Quantitative modeling of triacylglycerol homeostasis in yeast – metabolic requirement for lipolysis to promote membrane lipid synthesis and cellular growth Jurgen Zanghellini1,*, Klaus Natter2,*, Christian Jungreuthmayer3, Armin Thalhammer1, Christoph ă F Kurat2, Gabriela Gogg-Fassolter2, Sepp D Kohlwein2 and Hans-Hennig von Grunberg1 ă Institute of Chemistry, University of Graz, Austria Institute of Molecular Biosciences, University of Graz, Austria Trinity Center of Bioengineering, Trinity College Dublin, Ireland Keywords dynamic flux-balance analysis; lipid metabolism; Saccharomyces cerevisiae; systems biology; triacylglycerol degradation Correspondence J Zanghellini, Institute of Chemistry, University of Graz, Heinrichstraße 28, A-8010 Graz, Austria Fax: +43 316 380 9850 Tel: +43 316 380 5421 E-mail: juergen.zanghellini@uni-graz.at *These authors contributed equally to this work (Received 11 July 2008, revised September 2008, accepted September 2008) Triacylglycerol metabolism in Saccharomyces cerevisiae was analyzed quantitatively using a systems biological approach Cellular growth, glucose uptake and ethanol secretion were measured as a function of time and used as input for a dynamic flux-balance model By combining dynamic mass balances for key metabolites with a detailed steady-state analysis, we trained a model network and simulated the time-dependent degradation of cellular triacylglycerol and its interaction with fatty acid and membrane lipid synthesis This approach described precisely, both qualitatively and quantitatively, the time evolution of various key metabolites in a consistent and self-contained manner, and the predictions were found to be in excellent agreement with experimental data We showed that, during pre-logarithmic growth, lipolysis of triacylglycerol allows for the rapid synthesis of membrane lipids, whereas de novo fatty acid synthesis plays only a minor role during this growth phase Progress in triacylglycerol hydrolysis directly correlates with an increase in cell size, demonstrating the importance of lipolysis for supporting efficient growth initiation doi:10.1111/j.1742-4658.2008.06681.x Triacylglycerols (TAG) are important storage compounds in pro- and eukaryotes Not only these lipids store chemical energy in the form of fatty acids (FA), they also serve to dispose of excess free FA from the cellular milieu, thus precluding FA-induced toxicity [1,2] Neutral fats, which in yeast consist of TAG and steryl esters (SE), are stockpiled in lipid droplets (LD) during periods of cellular growth [3] In times of starvation, esterified FA is then released by lipolysis and recycled into other lipids, or degraded via b-oxidation in order to provide the metabolic energy for cellular maintenance [4] Recent data have shown that TAG pools in yeast are filled when growth ceases as a result of carbon source (typically glucose) limitation, and cells enter stationary phase [5] TAG degradation during stationary phase occurs rather slowly and the specific activities involved have not yet been identified clearly Surprisingly, on glucose supplementation, quiescent cells rapidly initiate TAG degradation at a high rate when they re-enter the cell cycle [5] Accordingly, tgl3 tgl4 mutants lacking the ability to hydrolyze TAG show severe growth retardation These observations indicate that TAG degradation is an important Abbreviations CDP, cytidine diphosphate; DAG, diacylglycerol; DFBA, dynamic flux-balance analysis; FA, fatty acid; FBA, flux-balance analysis; LD, lipid droplet; MP, membrane particle; PA, phosphatidate; PC, phosphatidylcholine; PE, phosphatidylethanolamine; SE, steryl ester; TAG, triacylglycerol 5552 FEBS Journal 275 (2008) 5552–5563 ª 2008 The Authors Journal compilation ª 2008 FEBS J Zanghellini et al determinant of rapid growth initiation As peroxisomes – the only site of b-oxidation in yeast – are repressed by glucose, it was hypothesized that, during prelogarithmic growth, TAG-derived FA may be used as a precursor for membrane lipid synthesis rather than as an energy source [5] In this study, we used the well-established yeast model and combined theoretical and experimental approaches to describe quantitatively the role of TAG degradation in growing cells and the metabolic flux of FA We reconstructed the metabolic pathway of TAG lipolysis in yeast in silico and specifically addressed the question of whether FA derived from TAG hydrolysis in growing cells is channeled into b-oxidation or towards membrane lipid synthesis by a systems biological approach [6] Our theoretical model is based on the well-established concept of flux-balance analysis (FBA) [7], a structural network model that replaces a full kinetic description which, because of a lack of experimental parameters, is as yet out of reach FBA uses stoichiometric information about all possible reactions which comprise the metabolic network of yeast cells By assuming stationarity, FBA allows for the identification of the