Biotechnology and Bioengineering Published by Wiley Peiodicals, Inc.This paper investigates the scaling‐up of cyanobacterial biomass cultivation and biohydrogen production from laboratory to industrial scale. Two main aspects are investigated and presented, which to the best of our knowledge have never been addressed, namely the construction of an accurate dynamic model to simulate cyanobacterial photo‐heterotrophic growth and biohydrogen production and the prediction of the maximum biomass and hydrogen production in different scales of photobioreactors. To achieve the current goals, experimental data obtained from a laboratory experimental setup are fitted by a dynamic model. Based on the current model, two key original findings are made in this work. First, it is found that selecting low‐chlorophyll mutants is an efficient way to increase both biomass concentration and hydrogen production particularly in a large scale photobioreactor. Second, the current work proposes that the width of industrial scale photobioreactors should not exceed 0.20 m for biomass cultivation and 0.05 m for biohydrogen production, as severe light attenuation can be induced in the reactor beyond this threshold. Biotechnol. Bioeng. 2015;112: 2429–2438. © 2015 The Authors.  Spirulina and Chlorella have been consumed as a food supplement in Mexico since antiquity, and are currently being cultivated for the commercialization of nutrient products in China, the United States and Thailand  greatly restricts the application of microalgae in both biofuels and biomass production  was supplied to enhance biomass growth rate, recent work found that green algae such as Nannochloropsis sp. can only peak at a density of 2.48 g L−1 in a 1000 L flat plate PBR with a width of 0.10 m, and 1.20 g L−1 in a 500 L flat plate PBR with a width of 0.17 m  h−1 from green alga Chlamydomonas reinhardtii, and 2.33 mL L−1 h−1 for cyanobacterium Cyanothece sp. in a 1 L flat plate PBR with a width of 0.025 m  sufficiency. Because the main focus of the current study is on the effects of light intensity and reactor configuration, temperature in the current experimental research is fixed at 35°C and its effects on cyanobacterial growth and decay rates are not considered. The detailed model is shown in Equations (1–3), and the parameters in the model are listed in Table cτ is the light absorption coefficient by cyanobacteria. max,aμ and d,aμ are the maximum specific growth rate and decay rate, respectively. I is the local light intensity and I0 is the incident light intensity. X is biomass concentration. sk is a light saturation parameter for cell growth, and ik is a photoinhibition parameter for cell growth.A previous study  and temporal variations as they constitute a dynamic model. To eliminate the spatial variation, the Trapezoidal rule is used to replace Equation (3) by Equation (4) in  are the same as those in Table h¯(I), given in Equation (5h), and represents the effect of light intensity on hydrogen production.μmax,h and μd,h are the maximum specific photo‐heterotrophic growth and decay rate, respectively. q is the normalized nitrogen quota. kq denotes the normalized minimum nitrogen quota. N, C, H2, and O2 are the concentration of nitrate, glycerol, hydrogen, and oxygen, respectively. KN and KC are the half‐velocity constant of nitrate and glycerol, respectively. Yq/X, YN/X, YC/X, YO/X. YH/X, YC, YOd are the yields of nutrients and products as indicated by the respective subscripts of the symbols used. ks,H2 and ki,H2 are the light saturation and photoinhibition terms, respectively, for hydrogen production.In this model, parameters in Equation (5g) have been estimated in previous work , the oxygen generation rate is assumed to be a function of nitrate concentration instead of nitrogen quota. This is because the cyanobacterial nitrate uptake process requires a high amount of ATP which has to be synthesized through the photosynthesis pathway where molecular oxygen is generated , the orthogonal collocation method is selected to discretize the current model. The implementation for the optimization problems shown in this work is done in Pyomo, a Python‐based optimization environment. Specifically, Pyomo , as shown in Figure In the current research, the laboratory scale photobioreactor is a 1 L flat‐plate photobioreactor with a width of 0.025 m  with respect to the normalized change of the parameters (x). A positive sensitivity value indicates that an increase in x results in an increase in y, while a negative sensitivity value indicates that increasing x will lead to a decrease in y.y is the model output and x is any parameter in the overall process model.Sensitivity analysis is widely used in this work to explore the variability of model outputs (process substrates and products) with respect to changes in model inputs (parameters in dynamic models)  even if biomass concentration