional data file.S4 FigE. coli reported by different authors, using diverse methods to determine net fluxes. Central pathways are shown. For each reaction (represented by an arrow) a box containing six values of flux is shown, corresponding to the velocity of reaction reported by Gerosa et al. (2015), Haverkorn van Rijsewijk (2012), Taymaz-Nikerel et al. (2010), Zhao & Shimizu (2003), Holms (1996) and Walsh & Koshland Jr. , respectively. With the exception of Taymaz-Nikerel et al. (2010), all studies used 13C isotope labeling to determine flux distributions. The fluxes are represented as a percentage of the corresponding acetate uptake rate. If the direction of a reaction is opposed to that of the arrow, the value of flux possesses a negative sign. Dehydrogenases are represented by green and red circles, indicating NADP+ and NAD+ specificity, respectively. Only the NADP+-dependent malic enzyme is shown. n.d., not determined.Flux distributions in acetate metabolism of (TIFF)Click here for additional data file.S5 FigicdNAD strain under sub-optimal conditions of growth. In this case we used as upper bound the 75% of the optimal flux of PntAB. The histograms of the main sources of NADPH are shown . If the optimal flux was not zero, vertical red lines indicate this value. The flux distributions selected were gathered from the samples used for Distribution of selected-sampled points from the solution space of the (TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Type 2 diabetes is one of the leading non-infectious diseases worldwide and closely relates to excess adipose tissue accumulation as seen in obesity. Specifically, hypertrophic expansion of adipose tissues is related to increased cardiometabolic risk leading to type 2 diabetes. Studying mechanisms underlying adipocyte hypertrophy could lead to the identification of potential targets for the treatment of these conditions.iTC1390adip, a highly curated metabolic network of the human adipocyte presenting various improvements over the previously published iAdipocytes1809. iTC1390adip contains 1390 genes, 4519 reactions and 3664 metabolites. We validated the network obtaining 92.6% accuracy by comparing experimental gene essentiality in various cell lines to our predictions of biomass production. Using flux balance analysis under various test conditions, we predict the effect of gene deletion on both lipid droplet and biomass production, resulting in the identification of 27 genes that could reduce adipocyte hypertrophy. We also used expression data from visceral and subcutaneous adipose tissues to compare the effect of single gene deletions between adipocytes from each compartment.We present We generated a highly curated metabolic network of the human adipose tissue and used it to identify potential targets for adipose tissue metabolic dysfunction leading to the development of type 2 diabetes.The online version of this article (doi:10.1186/s12918-017-0438-9) contains supplementary material, which is available to authorized users. Type 2 diabetes is one of the most prevalent non-infectious diseases in the world and affects an increasing number of patients every year. The development of insulin resistance in multiple tissues and impaired insulin secretion by pancreatic β-cells in response to glucose are the two main pathophysiologic characteristics of type 2 diabetes . Both arExposure of lean-tissues to fatty acids is regulated by adipose tissues. Deficits in adipose tissue expansion during chronic positive caloric imbalance is linked to increased levels of circulating fatty acids that contribute to ectopic fat deposition and lipotoxicity in lean tissues . AdiposeImproved knowledge of the mechanisms leading to adipose tissue hypertrophic vs. hyperplasic expansion during weight gain could lead to the identification of new targets for the prevention and treatment of type 2 diabetes. One method for studying cells at the systemic level is the use of metabolic networks. Metabolic networks serve as a representation of metabolism at the cellular scale and can be used to identify metabolites, reactions, genes and pathways linked to various diseases –12. ReacTwo genome-scale metabolic networks, Recon1  and the Various, more specific models were developed over the years to study the effect of diabetes in various tissues –19. ThesiAdipocytes1809 was developed by Mardinoglu et al. [BCAT1) as a potential target for the treatment of obesity [A genome-scale metabolic network of human adipocytes called u et al. . It is s obesity . UnfortuiAdipocytes1809 and used the resulting metabolic network to perform a number of analyses. Among these, we performed in silico gene deletions in which we used biomass and lipid droplet production as proxies for hyperplasia and hypertrophy respectively, to identify genes that could tilt the balance away from adipocyte hypertrophy. Furthermore, we used experimental postprandial concentration curves of circulating glucose, non-esterified fatty acids (NEFA) and triacylglycerol (TAG) to simulate the adipocyte metabolic responses over time after meals. Studying variation in the production rate of