The “division-of-labor” between compartments was investigated using a minimum energy (photon) objective function. Random sampling of the solution space was used to explore the flux distributions under different scenarios as well as to identify highly coupled reaction sets in different tissues and organelles. Efficient identification of these sets was achieved by casting this problem as a maximum clique enumeration problem. The framework also enabled assessing the impact of energetic constraints in resource (redox and ATP) allocation between leaf, stem, and root tissues required for efficient carbon and nitrogen assimilation, including the diurnal cycle constraint forcing the plant to set aside resources during the day and defer metabolic processes that are more efficiently performed at night. This study is a first step toward autonomous modeling of whole plant metabolism.Genome scale metabolic modeling has traditionally been used to explore metabolism of individual cells or tissues. In higher organisms, the metabolism of individual tissues and organs is coordinated for the overall growth and well-being of the organism. Understanding the dependencies and rationale for multicellular metabolism is far from trivial. Here, we have advanced the use of AraGEM  in a multi-tissue context to understand how plants grow utilizing their leaf, stem and root systems across the day-night  cycle. Six tissue compartments were created, each with their own distinct set of metabolic capabilities, and hence a reliance on other compartments for support. We used the multi-tissue framework to explore differences in the “division-of-labor” between the sources and sink tissues in response to: (a) the energy demand for the translocation of C and N species in between tissues; and (b) the use of two distinct nitrogen sources (NO Genome-scale reconstruction and modeling of metabolism enables the integration of knowledge at different levels of the cascade from genes over proteins to metabolic fluxes. This is pivotal to develop an understanding of how individual components in a system interact and influence overall cell function  , and sugarcane (Saccharum officinarum) , exchange of metabolites between tissues (common pools), temporal storage and retrieval of metabolites and in particular, the diurnal cycle constraint.The multi-tissue modeling framework developed here differs from the approaches used in modeling microbiome . CP has no storage capacity, so transport to the pool from one tissue must be matched by transport to other tissues. A model may have several CPs to describe distinct pools shared by some but not all tissue types. A simple C4 model, for example, would have a CP describing exchange between bundle sheath and mesophyll tissue as well as a CP describing translocation through the vasculature. Transport mechanisms fall into two catagories passive and active. Passive transport mechanisms do not require the cell to do work for the substance to enter or leave the cell . Active transport mechanisms involve the cell to use cellular energy usually in the form of ATP. In the multi-tissue model, active transport is captured by coupling transport to ATP hydrolysis. The difference in energetics of loading and unloading can be captured by introducing separate transport for export and import. For the purpose of illustration, the current study will consider a three-tissue  model with two interstitial CPs, one for leaf-stem exchange and one for stem-root exchange Figure .Temporal storage and retrieval of metabolites is managed through the introduction of a storage pool (SP). We can divide time into as few or many periods as required. The key assumption is that there is no net accumulation across all periods, i.e., whatever is stored in one period must be retrieved in the other periods. For illustration purposes, we will here consider a day-night cycle with leaf starch being the only stored compound.S  and three types of transport matrices. Matrix E represents the exchange reactions of metabolites in S with the environment, matrix T the transport reactions of metabolites in S with the common pool CP, and matrix A the accumulation reactions of metabolites in S into a temporal storage pool SP. The number of rows in matrix CP or SP depends on the number of metabolites shared or stored, respectively.We extended the concept of stoichiometric modeling to describe the metabolite balance constraints for the whole-plant system. Using an existing genome-scale model, we can define an internal stoichiometric matrix l, s, r) exchanging with the environment and common pools for a given period is shown below:The stoichiometric matrix block representing the leaf, stem and root tissues  were introduced to convert intrinsic tissue fluxes (moles/g DW/h) to extrinsic fluxes (moles/h), in order to balance fluxes between tissues.Mass fractions ωTemporal separation  was achieved by duplicating compartments for both tissues and common pools and introducing SP. The large stoichiometric matrix linking resource storage between day-night cycle is:d, ωn were introduced to represent the fraction of hours day light is available to the plant, in order to convert rates to total flows. The introduced mass and time fractions are shown in Supplemental Material .Where time fractions ωf and a set of flux boundary constraints .Flux balance analysis (FBA) –(3) define a generic FBA model for a given genome scale stoichiometry with a defined set of tissues and pools. Specific models are formulated by specifying the objective function weights, We assume that plant metabolism  has evolved to become efficient at utilizing photons for growth (Equation 3). Efficient resource usage has been successfully applied as the metabolic objective to predict single and two-tissue metabolic function  considering both day and night period. Phloem loading and unloading of amino acids and sucrose is dependent on an energy-requiring mechanism  for the species translocated between tissues (captured by coupling transport to ATP hydrolysis). For example, the penalty weight for sucrose translocation can be described as:pwsucroseleaf, CP > 0, when the translocation process of sucrose from leaf to the common pool is considered active. Passive diffusion is described by defining zero penalty, e.g., pwsucroseleaf, CP = 0.The tissue model defines both the tissues and the metabolites pools used for interaction Figure . The boupw, we were able to evaluate the main changes in the metabolic network caused by an active translocation process compared to a passive translocation process for glutamate, nitrate and sucrose between tissues.By varying vNH3 uptakeroot = 0, to describe nitrate metabolism.The model was used to contrast two nitrogen sources: nitrate and ammonium. The contrast was implemented by constraining uptake of the alternate nitrogen source, e.g., ammonium uptake was constrained to zero, 1-norm (taxicab norm):Zopt denotes the solution found in Equation (3).The solution to any genome scale FBA problem (Equation 3) is degenerate, i.e., multiple solutions produce the same optimal value. Comparisons can be made using representative values determined by adding an additional criterion, for example, choose the optimal solution that minimizes total enzyme effort required. This can be calculated as an optimal solution to Equation (3) that also minimizes the LIf no additional criterion is introduced, Flux Variability Analysis (FVA)  was calculated.FVA identifies the extreme values for each individual reaction. Uniform sampling of the solution space has been increasingly used for studying the correlation structure of metabolic networks  was constructed based on the reactions  with correlations higher than a correlation cut-off (|ρij| > ρcut−off). This matrix represents an undirected graph G =  consisting of a finite sets of vertices V (reactions) and a finite set of edges E (coupled reaction pairs). The problem of finding the largest sets of fully connected reaction modules can now be cast as listing maximal cliques. A clique is a subset C of the vertex set (C ⊆ V) such that every vertex in C is connected to all the other vertices in the clique, i.e., C is a complete graph. All maximal cliques were enumerated using the Bron–Kerbosch algorithm based on recursive backtracking  within the MATLAB 2013a environment . MATLAB scripts, along with instructions to use the scripts, are provided in Supplementary Materials.