is low (0.5 g L−1). Figure Light attenuation in a PBR is mainly caused by bubble scattering and cell absorption , the distributions of the cyanobacterial growth rate and the hydrogen production rate in the reactor are relatively uniform , both the cell growth rate and the hydrogen production rate are higher than those at low biomass concentration in the major part of the reactor, since more cells are exposed to illumination. However, because of the increased light attenuation , both rates are significantly improved in the front part of the reactor. This is due to the fact that local illumination intensity in the front part of the reactor is not much affected by light attenuation as it is proximity to the light source. As biomass growth and hydrogen production rates depend on the biomass concentration , both rates increase with the increasing biomass concentration. The major volume of the reactor, however, is subject to the severe photo‐limiting environment, and both rates are much lower than those at the low biomass concentration , the photo‐autotrophic growth culture is mainly selected for biomass cultivation. Therefore, in the current study, for biomass cultivation the photo‐autotrophic growth model will be selected and for hydrogen production the photo‐heterotrophic growth model will be chosen.Dunaliella tertiolecta have been isolated recently  is proportional to the mass fraction of chlorophyll in cells, its value will also be reduced in a low‐chlorophyll mutant.σ is the cross‐section of PSU unit, m2 J−1; v is the equivalent turnover time of PSU, s−1; K is the ratio of the protein damage constant to the protein recovery constant, s−1.Previous research has demonstrated that parameters in the Aiba model have their specific biological meaning  can enhance cell growth rate, which accelerates the consumption of nitrogen quota. As a result, cell decay due to the lack of nitrogen source will be enhanced and the hydrogen production rate will be reduced. Therefore, a laboratory scale PBR might not show the potential enhancements possible at industrial scale, that is, the potential benefits might be simply overlooked as there is usually no scale‐up assessment carried out when collecting laboratory data. This fact is also consistent with the conclusion from sensitivity analysis that identifying the optimal operating conditions is more important for a small‐scale process.To explore further the effects of PBR configuration on biomass cultivation and hydrogen production, the current work calculates the maximum biomass concentration and average hydrogen production rate in different configurations of PBRs and compares them in Table For biomass cultivation, it is found that the reactor width should not exceed 0.2 m, as the maximum biomass concentration is severely reduced . With regards to incident light intensity, using a low intensity on two exposure surfaces is a better strategy to enhance biomass concentration and save lighting costs. For hydrogen production, very similar observations are made. However in this case the width should not be greater than 0.05 m  as the metabolic pathway of hydrogen production is more energy demanding and a uniform light distribution is vital.This contribution presents the detailed modelling of cyanobacterial photo‐heterotrophic growth and hydrogen production. To the best of our knowledge, it is the first time a dynamic model is presented, which is capable of simulating all phases of this bioprocess, and also includes the effects of limiting nutrient concentration, light intensity, and light attenuation.Based on a sensitivity analysis, it is shown that identifying the optimal operating conditions is more effective than developing mutants for increasing hydrogen production in a laboratory scale reactor.Cell absorption has been determined to be the primary limiting factor for light attenuation in the present laboratory scale PBR. It is found that cultivating a low‐chlorophyll mutant is an efficient way to enhance both biomass and hydrogen productions in a large‐scale PBR, although this effect might not be observed at laboratory scale. Finally, for the design of pilot‐scale PBRs, the present research calculates the maximum biomass concentration and the average hydrogen productivity in different configurations of PBRs. It is concluded that using lower incident light intensity but double exposure surfaces is a better strategy for the simultaneous production of biomass and biohydrogen.D. Zhang gratefully acknowledges the support from his family. P. Dechatiwongse is supported by a scholarship from the Royal Thai Government, Thailand. The Solar Hydrogen Project was funded by the UK Engineering and Physical Sciences Research Council (EPSRC), project reference EP/F00270X/1. E. A. del Rio‐Chanona is funded by CONACyT scholarship No. 522530 from the Secretariat of Public Education and the Mexican government.