optimum flux distribution to sustain a particular biological function However, FBA is unable to describe the kinetics of individual chemical reactions and their regulation, as the analysis of the network behavior is based on steady-state solutions Time-dependent effects can be taken into account by adopting a dynamic extension to conventional, stationary FBA (dynamic flux-balance analysis, DFBA) In brief, DFBA approximates the observed temporal behavior by a series of steady-state solutions Based on technically mature theoretical methods, this systems biological program has been applied successfully to simulate a number of complex biological networks [7,8] The approach in this study differs from previous implementations of stationary and dynamic FBA [9– 13] insofar as we experimentally determined the time dependence of glucose and ethanol concentrations, as well as of cell mass (growth) These data were used as constraints to iteratively impose the observed functional behavior on our in silico model in order to reduce its degrees of freedom We successively applied different cellular objectives and locked the resulting network response The trained model was then utilized to predict cellular TAG levels in response to altered metabolic parameters To confirm these results, the average TAG content per cell during growth, and the cell size, were determined experimentally Our study: (a) identifies TAG lipolysis during early growth as an important, genuine effect; (b) shows that Triacylglycerol mobilization in yeast TAG degradation is most prominent during the initial lag phase after the inoculation of cells into fresh culture medium; and, most importantly, (c) yields a quantitative description of the utilization of TAG depots for the production of membrane lipids in order to initiate rapid growth, in accordance with experimental evidence Taken together, we present, for the first time, a consistent and accurate quantitative analysis of a lipid metabolic pathway in yeast Results DFBA satisfactorily models the time-dependent metabolic behavior of S cerevisiae The glucose uptake and growth rate of a wild-type yeast culture were determined and subjected to DFBA to predict the time evolution of the maximum possible ethanol concentration in the medium As a unique DFBA solution requires an optimization criterion, we employed the maximization of ethanol production as the objective (Table 3, run 1) As illustrated in Fig and in accordance with experiments, ethanol (thin full line) is secreted during all growth phases up to 35 h Deviations between the calculated and measured ethanol concentrations result from ethanol loss because of evaporation Fig DFBA simulations and experimental data for cell density (dotted line and open squares, respectively), glucose concentration (broken line and open circles) and ethanol concentration (full line and filled diamonds) The input data for the simulation (glucose uptake and cell density) were first fitted to analytical functions (dashed and dotted lines) to facilitate easy handling of the data The thin full line was obtained by assuming that all available sugar is converted into ethanol The shaded area underneath represents an estimate of the portion of ethanol being evaporated The thick full line represents a DFBA calculation, where the maximum ethanol secretion rate has been constrained in order to fit the experimentally measured values (filled diamonds) FEBS Journal 275 (2008) 5552–5563 ª 2008 The Authors Journal compilation ª 2008 FEBS 5553 Triacylglycerol mobilization in yeast J Zanghellini et al When vaporization was taken into account, all experimentally measured ethanol concentrations were in accordance with our calculation In Fig 1, the loss caused by volatilized ethanol is represented by the shaded area We trained our computer model by constraining the ethanol secretion rate (Table 3, run 2) such that the experimentally measured concentrations (Fig 1; filled diamonds) were best matched using leastsquares fitting The fitting procedure used was to reduce the maximum ethanol secretion rate, solve the corresponding DFBA problem, correct for evaporation and calculate the sum of squares of vertical deviations This sequence was repeated until the best fit was achieved, resulting in a correlation coefficient of r = 98.2% The maximum ethanol secretion rate per gram dry weight of biomass was found to be 18.8 mmolỈg)1Ỉh)1, which is comparable with the values reported by Velagapudi et al [14] (18.2 ± 1.5 mmolỈg)1Ỉh)1) and Duarte et al [15] (11.98 mmg)1Ỉh)1) The data in Fig illustrate the resulting evolution of the ethanol concentration (thick full line), and confirm that our implementation of DFBA matches all measured data within the error bounds, and thus accurately describes the dynamic behavior of S cerevisiae LD turnover in growing cells cannot solely be explained by dilution It has previously been shown that the relative volume of LD decreases by some 80% when stationary phase (starving) yeast cells re-enter the cell cycle after transfer into fresh medium containing glucose as carbon and energy source (see Fig 2, left panels) [5] One explanation for the time dependence of cellular neutral lipid content may be simple dilution, i.e existing LD is distributed amongst a growing number of cells, without active degradation Such a mechanism can explain the decrease in the relative LD content per cell as a consequence of the sharing of a constant amount of LD between an increasing number of cells From our measurements, and in agreement with published data [16,17], we found that LD typically consists of 52 mol% SE and 48 mol% TAG Assuming that the composition of LD does not change during hydrolysis, we have focused on the TAG content of LD The ‘dilution only’ model was calculated by assuming the initial, total mass of TAG of the yeast culture to be constant throughout the subsequent growth period, mTAG(t0)X(t0) = mTAG(t)X(t) = constant Here, mTAG and X denote the mass of TAG per 5554 cell and the cell number as a function of time t, respectively, with the initial time t0 In Fig (top right panel, full line), we show the expected evolution of TAG levels based on dilution and the experimentally determined mass levels (dotted line), demonstrating a major deviation of the observed TAG levels from the content expected as a result of simple dilution The difference (bottom right panel) indeed represents the loss of TAG caused by lipolytic activity, and shows that LD is rapidly catabolized, reaching a minimum level after h After this period, first cell divisions occur, yet the deviation of TAG levels between calculated dilution and measured data remains fairly constant throughout the following h Figure clearly shows that the lipolytic activity peaks before the cells enter exponential growth and continues for several hours into logarithmic growth FA derived from TAG mobilization are not used for energy production To simulate LD mobilization, we employed DFBA based on quantitative data of LD composition (Table 1) Computationally, we modeled LD by adding a reservoir of various neutral lipids (Table 1) to our in silico model Glucose uptake, calibrated ethanol production and cellular growth were used as input values for the calculations To uniquely define the internal flux distribution, FBA requires an optimization criterion, which, in biological terms, represents a certain physiological goal for the cell Typically, the maximization of cellular growth is chosen as an objective [15,18,19] As the time-dependent growth behavior of our system is already determined by the input data, we were especially interested in identifying conditions with high lipolytic activity in silico to explain the experimental data Therefore, maximum LD mobilization was chosen as an objective (Table 3, run 3) The calculation revealed that, in the absence of additional metabolic fluxes, no change in TAG levels, and thus no LD mobilization, takes place The inability to catabolize TAG under these conditions clearly indicates that the release of FA and their degradation by peroxisomal b-oxidation are not possible To confirm this result, we simulated growth with the objective of maximizing acetyl-CoA generated by FA degradation (Table 3, run 4) Yet, even under these conditions, a negligible amount of TAG was mobilized (3 · 10)5 mmolỈg)1Ỉh)1) We therefore conclude that peroxisomal b-oxidation does not contribute to the experimentally observed LD mobilization This inability to break down FEBS Journal 275 (2008) 5552–5563 ª 2008 The Authors Journal compilation ª 2008 FEBS J Zanghellini et al Triacylglycerol mobilization in yeast Fig Measured LD mobilization during early growth in comparison with LD kinetics caused by dilution Top left panel: cellular growth X(t) in complete medium Bottom left panel: time profile of the TAG content per cell: mTAG(t) Top right panel: measured (filled circles) and calculated (open squares) normalized mass of TAG per cell as a function of time The calculation assumes that, during the growth period, LD is not metabolized, but shared between mother and daughter cells, hence diluting the initial LD concentration in the cell culture Bottom right panel: deviation D between the measured and calculated normalized LD mass, defined as mTAG(t) ⁄ mTAG(t0) – X(t0) ⁄ X(t) Note that the largest deviation occurs approximately h before the TAG content reaches its minimum Table LD components and their FA composition as obtained from mass spectroscopy Fatty acid (mol%) Compound w ⁄ w (%) 10 : 12 : 14 : 16 : 16 : 18 : 18 : Ergosterol Episterol Fecosterol Lanosterol Zymosterol Triacylglycerol 19.1 8.1 6.1 1.0 12.2 53.5 – – – – – 2.0 – – – – – 6.0 0.2 1.8 1.8 0.7 0.4 19.9 54.3 8.6 8.6 20.8 4.3 39.2 0.1 63.1 63.1 38.1 50.1 17.0 43.3 22.2 22.2 33.3 41.7 8.4 2.1 4.3 4.3 7.1 3.5 27.0 free FA indicates that the cell transfers FA from TAG to another acceptor molecule, as a balanced flux distribution is otherwise unachievable Accumulation