−3; usually the most abundant form), ammonium (NH+4) and to a lesser extent, as proteins or amino acids -based fertilizers and nitrification inhibitors can effectively alleviate the two main environmental problems associated with nitrogen fertilization, namely water pollution caused by nitrate leaching and gaseous emissions of nitrogenous compounds. As such, the use of NH+4 has been proposed as a good alternative to nitrate-based fertilizers  most of NH+4 is assimilated in roots rather than translocated to source tissues; (iii) starch is the major storage component and is accumulated in leaves; (iv) growth is constant day and night; and (v) the whole plant will use the minimum energy capture .The current framework is flexible and allows for translocation between tissues and temporal storage of any number of components. For the sake of simplicity, we here use the simplest possible model based on the following assumptions: (i) most of C and N species are translocated in between tissues in the form of NO−3 or NH+4) used. The model-highlights discussed here are to show the potential use of the framework in whole-plant systems only. Biological interpretation requires a more detailed model as well as experimental validation.Under these assumptions, we used the multi-tissue framework to explore differences in the “division-of-labor” between the source and sink tissues as a function of (a) the energy demand for the translocation of C and N species in between tissues, and (b) the nitrogen source (NO−3 to NO−2 occurs in the cytoplasm and it is carried out by nitrate reductase (step 9 and 15). Further reduction of NO−2 occurs in the plastid/chloroplast by nitrite reductase, which converts NO−2 to NH+4 (step 10 and 16). NH+4 assimilation takes place in the plastid and leads to the formation of glutamine and glutamate (step 11–12 and step 17–18) through glutamine synthetase/glutamate synthase (GS/GOGAT). Nitrate and nitrate reductases are available in all tissues and various isoforms of GS/GOGAT enzymes -GOGAT: EC1.4.7.1) enable these reactions in both photosynthetic and non-photosynthetic tissues.Random sampling of the solution space was used to compare active  vs. passive  tissue translocation under nitrate as sole N source Figure . The modThe histogram for each reaction shows the change in flux distributions caused by the tissue translocation penalties. The flux distribution shapes give information about the sensitivity of the solution space to each constraint. Introduction of an energy cost of transportation greatly affected the probability distributions through many reactions in the network. For instance, the flux distribution for glutamate translocation changed not only in shape, but also in direction (step 14). Under free tissue-translocation , the model predicts that nitrate is taken up in roots (sink tissues) but is preferentially assimilated into glutamate in leaves (source tissues). However, under high energy demand for tissue translocation , nitrate is preferentially assimilated in roots.According to the simulations, the translocation constraint is likely to affect many reactions involved in nitrogen metabolism, whereas steps through carbon fixation, photon uptake, starch synthesis/degradation, nitrate uptake and glutamate synthase (in source tissues) are less likely to be affected based on the very similar flux distributions . Differences in sensitivity of fluxes carried by isoforms of GS/GOGAT enzymes are also highlighted. Our results suggest that the flux through plastidic glutamate synthase in root is likely to be more sensitive to the translocation constraint than its isoform in leaves.Uniform random sampling of the steady-state flux space was used to calculate the correlation coefficient between subsets of fluxes of the N uptake and assimilation pathways under different tissue translocation constraints Figure . The metIdentification of the correlated reaction sets can aid experimental design. The measurement of any flux in a perfectly correlated reaction set determines the steady-state flux level though all the reactions  is only perfectly correlated to fluxes through nitrate/nitrite reductases in root (step 15–16) under none or minimum restriction in energy requirements for tissue translocation.In the absence of experimentally determined cost of active transport, the subsequent studies of the effect of nitrogen source was performed assuming no  tissue translocation penalties.−3 and NH+4 assimilation pathways are active over the diurnal cycle at least in tobacco roots  was performed under two nitrogen sources. The predicted uptake rates of nitrate or ammonium are the same during day and night, approximately 7 mmol/g tissue.h under the tested conditions  is required on NO−3 compared to NH+4 to sustain the same plant growth. This is a direct result of the cost of nitrate and nitrite reduction.The model shows significant metabolic changes in N uptake and assimilation pathways due to the use of N source Figure . The pla−3 metabolism. An increase of approximately 27% in starch accumulation in leaf tissues is required in order to keep the same leave biomass composition  and to sustain the same plant growth rate. The increase in starch accumulation is achieved by increasing carbon fixation and photons usage. Stored starch in the source tissue during the day is degraded during the night into sugars that are then used to sustain leaf tissue biomass and some are translocated to sustain the sink tissues. Our simulations show no changes in starch degradation during the day. However, flux through starch degradation increases over night in leaf tissues to sustain plant growth  the supply of assimilates via photosynthesis, (ii) nitrogen source, and (iii) ability of different organs to utilize the available supply.A. thaliana metabolomics data and to obtain further information about metabolite relationships  under optimal photon uptake . Each coupled reaction pair is represented as two vertices connected by an edge. Coupling analysis enabled us to identify and visualize highly coupled reactions from different tissues, organelles and day periods under different conditions. For instance, two reaction modules coupled between source and sink tissues under nitrate condition  and temporal  processes.This approach may generally be coupled to extensive omics data sets in a two way interaction where data narrows the solution space and the network model enable us to gain biologically meaningful insights into complex networks and system level interactions.In the current study, we did not constrain the tissue models using gene expression data for leaf, stem or root tissues. Instead, we used the minimum set of constraints for key biochemical reactions that distinguish phototrophic (leaf) and heterotrophic (stem and root) tissues. This enabled us to explore the optimum distribution of metabolic fluxes in the whole plant system, assuming that plant metabolism  has evolved to become efficient at utilizing photons for growth.−3 rather than NH+4 as nitrogen source. Identification of highly correlated reactions sets enabled visualization of key pathways linking metabolic reactions from different tissues, organelles and day periods under different conditions. The examples of in silico flux predictions illustrate the potential of this framework to interrogate plant metabolism at the multi-tissue level.With minimal constraints, the solution space remains large. Using random sampling, however, we were still able to unravel network-wide effects of (a) imposing energy penalties to account for active transport and (b) using NOThe 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.


Pseudomonas putida KT2440 can use glycerol to synthesize medium-chain-length poly (mcl-PHA), a class of biopolymers of industrial interest. Here, glycerol metabolism in P. putida KT2440 was studied on the level of gene expression (transcriptome) and metabolic fluxes (fluxome), using precisely adjusted chemostat cultures, growth kinetics and stoichiometry, to gain a systematic understanding of the underlying metabolic and regulatory network.Given its high surplus and low cost, glycerol has emerged as interesting carbon substrate for the synthesis of value-added chemicals. The soil bacterium P. putida KT2440 has a maintenance energy requirement [0.039 (mmolglycerol (gCDW h)−1)] that is about sixteen times lower than that of other bacteria, such as Escherichia coli, which provides a great advantage to use this substrate commercially. The shift from carbon (glycerol) to nitrogen (ammonium) limitation drives the modulation of specific genes involved in glycerol metabolism, transport electron chain, sensors to assess the energy level of the cell, and PHA synthesis, as well as changes in flux distribution to increase the precursor availability for PHA synthesis (Entner–Doudoroff pathway and pyruvate metabolism) and to reduce respiration (glyoxylate shunt). Under PHA-producing conditions (N-limitation), a higher PHA yield was achieved at low dilution rate (29.7 wt% of CDW) as compared to a high rate (12.8 wt% of CDW). By-product formation  was specifically modulated under these regimes. On top of experimental data, elementary flux mode analysis revealed the metabolic potential of P. putida KT2440 to synthesize PHA and identified metabolic engineering targets towards improved production performance on glycerol.Glycerol-grown P. putida KT2440.This study revealed the complex interplay of gene expression levels and metabolic fluxes under PHA- and non-PHA producing conditions using the attractive raw material glycerol as carbon substrate. This