metabolites as a function of time opens the possibility to use metabolic networks as a window to the internal state of adipocytes as a function of time. Considering that a computational model of adipocyte metabolism gives access to measurements of intracellular metabolites that may be difficult to perform experimentally, this approach generates hypotheses that could lead to a better understanding of adipocyte function.In this work, we generated a highly curated, freely available human adipocyte metabolic network improving on the published iAdipocytes1809 metabolic network we found that it had some limitations. It contains genes that are not expressed in adipocytes, various erroneous gene-reaction associations, reactions that do not occur in adipocytes and lack of critical information regarding protein complexes that are essential to the determination of the effect of in silico mutations. Furthermore, iAdipocytes1809 also lacked a precisely defined biomass reaction, necessary for the flux balance analysis (FBA) calculations we wished to produce [During our use of the  produce . FBA is iAdipocytes1809: 1) removal of genes not expressed in adipocytes; 2) removal of incorrect gene/reaction associations; 3) removal of non-metabolic reactions; 4) reduction of the number of dead-end metabolites; 5) addition of new genes, reactions and gene/reaction associations; and lastly, 6) addition of information relative to protein complexes. These steps resulted in the creation of a new network, iTC1390adip. The network was validated using a battery of 245 tests and used in various analyses. These steps are described next.The following steps were undertaken in order to generate an improved version of iAdipocytes1809 network supplementary data provided by Mardinoglu et al. [We used the u et al.  to removu et al.  data as u et al. . We callCEL) gene, which is expressed in the pancreas and mammary glands and encodes an enzyme excreted into the digestive tract and breast milk [iAdipocytes1809 network even though all expression data available indicated that this gene is not expressed in adipose tissue. Unexpressed genes whose removal would block the production of biomass or lipid droplet were kept in the network. We also removed the synthesis of certain hormones such as thyroxine and melatonin for which there is no evidence of production by adipocytes [Genes not tested in any of the sources of expression data described above were kept in the network. Genes for which all available data indicated that the gene was not expressed were removed from the network. If such a gene was the only one related to a particular reaction, that reaction was removed from the network, otherwise it was retained. For example, the Carboxy Ester Lipase  gene, responsible for phosphorylation of pyruvate as part of glycolysis (EC: 2.7.1.40), was associated with glutathione degradation converting glutathione into cysteinyl-glycine and glutamate (EC: 3.4.19.13) even if the gene and reaction are not linked in any ways. Some associations in which the gene and reaction had the same ECN were also removed when the reaction was not consistent with the specificity of the gene product. For example, the Succinate-CoA Ligase, ADP-Forming, Beta Subunit (SUCLA2) gene is an ATP-dependent succinate-CoA ligase which was associated to the GTP-dependent version of the reaction.We used the Enzyme Commission Numbers (ECN)  associateneCards  to manuaiAdipocytes1809 network are not directly involved in metabolism, as for example, protein synthesis and degradation, post-transcriptional modification, DNA methylation, PPARA activation. Although all molecular processes are interconnected, we proceeded with the assumption that the elements above should not be part of a metabolic network because they lead to a blurring of the conceptual boundaries between intracellular sub-systems  as well as an artificial inflation of the size of the network. For example, iAdipocytes1809 contains 62 different genes linked to protein-L-tyrosine phosphorylation. We manually verified pathways and metabolites to identify those linked to such non-metabolic functions and removed the appropriate genes and reactions.Several pathways and reactions in the iAdipocytes1809 as well as the partially curated network following the steps above contained a large number of dead-end metabolites. Dead-end metabolites are defined as metabolites that can only be either consumed or produced within the network. All input metabolites to the network need to be provided and are dead-ends by definition but are necessary. Likewise, output metabolites  are also dead-end but their production is the objective of the simulated network. Unlike these, dead-end metabolites within the network are biological impossibilities. The detection of dead-end metabolites involves standard graph analysis techniques and are part of the different packages to analyze biological networks. We used the Sybil package version 1.2.6 [on 1.2.6 . Some deiAdipocytes1809 network. We searched KEGG [Using the gene expression data described above, we identified genes expressed in adipose tissue that were not present