of free FA can be excluded due to their lipotoxic effects and hence, free FA have to be processed further Computationally, we introduced virtual membrane particles (MP), which contain glycerophospholipids and membrane sterols in a single entity that reflects the typical lipid composition of cellular membranes The chemical composition of MP is listed in Table TAG are hydrolyzed exclusively to provide precursors for membrane lipid synthesis Table Composition of virtual membrane particle Compound w ⁄ w (%) It has been suggested that, during pre-logarithmic growth, FA released from TAG and SE may be used as precursors for membrane lipid synthesis [4,5] To test this hypothesis, we simulated TAG mobilization by DFBA under the assumption that the production and storage of excess membrane material is possible by including a pool of membrane lipids in our model Ergosterol Phosphatidate Phosphatidylcholine Phosphatidylethanolamine Phosphatidylinositol Phosphatidylserine Zymosterol 1.9 4.3 29.6 23.2 27.2 9.9 3.9 FEBS Journal 275 (2008) 5552–5563 ª 2008 The Authors Journal compilation ª 2008 FEBS 5555 Triacylglycerol mobilization in yeast J Zanghellini et al The basic metabolic pathways involved in the production of membrane lipids and MP, and their interaction with TAG mobilization, are illustrated in Fig By Fig Schematic representation of FA, neutral, and phospholipid metabolism implemented in our reconstructed yeast network Broken arrows mark the Kennedy pathway, which is turned off in our calculations Full arrows indicate the direction of metabolic fluxes according to simulation listed in Table The circular areas represent the relative amount of FFA derived from LD mobilization (large circle) and de novo synthesis (small circle) For further details, see text DAG, diacylglycerol; FFA, free fatty acid; LD, lipid droplet; MAG, monoacylglycerol; MP, (virtual) membrane particle; PA, phosphatidate; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PI, phosphatidylinositol; PS, phosphatidylserine; SE, steryl esters permitting or forbidding a flux from TAG degradation products to virtual MP, in DFBA, we are able to dissect the contribution of lipolysis to membrane lipid synthesis Indeed, these DFBA calculations confirmed the hypothesis that LD are only degraded if cells are able to generate membrane material which utilizes products of TAG hydrolysis (Table 3, runs and 5, respectively) Figure illustrates that the predicted lipolytic activity (degradation rate of 1.5 · 10)2 mmolỈg)1Ỉh)1; thick full line) is in excellent quantitative agreement with experimental observations (filled circles) if the production of MP is permitted If MP production is disabled in the simulation, no lipolysis occurs (LD degradation rate of · 10)5 mmolỈ g)1Ỉh)1) Figure plots the resulting time dependence of the TAG concentration per cell for these two cases The difference between the simulation with and without enabled membrane production (difference between the full and broken lines in Fig 4) is indeed dramatic, and MP production is increased by three orders of magnitude if metabolically accessible TAG pools are provided (inset in Fig 4) In both simulations, the impact of lipolytic activity was found to be restricted to the production of membrane material, as we could not detect any significant changes in other metabolite concentrations On the basis of these results, we suggest that TAG Table Summary of the simulation arrangements together with their main features Additional parameters used in the simulations are listed in Table Run Input (time no dependent) 5556 Glucose uptake Growth rate Glucose uptake Growth rate Glucose uptake Growth rate Ethanol secretion Glucose uptake Growth rate Ethanol secretion Glucose uptake Growth rate Ethanol secretion Glucose uptake Growth rate Ethanol secretion Glucose uptake Growth rate Ethanol secretion Constraint Objective function Maximum ethanol production Ethanol secretion £ C Maximum ethanol production C [12, 20] mmolỈg)1Ỉh)1 Excess MP production = Maximum LD mobilization Output (time dependent) Comment Ethanol concentration Overestimates data Fig 1, thin full line Ethanol concentration Fitting ethanol concentration Fig 1, thick full line TAG concentration Inconsistent with experiment Fig 4, broken line Excess MP production = Maximum acetyl-CoA production TAG concentration Inconsistent with experiment Fig 4, broken line Maximum LD mobilization Consistent with experiment Fig 4, full line Maximum MP production LD mobilization = TAG concentration MP concentration TAG concentration MP concentration Consistent with experiment Maximum MP production MP concentration Growth retardation Fig 5, broken line FEBS Journal 275 (2008) 5552–5563 ª 2008 The Authors Journal compilation ª 2008 FEBS J Zanghellini et al Triacylglycerol mobilization in yeast Fig Experimentally measured (filled circles) and calculated (full and broken lines) TAG mobilization during pre-logarithmic growth as a function of time The thin dotted