knowledge will form the basis for the development of future metabolically engineered hyper-PHA-producing strains derived from the versatile bacterium  Pseudomonas putida is well known for its capacity to use a wide range of carbon sources, including aromatic compounds, sugars, fatty acids and polyols [Pseudomonas in comparison to other microbes, when adverse environmental conditions are present [P. putida can cope with fluctuations in nutrient availability through the accumulation of polyesters [P. putida species can synthesize a wide range of poly (PHAs), whereby the monomer composition of the polymer varies with carbon source and other environmental factors [P. putida KT2440 is most suitable to synthesize PHA from raw glycerol among different P. putida strains due to reduced by-product formation under PHA-producing conditions [P. putida KT2440. This led to the discovery of specific regulatory genes [glpR (PP_1074) controls utilization of the substrate. Interestingly, inactivation of this regulator leads to increased synthesis of mcl-PHA in P. putida KT2440 [P. putida KT2440 indicate a mixed glycolytic and gluconeogenic pathway use, a rather complex metabolic adjustment, which strongly differs from that of cells, growing on glucose and succinate, respectively [P. putida for the production of biopolymers from glycerol, it now appears straightforward to further quantify and integrate metabolic function and regulation as well as physiological parameters in a systematic fashion. Such systems biological approaches have proven valuable to understand cellular physiology [P. putida KT2440, several genome-scale models have been developed from genome-annotation [P. putida KT2440 in order to provide an integrated insight into its metabolic and regulatory networks during growth and PHA production on glycerol. A set of fine adjusted chemostat cultures under carbon- and nitrogen-limited conditions, and the integration of experimental and modelling data provided the basis for this systematic analysis. polyols . The bro present , 3. In alyesters , 5, formlyesters . During lyesters . Most P. factors , 9. In t factors . PHAs al factors , 12. Muc factors , 14, eff factors , 16. The factors . High ti factors , polyeth factors , and, pa factors , 21. Recry genes , 23. Wheectively . To bettysiology –27 and eysiology –30. As bnotation –33, unraP. putida. In this regard, the so far unknown maintenance coefficient for P. putida KT2440 on glycerol was quantified. For this purpose, we carefully inspected growth kinetics and stoichiometry of the bacterium, using glycerol-limited chemostat cultures at different dilution rates between 0.044 and 0.21 h−1. At a higher dilution rate of 0.22 h−1, washout of cells from the system occurred, indicating that this value approached the maximum specific growth rate of the strain on glycerol. In addition, accumulation of glycerol in the broth was observed at these elevated dilution rate. From the obtained data, specific rates for glycerol uptake (qglycerol) and biomass yield (YX/glycerol) were calculated  can be inferred from the correlation between the specific consumption rate of glycerol (qglycerol) and the corresponding dilution rate, i.e. the specific growth rate (μ) in the chemostat [Yx/glyceroltrue).According to the Pirt model Eq. , the maihemostat . In addimglycerol = 0.039 (mmolglycerol) (gCDW)−1 h−1 and Yx/glyceroltrue = 0.505 gCDW (gglycerol)−1, respectively. Subsequently, the energy requirement for maintenance (mATP) was derived (see CDW)−1 h−1, the ATP amount required for maintenance in glycerol-grown P. putida KT2440. Additionally, the amount of accumulated PHA and of by-products was quantified for each dilution rate. Organic acids were not detected by HPLC. The cellular PHA content was less than 3 % of the cell dry weight (CDW) at all dilution rates, which matches with natural amounts during growth on non-PHA-synthesis-related substrates [P. putida strains [2 and biomass. As consistency check, the carbon recovery was determined from the experimental data, involving thorough balancing of carbon consumption and production, respectively. The C-mol content in the biomass was assumed to be constant, corresponding to a molecular weight of 27 g (C-mol biomass)−1 [As shown in Fig. ived see  as: \doc of 1.75 , reflectbstrates   and a high (0.12 h−1) dilution rate was adjusted, which corresponded to approximately 20 and 50 % of the maximum specific growth rate, respectively. Table Next, the PHA production capacity of mitation , 41. To  high 0.1 h−1 dilu high 0.1 h−1 dilu high 0.1 h−1 dilu−1) and low (0.044 h−1) dilution rate, and C-limited growth at high (0.12 h−1) and low dilution (0.044 h−1) rate, respectively  are listed. Obviously, a larger number of genes was affected by the imposed nutrient limitation than by the growth rate  and an integral membrane sensor (PP_2671) showed the highest change in gene expression level, which was more than 30-fold. PAS domains are important elements that sense both, fluctuation of signals in the environment and the overall energy level of the cell [PAS and LuxR genes is likely related to the energetic state of the cell, as N-limiting cultures promote not only accumulation of PHAs, but also interactions between PAS domains and electron transport systems [P. putida overexpressed genes, encoding for proteins of the cytochrome c-type . These are essential enzymes in the electron transport chain and define the final redox state of the cell.To gain a more detailed insight into regulation patterns under different nutrient environments, four scenarios were selected for global gene expression analysis: N-limited growth at high 0.12 h h−1 and d low 0.04 h−1 dilthe cell . In addi systems . High yi systems , 43. Propgm, eno, acoA and pykA) and in the pentose phosphate (PP) pathway , whereas the type of nutrient limitation rather impacted the expression of genes of the Embden-Meyerhof–Parnas (EMP) pathway , the Entner–Doudoroff (ED) pathway (eda) and the tricarboxylic acid (TCA) cycle , respectively.Concerning central carbon metabolism Table , increasP. putida we next performed flux balance analysis to additionally evaluate the performance of the underlying metabolic network for the different scenarios. Metabolic fluxes of central carbon metabolism were inferred by flux balance analysis, based on a detailed metabolic network of P. putida . No by-product formation was detected under carbon-limitation. In contrast, a minor flux of carbon was funneled to malate and succinate synthesis from the TCA cycle under nitrogen-limiting conditions. These apparent differences in carbon partitioning were associated with a 7- and 20-fold increased PHA biosynthetic flux under nitrogen-limitation at high and low dilution rate, respectively. Concerning redox equivalents, more NADPH was needed to satisfy the increased demand under nitrogen limitation, related to the enhanced PHA synthesis.As predicted by the modeling approach, When comparing the different growth rates, approximately the same level of PHA synthesis was achieved for both conditions. Apart from a slightly increased ED pathway  and TCA cycle  at high dilution rate, no apparent differences were observed in carbon partitioning. The carbon flux through glyoxylate shunt and PHA synthesis remained almost unchanged.P. putida’s productivity needs to be maximized. A first inspection is provided by integration of the achieved performance under the different conditions into the overall flux space . Although this extreme value at zero growth seems not reachable in practice and an achievable optimum will be realistically lower, the location of the experimental data in the flux space indicate a significant remaining optimization potential  ac glucose , and    showed no repression at low dilution rate, accompanied by a higher flux through its reaction  was overexpressed, when comparing cells growing at a high dilution rate against those at low dilution rate under nitrogen limitation  was used in this study.The wild-type 2HPO4·7H2O, 3 g KH2PO4, 4.7 g (NH4)2SO4, 0.5 g NaCl, 0.12 g MgSO4·7H2O, 6.0 mg FeSO4·7H2O, 2.7 mg CaCO3, 2.0 mg ZnSO4·H2O, 1.16 mg MnSO4·H2O, 0.37 mg CoSO4·7H2O, 0.33 mg CuSO4·5H2O, 0.08 mg H3BO3, and 3 g glycerol as sole carbon source. Cells were grown under aerobic conditions at 30 °C in an orbital shaker . By taking a calculated volume of the overnight-grown cell suspension, cells were transferred into 500 mL baffled Erlenmeyer flasks with 100 mL M9 medium and cultivated as described above. This second pre-culture was used as an inoculum for the continuous process, operated in a lab scale bioreactor. The feed solution consisted of minimal medium  supplemented with 32.5 and 4.1 g L−1 of glycerol, for the nitrogen- and carbon-limited conditions, respectively.Cells were kept as frozen stock in 25 % (v/v) glycerol at −80 °C. To obtain single colonies, cells were plated onto Luria–Bertani agar plates. After 1 day incubation at 30 °C, liquid cultures were prepared by picking up a single colony from the