In contrast with normal cells, most of the cancer cells depend on aerobic glycolysis for energy production in the form of adenosine triphosphate (ATP) bypassing mitochondrial oxidative phosphorylation. Moreover, compared to normal cells, cancer cells exhibit higher consumption of glucose with higher production of lactate. Again, higher rate of glycolysis provides the necessary glycolytic intermediary precursors for DNA, protein and lipid synthesis to maintain high active proliferation of the tumor cells. In this scenario, classical control theory based approach may be useful to explore the altered dynamics of the cancer cells. Since the dynamics of the cancer cells is different from that of the normal cells, understanding their dynamics may lead to development of novel therapeutic strategies.We have developed a model based on the state space equations of classical control theory along with an order reduction technique to mimic the actual dynamic behavior of mammalian central carbon metabolic (CCM) pathway in normal cells. Here, we have modified Michaelis Menten kinetic equation to incorporate feedback mechanism along with perturbations and cross talks associated with a metabolic pathway. Furthermore, we have perturbed the proposed model to reduce the mitochondrial oxidative phosphorylation. Thereafter, we have connected proportional-integral (PI) controller(s) with the model for tuning it to behave like the CCM pathway of a cancer cell. This methodology allows one to track the altered dynamics mediated by different enzymes.The proposed model successfully mimics all the probable dynamics of the CCM pathway in normal cells. Moreover, experimental results demonstrate that in cancer cells, a coordination among enzymes catalyzing pentose phosphate pathway and intermediate glycolytic enzymes along with switching of pyruvate kinase (M2 isoform) plays an important role to maintain their altered dynamics. Decades ago Otto Warburg recognized this altered metabolism in cancer cells , 2. ThisOne of the most common approaches for analyzing metabolic pathways is flux balance analysis –5. The me.g., large scale production of a metabolite) or clinical relevance . In this fashion, the properties of metabolic pathways under various conditions can be well understood. However, metabolic control analysis determines only the changes of a state (metabolite) with respect to certain parameters. Moreover, it does not provide any supervisory controller to manipulate concentrations of enzymes/metabolites to attain certain objectives/needs of cells.Unlike flux balance analysis, metabolic control analysis –9 can beThus, we have used principle of the classical control theory to explore the altered dynamics of mammalian central carbon metabolic (CCM) pathway in cancer cells. It is capable of dealing with the behavior of nonlinear dynamic systems. Metabolic pathways can be considered as a process based on classical control theory because of its nonlinearity in nature. An earlier investigation  shows hoIn our approach, the reactions are modeled by standard Michealis Menten kinetics  with some.g., nucleotide). Thus, we have developed a model which behaves like the CCM pathway in a mammalian cancer cell , the concentration of a substrate X. The equation is given byMichaelis Menten kinetics expresses the relationship between substrates and enzymatic interactions of a metabolic pathway at a particular instant. Moreover, the equation based on Michaelis Menten kinetics describes the rate of enzymatic reactions. It relates an initial reaction rate Vmax (= K ⋅ [E]) represents the maximum rate achieved by the reaction at maximum (saturating) substrate concentrations. [E] is the total enzyme concentration, i.e., the sum of the free and substrate-bound enzyme concentrations. Michaelis constant Km is the substrate concentration at which the reaction rate is half of Vmax. A small value of Km indicates high affinity depicting that the rate of the reaction will approach more quickly to Vmax, at saturating substrate concentrations  assuming them as needs of cancer cells. Each of them is considered as e.g., energy demand) of cancer cells. In this process, the error input ϑ(t) is applied to the PI controller(s). Besides, the outputs of the PI controller(s) affect concentrations of different input metabolites We have modeled each of the CCM pathway reactions in an individual module . Each ofc(t) in the time-domain t of a PI controller is driven by the error function ϑ(t). Thus, c(t) is governed by the following equation.Kp and Kint are the proportional and integral gains respectively. Kd = 0. The term c(t) is responsible to alter appropriate parameters  includes several small steps for a reaction. For example, in the case of the reaction pyruvate → acetyl-CoA, there are three intermediate steps catalyzed by the same enzyme pyruvate dehydrogenase. They can easily be treated as a single reaction without affecting the nature of dynamic responses of the system  except introducing some amount of differences in the values of metabolite concentrations/reaction fluxes at steady state. Since, our main objective is to explore the relative effect of different key metabolites, enzymes and perturbations in a metabolic network, these differences may be neglected. They do not affect the pattern of the dynamic responses of the metabolic pathway. Besides, the system remains stable despite this reduction.We have applied the order reduction technique to reduce the order of CCM pathway . In thisi.e., X1, X2 and X3, along with 3 reactions  catalyzed by the enzymes E1, E2 and E3 respectively. The initial fluxes of the three reactions are V1, V2 and V3 respectively. Besides, the metabolite X1 is supplied by a separate reaction I → X1 catalyzed by an enzyme E0. The reaction flux V1 is inhibited by the accumulation of X3. Subsequently, there is a perturbation Xd which activates/inhibits the flux V1. The stoichiometric matrix of the hypothetical pathway is given byNCCM for CCM pathway.Let us consider a simple hypothetical metabolic pathway with feedback inhibition as depicted in X1 → X2 in X1 is consumed in a reaction with turnover number/rate constant K1 and Michaelis constant Km1, which is inhibited by a substrate X3, then initial reaction rate is modified asX3 accelerates the reaction that consumes X1. Then the modified Michaelis-Menten kinetics becomesAs defined in Xd which has been considered in Here we have ignored the perturbation y represents resultant flux vector of the reactions participating in the metabolic pathway.v = [v1v2v3]T and metabolite concentration vector be x = [x1x2x3]T, while I is the input substrate. The term xd is a perturbation which either inhibits the reaction/enzyme or activates it. Each reaction flux of v1, v2 and v3 is modeled using modified Michaelis-Menten kinetic equation v = . Similarly, we have defined each reaction flux for both of the original (47 reactions) and reduced (28 reactions) CCM pathway as depicted in Here we include a new term F1 and F2 values just shift the steady state values of different metabolites (states) and reaction fluxes (outputs), as depicted in xd inhibits the reaction flux v1. Moreover, we have taken fixed values (0.1) of F2 and F1  do not affect the pattern of the dynamic responses of a biological system (metabolic pathway). Different choices of feedback constant values only change (increase/decrease) the steady state values. Moreover, the responses of the system always reach the steady states. The results never show any oscillating or exponential nature. Thus, the proposed model is quite robust with respect to the choice of feedback constant value.Different choices of  and F1  Figs. Sut) derivative of state vector  an an11) an·[v1v2v3]andy=[v=[v1v2v3]Thus we t) derivative of state vector and output vector in the appropriate function blocks  for the reactions depicted in Develop the stoichiometric matrix (Define reaction fluxes of the CCM pathway with modified Michaelis-Menten kinetic equations as depicted in t) derivative of state (metabolite) and output (reaction flux) vectors for the CCM pathway.Use Eqs  and 12)12) for t– Develop subsystems for each of the states (metabolites) belonging to the CCM pathway using simulink platform of matlab. Each subsystem consists of the components as shown in t) derivative of state (metabolite) into the function (Fcn) block of individual subsystem.– Put the expression for the time (– Replace the variables in the expression by u(1), u(2), u(3), …t) derivative of state) for the mux block.– Set the number of inputs  to the integrator block.– Finally, connect all such subsystems to build a super subsystem representing the state (metabolite) vector.For the state vector:Follow the above steps to build a super subsystem . Choose initial input values for Supply initial input values for Vary the input values one by one  by step input block/signal builder block. Connect scope block to the varying input to track it.Connect scope blocks to each output state (metabolite) and reaction flux to track the changes in responses of the CCM pathway.Configure the simulation parameters as described in Here we have perturbed the normal model and connect PI controllers with it. Thereafter, we have tuned PI controller to mimic the altered dynamics of mammalian CCM pathway in cancer cells. The basic steps to develop the CCM cancer model from normal CCM model are as follows.Let i.e., D-ribose-5P or PEP. Let As cancer cells show high proliferation, they need sufficient amount of energy in the form of ATP to survive. Therefore, we have connected two PI controllers to the model  (NADH) is consumed in this reaction. As pyruvate and lactate formation rates decrease with the decrease in glucose level, consumption of NADH is low. Thus the rate of formation of NADH increases with decrease in glucose level . Again Nα-ketoglutarate. Isocitrate is produced from citrate in a reversible reaction. α-ketoglutarate is further transformed into succinyl-CoA. NADH acts only as inhibitor of these entire activities . According to some previous investigations , 33–35, Besides, Experiment 2 confirms that there is a switching among the enzymes catalyzing pentose phosphate pathway and intermediate glycolytic enzymes. In addition to this, the switching of PKM2 plays an important role for survival of cancer cells. Fluctuation of transaldolase/transketolase between low and high concentration plays an important role in this scenario. When energy is needed, D-ribose-5P is converted into intermediate glycolytic metabolites, switching PKM2 to its active tetramer form. As a result, desired amount of ATP is synthesized. Production of D-ribose-5P becomes high again during cell proliferation. In this scenario, PKM2 switches itself back to relative inactive dimer form. As a result, PEP gets accumulated (shown by Experiment 2). Afterwards, accumulated PEP helps in forming cell building materials and ATP as per need. Experiment 2 also indicates that the enhanced activities of hexokinase, PFK1, PFK2 and glucose-6-phosphate dehydrogenase contribute significantly to maintain the altered dynamics in cancer cells. Thus, the model can meet the needs of cancer cells (energy demand and high cell proliferation) efficiently. Moreover, it helps cancer cells in surviving in an initial low concentration of ATP. In this fashion, the model successfully mimic the altered dynamics of the mammalian CCM pathway in cancer cells.S1 FigThis two models show the similar pattern of dynamic responses of CCM pathway with variations of glucose and pyruvate kinase.(TIF)Click here for additional data file.S2 Figx1, x2, x3, v1, v2 and v3 Click here for additional data file.S3 Figx1, x2, x3, v1, v2 and v3 Click here for additional data file.S1 TableIt contains the simulation details and the model parameters which have been used to validate the order reduction technique using COPASI software.(PDF)Click here for additional data file.S2 TableIt contains the list of 47 reactions from KEGG database for mammalian CCM pathway under consideration. Here the reaction numbers correspond to the same serial numbers in (PDF)Click here for additional data file.S3 TableIt contains the list of reduced 28 reactions for mammalian CCM pathway under consideration. Here the reaction numbers correspond to the same serial numbers in (PDF)Click here for additional data file.S4 TableIt contains the list of equations of reaction fluxes associated with 47 reactions from KEGG database for mammalian CCM pathway under consideration. Here the serial numbers correspond to the same reaction numbers in (PDF)Click here for additional data file.S5 TableIt contains the list of equations of reaction fluxes associated with reduced 28 reactions from KEGG database for mammalian CCM pathway under consideration. Here the serial numbers correspond to the same reaction numbers in (PDF)Click here for additional data file.S6 TableHere, the simulation parameters for the proposed CCM pathway model are given.(PDF)Click here for additional data file.