in the hed KEGG  and Recohed KEGG  for reacProtein-protein interactions are important in the network since they can change the essentiality of a gene based on the involvement of its protein product in a protein complex. Protein complexes may be permanent or transient, and in the case of the latter, involve constant or variable  sets of partners . These diAdipocytes1809 network [Validation being an important part of a metabolic reconstruction, we submitted our network to the same 244 tests used to validate the  network . These tiAdipocytes1809 metabolic network used these data in their network to test the capacity of their network to produce lipid droplet. As additional validation of iTC1390adip, we also used the data from McQuaid et al. to recreate the lipid droplet production estimation experiment. FBA is an equilibrium technique; therefore, it is not possible to use FBA to study time-dependent processes. In order to do so, the data from McQuaid et al. for each time step was used to constraint the flux through particular reactions and as such create unique equilibrium networks representing each individual time step. Specifically, for each time point over the 24 h period, we used experimental data to constrain the lower and upper bounds of each associated exchange reactions and to calculate optimal production of both lipid droplet and acetyl-CoA independently. Other exchange reactions need to be active for these simulations to work. For Acetyl-CoA production, O2 absorption as well as H2O and CO2 excretion are active and we also added inorganic phosphate, ammonia and hydrogen ion uptake during lipid droplet synthesis.McQuaid et al. measured TAG and glucose uptake as well as NEFA release using multiple and simultaneous stable FA isotope tracers over 24 h in lean and obese subjects . The creLastly, we compared the effect of single gene deletions on biomass in our network using FBA to experimental results of gene deletion on the survival and proliferation of various human cancer cell lines. We compared our results to those of cancer cell lines, as large scale gene deletion data are not available for human adipocytes. Despite differences between cell types, broad agreement is expected. Blomen et al.  used genWe used our network to predict which genes have an effect on biomass and lipid droplet production using two different growth conditions. The growth media composition used for the network was based on Iscove’s Modified Dulbecco’s Medium (IMDM)  in whichiTC1390adip (as well as iAdipocytes1809 network) uses different definitions for lipids consisting of diacylglycerols, TAG, cholesterol and cholesterol-esters as well as a variety of phospholipids. Adipocyte biomass usually includes the lipid droplet but since our goal is to identify genes affecting lipid droplet production without affecting cellular metabolism, we decided to remove the lipid droplet from our definition of biomass. The objective function used for the production of lipid droplets the same used by the iAdipocyte1809 network [The biomass reaction is used in metabolic network analysis to simulate cellular growth. The biomass reaction is a simplified representation of the production of the major macromolecules and metabolites necessary to produce a new cell. Specifically, biomass consists of proteins (in terms of the amino acid composition of the proteome of the given cell), nucleic acids (RNA and DNA in terms of their composition) and cellular membranes. The biomass components capture the major components of a cell but should not be considered more than a rough estimate. We used a modified version of the biomass definition from the Recon2 metabolic network . The mod network . In our iTC1390adip network.We used flux variance analysis (FVA) to identify the maximum fluxes attainable by each of the network’s reactions during unrestricted optimization of biomass and lipid droplet production. FVA is a variation of FBA in which the network returns a range of possible fluxes for each reaction which are compatible with the optimization of the objective function instead of a single optimized solution. FVA uses the same objective functions (biomass) as FBA. Using the same DNA microarray data we used to identify reporter metabolites and the maximal fluxes obtained using FVA, we restricted the maximal flux of each reaction using the smallest expression fold difference of genes associated to the reaction, keeping the maximal flux of the reaction in the tissue with more expression to the value obtained by FVA and dividing that value by the fold difference for the other tissue. If a reaction had genes more highly expressed in both tissues we considered the reaction as neutral and did not change its maximal flux value. Using this method, we produced two networks with maximal fluxes representing subcutaneous and visceral adipose tissues. We did the same gene deletion analysis on both of these networks then on the All validations and simulations in this article except for the reporter metabolites analysis were performed using the Sybil package version 1.2.6  availabliAdipocytes1809 to the proteomic data provided with