plate and inoculating it into a 50 mL shake flask with 10 mL liquid M9 medium. M9 medium contained (per liter): 12.8 g Na−1 in a 2.0 L top-bench BIOSTAT B1 bioreactor  with a working volume of 1.0 L at 30 °C. The aeration rate was set to 0.4 L L−1 min−1 . The dissolved oxygen level was kept above 30 % air saturation by control of the agitation speed up to a maximum of 700 rpm. The pH was maintained at 7.0 ± 0.2 by automatic addition of 0.5 M H2SO4 and 1 M KOH, respectively. A gas analyzer, equipped with both a paramagnetic O2 and an infrared bench CO2 analyzer , which recorded the concentration of carbon dioxide and oxygen, was coupled to the gas outlet of the bioreactor. The working volume of the fermenter was kept constant by removing the fermentation broth through a peristaltic pump and recording the weight with a balance, placed under the bioreactor.Continuous cultivations were carried out under aerobic conditions with dilution rates ranging from 0.044 to 0.205 hnm . Cell dry weight was determined gravimetrically after collection of 10 mL culture broth  in pre-weighed tubes, including a washing step with distilled water, and drying of the obtained pellet at 100 °C until constant weight. The ammonium concentration in cultivation supernatant was measured by a photometric test . The concentration of glycerol and organic acids  in cultivation supernatant was analyzed by HPLC , equipped with an 8 mm Rezex ROA-organic acid H column , operated with 0.013 N H2SO4 as mobile phase at 0.5 mL min−1 and 65 °C and detection using a refractive index detector .Cell growth was recorded as optical density (OD) at 600g), followed by a washing step with distilled water. The supernatant was discarded and the cell pellet was kept at −20 °C for further processing. Methanolysis was then carried out by re-suspending 5–10 mg of lyophilized aliquots in 2 mL chloroform and 2 mL methanol, containing 15 % (v/v) sulfuric acid and 0.5 mg mL−1 3-methylbenzoic acid as internal standard, respectively, followed by incubation at 100 °C for 4 h. After cooling down to room temperature, 1 mL of demineralized water was added and the organic phase, containing the resulting methyl esters of the PHA monomers, was analyzed by GC–MS. Analysis was performed in a Varian 450GC/240MS ion trap mass spectrometer  and operated by the software MS Workstation 6.9.3 . An aliquot (1 mL) of the organic phase was injected into the gas chromatograph at a split ratio of 1:10. Separation of compounds of interest, i.e. the methyl esters of 3-hydroxyexanoate, 3-hydroxyoctanoate, 3-hydroxydecanoate, 3-hydroxydodecanoate, 3-hydroxy-5-cis-dodecanoate, 3-hydroxytetradecanoate, was achieved by a FactorFour VF-5 ms capillary column . Helium was used as carrier gas at a flow rate of 0.9 mL min−1. Injector and transfer line temperature were 275 and 300 °C, respectively. The oven temperature program was: initial temperature 40 °C for 2 min, then from 40 °C up to 150 °C at a rate of 5 °C min−1 and finally up to 280 °C at a rate of 10 °C min−1. Positive ions were obtained using electron impact ionization at 70 eV and mass spectra were generated by scanning ions from m/z 50 to m/z 650. The PHA content (wt%) was defined as the percentage of the cell dry weight (CDW), represented by the polyhydroxyalkanoate.Monomeric composition of PHA, as well as its cellular content, were determined by gas chromatography mass spectrometry (GC/MS) of the methanolyzed polyester. For this purpose, 10 mL culture broth was placed in a falcon tube and centrifuged . Cell pellets were frozen at −80 °C until further processing. Isolation of total RNA was performed using RNeasy kits , according to instructions by the manufacturer. Extracted RNA (2 μg) was labeled with either Cy3 or Cy5 using the ULS-system  according to the manufacturers manual. Equal amounts of Cy3 or Cy5-labelled RNA, one of them corresponding to the control and the other one to the condition to be analyzed, were mixed by pipetting. Labeled RNA (600 ng) was then fragmented and hybridized to the microarray. Agilent 8 × 15 K two-color microarrays , specifically designed for P. putida KT2440, was used for all transcriptional analyses. The microarrays were scanned using a GenePix Pro 4001 scanner and the GenePix 4.0 software . Subsequent analysis of the microarrays was conducted with software packages (see below) from the Bioconductor suite. The image analysis results were read using the ‘limma’ packages [Aliquots of 10 mL culture broth were placed in RNAprotect buffer  and centrifuged  was performed as described previously , using lElementary flux modes (EFM’s) were calculated with efmtool, based on the null space approach and recursive enumeration with bit pattern trees . The matThe metabolic network model, used for both analyses, was adapted from previous work . Reactio


Current computational tools can generate and improve genome-scale models based onexisting data; however, for many organisms, the data needed to test and refine suchmodels are not available. To facilitate model development, we created the forcedcoupling algorithm, FOCAL, to identify genetic and environmental conditions such thata reaction becomes essential for an experimentally measurable phenotype. Thisreaction's conditional essentiality can then be tested experimentally to evaluatewhether network connections occur or to create strains with desirable phenotypes.FOCAL allows network connections to be queried, which improves our understanding ofmetabolism and accuracy of developed models. There are currently over 3,000 completely sequenced bacterial genomes . For manin silicometabolic and regulatory models depends on their ability to capture the underlyingcharacteristics of the biochemical networks in the microbe of interest. With increasingimprovements in genome sequencing technologies and annotation, and in metabolic networkreconstruction [Escherichia coli have beenpainstakingly developed and refined over the past 20 years, using analysis ofexperimental data acquired over the past 50 years from hundreds of laboratories.Spending this level of time, effort, and resources to obtain a good understanding ofmetabolism for every microbial organism of interest is simply intractable. Thus, tostreamline the process of model curation, future experiments should be designed toreduce experimental efforts while still effectively probing the biological system ofinterest. Having the ability to quickly design experiments to test reactions is criticalfor improving the accuracy and utility of genome-scale models, particularly forless-characterized microorganisms where existing experimental data are limiting.Mathematical representations of genome-scale networks - known as genome-scale models(GEMs) - enable a quantitative and systematic approach to address this issue. Bydeveloping GEMs, the microbial reaction networks can be interrogated to predict growthphenotypes, guide metabolic engineering strategies, elucidate network components andinteractions, and facilitate hypothesis-driven discovery -6. Howevtruction , the abitruction . ExistinA GEM can be refined when discrepancies are found between model predictions andexperimental observations. Several automated computational approaches have beendeveloped to suggest model improvements based on such discrepancies between modelpredictions and existing experimental data. Constraint-based model refinementalgorithms, such as OMNI, SMILEY, GrowMatch, and GeneForce -11, workCurrently, there are no constraint-based methods to efficiently design new experimentsto test the accuracy of a given genome-scale metabolic model, and its associatedmetabolic network reconstruction. To address this limitation, we sought to develop anapproach that would identify media and gene knockout conditions under which a chosenreaction is essential for some measurable phenotype . A prior studyhas used minimal cut sets (MCSs) to identify minimal sets of reactions that if deletedwill disable growth , and oncchosen/vmeasured) is positive, then it implies that a chosenflux, vchosen, must be non-zero if another experimentally measurable flux,vmeasured, is non-zero (vmeasured → vchosen).For our purposes, reactions are considered coupled if the minimum flux ratio is positiveor the maximum flux ratio is a finite number; otherwise, they are uncoupled. Thesereaction couplings are highly dependent on the network and the environmental conditionsused [To address this experimental design challenge, we used concepts from flux couplinganalysis to efficiently identify media and knockout conditions under which a chosenreaction is required to enable flux through another experimentally measurable reaction. Flux coupling analysis characterizes the relationships betweenreactions in a fixed network , and hasonsused  and so fBy finding coupling conditions in which biomass production depends on flux through achosen reaction(s), we can design new growth phenotyping experiments to detect whether achosen reaction occurs by simply monitoring cellular growth. Experimentally testingthese