line represents a linear fit of the experimental data in the range 0.25–4 h The calculated lines are obtained via DFBA assuming maximum TAG mobilization The full line represents a simulation, which allows for the production of excess membranes, and the broken line is the result of a simulation suppressing such membrane production In both simulations, glucose uptake, ethanol production and cellular growth are used as shown in Fig as input The inset shows the total rate of membrane production with (filled) and without (open) TAG mobilization at t = h (marked by arrows) Note the logarithmic scale in the inset mobilization and membrane production are interchangeable objectives, leading to similar results in the simulation To corroborate this hypothesis, we repeated the DFBA calculation by optimizing with respect to maximum MP production instead of maximum TAG mobilization (Table 3, run 6) For both objectives, we obtained identical results, demonstrating that these optimization criteria are equivalent, as the MP production rate is directly linked to TAG degradation We conclude that, during pre-logarithmic growth, TAG is mobilized with the sole purpose to supply precursors for membrane synthesis, and that, conversely, TAG degradation products are solely used for MP production This result is further supported by flux variability analysis [20], as testing maximum MP production across alternate optimal solutions showed that TAG lipase activity remained unaltered by changes in the internal flux distribution It is noteworthy to mention that the data shown in Fig are the result of a DFBA simulation following the procedure described above No additional adaptations were necessary, which clearly demonstrates the potential of this approach to simulate correctly in vivo TAG mobilization TAG mobilization is proportional to the rate of cell surface growth The production of (excess) membrane lipids derived from TAG degradation raises the question of storage options for these membranes in a biological context The most obvious solution would be to increase cell size If the subdivision of membrane material between the organelles remained constant, the rate of membrane production should be proportional to the change in the surface area of the cell As membranes are typically of constant thickness, any increase in membrane material results in a gain of membrane surface In fact, Fig shows that the normalized MP production rate closely mimics the experimentally determined change in the mean cellular surface area, which was calculated from the measured mean cell volume by assuming spherical cells These data demonstrate that the rate of MP production during the first h of growth directly correlates with the change in the cell surface area As MP production is caused by TAG mobilization, these simulations suggest that the increase in cell size during pre-logarithmic cellular growth can be traced back to lipolytic activity This interpretation is consistent with the observation that after h – when lipolysis ceases FEBS Journal 275 (2008) 5552–5563 ª 2008 The Authors Journal compilation ª 2008 FEBS 5557 Triacylglycerol mobilization in yeast J Zanghellini et al run 7), the MP production rate was 29 mmolỈg)1Ỉh)1, which is less than one-third of the rate calculated when lipolysis takes place, supporting the predominant role of TAG to provide precursors for phospholipid synthesis during the initiation of cellular growth However, these simulations show that the cell is able to respond to a lack of TAG degradation by increasing de novo FA synthesis Utilization of diacylglycerol (DAG) generated by TAG lipolysis Fig Normalized cell surface area as a function of time for the cell culture shown in Fig Filled diamonds present the estimated values for the cell surface based on the measured mean cell volume, assuming spherical yeast cells The full line represents the normalized increase in membrane lipids predicted by DFBA and assuming maximum TAG mobilization The broken line shows the result for maximum membrane production if TAG is not available (see Fig 2, top right panel) – the relative cellular surface reaches its maximum (Fig 5) TAG lipolysis rather than de novo synthesis is the predominant source of FA in the lag phase Although membrane production clearly correlates with increased cell size (Fig 5), we considered the possibility that processes other than TAG degradation contribute to membrane lipid synthesis The only pathway, which may indeed contribute considerably to the supply of FA, is de novo synthesis from acetylCoA To elucidate the role of this pathway during the early growth period, we analyzed the fluxes leading to phosphatidate (PA), the primary intermediate in phospholipid synthesis By comparing the FA fluxes into PA (1.8 · 10)2 mmolỈg)1Ỉh)1) versus the release from TAG (1.5 · 10)2 mmolỈg)1Ỉh)1), we found that, in total, 80 mass% are indeed derived from LD This ratio is smaller for FA which are found in lower concentrations in