the network, we identified 272 genes where proteomic data indicated that the gene was not expressed in adipocytes. Independently, 388 genes were indicated as not expressed when comparing the genes in the network with the DNA microarray data. Comparing the two sources of data, we obtained 77 genes that were not expressed in any sample, 153 genes that were not expressed but only tested in one of the analyses and the remaining 353 genes considered as unexpressed in one analysis and expressed in the other. Of the 230 genes considered as unexpressed, Ornithine Carbamoyltransferase (OTC), Elongation of Very Long Chain Fatty Acids Protein 4 (ELOVL4), Mevalonate Decarboxylase (MVD) and Phosphoribosylformylglycinamide Synthase (PFAS) were kept in the network because their removal blocked the production of biomass. The genes above are essential to enable biomass and lipid droplet production but we were unable to find alternative pathways that could compensate their function.Comparing the list of genes in The ECNs of each gene was compared to that of each reaction associated to any given gene. Of the 14,985 associations present, 1439 had different ECNs for the gene and reaction, 1693 had incomplete ECNs for the gene, the reaction or both, 5029 had no ECN associated with at least one of the elements and the remaining 6824 had the same ECN for both the gene and reaction. Of those 14,985 associations in the network, we removed a total of 3583 after individual verifications using KEGG, NCBI, GeneCards and Brenda. From the removed associations, 1137 had different ECNs for the gene and reaction, 1332 and 1056 respectively had incomplete or no ECN for at least one of the elements and 58 had the same ECN for both gene and reaction but with different specificity. Removal of these associations eliminated 660 reactions and 332 genes. Removal of hormone syntheses not occurring in the adipocyte led to removing 44 reactions and 3 genes. We kept the conversion of the inactive thyroxine to the active triiodothyroxine as well as the import and export of those metabolites because these activities are known to occur in adipocytes .From the list of genes expressed in adipocytes identified in the DNA microarray analysis that were not present in the network, we searched in the KEGG database and the Recon2 human metabolic network for reactions associated to those genes. This search led to the addition of 93 reactions associated to 49 new genes as well to 45 genes already present in the network. Twenty-four of the new genes and an additional 105 new genes were added to the network and associated with reactions which were already present. The use of interaction data from Uniprot about all the genes in the network led to the addition of 473 complexes to the network, including 223 genes already in the network, 7 genes added previously and 62 new genes, multiple complexes being variants of the ATP synthase complex which has a lot of redundancy in subunits. There was a total of 49 unique complexes, which are involved in various enzymatic reactions.PPARA activation, reduced the size of the network without affecting the results of our FBA analysis. This step included 442 metabolites taking part in 499 reactions leading to the removal of 418 genes. Additionally, to reduce the number of dead-ends, we added 8 reactions back to the network with 5 genes including 3 new genes. We also added 58 exchange reactions to the network for dead-end metabolites that were extracellular and should be able to be exchanged with the environment. During this step, we also removed an additional 197 reactions and 22 genes pertaining to dead-ends.The removal of non-metabolic elements from the network that included but was not limited to protein production, degradation and post-transcriptional modification, DNA methylation and iTC1390adip failed but the reintroduction of 13 genes removed in the first step of curation led to all the tests being successful, except thioredoxin synthesis  including 195 exchange metabolites. The iTC1390adip network is available in SBML format usable with the sybilSBML package [Initially, 17 of the 244 tests  . Of those, 1 was associated to a human disease and had a mouse deletion model, 1 was only associated to a human disease, 1 was associated to a mouse deletion model and the last 1 was associated to neither. Deletion effect of all 20 genes are available in Additional file Fold differences in expression between subcutaneous and visceral adipose tissues were available for 882 of the 1390 genes present in the iAdipocytes1809 metabolic network provided opportunities for extensive manual curation of the present genome-scale metabolic network reconstrution. While the automatic generation of metabolic networks [iAdipocytes1809. For example, identification of Lipase A (LIPA) as a gene of interest would not have occurred if the CEL gene, which is not expressed in adipocytes, was still in the network, because they were associated to the same reactions. In some cases, we predicted complexes having an interesting effect that could not have been made with iAdipocytes1809 due to the absence of complexes.The