coupling conditions allows for a variety of interesting conclusions to be madeabout the metabolic network. First, if no growth under the proposed coupling conditionsoccurs, then there is a problem with the model. In this case it is possible that thechosen reaction does not occur because the associated enzyme is not expressed under thiscondition (due to regulation) or that the enzyme does not catalyze the reaction ofinterest (incorrect annotation). This means that regulatory, reaction and/or GPR changesare needed to correct the model. Second, if the chosen reaction is found to beconditionally essential under the coupling condition , then the chosen reaction and its associated GPR relationships appear to becorrect within the model. Third, if the chosen reaction is not conditionally essentialunder the coupling condition, then components  aremissing from the network and can be suggested using computational approaches,9.A cycle of model testing and improvement can be established by iteratively using FOCALto design experiments, conducting the FOCAL designed experiments, and adjusting themodel when discrepancies between model predictions and experimental results are foundFigure .By enumerating and testing such coupling conditions, it is possible to not only confirmthe presence of existing network components and interactions, but also to discover newinteractions within the cellular network when the experimental results do not agree withmodel predictions. Additionally, since GEMS are powerful tools for enhancing biochemicalproduction , we havechosen) becomes essential for another measurable flux(vmeasured). While a variety of different types of flux couplingrelationships exist [measured, implies the existence of flux through another reaction,vchosen . Here, we discuss FOCAL's proposedsolutions for coupling reactions to biomass in four genome-scale metabolic models. Usingthese results, we illustrate FOCAL's utility for systematically evaluating and refiningmetabolic models by comparing FOCAL predictions to new and existing experimentalresults. We further show how FOCAL led to the discovery of a new isozyme (YeiQ) for tworeactions in glucuronate and galacturonate catabolism. Finally, we demonstrate the useof FOCAL to design more complex phenotypes, such as mutants that must concurrentlyutilize glucose and xylose in order to grow.FOCAL builds on concepts from the flux coupling framework , where tps exist , FOCAL l2 flux occurs. In the wild-typenetwork  andv2 are uncoupled due to alternative ways of making the two biomasscomponents, F and H . FOCAL indicates that coupling between v2and vbio (vbio → v2) can be obtained byusing metabolite A as the sole minimal media component and deleting genes associatedwith v8 , are required in orderfor the cell to grow (vbio → v1 and v10). Toachieve this, FOCAL recommends deleting genes associated with v3 andv8 and using both metabolites A and G in the minimal media. Theresulting mutant requires both metabolites A and G to produce biomass components Fand H, respectively. In some instances, alternative FOCAL solutions will exist andthese can be found using additional integer cut constraints .Using a small reaction network, we will first demonstrate how FOCAL works and how tointerpret its results Figure . FOCAL pk Figure , biomass8 Figure . FOCAL cEscherichia coli [Bacillus subtilis [Pseudomonas putida [measured.For each model, we specified sets of selectable carbon sources, nitrogen sources,electron acceptors, and additional nutrients that can be used to compose the minimalmedia  were distributed across different metabolic subsystems.We also investigated if the gene deletions selected by FOCAL for the iJR904 E. coli network that could not becoupled to biomass. These 115 reactions were subsequently re-evaluated with FOCALusing a higher gene deletion limit (up to 10 gene deletions), adding more measurablereactions that FOCAL could use as vmeasured besides biomass production , andexpanding the list of additional nutrients. With these three changes, approximately athird of the previously uncoupled reactions were coupled by FOCAL to a measurableflux , coupling conditions werefound for a lower percentage (approximately 60%) of the unblocked reactions in themost recent E. coli model (iJO1366) [Compared to the smaller iJO1366) . We furtThus, an increased number of alternative reactions, recycling reactions andmultifunctional enzymes in iJO1366 reduces the number of reactions that can becoupled to biomass. As such, the increase in uncoupled reactions is not a failing ofFOCAL, but rather a feature of the more comprehensive network. Future research couldlook to overcome this by instead generating coupling conditions for genes rather thanreactions; in this way conditionally essential genes could be identified that wouldindicate that some of these uncoupled reactions take place. Additionally, whilemanual efforts were used to identify why particular reactions cannot be coupled tobiomass, this process could be semi-automated, by identifying clusters of reactionsthat share common genes and by determining cycles in metabolism  were compared to previous studies where E. coli singleknockout strains were tested for aerobic growth in glucose [aroD and ΔnadC) were shown tobe unable to grow on glucose in other experiments [folB andΔfolP) were shown to have gene duplications [FOCAL coupling conditions for  glucose  and glyc glucose minimal  glucose .Of the eriments  and anotications , indicatications  and invoyneI) has now been identified [gabD mutant for aerobic growth on putrescine (ptrc) as a carbonsource or on 4-aminobutanoate (4abut) as either a carbon or a nitrogen source. Bothptrc and 4abut are ultimately broken down into succinic semialdehyde , whichmust be subsequently consumed by one of the two succinic semialdehyde dehydrogenases.The ΔgabD mutation prevents the NADP-dependent SSALy reaction fromoccurring and leaves NAD-dependent SSALx reaction as the sole succinic semialdehydedehydrogenase. Consequently, the ΔgabD mutant in one of theseptrc/4abut media conditions must use SSALx to grow and the desired flux coupling isobtained. Experimentally the ΔyneIΔgabD double mutantcannot grow during growth on putrescine as a carbon source [By determining coupling conditions for reactions with unknown GPRs, it is alsopossible to use FOCAL results to design high-throughput screens to identify genesassociated with these so-called orphan reactions. Of the 39 orphan reactions iniJR904 that are not transporters, coupling conditions were found by FOCAL for 27reactions  and uxuB . Subsequent removal ofyeiQ, uxaB, and uxuB eliminated the ability of strains to growon glucuronate and galacturonate that will force coupling between a chosen flux of interest and a measurable flux . As a result, FOCAL can design experiments to assess theaccuracy and usage of metabolic reactions and their associated genes. FOCAL has numerousapplications, including validating network elements, discovering new GPR associationsand designing strains with unique and complex phenotypes. In addition, FOCAL couplingconditions could be used to select for improved enzyme activities since selection forimproved growth would require improved flux through these reactions. Future work willlook to reduce the total number of experiments needed to probe entire networks  and incorporate more advanced modeling componentssuch as regulatory information to improve strategies proposed by the forced couplingalgorithm.Coupling) and the chosen reaction . Since FOCAL calculatesnon-linear flux ratios, the problem must first be linearized in order to solve theproblem as a MILP. Therefore, the non-linear problem is transformed to its linearform as described previously [FOCAL is a mixed-integer linear program (MILP) that works to propose media conditionsand gene deletions such that a chosen flux  becomes coupledwith another measurable flux , meaning that fluxthrough the measured reaction requires flux through the chosen reaction. FOCAL, which ensurethat there is no net production or consumption for the set of all metabolites, M.Equations 8 to 10 are identical to those reported previously [t. Here, Exch is the set of all exchange reactions, andj,for cand j,revc, areused to determine which flux, from a specified set of measurable fluxes(Coupling), chosen vis being coupled with. Anydeleted reactions , indicated bybinary variable, ja, have their flux set to 0 in both the forwardand reverse directions using Equation 14. All conditional constraints , were implemented using GAMS  indicator constraints.The inner problem (Equations 6 to 15) minimizes the ratio of the two fluxes for bothchosen reaction directions such that if no coupling exists the inner objective iszero. This effectively amounts to solving the flux coupling framework problemproposed by Burgard et al.  todetereviously  to constMinimal is the set of exchange fluxes that areessential for cellular growth irrespective of the carbon, nitrogen or electronacceptor selected .To allow changes in minimal media