lipids of LD, such as C10:0, but never falls below a contribution of about 60% for a specific FA According to our calculation, only 20 mass% of FA in newly synthesized PA are derived from de novo FA synthesis, if TAG lipolysis is enabled To address the question of whether this flow could be increased if the supply of FA from TAG was prevented, we adjusted our calculations towards maximized membrane production in the absence of TAG mobilization (broken line in Fig 5) Under this condition (Table 3, 5558 The synthesis of membrane-forming phospholipids occurs via two independent pathways In the de novo pathway, PA is converted to cytidine diphosphateDAG (CDP-DAG), which, in turn, is further metabolized to phosphatidylinositol, phosphatidylserine and phosphatidylglycerolphosphate ⁄ cardiolipin Decarboxylation of phosphatidylserine gives rise to phosphatidylethanolamine (PE), which is subsequently methylated to phosphatidylcholine (PC), the major phospholipid in yeast To activate this pathway during the lag phase of growth, cells entirely rely on the supply of FA for PA synthesis, or on the activity of the recently published DAG kinase [21,22], which may utilize DAG that is generated by a single de-acylation step from TAG Alternatively, PE and PC can be synthesized via the Kennedy pathway by transfer of CDP-ethanolamine and CDP-choline to DAG if choline and ethanolamine are present in the medium In our study, this pathway was disabled to reduce the complexity of analysis, thus forcing the cells to rely entirely on the de novo phospholipid biosynthetic pathway through the production and utilization of PA By analyzing the calculated flux distribution, we found that TAG is converted to DAG and directly phosphorylated to yield PA with a rate of 1.9 · 10)2 mmolỈ g)1Ỉh)1 This pathway is less energy costly than the synthesis via total hydrolysis of TAG or DAG, and the subsequent re-acylation of glycerol-3-phosphate (Fig 3) Interestingly, inactivation of DAG kinase activity resulted in a comparable TAG mobilization rate; in fact, our analysis shows that, after 5.5 h of growth, the difference in the relative TAG mass per cell for both cases, with active or inactive DAG kinase, is below 3% of the initial TAG concentration Discussion TAG have only recently been acknowledged as important metabolic compounds, not only to provide FA as a source for energy, but also playing important roles in cellular FA and complex lipid FEBS Journal 275 (2008) 5552–5563 ª 2008 The Authors Journal compilation ª 2008 FEBS J Zanghellini et al homeostasis Synthesis of TAG and its storage in the biophysically rather inert neutral lipid core of LD may serve as a rescue pathway when excess FA need to be withdrawn from active cellular metabolism to prevent lipotoxicity [1,2] In addition, catabolites derived from TAG hydrolysis, especially DAG, have been suspected to be substrates in pathways of phospholipid synthesis, such as the Kennedy pathway for PE and PC synthesis [23–25] In this study, we have analyzed neutral lipid mobilization and its underlying metabolic fluxes within a DFBA framework Using DFBA, or any other FBAbased approach for that matter, requires the knowledge of a physiologically relevant objective function [26–28] This is necessary as DFBA optimizes the flux distribution through a reaction network with regard to that function However, although different schemes to identify the most probable objective function for a given biological system have been put forward [29,30], the choice of a particular goal function is still anything but obvious Typically, maximization of the cellular growth rate is chosen as an objective [15,18,19], but other objective functions have also been suggested [31,32] As our interest was focused on the flux of catabolites derived from TAG hydrolysis, rather than on predicting the growth behavior, we were able to utilize the measured growth parameters as input for our in silico yeast model and optimize with respect to maximum TAG mobilization or membrane production We used cellular growth as input data, which allowed a change in the objective function without altering the cellular response Moreover, by adopting various objective functions – which may change as the cell faces nutritional and environmental alterations – our simulations matched all experimental observations For example, we first optimized with respect to ethanol secretion and calibrated our calculation to meet the experimentally determined ethanol concentration in the culture medium We then used the time dependence obtained as an additional input parameter, and chose a different objective function in order to further reduce the degrees of freedom in the model Rather than guessing the ultimate physiological objective of the cell in one optimization criterion, this approach enabled us to iteratively train the computer model with experimental data using different objectives This successive calibration sets our approach apart from conventional FBA and