presence of inconsistencies such as genes unexpressed in adipocytes and incorrect gene-reaction associations in the networks  is an imnetworks . SeveralRRM1) and either Ribonucleotide Reductase M2 (RRM2) or Ribonucleotide Reductase M2 B (RRM2B). Both RRM1 and RRM2 are experimentally essential. In our network only RRM1 is predicted to be essential due to the presence of RRM2B, which can compensate RRM2 in its absence. However, RRM2B cannot compensate RRM2 experimentally due to lower catalytic activity of the RRM1/RRM2B complex (compared to RRM1/RRM2), that is not sufficient to maintain growth experimentally [When using the experimental data on gene deletion and comparing them to our prediction on the production of biomass, we obtained a maximum of 56.6% sensitivity. This low sensitivity may in part be explained by the fact that some experimentally essential genes are linked to functions not directly related to biomass production. For example, multiple subunits of the vacuolar proton pump, which is responsible for the acidification of the peroxisome and other intracellular compartments, are experimentally essential but have no effect in the network since proton accumulation leading to protein degradation is not part of the network. Another source of error is the varying enzyme kinetics for genes with the same functions. For example, the ribonucleotide reductase complex is formed of two subunits, Ribonucleotide Reductase M1  or Lecithin-Cholesterol Acyltransferase (LCAT) are resistant to diet induced obesity and insulin resistance [AGK) whose disruption in humans is associated to the development of the Sengers Syndrome, an autosomal recessive disease related to abnormality of the mitochondria and in the storage of lipids and glycogen, cataracts and lactic acidosis [CEPT1), the muscle specific knockout of CEPT1 led to improved insulin sensitivity and levels of CEPT1 expression were inversely correlated with insulin sensitivity in obese human subjects [HMGCS1) and Fatty Acid Desaturase (FADS1) where both identified as being overexpressed in obesity and linked to higher risks of developing type 2 diabetes [HMGCS1, the gene coding for the protein needed for the step just before the reaction linked to 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR), the target of statins in the cholesterol synthesis pathway, is essential for optimal growth in all the experimentally tested cell lines used during the validation of the network. HMGCR was also essential in all tested cell lines but still represented a good target in the treatment of metabolic abnormalities. This indicates that genes that are essential in the various cell lines should still be investigated despite the essentiality data. FADS1 deficiency in mice leads to the synthesis of arachidonic acid-derived eicosanoids, which results in failure to thrive and death by 12 weeks of age [Some of the potential target genes can serve as a validation of the network due to previously known implication in the development of type 2 diabetes or its complications. Mice deficient in either Diacylglycerol O-Acyltransferase 1  is not associated with any disease or mouse models and was also not essential for the optimal growth of the various tested human cell lines. FAR2 has a paralog, Fatty Acyl CoA Reductase 1 (FAR1), which is not expressed in adipocytes based on our gene expression data. Theoretically, targeting only FAR2 could reduce the risk of side effects since FAR1 would compensate the loss of function of FAR2 in tissues other than adipocytes. CEPT1 was essential for growth in only one of the cell lines, 6 other genes were also essential in one or two of the tested cell lines (Phosphatidylserine Synthase 1 (PTDSS1), Elongation of Very Long Chain Fatty Acids Protein 1 (ELOVL1), Trans-2,3-Enoyl-CoA reductase (TECR), Phosphate Cytidylyltransferase 2 (PCYT2), Succinyl-CoA Ligase subunit A (SUCLG1) and Alkylglycerone Phosphate Synthase (AGPS)). These 7 genes might be essential in their respective cell lines due to other mutations present specifically in these cancer cell lines. Homozygous mouse models for two of the previously mentioned genes (PCYT2 and ELOVL1) die at the embryonic level or shortly after birth [ENO1), Dihydrolipoamide Dehydrogenase (DLD), Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) and Hydroxysteroid 17-Beta Dehydrogenase 12 (HSD17B12)) are also essential in most of those cell lines and homozygous null mouse models for each of these genes die at the embryonic state [LPL), ELOVL4 and the Mitochondrial Trifunctional Protein Alpha and Beta Subunits (HADHA/HADHB)) caused mice to die sooner than expected when they were individually deleted [DLD, Dihydrolipoamide S-Acetyltransferase (DLAT), Pyruvate Dehydrogenase Beta (PDHB), Pyruvate dehydrogenase Component X (PDHX)) results in lactic acidemia [DLD deficiency is more severe and causes other complications because it also participates in the branched-chain alpha-keto acid dehydrogenase complex and the alpha-ketoglutarate dehydrogenase complex. Contrary to our predictions, DLAT silencing was linked to an increase in lipid accumulation in adipocytes