conditions, four sets of media components weredefined, each set containing exchange reactions used to import metabolites as sourcesof carbon, nitrogen, electron acceptors, or additional nutrients. Each mediacomponent set was specific for individual models. If experimental information was notavailable, FBA  was usedobjr, is usedto determine whether the desired coupling criterion has been satisfied  wereimplemented as part of FOCAL's outer problem in which x,k mis abinary indicator variable used to select a metabolite exchange, x, from oneof the created media component sets, while x,k mediais a binarymatrix indicating whether metabolite exchange, x, belongs to the mediacomponent type, k. K is the set of four media component types , and xhis a binary variable used to control the media composition and uptakerates in the inner problem (Equation 15). An optional constraint (Equation 19)prevents a given metabolite exchange from being selected as more than one mediacomponent type. Equation 20 also limits the total number of metabolite exchanges thatcan be used for each media component type. The parameter kmaxMediawas set to one, except for the co-utilization case, where it was set totwo to enable use of two carbon sources.In the outer problem, a binary indicator variable, g = 0) to associatedreaction deletions (aj = 0). Limits on the maximum and minimum number ofgene deletions were also imposed considering the set of genes in the model, G(Equation 22), using parameters Δmax and Δmin. Forthese studies, Δmax and Δmin were normally set to 5and 0, respectively. In FOCAL's outer objective function (Equation 5), αand β are positive real numbers used to penalize the use of genedeletions and metabolites from the additional nutrient set. For this study, values ofα = 1.0 and β = 0.25 were used so that addingadditional nutrients would be favored over creating extra deletions, which take moretime experimentally. FOCAL is not very sensitive to the penalty values, so thesevalues can be changed to modify the type of proposed experiments as long as themaximum possible combined penalties do not exceed the increase in the objectiveresulting from the desired coupling. To solve the bi-level problem using availableMILP solvers, the inner problem is rewritten using duality such that the primal andits dual are solved simultaneously and their objectives set equal to one another.This guarantees that the inner problem objective is met prior to maximizing the outerobjective [FOCAL is subject to GPR deletion rules Equation 21), which were implemented asdescribed previously . Such rubjective . Complet1, which E. coli [B. subtilis [P. putida [Using FOCAL, coupling conditions were proposed for reactions within the genome-scalemodels of E. coli ,21, B. ssubtilis , and P. . putida . Given t. putida . We alsoCoupling) that a chosen reaction couldbe coupled with initially only contained the biomass flux. For E. colireactions for which FOCAL could not initially find a coupling condition, FOCALwas re-run using an expanded Coupling set that included ethanol, formate,and succinate secretion in addition to the biomass, since these metabolites arecommon anaerobic by-products and can be easily measured.For simplicity, a reaction flux was considered coupled to a measurable flux if amedia and gene knockout strategy could be generated for either its forward or reversecomponent (Equations 6 and 16). To improve run-time performance, the set of possiblemeasurable fluxes , the algorithm was allowed only 3 hours to find a solution forany given reaction coupling problem. To further reduce the time spent solving for anoptimal solution, once a feasible solution to the coupling problem was discovered,the algorithm was only allowed an additional 10 minutes to search for a bettersolution using the GAMS BCH facility. To minimize the number of different minimalmedia conditions proposed and to prune simple coupling problems, a reduced set ofmetabolite exchange reactions composed of glucose, ammonium, and oxygen exchanges aswell as the entire additional nutrient set was used for the initial 10 minutes ofsolution time. If no solution was found within this time period, then a moreexhaustive search was performed using all elements within the various media componentsets for the remainder of the allotted 3 hours. This amount of time is comparable toother bi-level MILP methods given the number of decision variables involved. Furtherimprovements in run-time performance may be possible by constraining the dualvariables  or elimiFOCAL will initially only propose a single coupling condition that best maximizes theobjective. Under certain circumstances, alternative solutions may exist and can befound by adding integer cut constraints that make prior FOCAL solutionsinfeasible:OldKO is the set of genes deleted in a past solution and OldMediais the set of media components proposed in that same solution. Such a cutprevents FOCAL from proposing a solution that is identical to or a superset of aprevious solution. Additionally, one can omit the knockout or media component of theinteger cut depending on the type of alternative solutions one is interested inobtaining.where E. coli strains from the Keio collection [uxaA::kan, uxaB::kan,uxaC::kan, uxuA::kan, uxuB::kan, yeiQ::kan,and E. coli K-12 BW25113, were used in FOCAL designed experiments.Additionally, three double mutants  and a triple mutant (ΔyeiQ::kan ΔuxuBΔuxaB) were generated using sequential removal of the kan geneusing FLP recombinase [llection , specifiombinase  and P1tombinase  followed600 value in a Biomatespectrophotometer with a 1 cm path length  using 96-well plates. OD measurements weretaken at 600 nm every 15 minutes with linear shaking . Tecan ODmeasurements were converted to an equivalent ODgth ,which can be freely downloaded at [oaded at .Click here for file


Arabidopsis thaliana and their variation in genetically diverse accessions. For this project, we used the 19 parental accessions of the Arabidopsis MAGIC collection. Plants were grown in a hydroponic system, their exudates were harvested before bolting and subjected to UPLC/ESI-QTOF-MS analysis. Metabolite profiles were analysed together with the genome sequence information. Our study uncovered distinct metabolite profiles for root exudates of the 19 accessions. Hierarchical clustering revealed similarities in the exudate metabolite profiles, which were partly reflected by the genetic distances. An association of metabolite absence with nonsense mutations was detected for the biosynthetic pathways of an indolic glucosinolate hydrolysis product, a hydroxycinnamic acid amine and a flavonoid triglycoside. Consequently, a direct link between metabolic phenotype and genotype was detected without using segregating populations. Moreover, genomics can help to identify biosynthetic enzymes in metabolomics experiments. Our study elucidates the chemical composition of the rhizosphere and its natural variation in A. thaliana, which is important for the attraction and shaping of microbial communities.Many metabolomics studies focus on aboveground parts of the plant, while metabolism within roots and the chemical composition of the rhizosphere, as influenced by exudation, are not deeply investigated. In this study, we analysed exudate metabolic patterns of  Arabidopsis thaliana , natural genetic variation has been intensively exploited to study a variety of traits related to plant development, stress response and nutrient content  ratio, retention time (RT) and intensity, so-called features, can be annotated. Depending on the nature of the compound, they are more likely to be detected upon electrospray ionization in the positive (ESI(+)) or negative mode (ESI(−)).Non-targeted metabolite profiling of secondary metabolites by liquid chromatography coupled to mass spectrometry (LC/MS) is an ideal analytical platform to link natural metabolite variation to biosynthetic pathways. It allows for the detection and quantification of semipolar compoundsA. thaliana accessions, which show a large degree of geographic and phenotypic diversity  were found in the same cluster ; cluster 5 ESI(+)) in both ion modes. Ct-1 and Edi-0 (purple) also displayed high similarity in their metabolic profiles. Sf-2 and Kn-0 (green) were in close proximity and would have been in the same clade when cutting the ESI(+) dendrogram at a different threshold. Similar metabolic phenotypes were also detected in the exudation patterns of Wu-0 and Tsu-0, and additionally Mt-0 (orange). These three accessions either clustered in dendrogram branch 2 (ESI(−)) or 3 (ESI(+)).In both metabolic dendrograms, one Oy-0 sample was observed as an outlier, which did not cluster with the other replicates of Oy-0. For Hi-0 and Ws-0, mixed clusters were observed. The positive ion mode generally harboured more outliers. As obvious from the quality control plots in er-0, Kn-0, Wil-2; Ws-0, Ct-1, No-0; Hi-0, Tsu-0, Mt-0, Wu-0, Col-0, Rsch-4) had less than 825,000 mismatches (dashed line) while the outliers Bur-0, Sf-2, and