Validation was achieved by demonstrating agreement of experimental measurements of the metabolic rates of oxygen consumption, glucose consumption, lactate production, and glutamate pool size with independent measurements of these parameters in cultured human DB-1 melanoma cells. These cumomer models have been applied to studies of DB-1 melanoma and DLCL2 human diffuse large B-cell lymphoma cells in culture and as xenografts in nude mice at 9.4 T. The latter studies demonstrate the potential translation of these methods to in situ studies of human tumor metabolism by MRS with stable 13C isotopically labeled substrates on instruments operating at high magnetic fields (≥7 T). The melanoma studies indicate that this tumor line obtains 51% of its ATP by mitochondrial metabolism and 49% by glycolytic metabolism under both euglycemic (5 mM glucose) and hyperglycemic conditions (26 mM glucose). While a high level of glutamine uptake is detected corresponding to ~50% of TCA cycle flux under hyperglycemic conditions, and ~100% of TCA cycle flux under euglycemic conditions, glutaminolysis flux and its contributions to ATP synthesis were very small. Studies of human lymphoma cells demonstrated that inhibition of mammalian target of rapamycin (mTOR) signaling produced changes in flux through the glycolytic, pentose shunt, and TCA cycle pathways that were evident within 8 h of treatment and increased at 24 and 48 h. Lactate was demonstrated to be a suitable biomarker of mTOR inhibition that could readily be monitored by 1H MRS and perhaps also by FDG-PET and hyperpolarized 13C MRS methods.We present the first validated metabolic network model for analysis of flux through key pathways of tumor intermediary metabolism, including glycolysis, the oxidative and non-oxidative arms of the pentose pyrophosphate shunt, the TCA cycle as well as its anaplerotic pathways, pyruvate–malate shuttling, glutaminolysis, and fatty acid biosynthesis and oxidation. The model that is called Bonded Cumomer Analysis for application to  Analysis of these dynamic isotopolome (labeled metabolome) data used to require solution of hundreds of differential equations that could only be accomplished with the help of supercomputers  spectrometers before and following treatment with appropriate therapeutic agents whose choice will be at least partially dictated by metabolic flux analysis of tumors following administration of appropriate 13C-labeled substrates. Changes in tumor metabolism indicated by these labeling experiments will point to the probable success or failure of drug delivery and will monitor the effect of these agents on tumor metabolism.We present data on human DB-1 melanoma and models of various non-Hodgkin’s lymphomas (NHLs). DB-1 melanoma has served as the principal tumor model on which this technology has been developed and validated , 14. Thi13C metabolomics/isotopolomic studies are expensive and labor intensive, it may prove advantageous to identify surrogate biomarkers of therapeutic response whose metabolism could be followed by more conventional methods, such as lactate imaging by 1H MRS or 1H chemical exchange of saturation-transfer (1H CEST), hyperpolarized 13C MRS or PET/CT with FDG or other appropriate metabolic probes. This two-stage approach – utilizing 13C MRS for initial exploration of overall tumor metabolism and identification of suitable biomarkers for subsequent follow-up by 1H MRS or other appropriate method – is a general strategy that we are proposing for monitoring signal-transduction inhibitors. The feasibility of achieving this “personalized medicine dream” or at least the 13C MRS exploratory phase is already demonstrated through the pioneer work of investigators at the University of Texas Southwestern, who have monitored the metabolism of 13C-labeled substrates by ex vivo analysis of surgical specimens obtained from patients with brain (13C MRS (see below) will further demonstrate the potential feasibility of non-invasive monitoring of tumor metabolism. The purpose of this manuscript is to describe the technical details on how 13C isotope kinetics can be monitored and quantitatively analyzed.Non-Hodgkin’s lymphoma tumors have served as models for developing methods for monitoring response to signal-transduction pathway inhibitors  as well th brain  and lungth brain  tumors. 13C-labeled substrates, one can monitor the time course of distribution of the isotopic label among the various metabolites of the substrate either directly by 13C MRS or indirectly through 1H MRS of the protons directly bonded to the isotopically labeled carbon atoms. These methods have been extensively reviewed by de Graaf et al.  that will depend on relaxation times, the fraction of the relaxation occurring through dipolar mechanisms and the broadband decoupling technique . There will be differences in signal enhancement, and these will have to be calibrated. For in vivo applications, the NOEs vary between 1.3 and 2.9 for protonated carbons, and are 1.0 for unprotonated carbons or carbons that relax by non-dipolar mechanisms. Alternatively, one could use dynamic polarization-transfer excitation for direct 13C excitation , freeze clamped the hearts at various time points, extracted the water soluble metabolites with perchloric acid, and analyzed the extracts with direct 13C-{1H} MRS to measure the time course of isotopic labeling of glutamate, alanine, and aspartate. The data were fit by least-squares analysis to an idealized TCA cycle model consisting of oxaloacetate in equilibrium with aspartate, citrate, α-ketoglutarate, and reactions involving glutamate, succinate, and malate. The analysis required about 325 differential equations that were solved with the aid of a Cray supercomputer. The model fit estimates of isotope enrichment of carbon atoms in glutamate and aspartate, and calculated the rate of TCA cycling, but did not attempt to validate this rate. This was accomplished later by Chatham et al. (2) from the rate of production of reducing equivalents assuming that the system was at equilibrium. They compared the calculated rate with the experimental rate measured with oxygen electrodes placed at the inlet and outlet ports of the Langendorff perfusion apparatus; MRS and MRO2 data acquisition were performed directly in the magnet on the beating heart, and the mathematical fitting was performed with a simple laptop computer. The results demonstrated that the original model proposed by Chance et al.  in Langendorff perfused rat hearts , monocarboxylic acid (MCT), and glutamine (GLN) transporters to the cytosolic compartment are included in the cytosolic membrane. Tumors generally express GLUT1 and GLUT3, but only GLUT1 is currently included in the model; similarly DB-1 contains MCT1 and MCT4 in approximately equal proportions  oxidation.The following metabolic pathways are included in the network: glycolysis, TCA cycle, α-ketoglutarate–glutamate, and oxaloacetate–aspartate exchange through the malate-aspartate shuttle, anaplerosis through (a) pyruvate carboxylase (PC) activity, (b) at the succinyl-CoA level, and (c) glutaminolysis through mitochondrial glutaminase (GLS and GLS2), pyruvate recycling through cytosolic (ME1) and mitochondrial malic enzymes (ME2 + ME3), lactate dehydrogenase (LDH) activity, and transport processes. Oxidative and non-oxidative branches of the pentose phosphate pathway (PPP) fluxes were included in the modeling analysis as well as In order to permit access of pyruvate to the pyruvate dehydrogenase complex (PDH) complex for conversion to acetyl-CoA and incorporation into the TCA cycle, we have included the mitochondrial pyruvate carrier (MPC), which was recently identified in all mammalian cells –56. Pyru13C time courses of labeled lactate, glutamate and FAs to determine the corresponding metabolic fluxes and the transport parameter for bidirectional lactate transport through the cell membrane, we constructed a BC dynamic model adapted to perfusion bioreactor experiments. In the model, the perfused  glucose and glutamine  are transported from the extracellular medium to the DB-1 cells. Reversible non-steady-state Michaelis–Menten facilitative transport kinetics are assumed to occur through glucose (GLUT family) or glutamine (ASCT2) transporters:i between media and cytosol or cytosol and mitochondria; i, In order to fit iM is the intracellular concentration of the i species, i between media and cytosol or cytosol and mitochondria, j) or utilize (k) cellular species i, and eM, and ier is the ratio of cell volume to media volume. We also included an additional term for the medium flow rate. Note that we do not assume here that media boundary metabolites are at constant concentration.Two types of mass balance equations for chemical and isotope variables were expressed mathematically using the mass balance equation for the total metabolite concentration in the medium, cytosol, and mitochondria:N − 1 independent variables for a molecule with N different carbon atoms. Because not all possible isotopomers are detectable by NMR, a model including all possible isotopomers would be unnecessarily complex. The concept of bonded cumomers leads to a reduced number of equations as well as a more simple derivation of these equations compared to a model, including all possible istopomers, while retaining all the NMR measureable isotopomer information.One can monitor flux through the biochemical network model by measuring changes in isotopic enrichment of individual carbons in a molecule containing N carbon atoms. This yields a total of N-independent time-courses or steady-state values. However, by considering all the different combinations of labeled and unlabeled carbons, isotopomer models take full advantage of the biochemical information that can be obtained from the NMR spectra, yielding a total of 2{i} denote the isotopomer of a metabolite M, where {i} is a set of integers i1, i2,…, in indicating the positions of all the labeled carbons. We refer to m{i} as the isotopomer fraction [M{i}]/[M] of molecules M labeled exactly at positions i1, i2…, in. A cumulative isotopomer fraction (or cumomer fraction), noted as pM{i} is by definition, the sum of isotopomer fractions for all isotopomers labeled at least at positions i1, i2…, in, whatever the label at the other positions. We refer to the size n of the set {i} as the order of the p-function. For example, assuming that M has a total of four carbons, the second order p-function pM{1,3} can be expressed as:Let MM{1,3} for cumomer M{1,3} is the probability that both the first and third carbon atoms of this molecule are labeled. Shestov et al.  are formulated as an initial value problem for ordinary differential equations (ODE) in terms of Bonded Cumomer fractions as the state variables. Metabolite 13C cumomer mass balance for parallel monomolecular reactions was expressed