instead of the reduction we predict but this could be due to accelerated adipogenesis, which would be an interesting effect to study [GPIHBP1), LIPA and Alcohol Dehydrogenase 5 (ADH5)) were essential in any of the tested cancer cell lines, but some of them were associated to various human diseases or have homozygous null mouse models with various phenotypes [Fatty Acyl CoA Reductase 2  were already identified in the previous analysis and are discussed above while the remaining 4 , Lipin 1 (LPIN1), Choline Phosphotransferase 1 (CHPT1), Carnitine Palmitoyltransferase 2 (CPT2)) are additional new potential targets for reducing hypertrophy in adipocytes. CHPT1 is neither associated to a human disease nor possesses a mouse deletion model. CPT2 deficiency leads to a broad range of phenotypes, from neonatal death to late onset of manifestations in adulthood. All forms of the disease are associated to reduced carnitine metabolism leading to various clinical symptoms from myoglobinuria and hypoglycemia to seizures and death [AKR1A1 was the only new gene identified in visceral adipose tissues due to the deletion having no effect on biomass compared to the subcutaneous tissue. AKR1A1 deficiency is not associated to any disease in humans while mouse deletion models for AKR1A1 present reduced levels of ascorbic acids and an accumulation of d-glucuronic acid and saccharate [LPIN1 deficiency leads to the development of recurrent myoglobinuria before age 5 [LPIN1 in mice leads to the development of neonatal fatty liver that resorbs between 14 and 18 days. These mice also develop peripheral neuropathies, reduced plasma leptin levels and insulin resistance associated with a reduction of 50–90% in white and brown fat pad mass [LPIN1 in mouse adipose tissues leads to increased adiposity and higher insulin sensitivity [LPIN1 deficiency in human is restricted to muscles and is less severe than in mice due to the near ubiquitous expression in humans of 2 other LPIN genes, LPIN2 and LPIN3, which encode related proteins [LPIN1 is necessary for the development of mature adipocytes via the induction of adipogenic genes such as peroxisome proliferator-activated receptor γ (PPARγ) and CAAT enhancer binding protein-α (C/EBPα) [Multiple techniques exist to use expression data in conjunction with metabolic networks –76. To ond death . AKR1A1 ccharate . Lastly,re age 5 . Deletiopad mass , 81. Higsitivity . The effproteins . LPIN1 i(C/EBPα) . This inIn this work we develop iTC1390adip, an improved human adipocyte metabolic network with less inconsistencies and additional knowledge on protein complexes. The creation of iTC1390adip shows that extensive manual curation is still necessary in the development of metabolic networks. Methodologically, we optimize two objective functions, one for biomass production and another for lipid droplet production. We used our network to predict genes that could potentially reduce adipocyte hypertrophy (decrease lipid droplet production without affecting biomass production), a negative factor for adipose tissue metabolic dysfunction contributing to type 2 diabetes. We also identified genes differentially regulated between visceral and subcutaneous adipose tissues which may help explain some known differences between the two tissues.Additional file 1:Results from every deletion experiment in the article as well as the assignment of gene essentiality for optimal growth from the 6 cell-lines of the two studies used in the validation of the network. (XLSX 54 kb)Additional file 2:R code for the simulation and validation if iTC1390adip. (ZIP 452 kb)Additional file 3: Figure_S1. Table_S1.docx. Biomass and lipid droplet constituent with stoichiometry as well as growth media definition. Table_S2.xlsx. List of metabolic tasks used to insure proper network behavior under various circumstances. Table_S3.xlsx. List of fluxes for imports and exports in the network at each time point when optimising for lipid droplet production with restrictions to the values of TAG extraction, glucose uptake and NEFA release to the experimental values from obese and lean subjects. Table_S4.xlsx. List of fluxes for imports and exports in the network at each time point when optimising for acetyl-CoA production with restrictions to the values of TAG extraction, glucose uptake and NEFA release to the experimental values from obese and lean subjects. Table_S5.xlsx. Effect of gene deletion in mouse models for the genes predicted to have an effect on adipocyte hypertrophy. Table_S6.docx. Number of genes having an increased effect on lipid droplet and biomass production in either of the adipose tissues compared to the other. Table_S7.xlsx. List of genes identified as potential targets when restricting the flux of reactions using gene fold differences between subcutaneous and visceral adipose tissues. iTC1390adip.xml and iTC1390adipRaven.xml files containing the iTC1390adip network in SDML and raven formats as described above. (ZIP 911 kb)Graph of the effect of each gene’s deletion on both biomass and lipid droplet production compared to the wild type network when glucose and TAG uptake are restricted.