Can-0 had increasing numbers of polymorphisms. Oy-0 and Po-0 formed a small cluster and were found in proximity to Edi-0, Zu-0 and the large dendrogram branch.For the analysis of genetic diversity, sequence polymorphisms in coding sequences (CDS) extracted from the 19 genomes projectm/z ratios, RTs and intensities. These characteristics are not sufficient to investigate the underlying molecules, its biosynthetic pathway and its potential in plant signaling. Annotations and identifications of metabolites, as shown in the next paragraph, are required to interpret non-targeted metabolic profiles in the biological context.The metabolic analysis was based on a non-targeted metabolite profiling approach considering metabolic features characterised only by their Only 25 and 22 of the metabolic signals (455 (ESI(−)), 475 (ESI(+), respectively) could be assigned to metabolites which have been previously described as exudate-characteristic for Col-0S-oxide had low abundance in the exudates of Sf-2 and was enriched in Mt-0.Among the differential metabolites, there were several compounds with an aromatic moiety, such as the nucleoside thymidine and the amino acids Phe and Tyr. The amino acid derivative hexahomo-Met 2NH2), while Sf-2 was missing the malonyl-glucoside of 6-hydroxyindole-3-carboxylic acid -I3CH2CO2H). An unknown indole derivative (C10H9NO3) was highly abundant in the exudates of Ct-1 and Wil-2, and lowly abundant in Sf-2. Generally, large amounts of the glucosinolate precursor and hydrolysis products were detected in the exudates of Ler-0, Mt-0 and Wil-2.A range of glucosinolate degradation products was characteristic for the exudates of some accessions. Edi-0 had rather low levels of indolic compounds and the isothiocyanate hydrolysis product of 8-MeSO-Octyl glucosinolate. Wu-0 showed a clear absence of the neoglucobrassicin (1-MeO-I3M) hydrolysis product 1-methoxy-indole-3-ylmethylamine  O-sulfate was another differential plant hormone catabolite in A. thaliana exudates with low levels in Bur-0, Can-0 and Zu-0 and high levels in Col-0, Kn-0, Po-0, Rsch-4 and Wu-0.Plant hormone-derived metabolites also differed between the 19 accessions. Two salicylic acid (SA) catabolites, 2,3 and 2,5-dihydroxybenzoic acid (DHBA) pentosides, were highly abundant in Col-0, Kn-0, L4H10O Hex-DeoxyHex, C12H16O5 Hex, C7H14O4 Malonyl-Hex) were among the differentially abundant metabolites which were described for Col-0 exudatesAmong the phenylpropanoids, the coumarin scopoletin and its glycosides differed in the exudates of the 19 accessions. A hexose-pentose conjugate of scopoletin as well as three other glycosides (CO-4)FA O-sulfate consisting of coniferyl alcohol (G) and ferulic acid (FA): it was present at high levels in Kn-0 and Wil-2 exudates. Two hydroxylated fatty acids also showed natural variation and were highly abundant in Mt-0.Other differential phenylpropanoids include the monolignol glucoside syringin as well as both isomers of the sulfated dilignol G(8-m/z 739.21) were detected at different RTs indicating differences in sugar conjugation. The investigation of these putatively annotated metabolites can be facilitated by exploring polymorphisms in genes encoding their biosynthetic enzymes.Several isoforms of known glycosylated metabolites spermidine sulfate previously identified in Col-0er-0  analysis revealed a sulfur trioxide loss in the fragmentation pattern similar to the sulfated cyclic didehydro-di(coumaroyl)spermidine conjugate. Can-0 carries a premature stop codon in the gene AT2G25150 encoding spermidine dicoumaroyl transferase (SCT), whereas in Ler-0, a large deletion is present in the CDS of this geneAmong the seven metabolic features with absence in two accessions, three were characteristic for Can-0 and Lol-0er-0 . This coer-0 possess SCT activity to most likely produce cyclic didehydro-di(coumaroyl)spermidine sulfate and its isomer. To further support the data observed with these two accessions, we analysed the exudates of the homozygous knockout line SALK_098927C (Col-0 background), which indeed did not display any peaks with m/z 514.17 ESI(−) at 3.6 min, as shown in Thus, neither Can-0 nor Ler-0 pattern showed the feasibility of such an association analysis to link compounds to their biosynthetic pathways. In specific cases, there is a direct connection between metabolic phenotype and genotype. Therein, metabolite variation among Arabidopsis accessions can be traced back to individual SNPs without trait segregation and QTL mapping.The above results for the Wu-0 and Can-0/LGenetic alterations may be exploited to characterise so far unknown compounds which are part of related biosynthetic pathwaysFor the annotation of metabolites, collision-induced dissociation (CID-) MS was performed for 17 selected MS1 ESI(−) features obtained by the above described screening.With the help of MS/MS spectra, nine out of 17 features were annotated and for five further features, the elemental composition was determined. An overview of compounds, fragment spectra and matching enzymes is given in m/z 739.21, RT = 4.3 min) that was not found in the exudates of Wu-0 (33H40H19) and fragment spectrum as kaempferol 3-O-Rha(1→2)Glc 7-O-RhaO-Rha-Gal 7-O-Rha) by an authentic standard having a galactose moiety instead of glucose in the diglycoside at the 3′ position (A compound ( of Wu-0  was idenposition . One outposition .O-fatty-acyltransferase gene (AT5G55360.1). The unsaturated variant 9,12,13-trihydroxyoctadec-10(E),15(Z)-enoic acid, however, could be detected in Edi-0 and Zu-0 exudates, but not in the Ct-1 accession, and accordingly, pointed to different polymorphism patterns. Besides, similar intensity distributions of both hydroxylated fatty acids were found across the exudates of the 19 accessions  was not present in the exudates of Edi-0 and Zu-0 . Its laccessions .These examples show that the direct search for a metabolite-enzyme-connection can provide valuable insights into biosynthetic pathways but require careful examination of the resulting candidate genes.A. thaliana accessions is reflected by a genetic clustering of polymorphisms in their CDS. The previously reported similarity of the German and Norwegian accession Po-0 and Oy-0This study showed how the exudation pattern of et al.et al.The similarity of the Wu-0, Tsu-0 and Mt-0 was present in both ESI dendrograms of the exudate analysis and seems to be genetically determined. The close genetic relation between the Japanese accession Tsu-0 and Mt-0 from Libya has already been reported by Nordborg et al.The clustering of metabolic profiles demonstrated that genetic variation between the 19 founder accessions of the Arabidopsis MAGIC population is indeed reflected in the exudate metabolome. This is in contrast to the previously reported only minor correlation between shoot metabolic and genetic similarityIn our study, a variety of glycosylated and sulfated compounds are the key metabolites that underlie natural variation in the exudates of the MAGIC parental lines. Scopoletin was found both as an aglycone and hexose-pentose conjugate. However, glucosinolates were only detected as degradation products . Currently, we cannot elucidate whether glucosinolate exudation is initiated by myrosinase activation or if hydrolysis was caused by the sample preparation procedure.et al.et al.A. thaliana accessions. Combined QTL and association mapping pointed to a locus on chromosome 5 within proximity of a gene encoding a putative UGT with pentose specificity. The findings of this study support the previously reported low ratio of pentose-hexose conjugates for Edi-0Previously, hormones were described as constituents of root exudateser-0. These data support the hypothesis postulated by Strehmel et al.O-Rha 7-O-Rha (kaempferitrin) is catalysed to produce robinin. However, the presence of the glucose-substituted isomer kaempferol 3-O-Rha(1→2)Glc 7-O-Rha implies the involvement of a different UGT not accepting galactose but rather glucose as a substrate. We hereby found that UGT91A1 might have similar flavonoid substrate specificity as UGT73C6 and UGT78D1Chemically related compounds often derive from the same biosynthetic pathway. The characterisation of these metabolites might be facilitated by combining metabolic patterns with genomic data. Thus, an analysis workflow was developed which compares metabolite and sequence polymorphism patterns. In order to reduce the complexity, qualitative metabolic patterns were extracted and compared with the presence of premature stop codons in enzyme-encoding genes. The absence of a sulfated cyclic di(dehydrocoumaroyl)-spermidine was traced back to a single genomic alteration diminishing SCT activity in Can-0 and Lm/z and RT in one accession but could erroneously not match its peak criterion in any replicates of another accession, the peak was defined as absent. ii) For a proof of concept, our workflow only included nonsense mutations in CDS of single genes. More complex studies would include amino acid exchanges in