in the form:M with pool size [M] is downstream of metabolites jS. While π(i) and πσj(i) represent the ith cumomer fraction of metabolite M and metabolite jS (i Bonded Cumomer), respectively. The total outflux ΣkF balances influx ΣjF.Isotopomer dynamics of this system together with the initial metabolome state vector 13C NMR spectra . Using matrix connection equations, one derives kinetic equations in the form of fine-structure spectroscopic-defined NMR data.Shestov and coworkers  have des13C2] glucose perfusion were: Glu4Tot , Glu4s, Glu4d34, Glu3Tot, Glu3d, Glu2Tot, Glu2s, Lac3Tot, and acyl methylene (–(CH2)n–) resonance, for a total of nine curves from which we determined the following free fluxes: melanoma TCA cycle FTCA, PC FPC, exchange between glutamate and α-ketoglutarate FX, anaplerotic exchange and net flux at the level of SucCoA, FANA, mitochondrial malic enzyme (ME2 + ME3) activity, de novo FA production, glutaminolysis, aspartate efflux, and three other parameters – Michaelis–Menten lactate transport 13C multiplets of glutamate, glutamine, and aspartate. Minimization of the cost function was performed using Broyden–Fletcher–Goldfarb–Shanno or Simplex algorithms. By verifying that goodness-of-fit values were close to expected theoretical values, we confirmed proper mean-square convergence. Monte Carlo simulations with experimental noise levels were used to calculate parameter errors  since no statistically significant change was observed between the different medium glucose concentrations.p is varied parameter and R is a system response, e.g., concentration or flux; the subscript ss indicates steady state. Sensitivity analysis measures the robustness of the metabolic model and network topology to variations in parameter values. The robust parameters with greatest influence on model simulation can help to identify critical fluxes and pathways. These fluxes also have less experimental error. Here, we extend the MCA technique to dynamic metabolic flux analysis in which the behavior of interest occurs in the temporal responses. First, we introduce the dynamic isotopomer control coefficient or the sensitivity function:(i) represents the ith cumomer fraction of metabolite M, and kF is a certain parameter, such as a flux. By introducing these variables, isotopomer sensitivity equations can be derived by differentiating dynamic bonded cumomer balance equations  was originally proposed to quantify sensitivity – i.e., to measure the effect of changes in any parameter of a system on the other variables in that system. This approach was developed independently by two groups in the 1970s , 60 and ions Eq.  with resE represents the minimum mean squared difference between model simulation and experimental data. These sensitivity values represent the degree to which the theoretical curves are sensitive to the value of individual parameters. High values of the sensitivity parameter iS indicate that changing a parameter ip results in a significant change in the simulated curves and in the MSE E.We also computed the sensitivity of the mean squared difference between experimental and corresponding model output  to finite changes in parameter values. The sensitivity to each parameter was calculated as the relative change in mean squared error (MSE) due to 5% change in the given parameter value:Figure 13C MRS and bonded cumomer analysis as well as LC–MS and fragmented cumomer analysis.To illustrate the application of these techniques to studies of human cancer, we will describe studies of human melanoma models grown in culture, as perfused tumor cells grown on solid microcarrier beads and as xenografts in immunosuppressed mice. The cell studies will be utilized to validate the bonded cumomer model and to estimate the contributions of glycolytic metabolism and mitochondrial metabolism to ATP production under hyperglycemic (26 mM) and euglycemic (5 mM) conditions. Effects of euglycemia and hyperglycemia on glutamine metabolism, flux through the pentose shunt and FA metabolic pathways will also be evaluated using both 13C MRS and LC–MS can be used to detect response to signal-transduction inhibitors, we will present data on the effect of rapamycin, a well known inhibitor of the mTOR pathway, on DLCL2 human diffuse large B-cell NHL cells grown in culture as perfused cells immobilized by encasement in agarose beads and as xenografts in immunosuppressed mice. We will demonstrate that response to this inhibitor can also be monitored by 1H MRS lactate imaging in cells and in xenografts. Ultimately, the goal will be to translate these methods into the clinic to manage human NHL patients. Preliminary studies leading to this approach have been published  non-essential AAs and 10 mM HEPES buffer. In tissue culture flasks, the doubling time was 48 h  before administration of any treatment. Cells were cultured from the tumor and cryopreserved after the 16th passage. Monoclonal antibodies were used to confirm cell surface antigens . DB-1 cewas 48 h . The V608 cells were grown on the surface of non-porous microcarriers that had a mean diameter of 170 microns . These microcarriers were coated with either collagen or ProNectinF® to enhance cell attachment and proliferation and tightly packed inside a 20 mm NMR tube in which they were perfused continuously in the open bioreactor system. Mancuso et al. , 10% fetal bovine serum, 10 mM HEPES, 4 mM glutamine, and 26 mM glucose (complete DMEM). Approximately 5 × 10o et al.  have pub8 cells in culture medium with an equal volume of the low-temperature-gelling agarose at 37°C and magnetically stirred the mixture after addition of 3 ml paraffin oil at 37°C generating the cell-encapsulated spherical beads with 100–200 μm diameters. The beads were cooled by continuous stirring, and the oil was removed by centrifugation. The encapsulated cells were then loaded into a 10 mm NMR tube connected to the perfusion system.The method of Bental et al.  was used2. A pH probe  was inserted downstream of the outlet oxygen probe. Adjustment of the level of CO2 in the gas-exchange module was used to modify/maintain the pH of the medium. The temperature of the medium entering the NMR tube was monitored with a thermocouple at the exit port of the perfusion chamber and was fine-tuned with a microstat-controller and an electrical resistance heater to 37.0 ± 0.2°C. The glucose concentration in the recirculating medium was maintained at a constant level (clamped) by continuously feeding fresh medium typically at a flow rate of 24 ml/h and removing depleted medium from a recirculation bottle while maintaining the total volume of recirculating medium at 120 ml. During 13C experiments, 32 mM  glucose was fed into the system, and the recirculating glucose level was clamped at 26 mM by adjusting the feed rate.Medium was circulated through the microcarriers or agarose gel at a flow rate of 12 ml/min with a peristaltic pump . A gas-exchange module consisting of a silicone membrane  was situated before the perfusion chamber along the medium flow path for removal of carbon dioxide and addition of oxygen. The oxygen level was measured continuously with a polarographic oxygen probe  situated at the inlet port to the perfusion chamber and maintained at a steady-state pressure near air saturation. The medium was then warmed to 40°C, and a second polarographic oxygen probe was used to detect the oxygen level of the medium coming out of the NMR tube permitting the oxygen consumption rate of the culture to be monitored continuously from the decrease in pO13C NMR spectra were acquired with standard 1H decoupled NOE 13C acquisition on a 9.4 T/89 mm vertical bore Varian spectrometer  with acquisition parameters: 60° pulse angle, 1.2 s repetition rate, 25,000 Hz spectral width, 16,384 points, and 750 transients per spectrum. Free-induction decays were apodized by exponential multiplication (2 Hz) for signal to noise enhancement and peak areas were measured with Nuts NMR  software. The number of cells in the NMR tube was determined from the total NTP level measured by 31P NMR as described previously  conditions to enhance lactic acidosis , 66 and in vivo studies. Tumors were developed by subcutaneous implantation of tumor cells to the flanks of nude mice. NMR studies on mice were performed using a home-built 13C/1H dual-tuned probe at 9.4 T (Varian Inc.). All animal studies were approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Pennsylvania, and performed in accordance with its regulatory standards.Immunosuppressed mice were used for t-test was used to calculate p-values. P-values <0.05 were considered significant.Student’s Determination of the relative levels of glycolytic and oxidative metabolism of specific tumors plays an important role in development of strategies for treating neoplasms and for choosing appropriate methods to detect tumor response to specific therapies. Development of a non-invasive method for quantifying the fraction of ATP production by tumors that occurs by glycolytic and mitochondrial metabolism would be very useful in the management of cancer patients and tailor-fitting of therapeutic regimens to their individual needs. In addition, various other pathways of tumor metabolism play critical roles in tumor proliferation by providing essential precursors for cell replication. We, therefore, sought to develop a method for quantitating tumor intermediary metabolism in cells with appropriate extension to animal tumor models and eventually to human cancer patients.G lucose in DB-1 Cells]13C labeling kinetics of DB-1 melanoma cells grown at a constant glucose level of 26 mM. Dynamic 13C NMR spectra obtained during the experiment appear in Figure 13C incorporation in C4 of glutamate (34.3 ppm) was detected; Figure 13C-13C coupling between glutamate-C4 and glutamate-C3 . Within 1 h, glutamate-C2 labeling was detected  steady-state 13C NMR lactate isotopomers. The following time courses were utilized in the curve-fitting procedure: Glu4Tot , Glu4s, Glu4d34, Glu3Tot, Glu3d, Glu2Tot, Glu2s, Lac3Tot, and acyl methylene (–(CH2)n–) resonance, yielding a total of nine curves. Estimation of the model-free parameters was achieved by fitting labeling patterns of glutamate, lactate, and FAs acyl methylene groups. Table We utilized Bonded Cumomer analysis to fit s Figure . Only th2 consumption rate (MRO2) with polarographic oxygen probes during the perfusion experiment. The fluxes obtained by fitting of dynamic 13C data produced good agreement between the experimentally observed oxygen consumption rate, 33 mmol/L-cell/h, and the calculated MRO2, 32 mmol/L-cell/h  to ATP production. We assumed that the oxidative phosphorylation P/O ratio equals 1.5 for FADHin vivo where tumors are often located in regions of hypoxia, which produce a Pasteur effect, and, hence, a shift toward glycolytic metabolism; however, it is likely that many tumors like melanomas will be strongly dependent on oxidative metabolism for their energy source, particularly in tumors that express the V600E BRAF mutation, about 50% of human melanomas.  