In this paper a new model of growing and dividing protocells is described, whose main features are (i) a lipid container that grows according to the composition of the molecular milieu (ii) a set of “genetic memory molecules” (GMMs) that undergo catalytic reactions in the internal aqueous phase and (iii) a set of stochastic kinetic equations for the GMMs. The mass exchange between the external environment and the internal phase is described by simulating a semipermeable membrane and a flow driven by the differences in chemical potentials, thereby avoiding to resort to sometimes misleading simplifications, e.g., that of a flow reactor. Under simple assumptions, it is shown that synchronization takes place between the rate of replication of the GMMs and that of the container, provided that the set of reactions hosts a so-called RAF  set whose influence on synchronization is hereafter discussed. It is also shown that a slight modification of the basic model that takes into account a rate-limiting term, makes possible the growth of novelties, allowing in such a way suitable evolution: so the model represents an effective basis for understanding the main abstract properties of populations of protocells. Protocells are cell-like structures that are simpler than present-day biological cells, but that are nonetheless able (i) to grow by some form of rudimentary metabolism, (ii) to reproduce giving rise to new protocells that are similar to their parents and (iii) to undergo evolution.While viable protocells do not yet exist, these structures are supposed to have played a key role in the self-organizing processes leading to the emergence of life; moreover their study could be important in order to create new “protolife” forms which are able to adapt and evolve . This eni.e., the “protogenetic material”) and the fission rate, evolution could in principle take place, so it would be appropriate to speak of a true protocell . The entire set of number of copies of the different species of the system is denoted by X = .The model describes a system in which simplified chemical species, cleavage, the cutting of a species made of more than one brick into two shorter species  and (ii) condensation, the concatenation of two species in a longer polymer . Reactions can occur only if catalyzed by reaction-specific catalysts, so no spontaneous reactions are allowed (by assuming a sufficiently high activation energy for each reaction). Condensation requires an intermediate step with the creation of a temporary complex between one of the substrates and the catalyst. Finally, the presence of backward reactions is also neglected, by assuming a sufficiently high Gibbs energy ∆G for each reaction . Thus, the reaction scheme can be summarized as follows:Cleavage: AB + C → A + B + CCondensation: (whole reaction: A + B + C → AB + C)○Complex formation: A + C → A C○Complex dissociation: A:C → A + C○Final condensation: A:C + B → AB + Cwhere A and B are two substrates involved in a specific reaction, C is the specific catalyst for that reaction and A:C represents the transient complex. The kinetic equations can be obtained by applying the law of mass action to this reaction scheme, as detailed in Only two kinds of reactions are allowed in the system, namely: (i) X and, in particular, each species i has a certain (fixed) probability ip of being the catalyst for a reaction chosen at random . Following Kauffman , catalyssion see ). In thesion see .The dynamics of the system is stochastic and simulated by means of an extension of the Gillespie algorithm ,38, descAppendix AThe dynamic behavior of catalytic reaction networks has been classically investigated in the case of a continuous stirred-tank reactor (CSTR) ,43,44,45i.e., those species that are shorter than an arbitrary length permL. Conversely, species longer that permL remain entrapped inside or outside the protocell. Another key modeling choice is that the concentration of the permeable molecules is assumed to be homogeneous inside and outside the protocell, by hypothesizing infinitely fast diffusion, both in the bulk phases and across the membrane . Then the equation for the growth rate of the container takes the form: rV is the internal volume of the protocell (where reactions occur) and [ix] is the concentration of catalysts in the internal aqueous phase; the kinetic coefficients ik are zero for all those species that do not contribute to the container growth. The kinetics of lipid formation are first-order with respect to the concentration of catalyst, because an infinite supply of lipid precursors is considered available inside the protocell; any lipids produced inside the protocell are assumed to go instantaneously to the membrane and not occupy the internal volume.Let C be a measure of the mass of the container, Protocells can grow and divide: during these processes their form and shape can change . HoweverrV is equal to: This hypothesis allows one to determine the relationship between C and the volume, so that Equation (1) provides a rule to determine the volume growth rate. Under the assumptions made, one can show see  that theAs discussed in Since the volume of the daughter protocell is approximately Different researchers have suggested several interesting models ,50,51,52A nice way of identifying the sets of chemical species able to catalyze their own growth is that of using a digraph representation  “comple where ciThis structure is therefore a possible component of a dynamic engine able to grow and reproduce ,29,30. HA RAF is the union of all the reactions allowing the complete self-reproduction of the involved chemical species, so it may be composed of several separate subparts each one able to separately self-replicate: if these subparts cannot be further decomposed they are called irreducible RAFs (irrRAFs) . IrrRAFsi.e., RAF sets, or strongly connected components, i.e., SCCs. Indeed, both depend upon the probability p that a molecule chosen at random catalyzes a reaction chosen at random: if this probability is very low, neither are observed, while, if it is high, the system is full of both. Apart from these trivial regimes, there is an interesting region of intermediate values. It has been observed  and [Miint] are the concentrations of the chemical i outside and inside the protocell, respectively.However, as has already been