DFBA implementations [9–13] Our approach also adds a new aspect to the usage of FBA By including pools in our simulation, we were able to analyze the impact of internal storage compartments These depots act either as sources or Triacylglycerol mobilization in yeast as sinks for internal fluxes From a biological point of view, they allow the cell to dispose of excess metabolites by storing them in an inert form When demand for these metabolites is high, they are readily available without the need for energy costly synthesis Our analysis demonstrated that it is essential to include storage compartments, as they are key players in supporting a flux equilibrium during nonlogarithmic growth If these cellular reservoirs were absent, a consistent interpretation of experimental observations was impossible Our simulations show – consistent with experimental data [5] – that lipolysis of TAG is a key process during lag and pre-logarithmic growth phases (Fig 2), and promotes the rapid initiation of growth of quiescent cells exposed to glucose-containing media TAG mobilization provides DAG and ⁄ or FA for the synthesis of phospholipids; forcing the system to utilize FA to produce energy via b-oxidation would result in halted TAG mobilization, which is not consistent with experimental data Peroxisomes are repressed in the presence of glucose; therefore, the utilization of lipolysis-derived FA for energy production during this phase of growth is also biologically irrelevant As we used a choline- and ethanolamine-free medium in both experiments and simulations, no net synthesis of the major yeast phospholipids, PE and PC, via the Kennedy pathway was observable Adding choline and ethanolamine in silico resulted in the production of considerable amounts of PC and PE by this route, which might be favored because of its lower energy demand The additional possibility for PE and PC synthesis further stimulated TAG mobilization to satisfy the demand for DAG, which is a major substrate in this pathway The direct phosphorylation of DAG to PA was favored over a complete hydrolysis to free FA and glycerol in our calculation Considering the energy balance, this result is not surprising, as the de novo pathway consumes about 80% more energy However, Han et al [21,22] proposed a regulatory role for the DAG kinase Dgk1 in PA homeostasis of the nuclear membrane, and it remains to be shown whether this pathway contributes considerably to net phospholipid synthesis Therefore, the complete hydrolysis of TAG to free FA, and their subsequent activation and assembly into PA, is the most likely pathway to synthesize phospholipids in the absence of choline and ethanolamine Our simulations yielded identical results for assuming both maximum TAG hydrolysis and production of membranes as objective functions Hence, yeast cells that re-adjust their metabolism from stationary phase FEBS Journal 275 (2008) 5552–5563 ª 2008 The Authors Journal compilation ª 2008 FEBS 5559 Triacylglycerol mobilization in yeast J Zanghellini et al to nutrient-rich conditions generate metabolites from TAG breakdown that are exclusively used for membrane lipid synthesis, but not for energy production Lag-phase cells are characterized by an increase in cell size, which depends on the availability of membranes, and which, in turn, relies on TAG lipolysis Accordingly, the absence of lipolysis in lipase-deficient tgl3 tgl4 mutants results in smaller cells and a major delay of their entry into vegetative growth after quiescence [5] Recently published data [33,34] and unpublished findings from our laboratory (C F Kurat and S D Kohlwein, unpublished results) indeed show a cell cycle-dependent regulation of enzymes involved in TAG homeostasis Initiation of DNA replication (S phase of the cell cycle) requires that cells have reached a defined minimum size (for a review, see [35]) and is delayed in the absence of lipolysis This implies an important role for TAG catabolism, not only during recovery from G0 (quiescence), but also for efficient cell cycle progression Future work will focus on the function of TAG stores as a buffer for specific membrane precursors, which become readily available at critical cell cycle checkpoints densitometry at 450 nm on a Camag TLC scanner (Camag, Muttenz, Switzerland) Network reconstruction We used the fully compartmentalized genome-scale metabolic model iND750 [38] as an in silico representation of S cerevisiae It captures the topology of the metabolic network by its stoichiometric matrix, S, and allows the simulation of steady-state behavior The description of the glycerolipid and phospholipid metabolism was extended by adding TAG, DAG, as well as monoglyceride lipases A list of all newly added chemical reactions may be found in the Doc S1 These reactions were elementally and charge balanced; hence, the pH value of 7.2 remained unaltered compared with the original iND750 model Dynamic flux-balance analysis (DFBA) Lipolysis was investigated within