of the classical PPP  or to cytosolic reductive carboxylation (RC) driven by glutaminase 1  or other glutamine–glutamate conversion enzymes in cytosol and isocitrate dehydrogenase 1 (IDH1) . It was,“Classical” glutaminolysis includes uptake of glutamine by the cell, transport to mitochondria, conversion to glutamate through mitochondrial glutaminase 1 or 2 (GLS and GLS2), and finally entry into the TCA cycle to α-ketoglutarate via GLUD1 and GLUD2 or via mitochondrial aminotransferases (GPT2 and GOT2). Alternatively, glutaminolysis could partially occur in the cytosol by producing glutamate or α-ketoglutarate from glutamine and eventually by entry of those metabolites in to the TCA cycle via α-ketoglutarate. Further flux in the TCA cycle then moves to malate where malate–pyruvate cycling occurs  indicates that the curves resulting from fitting of the C4-labeled glutamate multiplets, particularly the glutamate 3,4 doublet-glu4d34 resulting from simultaneous labeling of C3 and C4 (during the second TCA cycle turn) and the singlet resulting from glutamate labeling exclusively at C4 (during the first turn of the TCA cycle) are very sensitive to flux through the glutaminolysis pathway even when only natural abundance medium glutamine has been used. See for example Figure 13C, 15N, or 2H labeling) to evaluate glutaminolysis. Control simulation was based on the optimal set of extracted fluxes, and glutaminolysis flux was set at 5% of the TCA cycle flux. The plotted dynamic glutamate multiplet control coefficients indicate that the glutamate doublet 4d34 is the most sensitive glutamate multiplet with respect to glutaminolysis net flux during the first ~1.5 h when the sensitivity of the RGlu4d34 doublet is positive (positive mode) and afterwards when the RGlu4s singlet 4s becomes most sensitive in the negative operation mode. The data in the right panel of Figure We continue the analysis of data of DB-1 melanoma cells treated with 1,6-13Cglucose aWe then evaluated the contribution of glutaminolysis flux to overall energy production of DB-1 melanoma cells. We found that for this tumor line, glutaminolysis flux through the truncated TCA cycle and via pyruvate–malate cycling by malic enzyme ME2 is essentially 0. This conclusion resulted directly from the ability of the model to detect glutaminolysis flux by fitting of data on the glutamate multiplets (see above). To further confirm this conclusion, we forced the model to accommodate glutaminolysis flux between 5 and 20% of the TCA cycle flux. Invariably, this grossly deteriorated the goodness-of-fit of the isotope kinetic data to the model (results not shown).Even though glutaminolysis does not contribute substantially to mitochondrial ATP production, there is still substantial mitochondrial malic enzyme flux (combined activities of NADH producing ME2 and NADPH producing ME3), which is equal to approximately 40% of the TCA flux . Total FA content referenced to palmitate is at the level of several tens of mmol/L-cell. We iteratively varied the ratio of cytosolic to membrane FA and found the best fit at approximately 10 times more FA in the cytosolic membrane compartment  and potentially from cytosolic acetyl-CoA synthetase 2 (ACSS2) reaction, which involve participation of cytosolic acetate or acetate from the medium . NADPH could be produced by the oxidative branch of the PPP, ME, or IDH activities or by serine–glycine one-carbon metabolism (folate metabolism). Our calculated malic enzyme activities in mitochondria  would account for 4.3 mmol/L-cell/h and cytosolic NADPH flux  would not account for the required high level of NADPH flux. A recent elegant study . This constitutes ~80% in agreement with the NADPH consumption rate during FAs biosynthesis. Thus, the flux through the oxidative PPP appears to be ~80% sufficient to supply all of the NADPH required for FA production and is the main supplier of reducing equivalents in DB-1 melanoma. A 20% difference in the rates of NADPH production and consumption indicates that alternative sources of NADP reduction, considered above, need to be taken into account. We also found that mitochondrial-pyruvate–malate cycling was very high (4.3 mmol/L-cell/h). This could also contribute to defense against reactive oxygen species by producing sufficient NADPH via ME3.This modest flux for nt study  suggestent study , we estiThe effects of rapamycin on mTORC1 signaling and cell growth were first examined in four B-cell lymphoma cell lines, sub-classified either as germinal-center type DLBCL  or Burkitt (Ramos). As indicated by loss of mTORC1-dependent S6rp phosphorylation, rapamycin suppressed mTORC1 activity in all the cell lines and profoundly inhibited BrdU uptake-detected cell proliferation .1H MRS  with increased differences at 24 and 48 h. Changes of lactate and glucose concentrations in the medium indicated diminished glycolysis following rapamycin treatment with corresponding changes in lactate secretion and glucose uptake per cell. Lactate secretion and glucose uptake exhibited maximum differences between vehicle- and rapamycin-treated cell cultures at 24 h in contrast to intracellular lactate that remained approximately unchanged. The amount of secreted lactate per cell was three to four orders of magnitude greater than the intracellular lactate levels, which indicates that most of the lactate generated by cellular metabolism was secreted by the cell. Nonetheless, the level of intracellular lactate was more sensitive than secreted lactate levels for distinguishing metabolic differences between vehicle- and rapamycin-treated cells. This was, in part, due to the intracellular lactate concentration remaining essentially constant after rapamycin treatment.To determine how early the rapamycin-induced metabolic changes can be detected, we examined the intracellular lactate concentration at 2, 4, 8, 24, and 48 h in two B-cell lymphoma cell lines . While 2While inhibition of mTORC1 signaling by rapamycin reaches an optimum within minutes after drug administration –82, the in vivo, we evaluated xenotransplanted lymphoma models  of representative metabolic genes in DLCL2 cells (Ramos data are similar). The anticipated related alterations in the cellular metabolism indicate that in the DLCL2 cells mTOR inhibition diminishes not only glucose metabolism but also TCA cycle activity, de novo FA and sterol biosynthesis, pyruvate cycling activity, and flux through pentose phosphate pathway. Glutaminolysis should also be affected due to novel PPAT gene expression, which is responsible for enzymatic glutamate production from glutamine  media containing 5 mM  glucose over a 12-h period in a 10 mm diameter NMR bioreactor. Cells were studied before treatment and 24 and 48 h (not shown) after treatment with rapamycin. Using a two-compartmental enzyme kinetic model, we calculated lactate production rates. The results were 6.9 mM/h (pretreatment) and 1.6 mM/h (post-treatment), which indicate a profound decrease in glycolysis. There is also a decrease in labeling of Glu C4 reflecting a decrease in TCA cycle flux. Alanine and fatty acid labeling are also diminished, and glycogen stores have been completely depleted, probably as a result of decreased energy production by both glycolytic and TCA cycle pathways.Figure 13C MR spectra measured after infusion of  glucose into a mouse with a 12 mm DLCL2 xenograft (500 mm3) growing on its flank. The inset to this figure shows the MR image of the tumor and the Hadamard voxel that localizes the spectral data in the tumor . These studies demonstrate the feasibility of performing 13C MRS studies non-invasively in these in vivo lymphoma tumor models in mice. The spectra show the time course of labeling of tumor metabolites by transfer of 13C by metabolism of the labeled glucose over 120 min with each spectrum measured in 15 min. The same animal was studied before and 4 days later after seven doses of rapamycin (10 mg/kg) with two doses administered per day . The model included glycolysis, alanine production, and exchange of cytosolic and vascular/interstitial lactate through MCT1 (the dominant monocarboxylic acid transporter). The calculated fluxes indicate a decrease in glycolytic metabolism caused by treatment with rapamycin. Clearly a more comprehensive analysis, including other metabolic pathways that contribute to lactate production is required, but this demonstrates feasibility of our precise data analysis approach.A computational analysis Figure  of the t13C] lactate, citrate, and succinate as internal standards. The extracted metabolites were separated by a reverse-phase ion-paring chromatography and analyzed by MS. Note that these are steady-state measurements, which only provide information about relative fluxes. Kinetic data are required for absolute flux measurements. Significant changes are evident in levels of glycolytic metabolites , pentose shunt metabolites (6-phosphogluconate and 5-carbon phosphates), and TCA cycle   , 101.13C] glutamine; after 6 h steady state was achieved. A preliminary analysis fitted the LC-MS isotopomer data  to all the metabolic pathways included in the fragmented cumomer model   , 49. TheWe have for the first time formulated and validated a detailed metabolic network model of tumor intermediary metabolism that includes all the key pathways except glycogen and phospholipid metabolism as well as serine–glycine one-carbon metabolism. These pathways can be added in the future. We have demonstrated that glutamine metabolism, while critical to AA and nucleoside/tide production, makes minimal contribution to energy production in DB-1 melanoma. Studies of FA metabolism have been initiated and need to be advanced further. Aerobic glycolysis and oxidative metabolism contribute approximately equally to energy production in this tumor and probably contribute in similar proportions to energy production by many tumors, with relative contributions of glycolysis being modulated by oxygen supply in the local environment of the tumor.13C MRS studies of tumor in situ and ex vivo13C MRS and LC-MS studies of tumor metabolism to identify key pathways that are modified by treatment of individual patients with specific inhibitors. Because of the intrinsic low sensitivity of 13C MRS, a key goal of these exploratory studies will be to identify biomarkers of therapeutic response that can be more readily studied by more sensitive methods, such as 1H MRS and CEST (13C-labeled pyruvate, forces them to be employed at abnormally high concentrations, which may modify the metabolic pathways that are being monitored. Despite its intrinsically low sensitivity, 13C MRS of glucose, lipids as well as low molecular weight metabolites, such as acetate and glutamine, should not be ignored, especially in view of the increasing availability of high-field instruments, such as 7 Ts, of which more than 60 are already available at leading institutions throughout the world. These are likely to soon gain FDA approval and will become much more useful as body coils and other probes become more available. The recent demonstration of the feasibility of ex vivo13C MRS and LC-MS analysis of tumor specimens that have been surgically excised (in vivo13C MRS study at 9.4 T of human melanoma and lymphoma xenografts in mice point to the feasibility of non-invasive human metabolic studies. Overall, metabolic studies are likely to complement genomic studies of human cancer in the near future.Using NHL as a model and rapamycin as a specific therapeutic agent, we have formulated a general strategy for monitoring therapeutic response of signal transduction. This strategy includes preliminary exploratory non-invasive and CEST  and PET/ excised , 19 in cThe study was conceived and coordinated by JG and AS; SL, KN, LG, DN, and JR designed and performed the experiments, and collected the data; AS, SL, KN, LG, IB, and JG analyzed and interpreted the experiments; AS performed computational metabolic analysis; MW and DL provided technical assistance and contributed to the preparation of the manuscript; JG provided conceptual advice; JG, SL, and AS wrote and edited the manuscript with the assistance of all authors.