CDS, alterations in promoter regions as well as cases of gene function redundancies.Future investigations will focus on the refinement of our approach by addressing the following points: i) When is a peak defined as absent? We relied on the decision of the peak-picking method centWaveLinking stop codons with metabolite absences helps with the elucidation of secondary metabolite pathways but still requires fragment spectra to be interpreted manually and gene annotations have to be carefully checked for a possible involvement within the biosynthetic pathway of the metabolite. The connection has to be validated by knockout lines of the respective candidate genes.A. thaliana. Combining nonsense mutations with metabolic patterns of the exudates facilitated to determine the genetic base of specific metabolite absences. Furthermore, the integration of sequence data can help to identify compound classes in metabolomics experiments. Our study can aid to further unravel biochemical and molecular processes in the rhizosphere by providing a metabolomics resource of root exudates . Future investigations should aim at correlating metagenomics with exudation profiles in order to deduce characteristics that can be exploited to circumvent limiting abiotic factors and decrease the susceptibility towards biotic stresses.Our study revealed natural variation in the root exudate composition of 19 genetically diverse accessions of er-0, Mt-0, No-0, Oy-0, Po-0, Rsch-4, Sf-2, Tsu-0, Wil-2, Ws-0, Wu-0, and Zu-0 of A. thaliana  according to Böttcher All mass spectra were acquired in centroid mode and recalibrated on the basis of lithium formate cluster ions.A detailed description of plant cultivation, sample preparation and metabolite profiling can be found in et al.Raw data files were converted to mzData using CompassXPort version 1.3.10 . Subsequently, the R package xcms version 1.41.0DataAnalysis 4.0  was used for generation of extracted ion chromatograms, deconvolution of compound mass spectra and calculation of elemental compositions. For relative quantification of compounds extracted ion chromatograms from the non-targeted analysis were integrated with QuantAnalysis 2.0  using the quantifier ions as listed in All statistical procedures were performed with the R statistical language version 3.0.0Before hierarchical clustering, remaining missing values were replaced with half of the minimum feature intensity. Feature intensities were logarithmized, z-transformed and checked for normality with a Kolmogorov-Smirnow test. Non-biological sources of variation were removed by surrogate variable analysis from the SVA package version 3.8.0Unspecific signals were more pronounced (87% vs. 75%) in ESI(+) vs. ESI(−). This had led to us to focus on ESI(−) in subsequent analyses.Genetic distances were estimated from the variant tables available from the 19 genomes projectPredicted amino acid sequences were processed with BioPerl  and aligned with the Clustalw algorithm with ktuple = 2 and a BLOSUM scoring matrix. Multiple sequence alignments were evaluated for premature ending with the R packages Biostrings version 2.30.1 and plyr version 1.8.1.A metabolic feature was defined as absent when below the limit of detection in all replicates of an accession. Applying this stringent definition, the peak list created from aligning all spectra from ESI(−) was screened for metabolic features with absence, thus reducing the number of features by 25% for exudates ESI(−). The distribution of absence across the 19 accessions is referred to as a pattern. The length of a pattern is the number of accessions that lack the same feature, i.e. a feature absent in Can-0 und Zu-0 is a pattern of length two. Out of the 455 metabolic features in the exudate data set (ESI(−)), 384 were missing in at least one accession. 46 were missing in exactly one accession (length = 1), 52 were absent in two accessions (length = 2) see . The R pftp.plantcyc.org/Pathways/BLAST_sets/aracyc_enzymes.fasta, Dec 2015) as annotated by TAIR10_functional annotations from TAIR.orgThese matching features or their corresponding quasi-molecular ion were subjected to fragmentation by MS/MS with 10, 20 and 30 eV. Stop codon patterns were derived from multiple sequence alignments of AraCyc enzyme genesHow to cite this article: Mönchgesang, S. et al. Natural variation of root exudates in Arabidopsis thaliana-linking metabolomic and genomic data. Sci. Rep.6, 29033; doi: 10.1038/srep29033 (2016).


Pyruvate dehydrogenase (PDH) occupies a central node of intermediary metabolism, converting pyruvate to acetyl-CoA, thus committing carbon derived from glucose to an aerobic fate rather than an anaerobic one. Rapidly proliferating tissues, including human tumors, use PDH to generate energy and macromolecular precursors. However, evidence supports the benefits of constraining maximal PDH activity under certain contexts, including hypoxia and oncogene-induced cell growth. Although PDH is one of the most widely studied enzyme complexes in mammals, its requirement for cell growth is unknown. In this study, we directly addressed whether PDH is required for mammalian cells to proliferate.PDHA1 gene encoding an essential subunit of the PDH complex and characterized the effects on intermediary metabolism and cell proliferation using a combination of stable isotope tracing and growth assays. Surprisingly, rapidly dividing cells tolerated loss of PDH activity without major effects on proliferative rates in complete medium. PDH suppression increased reliance on extracellular lipids, and in some cell lines, reducing lipid availability uncovered a modest growth defect that could be completely reversed by providing exogenous-free fatty acids. PDH suppression also shifted the source of lipogenic acetyl-CoA from glucose to glutamine, and this compensatory pathway required a net reductive isocitrate dehydrogenase (IDH) flux to produce a source of glutamine-derived acetyl-CoA for fatty acids. By deleting the cytosolic isoform of IDH (IDH1), the enhanced contribution of glutamine to the lipogenic acetyl-CoA pool during PDHA1 suppression was eliminated, and growth was modestly suppressed.We genetically suppressed expression of the Although PDH suppression substantially alters central carbon metabolism, the data indicate that rapid cell proliferation occurs independently of PDH activity. Our findings reveal that this central enzyme is essentially dispensable for growth and proliferation of both primary cells and established cell lines. We also identify the compensatory mechanisms that are activated under PDH deficiency, namely scavenging of extracellular lipids and lipogenic acetyl-CoA production from reductive glutamine metabolism through IDH1.The online version of this article (doi:10.1186/s40170-015-0134-4) contains supplementary material, which is available to authorized users. This phenomenon has been called aerobic glycolysis  because it occurs even when enough oxygen is present to support normal mitochondrial function. Evidence indicates that aerobic glycolysis supports cell survival and growth in numerous ways, including providing substrate for macromolecular synthesis glucose or 4-mM [U-13C]glutamine, in DMEM supplemented with 5 % dialyzed FBS and allowed to proliferate for 24 h. Metabolites were extracted using the Bligh-Dyer method glucose  or [U-14C]glutamine  in DMEM supplemented with 5 % tetracycline-reduced FBS and allowed to proliferate for 24 h, then lipids were extracted  glucose or [U-13C]glutamine were used to simultaneously fit the same network model to estimate intracellular fluxes. To ensure that a global minimum of fluxes was identified, flux estimations were initiated from random values and repeated a minimum of 50 times. A chi-square test was applied to test goodness-of-fit, and accurate 95 % confidence intervals were calculated by assessing the sensitivity of the sum of squared residuals to flux parameter variations [Steady state metabolic fluxes were calculated by combining extracellular flux rates, growth rates, and  package , which a package , 35. We riations .PDHA1 transcript. Induction of either of two hairpins greatly reduced PDH E1α protein levels . PDHA1 silencing reduced the presence of glucose-derived carbon in total lipids and in all fractionated classes of polar and non-polar lipids  and established mouse embryonic fibroblasts (MEFs) from male embryos transgenic for UBC-Cre-ER and hemizygous for the floxed PDHA1 allele. Deletion of exon 8 is thought to impair the interaction between the E1α subunit and the rest of the complex, dramatically reducing PDH activity [PDHA1 exon 8 in culture  were analyzed by Isotopomer network compartmental analysis (INCA), a form of metabolic flux analysis (MFA) that enables quantitation of many fluxes interacting with central carbon metabolism and fatty-acid synthesis [13C distributions, is described . These studies underline the plasticity of cellular metabolism with future studies being directed at ascertaining their relevance in vivo.