the framework of FBA [26,27] FBA assumes steady-state conditions, i.e no net production or consumption of metabolites occurs, leading to the mass balance equation [7] Sv ¼ Materials and methods Growth conditions and analytical methods A haploid yeast wild-type strain (MAT a his3D1 leu2D0 lys2D0 ura3D0), derived from tetrad dissection of BY4743 [European S cerevisiae Archive for Functional Analysis (EUROSCARF), Frankfurt, Germany], was used for all experiments Cells were grown at 30 °C in 500 mL minimal medium, containing 20 gỈL)1 glucose, 1.7 gỈL)1 yeast nitrogen base (Difco, Le Pont de Claix, France), gỈL)1 ammonium sulfate and the appropriate amino acids and bases Cells were isolated by RediGradÔ centrifugation [36] from cultures grown to stationary phase for 48 h These cells were inoculated into fresh medium to 106 cellsỈmL)1, and growth and cell size were monitored with a Casy TTC cell counter equipped with a 60 lm capillary (Scharfe Systems, ă Reutlingen, Germany) Glucose was measured with an Accu-Chek blood glucose monitor (Roche, Mannheim, Germany) Ethanol concentrations were determined with the alcohol dehydrogenase reaction For lipid extraction, 109 cells were harvested by centrifugation and frozen in liquid nitrogen Cells were disrupted and lipids were extracted by shaking with glass beads in chloroform–methanol (2 : 1) [37] Total lipid extracts were separated on silica gel plates (Merck, Darmstadt, Germany) with the mobile phase petrol ether–diethylether–acetic acid (40 : 15 : 0.5), and stained at 120 °C for 15 after submerging the plate in a solution containing 3.2% H2SO4 and 0.5% MnCl2 Lipids were quantified against appropriate standards by 5560 ð1Þ Here, S represents the stoichiometric matrix of the reconstruction metabolic network and v denotes the vector of all fluxes per gram of biomass through the network The flux vector contains both internal network fluxes and exchange fluxes, the latter capturing the interaction of the model with its environment For a typical simulation, various values for exchange fluxes were determined experimentally and used as input to compute the remaining flux values by solving Eqn (1) Usually, a biological system contains more reactions than metabolites, i.e the number of columns in S is larger than the number of rows Hence, the system of linear equations (Eqn 1) is under-determined and a linear objective function was adopted to single out an individual flux distribution using the freely available GNU Linear Programming Kit package, version 4.13 (http://www.gnu.org/software/glpk/; Department for Applied Informatics, Moscow Aviation Institute, Moscow, Russia) This purely static FBA was adapted to include dynamic processes by defining concentrations of external compounds [xe], which did not obey the steady-state condition (Eqn 1), but were allowed to change with respect to time t, according to the dynamic balance equations [9,1113,39] dẵxe ẳ ve tịẵXBM tị; dt ð2Þ where [XBM] denotes the concentration of biomass At any point in time, individual exchange flux values ve were either measured experimentally or resolved by solution of Eqn (1) Dynamic time profiles for external metabolites FEBS Journal 275 (2008) 5552–5563 ª 2008 The Authors Journal compilation ª 2008 FEBS J Zanghellini et al Triacylglycerol mobilization in yeast Table Time-independent constraints used in all simulations Constrainta (mmolỈg)1Ỉh)1) ACOAH = ATPM = GLUSx = O2t £ SBTR = Reaction name Compartment Chemical equation Acetyl-CoA hydrolase (EC 3.1.2.1) Non-growth-associated ATP requirement Glutamate synthase (EC 1.4.1.14) Cellular O2 uptake Sorbitol reductase (EC 1.1.1.21) [Cytosol] AcO) + CoA4) + H+ fi Acetyl-CoA4) + H2O [Cytosol] ATP4) + H2O fi ADP3) + H+ + HO4P2) [Cytosol] L-Gln [Cytosol] O2[extracellular] M O2[cytosol] 4) D-Glc + NADPH + H+ fi D-Sorbitol + NADP3) + 2-oxoglutarate2) + NADH2) + H+ fi 2L-Glx) + NAD) a The constraints and their abbreviations are identical to those used in the original yeast model iND750 [38], which have also been successfully applied in other situations [15,40] were then approximated by successively integrating Eqn (2) To facilitate integration, we assumed all fluxes to be constant during a single integration step GOLD within the framework GEN-AU program) to S.D.K of the Austrian References Medium composition and parameter estimation Experimentally determined glucose concentrations and cell densities were fitted using an asymmetric sigmoid function   Àa4 t À a3 ln21=a4 1ị a2 rfit tị ẳ a1 þ exp À ; ð3Þ a3 with the fitting parameters a1, a2, a3 and a4 The glucose uptake rate and growth rate were then calculated by differentiation Table lists every additional constraint except for ethanol The temporal medium composition was monitored and uptake fluxes were dynamically restricted if the corresponding metabolite was consumed All other fluxes were left unconstrained The ethanol production rate was constrained to 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