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 reviewer NM and handling Editor declared their shared affiliation and the handling Editor states that the process nevertheless met the standards of a fair and objective review.


A challenge for large-scale siRNA loss-of-function studies is the biological pleiotropy resulting from multiple modes of action of siRNA reagents. A major confounding feature of these reagents is the microRNA-like translational quelling resulting from short regions of oligonucleotide complementarity to many different messenger RNAs. We developed a computational approach, deconvolution analysis of RNAi screening data, for automated quantitation of off-target effects in RNAi screening data sets. Substantial reduction of off-target rates was experimentally validated in five distinct biological screens across different genome-wide siRNA libraries. A public-access graphical-user-interface has been constructed to facilitate application of this algorithm. Genome-wide high-throughput RNA interference (RNAi) screening has been widely applied in biomedical research for discovery of drug targets or illumination of unknown molecular machinery, and has proven to be an effective means for functional annotation of protein-coding genes in both normal and disease contexts . HoweverZ score derivations were consistent with the original publications (Z score (formula shown below) and adjusted for batch effect. That is, raw data were grouped by experimental batch and within each group, sample median and median absolute deviation were used to calculate robust Z score. Annotation of all siRNA/miRNAs pools and their associated Z scores can be found in Supplementary Tables S1, S2, S3 and S6.For the H1155 toxicity screens  and the Z scores were calculated as a standard score centered on the population mean of each screening run as described by the average of each triplicate experiment minus the standard deviation. Annotation of all siRNA pools and their associated Z scores can be found in Supplementary Table S4.For the WNT (int/Wingless) pathway siRNA screen , Z scoreβ0 + β1 Parkin + β2 siRNA + β3 Parkin × siRNA. Two-way ANOVA models were used to identify siRNAs that decreased Parkin-mediated mitophagy and Z scores were calculated as the statistical significance. Annotation of all siRNA pools and their associated Z scores can be found in Supplementary Table S5.For the selective autophagy siRNA screen , mitochoDecoRNAi approach has been tested in five distinct biological screens across different genome-wide siRNA libraries, and all data processing and ications .For theZ score is modeled as a linear combination of on-target effect  and seed sequence based off-target effects. The LASSO regression model was defined as below:iZ is the ith original Z score, jβ is the estimated off-target effect of the jth seed family, iY is the corrected Z score (on-target effect) and λ is the penalty parameter. X is denoted as below:The LASSO (least absolute shrinkage and selection operator) regression approach was adapted to quantify the strength of seed-link effects. For this analysis, each Z scores and vice versa. Based on empirical experience, λ is set to 0.001 as the default. We annotate those coefficients with absolute value >1 as indicating candidate off-target effects for all four datasets shown in this manuscript. However, all the parameters and cutoff values are tunable by users.For each seed family, we can thus estimate the coefficient that indicates the strength and direction of predicted off-target effects. A negative coefficient means the seed family tends to lower Z scores from primary screening, the empirical distribution function nF for Z scores from seed family S is defined as:Ns is the total number of Z scores from seed family S. The KS statistic for a given cumulative distribution function F(z) is as:For LASSO-selected off-target seed families, we further examine the statistical significance using the Kolmogorov-Smirnov test (KS-test). Taking P-value) was then determined by the KS statistic.The statistical significance .Significance: Specify the cutoff for significance  .®) supplemented with 5% fetal bovine serum  and 1% penicillin/streptomycin (Gibco®). All siRNAs were purchased from Dharmacon. The library contains 24 sets of four siRNAs each. The oligos targeting transmembrane protein 114 (TMEM114) from Dharmacon were used for siRNA negative control. The miR 4633-5p and the synthetic miRNA were from Ambion. Nontargeting miRNA control (IN-001005-01-05) was from Dharmacon. For reverse transfection, 1 μl siRNA (10uM) in 30 μl serum free media (SFM) was mixed with 0.4 μl RNAi Max (Invitrogen) in 10 μl SFM. 40 μl siRNA-reagent mix per well and 5000 cells per well, from a single cell suspension, were delivered in 100 μl media in 96-well microtiter plates. Cell viability was measured 96 h post-transfection with CellTiter-Glo (Promega) according to manufacturer's specifications.In secondary individual oligo screening, H1155 cells were grown in RPMI 1640  possible non redundant ‘seed sequences’ within any siRNA or small hairpin RNA (shRNA) library collection. As there are usually tens to hundreds of thousands of oligos in a given screening collection, the presence of a given seed sequence within many different siRNA/shRNA reagents presents the opportunity to identify ‘seed-driven’ phenotypic associations among reagents within a given screen. To begin to test this, we examined a whole-genome siRNA toxicity screen in H1155 non-small cell lung cancer cells . To defend against that, we developed DecoRNAi algorithm that estimated strength and direction of seed-driven effects using LASSO as a penalized regression model .A major determinant of the mRNA targets for translational suppression by a given miRNA, is partial mRNA/miRNA complementarity corresponding to a 6-nucleotide ‘seed sequence’ on the 5′ end of the miRNA . By thiser cells  .Application of DecoRNAi to the H1155 toxicity screen identified 13 potential seed-driven phenotypic associations corresponding to 365 siRNA pools . For experimental evaluation of candidate seed-driven effects, we chose four ‘off-target’ seed families  for a focused secondary screen . Twenty-four genes were selected and for each gene, the corresponding Dharmacon siRNA pool was retested as four individual siRNAs for consequences on H1155 cell viability. Of note, siRNA duplexes containing an oligo with the detected off-target seed sequence were consistently associated with stronger consequences on cell viability than the remaining siRNA duplexes designed to target the same gene  is present within the seed sequence of an annotated human miRNA (hsa-miR-4633-5p). As expected, introduction of the miRNA mimic corresponding to miR-4633-5p resulted in a similar viability defect Figure . To furtWe next applied DecoRNAi to four additional whole-genome siRNA screens employing distinct biological contexts and endpoint assays. These included a siRNA and miRNA mimic screen for host modulators of H1N1-cytopathogenicity, a siRNA screen for modulators of WNT reporter gene activation, a siRNA image-based screen for selective autophagy factors and one additional screen for lung cancer drug target discovery using a distinct whole-genome siRNA library.The H1N1-cytopathogenicity screen sought to return genes that modulate influenza virus replication in human bronchial epithelial cells  Supplem.The WNT screen sought to return genes modulating WNT pathway activation, and thus employed a highly specific endpoint assay using a WNT-specific and a WNT-independent reporter gene combination  , significantly reduced the experimentally determined false positive rate  is recently reported computational tool designed to identify off-targeted transcripts rather than to isolate and correct off-target phenotypes . We applTwo additional computational efforts designed to deflect spurious gene-level annotations from large-scale RNAi screens are ATARiS   and CSA http://galaxy.qbrc.org/root?tool_id=sirna_offtarget). The tool is based on Galaxy open source framework, accepts phenotypic measures (such as Z scores) from primary screens as input, and allows iterative parameter choices for data analyses. All of the user-specified parameters are documented in detail, and the intermediate outputs are provided for transparent analysis . To facilitate automated application of DecoRNAi to siRNA and shRNA library screening efforts, we have embedded pre-computed seed family annotations for three commonly used commercial RNAi libraries . In addition, we proved a tool for automated generation of seed family annotation of user-specific siRNA or shRNA oligonucleotide collections (In summary, DecoRNAi is a computational tool that fills an important unmet need for the functional genomics research community as it enhances the return of rigorous biologically meaningful observations downstream of screening efforts that otherwise consume huge time and reagent resources by following bad leads, or by weeding them out using strictly empirical approaches. Substantial reduction of off-target rates was experimentally validated in five distinct biological screens across different genome-wide siRNA libraries. A public-access graphical user interface has been constructed to facilitate application of this algorithm by any investigator.Supplementary Data are available at NAR Online.


Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the ‘human blood metabolome-transcriptome interface’ (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease. Biological systems operate on multiple, intertwined organizational layers that can nowadays be accesses by high-throughput measurement methods, the so-called ‘omics’ technologies. A major aim in the field of systems biology is to understand the flow of biological information between the different layers at a systems level in both health and disease. To unravel the complex mechanisms underlying those molecular processes and to understand how the different functional levels interact with each other, an integrated analysis of multiple layers, i.e. a ‘multi-omics‘ approach is required. In our present study, we investigate the relationship between circulating metabolites in serum and whole-blood gene expression measured in the blood of individuals from a population-based cohort. To this end, we constructed a correlation network that displays which transcript and metabolite show the same trend of up- and down-regulation. We derived a functional characterization of the network by developing a novel computational analysis. The analysis revealed systematic signatures of signaling, transport and metabolic processes on both a regulatory and a pathway level. Moreover, integrating the network with associations to clinical markers such as HDL-cholesterol, LDL-cholesterol and TG identified coordinately activated pathways or modules which might help to assess the molecular machinery behind such an intermediate phenotype. Blood is a connective tissue, which not only ensures nutrient and oxygen supply of all organs of the human body, but also the communication between them. Among the variety of key tasks performed by blood are immunological functions through white blood cells. Due to its diverse functionality, blood is heterogeneous and complex in its composition. Besides cellular constituents, which can be roughly divided into red and white blood cells, blood mainly consists of plasma. Plasma represents the aqueous phase containing proteins, peptides, signaling molecules and steroid hormones, but also other metabolites  which are consumed and released by the organs. This unique composition of blood, agglomerating both metabolic and transcriptional variation carrying molecular signatures of system-wide processes, together with its minimally invasive accessibility, makes blood a widely used system for integrative biomedical research ,2.omics technologies for different levels of molecular organization, a systematic analysis of biological mechanisms underlying the functionality  of a system became possible. In the case of transcriptomics data, an established framework to systematically investigate the constituents of involved biological processes and their interactions are network-based approaches, where pairwise associations between molecular entities (nodes) are modeled as network edges. Such studies commonly identify context-specific functional modules [With the development of high-throughput  modules , but als modules  from dif modules  and cell modules . When pa modules ,8 or glo modules –11 were  modules ,13 or mo modules .Similarly, for metabolomics data a variety of studies extensively analyzed interactions between metabolites in various tissues, conditions and species –17. Regaomics measurements (e.g. gene expression levels and metabolite concentrations) is an area of active research with many successful applications investigating the interplay between multiple organizational layers of a biological system [et al, who analyzed whole blood transcriptomics data in combination with blood lipid measurements and metabolites from a Finnish population cohort [et al was among the first to provide clear evidence for this immune system link in blood, suggesting that gene expression in white blood cells is responsive to changing blood metabolite levels. Thus, it can be concluded that even if not cell-specific, the signals derived from whole blood data still reflect organism-wide processes. This is also in line with previous studies conducted on whole blood transcriptomics or metabolomics data separately [The integration of multiple l system –28. Howen cohort . In theiparately ,30,31.omics layers , comprising 440 metabolites and 16,780 genes after filtering. We constructed a global correlation network to elucidate the complex interplay and regulation between these s layers . The corblood metabolome-transcriptome interface (BMTI) using different strategies. First, we manually investigated the strongest associations and provide evidence from literature wherever possible. Moreover, using a Mendelian randomization (MR) approach, we examined potential causal relationships between metabolites and transcripts. Second, using the most recent genome-wide human metabolic network Recon 2 [pathway interaction network (PIN) that allows for fast functional interpretation of the BMTI and furthermore provides insights into the cross-talk among distinct molecular pathways  with gene expression data of 16,780 uniquely mapping gene probes and metabolite concentrations of 440 metabolites . S1 Tabl−4 ± 0.0433, ρ = 0.56. Moreover, the distribution of inter-omics correlations showed a rather narrow shape, indicating generally lower correlations when compared to the intra-omics correlations . The metabolite-metabolite distribution was strongly skewed for positive correlation values, which is in accordance with our previous findings on a different metabolomics panel [Metabolite-mRNA Spearman correlation coefficients were symmetrically distributed around zero  of 0.01. This corresponded to an absolute correlation cutoff of ~0.181 and a p-value threshold at 1.07 × 10ff value . Accordiff value .SLC25A20 with 37 metabolites attached , consisted of 636 nodes  and 1109 edges, corresponding to a total network connectivity of ~0.015% . Out of attached .We used data from the DILGOM study, which included NMR metabolomics as well as transcriptomics data for 518 individuals, for independent replication of our correlations. In total, 17 metabolites  overlapped between the KORA F4 dataset and the DILGOM study, which allowed us to investigate the replication of 211 edges (~19% of the BMTI). 61 out of the 211 edges (~29%) reached a nominal significance  in the DILGOM study of which 38 (~18%) remained significant after multiple testing correction  a database of more general tissue gene expression signatures  two ures see . For theures see . The remures see .As a first step to characterizing the BMTI, we performed a manual literature lookup for the strongest absolute correlations in the network . In the DNA-Damage-Inducible Transcript 4 , which are known to play a role in stress response [NR3C1), it regulates various cellular processes like carbohydrate metabolism and the immune system by direct activation of target genes [DDIT4 was identified as a GR target gene in mouse hepatocytes [Suppressor Of Cytokine Signaling 1 , a major constituent of the cytokine signaling pathway and inflammatory response [Kruppel-Like Factor 9 (KLF9) and Dual Specificity Phosphatase 1 (DUSP1) . KLF9 is a ubiquitously expressed transcription factor involved in the regulation of diverse biological processes like cell development and differentiation in adipogenesis [DUSP1 is an enzyme involved in the response to environmental stress [The strongest association in the dataset was observed between cortisol and response . Cortisoet genes . Remarkaatocytes , rat hipatocytes  and alsoatocytes  deliveriresponse . We obseogenesis . DUSP1 il stress . Interesl stress  and peril stress .GATA Binding Protein 2 (GATA2), Histidine Decarboxylase (HDC), Solute Carrier Family 45, Member 3 (SLC45A3) and Chromosome 1 Open Reading Frame 186 (C1ORF186) . HDC is a cytosolic enzyme that catalyzes the conversion of histidine to histamine and thus represents an important immune system trigger molecule [GATA2, a key regulator of gene expression in hematopoietic cells [C1ORF186 and SLC45A3, two membrane-bound proteins, were all identified to play a role in the immune response [Another metabolite showing several strong associations to blood transcripts was 1-monoolein, which belongs to the class of monoacylgylcerols. This particular class of metabolites are bioactive compounds recently identified to be involved in various signaling processes of the immune system ,50. The molecule . In addiic cells , C1ORF18response .SLC25A20) occurred in 15 of the 25 top ranked correlations. This gene encodes an enzyme which transports acylcarnitines, i.e. the transport variant of fatty acids, into the mitochondria for subsequent ß-oxidation. Interestingly, the majority of SLC25A20-associated metabolites among our top 25 correlations belonged to the class of long chain fatty acids , which is in accordance with its function as a lipid transporter. Of note, among the metabolites associated with SLC25A20 beyond the top 25 correlations were also 5 acylcarnitines, although at lower correlation values.Carnitine-acylcarnitine translocase . It has been shown previously that circulating levels of branched-chain amino acids (BCAAs) affect a variety of metabolic processes such as glucose and lipid metabolism [We observed a significant, negative correlation between isoleucine and tabolism . ABCG1 itabolism . Interestabolism  and in atabolism .To assess whether metabolite-transcript links in BMTI contain causal effects, we performed a Mendelian randomization analysis . For eacIn order to further reveal the underlying mechanisms determining the observed associations, we systematically analyzed whether correlating pairs of metabolites and transcripts (i.e. enzymes) correspond to the structure of the underlying metabolic network. Specifically, we investigated if strong metabolite-transcript edges of the BMTI tend to be in close proximity within biochemical pathways. All pairwise associations between metabolites and transcripts were mapped to their corresponding network nodes in the Human Recon 2 metabolic network reconstruction . As a meWe could map 121 metabolites and 1,467 enzymes out of the 254 metabolites with known identity and 16,780 transcripts onto the metabolic network, respectively. While most pairwise correlation coefficients were closely distributed around zero for all investigated network distances, a distinct pattern was observable for statistically significant correlations. The majority of significantly