proposed in In this case, irrRAFs having growth rates equal to that of the already present ones but starting from very low concentrations have the possibility of increasing their presence and reach the already present irrRAFs a. MoreovWe believe that the model presented here could provide a convincing picture, at an abstract level, of the main features of the relevant processes involved in protocell growth and division. The model can be improved in different ways, some of which will be summarized below, but it can also be used in its present version to address some important issues in protocell research.First of all, let us recall that a population of units of evolution is “any population of entities with the properties of multiplication (one entity can give rise to many), variation , and heredity (like begets like)” . The proi.e., those that cannot be generated by the catalysts that are included in the “chemistry”, and that may come from outside or from rare spontaneous reactions) and make them grow—if the growth limiting terms are taken into account as it has been proposed in It is interesting to observe that the results shown above prove that not only protocells can be different, but that they may also host new types of molecules  possible answers to the question concerning the necessity of compartments: (a) the smallness of the vesicle; or (b) a possible active role of the membrane. The first one has already been discussed above: small vesicles can have an internal composition that is very different, not only from that of other protocells (thus ensuring heterogeneity), but also from that of the bulk, thus allowing different reaction pathways to dominate their internal environments.As far as alternative (b) is concerned, it is indeed highly plausible that the lipid membrane creates a local environment close to its boundaries that is markedly different from the bulk and that these surface effects modify the chemistry, by favoring some reactions and inhibiting others. In this case the membrane would itself act like a kind of catalyst, and would create a local environment different from the bulk. This would happen on both sides, but here the advantage of a closed vesicle is apparent: the molecules generated inside it will be trapped (if non permeable) in a small volume, so their concentrations will increase, while those that are generated on the outer side will freely diffuse in the bulk, without significantly affecting the external concentrations (at least until there are not very many protocells). It has also been shown that some superconcentration phenomena can appear in these cases . The actFinally, note that the presence of high protein concentrations in small vesicles are also experimentally reported, as for example in ; these dAs far as model improvements are concerned, there are of course many important directions. Indeed, we think that the picture that has been outlined above represents a rich class of models and opens up an interesting agenda for further research.A major topic concerns the robustness of our analysis  in different protocell architectures, like the Los Alamos Bug or the GARD models. Previous works on synchronization with different equations for the replicators makes us confident that the results can actually be robust, but this has of course to be proven.The appearance of novelties represents a major research topic: it would be important to better determine under which conditions the novelties are actually likely to survive and spread.p. Other improvements  involve considerations of backward reactions and the introduction of energy constraints.The model of the reactions could also be significantly improved, e.g. by considering a structure-dependent catalytic activity, instead of using a uniform probability of catalysis Let us recall, from the above discussion, that there are at least two different  explanations of the importance of closed containers: heterogeneity due to smallness, and an active role of the membrane. Both should be explored with appropriate versions of the model in order to understand their features.Other improvements are of course also possible. The model presented here can be the basis for a set of explorations of different alternatives, and it can be useful to cast basic questions about protocells in a precise form, amenable to analysis and simulation.


Such features are directly tailored to the more common experiment that performs only relative quantitation and compares fluxes between two conditions. We call our extension rKFP. Moreover, we examine the effects of common missing data and common modeling assumptions on (r)KFP, and provide practical suggestions. We also investigate the selection of measuring times for (r)KFP and provide a simple recipe. We then apply rKFP to 13C-labeled glucose time series data collected from cells under normal and glucose-deprived conditions, estimating the relative flux changes of glycolysis and its branching pathways. We identify an adaptive response in which de novo serine biosynthesis is compromised to maintain the glycolytic flux backbone. Together, these results greatly expand the capabilities of KFP and are suitable for broad biological applications.Fluxes are the central trait of metabolism and Kinetic Flux Profiling (KFP) is an effective method of measuring them. To generalize its applicability, we present an extension of the method that estimates the relative changes of fluxes using only relative quantitation of  Metabolism underlies all biological processes, and its quantitative study is crucial for our understanding. The central trait of metabolism, metabolic fluxes, cannot be directly measured and are estimated usually through modeling. Existing modeling methods, however, are limited by poorly-characterized parameters, crude precision, or labor-intensiveness. Motivated by these limitations, and recognizing a most common goal in the field of comparing the fluxes between two conditions, we develop an extension of an existing method that takes in time-series relative-quantitation data of isotope-labeled metabolites (a kind of data that modern metabolomic technologies readily generate), and outputs the relative changes of fluxes in the metabolic networks of interest. We also carefully examine some issues on model construction and experimental design, and improve the reliability and