correlating pairs accumulated at short distances and was dominated by positive correlations . To deteTo further characterize the underlying biochemical pathways, we calculated frequencies of functional annotations from Recon 2 among the significant associations for pathway distances 0 to 2 . At a diTaken together, the BMTI captured a systematic signal of metabolite-enzyme associations to be in close proximity when mapped onto a global metabolic network. Moreover, the strongest signals found for pathway distances of 0, 1 or 2 reflect distinct metabolic reactions mainly belonging to lipid, energy and amino acid metabolism, and transport mechanisms.Up to this point, our analysis was a reaction-centered approach limited to single edges only, thereby neglecting the global network structure and cross-talk between pathways captured in the BMTI. To derive a comprehensive functional description of the biological modules included in the BMTI, we developed a novel approach based on functional annotations which provides an integrated view on cellular processes. Briefly, the approach consists of three steps: First, we used pathway annotations to define groups of functionally related metabolites and transcripts. For metabolites, we used metabolic pathway annotations provided with the metabolomics dataset, and for transcripts we downloaded the Gene Ontology (GO) slim annotations. Second, an aggregated z-score (aggZ-score) was calculated for each functional category. Third, we calculated correlations between aggZ-scores of all functional categories. A schematic overview of this multi-step approach is provided in α = 0.01, p-value ≤ 2.2 × 10−6) this resulted in an overly dense connected network of 166 nodes and 1220 edges. To generate a visually interpretable version of this network, an ad-hoc stringent threshold of p-value ≤ 1.0 × 10−11 was applied to draw the network. This resulted in a PIN consisting of 113 nodes  connected by 244 edges  by drawing edges between significantly correlated categories. Interestingly, even when applying a stringent Bonferroni-corrected threshold  and Perilipin 2 (PLIN2), both involved in ß-oxidation and long chain fatty acid transport, were associated to 5 and 7 out of the 15 long chain fatty acids, respectively. Three further transcripts, Tumor Necrosis Factor Receptor Superfamily, Member 21 (TNFRSF21), Aldo-Keto Reductase Family 1, Member C3 (AKR1C3) and 1-Acylglycerol-3-Phosphate O-Acyltransferase 4 (AGPAT4) were correlated with 5,8-tetradecadienoate, a side product of oleate ß-oxidation. While AKR1C3 and AGPAT4 are enzymes mainly related to arachidonic acid metabolism and phospholipid metabolism, potentially indicating a branching point to other pathways of the lipid metabolism, TNFRSF21 is involved in T-cell activation and immune regulation [The first scenario contained the metabolic pathway “long chain fatty acid” and the gene ontology annotation “lipid metabolic process” . The subgulation .GATA2, Endothelial PAS Domain Protein 1 (EPAS1) and Notch1 are key regulators of hematopoiesis and as such are involved in the differentiation process of immune cells [EPAS1 and thioredoxin (TXN) are associated to the response to oxidative stress [Membrane-Spanning 4-Domains, Subfamily A, Member 2 (MS4A2) and Ectonucleotide Pyrophosphatase/Phosphodiesterase 3 (ENPP3) are involved in allergic responses mediated via the IgE receptor [Since whole blood transcriptomics measurements should mainly reflect the immune system, we have chosen the association between the metabolic class of “monoacylglycerols” and the GO-term “immune system process” as a second scenario . The corne cells ,64,65. Mreceptor . The tworeceptor .subpathways and 7 superpathways. The Methods section provides a detailed explanation of the process. A summary of all enriched TFBS can be found in The BMTI contains a prominent “flower-like” network topology, i.e. many transcripts associated to a single metabolite. We therefore asked whether these coordinated changes around a metabolite and also the network topology can be explained by common transcriptional regulatory processes through transcription factors TFs, . For theSterol Regulatory Element Binding Transcription Factor 2 (SREBF2), Peroxisome Proliferator-Activated Receptor Gamma  and Nuclear Factor, Interleukin 3 Regulated (NFIL3). SREBF2 is a major regulator of cholesterol metabolism [PPARG is known to be activated by fatty acid ligands, thereby regulating fatty acid ß-oxidation and other processes [In total, out of the 205 binding motif matrices used in the analysis, 189 reached a significant enrichment in at least one of the metabolite-derived gene sets, indicating a generally prevalent common regulation. Across all lists of enriched TFBS identified from our network, the motifs that occurred most frequently were tabolism  while PProcesses . NFIL3 irocesses .−8 and p-value = 2.36 × 10−10, respectively; SREBF1 and SREBF2 , a transcriptional repressor involved in the STAT-dependent interleukin 4 response of B-cells [BCL6 is negatively correlated with methionine and tyrosine in our network , while no significant enrichment was observable for the genes connected to tyrosine. The second motif was identified around Nuclear Receptor Subfamily 4, Group A, Member 2 (NR4A2), which was associated to 7 metabolites in our network. The 22 neighboring genes of one of those metabolites, kynurenine, showed significantly enriched binding sites for this transcription factor . We then projected the −log10 transformed p-values from this regression as colors onto the corresponding nodes in the two networks. Similarly, the analysis was performed using aggZ-scores of pathways / GO terms as explanatory variables and mapped to the PIN , 28  and 1,124  statistically significant associations for HDL-C, LDL-C and TG, respectively. Of those associations, 64%, 28% and 25%, were contained in the BMTI, respectively see  Table. Wρ = −0.53). This opposing pattern also holds for the categorical networks  which was even more pronounced between the PIN-HDL-C and PIN-TG networks . At the pathway level, processes like “generation of precursor metabolites and energy” or “catabolic process” are negatively associated with HDL-C, while “nucleic acid binding transcription factor activity” and “signal transducer activity” are positively associated . Moreover, gamma-glutamyl variants of BCAAs belonging to “gamma-glutamyl metabolism”  and “Valine, leucine and isoleucine metabolism”  displayed profound negative associations to HDL-C . This relatively low number of replicated associations might have various reasons. For example, 1) The DILGOM study used an NMR-based metabolomics platform in contrast to the mass spectrometry-based methodology used in KORA. 2) The smaller sample size of the DILGOM study might limit the power to detect existing associations between metabolites and transcripts. 3) Differences in laboratory procedures and protocols or the population structure can affect replication across cohorts. Future high-powered studies with more similar measurement platforms can further address the stability of metabolite-transcript correlations across studies.DDIT4 or between SLC25A20 and multiple long chain fatty acids, were in consent with known signaling or metabolic pathways. Others support and extend results from previous studies. As one example, nearly all transcripts included in the lipid-leukocyte (LL) module identified by Inouye et al [CP3A, FCER1A, GATA2, HDC, MS4A2, and SLC45A3, core genes of the LL module, and leucine, isoleucine, and several lipids  might have been too low . We therComparison of the blood metabolome-transcriptome interface with the most recent human genome-scale metabolic reconstruction  allowed Our model-based analysis has several limitations. Obviously, any such analysis is heavily dependent on the quality of the underlying metabolic reconstructions, which are still far from being complete . This inTo further functionally characterize the blood metabolome-transcriptome interface at a global level, we developed a network approach based on functional annotations. To this end, we aggregated z-score transformed measurements of metabolites and transcripts into their corresponding metabolic pathways and gene ontology categories, respectively. This approach allowed us to calculate correlation values between different functional categories, rather than between single metabolites and transcripts only. From these associations, we generated a pathway interaction network (PIN) of associated metabolic pathways and Gene Ontology terms, substantially reducing the complexity of the original network and thus facilitating functional interpretations. Detailed inspection of the PIN revealed that correlating nodes resembled not only signatures of well-known biological processes, like the carnitine shuttle, but also suggested novel interactions such as a crosstalk between monoacylglycerols and immune system processes. Taken together, the pathway interaction network enabled us to verify and elevate observations from the single reaction level  onto a pathway level. Moreover, we are now able to explore associations between biological processes/pathways across different biological scales including those that are not necessarily covered by metabolism, such as signaling or transcriptional processes.BCL6 and NR4A2, their target genes and the metabolites methionine and kynurenine, respectively. Remarkably, in a study conducted on mice fed a methionine and choline deficient diet, a significant reduction in the expression of BCL6 was observed [Given the high interconnectivity of the BMTI and the PIN, we asked whether these associations contain information about regulatory interactions across the different metabolite classes and pathways. Enrichment analysis of transcription factor binding sites in the promoter regions of the genes contained in our network identified regulatory signatures for transcripts associated to the same metabolite, which are additionally largely shared between metabolites belonging to different metabolic pathways  Figs. Thobserved . It is wBesides transcriptional regulation mediated by TFs, a substantial fraction of transcripts are expected to be regulated by epigenetic processes . CompariFurther following the scheme of a systems genetics approach, we integrated the two identified networks with intermediate clinical trait data. To this end, we investigated the relationships between changing levels of HDL-C, LDL-C and TG and all measured metabolites and transcripts, metabolic pathways and GO terms . A similSREBP1C which leads to hypertriglyceridemia, further supporting the suggested regulatory cross-link between HDL-C, TG and BCAAs [Combining results from all analysis steps allows for novel hypothesis generation. For example, for the well-known interactions between HDL-C, TG and BCAAs ,76, we dnd BCAAs –88 and oIn summary, our study highlights the potential of a systems genetics approach for understanding interactions across multiple biological scales – in this case circulating metabolites and blood cellular gene expression—and how those insights can be used to generate novel hypothesis on mechanisms underlying common diseases.superpathways and one out of 61 subpathways by Metabolon, Inc., representing two different levels in the metabolic pathway classification hierarchy  study is a series of independent population-based epidemiological surveys and follow-up studies of participants living in the region of Augsburg, southern Germany ,90. In tReplication of the significant metabolite- mRNA associations identified in the KORA dataset was carried out with the Finish DILGOM cohort dataset which included whole blood NMR metabolomics data as well as transcriptomics data for 518 individuals. A detailed description of the sample acquisition as well as data preparation can be found in ,29.http://www.bioconductor.org). To account for technical variation, gene expression intensities data were adjusted for RNA amplification batch, RNA integrity number and sample storage time. Only probes with the annotation flag QC_COMMENT “good” as provided in the updated Illumina Human HT-12 v3 BeadChip annotation file were considered for analysis [To ensure data quality, metabolites with more than 50% missing values were excluded, leaving 440 metabolites (254 knowns and 186 unknowns) for further analysis. The remaining metabolite concentrations were log-transformed, since testing for normality indicated that for most cases the log-transformed concentrations were closer to a normal distribution than the untransformed values . For genanalysis . In addi−06. To get a unique network node per gene, redundant probes matching the same gene were removed. One representative probe per gene was chosen based on the maximum correlation strength to any metabolite, leaving 16,781 unique probes for subsequent analysis. It has to be noted that the applied significance level was still calculated on the whole dataset (including multiple matching probes per gene) to properly account for multiple testing. Network density was calculated as described in [http://www.yworks.com).The metabolite-transcript interface was constructed based on Spearman’s correlation coefficients between the concentrations of all possible metabolite-transcript pairs  across the individuals of the study cohort. Correlation calculation was performed separately for each variable pair, only considering samples without missing values for the metabolites. Statistical significance of correlations was determined at an FDR of 0.01 , correspribed in . More prhttp://www.influenza-x.org/~jshoemaker/cten/db_info.php and comprised markers for 84 different human tissues/cell types [BMTI genes were mapped to three published lists of tissue- and cell-specificity based on gene expression microarrays from purified cells or tissues. The first two marker gene lists were taken from Palmer et al. , who defll types . The thrEstimation of causal effects within the BMTI was performed using a Mendelian randomization (MR) approach . A totalhttp://humanmetabolism.org as of October 2013 [Metabolic reactions were extracted from the consensus metabolic reconstruction “Recon 2”, v. 02 available at ber 2013 . Comparthttp://www.geneontology.org/GO.slims.shtml). Generic GO Slim is a broad and non-redundant subset of all Gene Ontology terms consisting of 148 unique terms covering all three GO domains . Metabolic pathways (MP) with less than two metabolites were excluded from the analysis, leaving 48 metabolic pathways.Functional annotations were retrieved from two different sources. For transcripts, the generic GO Slim catalogue was downloaded from Gene Ontology . The thi enumerates all members in this set, and Zi,j is the z-score of the gene/metabolite with index i in sample j. Spearman’s rank cross-correlation between the aggZ-Scores of all possible GO-MP combinations was then calculated  was tested with all three phenotypic traits using the aggZ-Score for the particular annotation of x. Note that for this analysis, aggZ-Scores were calculated only on those members of a particular annotation that are also contained in the BMTI. Each network node was then color-coded with the −log10 × sign(β1), where the p-value and β1 were derived from the linear regression with the respective metabolite, gene or annotation category. To assess statistical significance of the determined associations, a Bonferroni-corrected threshold of 0.05/ ≈ 2.9 × 10−6 was applied.Linear regression analysis was performed with age and sex as covariates:−7 which lies in the recommended optimal range given the numbers of input sequences we used in this study (mean number of input sequences: 62) [−7 for a dataset of 100 input sequences.To investigate regulatory signatures in the BMTI, an enrichment analysis of transcription factor binding sites was performed. Sets of input sequences were created from the neighbors of each metabolite with a degree ≥ 3 (at least 3 connected genes). Analogously, the pathway interaction network was used to construct sequence sets based on the neighborhood of a metabolic pathway node. For each set of input sequences, a separate search for overrepresented TFBS was performed with the sequences of all remaining genes as background model. Promoter regions  and TSS positions of all genes were extracted from the UCSC database using the R package BSgenome.Hsapiens.UCSC.hg19 version 1.3.1. Position-specific weight matrices of the transcription factor binding motifs were taken from the vertebrate collection of the Jaspar database version 5.0 alpha . Enrichmces: 62) . The autS1 Datasethttp://www.cytoscape.org/; the .graphml file can be viewed with http://www.yworks.com/en/products/yfiles/yed/The .cys file can be viewed with (ZIP)Click here for additional data file.S1 Figth correlation of the ordered list of correlation coefficients is plotted. We observe a high agreement between the two measures, with a correlation (of correlations) of r = 0.92.Note that due to the high number of pairs (440x16780), only every 200(PDF)Click here for additional data file.S2 FigThe fraction of edges above cutoff against all possible edges, plotted as a function of the absolute correlation coefficient. Red dotted line represents the correlation cutoff used in this study (0.01 FDR).(PDF)Click here for additional data file.S3 FigThe first column  contains mapped pairs with non-infinite distances. The second column  contains all pairs of metabolites and transcripts that could be mapped to the pathway network. The third column  shows all pairs, indicating that the larger fraction of pairs cannot be mapped to the pathway network. The first row (‘significant’) only contains significantly correlating pairs of metabolites and transcripts, whereas the second row  shows all pairs. The increase in fraction size from ‘all’ to ‘significant’ is assessed by a Fisher’s exact test and shown in (PDF)Click here for additional data file.S4 FigCarnitine Palmitoyltransferase 1A (CPT1A) at the outer mitochondrial membrane and subsequent internalization by carnitine-acylcarnitine translocase (SLC25A20). Upper triangle matrix indicates Spearman correlation coefficients. For direct substrate/product–enzyme pairs of this path we observe weak, insignificant associations ranging from ρ = 0.0016 to ρ = 0.13. In contrast, the strongest correlation was observed between stearate (18:0) and SLC25A20 , which are two reaction steps apart.Note that this path corresponds to the one depicted in (PDF)Click here for additional data file.S5 FigSimilar to shorter distances, the strongest associations between metabolites and transcripts at a distance of two mainly resemble paths describing transport processes (28%) or those belonging to energy metabolism (oxidative phosphorylation 25%), amino acid metabolism  and lipid metabolism (fatty acid oxidation 14%) besides others.(PDF)Click here for additional data file.S6 FigThe left-hand side of the flowchart shows an exemplary set of interactions between 4 transcripts and 4 metabolites, with annotations to two gene ontology terms and two metabolic pathways. For each GO-pathway pair we then determine the transcripts and metabolites which generate the connection between those two (middle panel). For example, GO1 and MP1 are connected through 3 transcripts and 2 metabolites, whereas GO1 and MP2 do not share any connection. Aggregated z-scores are then calculated for each pair based on the shared transcripts and metabolites to generate the pathway interaction network (left-hand side).(PDF)Click here for additional data file.S7 FigOn this fine grained level of single metabolites, several signatures of a shared regulation are observable, e.g. between amino acids and various other metabolites. Note that the color-scale is capped at 15, the maximum number of shared binding sites was 27 between histidine and X-03094.(PDF)Click here for additional data file.S8 FigOn this coarse level, only few but highly significant shared regulatory signatures are observable. Note that the color-scale is capped at 15, the maximum number of shared binding sites was 4 between carbohydrate and peptide metabolism. The category “unassigned” includes all unknown metabolites.(PDF)Click here for additional data file.S9 FigComparison of beta values from linear regression analysis. Each dot represents either a transcript or a metabolite in the BMTI. We observe a strong anti-correlation between HDL and TG associations, modest positive correlation between TG and LDL and a very weak anti-correlation for HDL and LDL.(PDF)Click here for additional data file.S10 FigComparison of beta values from linear regression analysis. Each dot represents either a GO term or a metabolic pathway in the PIN. Even more profound than for single transcripts and metabolites , there i(PDF)Click here for additional data file.S11 FigSimilar to the results of transcripts and metabolites , we obse(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.