strength of the method. We apply our method to data collected from cells in normal and glucose-deprived conditions, demonstrate the efficacy of the method and arrive at new biological insight. PLOS Computational Biology Methods article.This is a In recent years there has been renewed interest in metabolism resulting from discoveries of its connections to gene regulation Among the isotope-based methods, Kinetic Flux Profiling (KFP) The basic idea of KFP can be illustrated using a toy metabolic network. Consider a system of only one metabolite tate so  . KFP worThe two terms ques see . Its soland geometrically corresponds to a family of curves parameterized by  and  .why KFP should work in this case. First, it is easy to see from Eq. 2 that parameter scale and rate. To highlight this, we define a rate parameter, identifiable when applying KFP to the system in With some measurements of en when  . Measureth  and  . The disApplying KFP to systems of arbitrary size and network topology and with multiple influxes is less straightforward and requires care absolute quantitation of metabolites, meaning that their absolute concentrations have to be measured, while many experimental techniques such as mass spectrometry can only perform relative quantitation readily, meaning that the measurement output is scaled from the absolute concentration by a metabolite-specific unknown constant; going from relative quantitation to absolute quantitation typically requires performing relative quantitation on some reference samples whose absolute concentrations are known, which can often be a challenge due to the increased effort of both additional experiments and procurement of reference samples. Second, often it is the relative changes of fluxes  between two conditions that are of interest or biological relevance  and the absolute quantitation measurements (concentrations) for the two conditions:where an upper-case letter denotes concentration and lower-case signal, more natural in this situation: although relative quantitation leaves us oblivious to the scale, we can still normalize the measurements to uncover the rate.The above two equations highlight a simple but important fact: Defining the relative changes of pool size Since tifiable , so is .To summarize the mechanics of rKFP for the system, one sets up the following two equations:which form a model that is parameterized by For larger networks consisting of single-substrate reactions arranged in linear pathways and branch points, rKFP proceeds in a similar way: equations like Eq. 7 are constructed for each metabolite in the network, and relative quantitation measurements of the metabolites allow for estimating the relative changes of all the independent fluxes with precision. However, for metabolic networks involving reactions of multiple substrates, especially cycles .Formally, one can plug some metabolites  use a reduced model where the network components corresponding to the missing data are removed; (2) use a full model where all components are kept and the part of the model corresponding to missing data represents additional degrees of freedom unconstrained by data; (3) use the full model but incorporate prior information for the part of model uncovered by data; (4) spend additional effort to collect all data and use the full model. We observe that a common practice in applying KFP is to choose the first option and use reduced models when there is missing data e.g., . We ackni.e., how well one a priori knows the missing piece. Hence it has to be judged on a case-by-case basis and cannot be discussed generally. We therefore exclude option (3) in our subsequent analysis, and only note that in the limit of a correct tight prior the case converges to option (4) of completing the data, and in the limit of a loose prior the case converges to option (2) of effectively having no prior information.Consider the following example. missing metabolite, and the corresponding procedure of constructing reduced models metabolite removal. We identify and name three additional common scenarios of missing data with their associated reduction procedures: first, missing pathway, where a branching pathway has poor data coverage with most metabolites unmeasured, associated with pathway removal, where the branching pathway is removed from the model; second, undistinguished metabolites, where the individual identity of a set of metabolites in a measurement cannot be resolved, associated with lumping, where the undistinguished metabolites are lumped into a single pool; third, unknown reversibility, where the extent of reversibility of a reaction is unknown, associated with assuming irreversibility, where potentially reversible reactions are modeled as irreversible for simplicity. In the remainder of this section, we describe in details the effects of metabolite removal on both KFP and rKFP, for this case is the simplest and hence most illustrative, and also for this case turns out to be important (see below); after that we briefly describe the results on pathway removal and lumping, leaving the details to the supplementary text; results on assuming irreversibility are discussed in the next section.We call this scenario of missing data in underestimateBack to vs. the green error bars). These observations suggest the following three summary statistics:bias, defined as cost, the cost of fitting using the reduced model.error ratio, defined as a priori to have small pool sizes, one should use full models in KFP.These statistics summarize the effects and pitfalls of missing data: error ratio quantifies their adverse effects on parameter estimation , and bias and cost quantify the harm of using reduced models. We next examine how metabolite removal affects rKFP . We haveelasticity regimes flux controlmore robust to metabolite removal than KFP. However, we also note in To summarize, we have demonstrated above the effects of missing metabolites on (r)KFP and the pitfalls of removing them; we suggest that unless one has good prior knowledge about the missing pool size or its relative change between two conditions that ensures a small bias, full models should be used. The approach we use for the demonstration is an intuitive and computational one, and in supplementary flux-force relationship: Biochemical reactions often operate close to equilibrium This suggests another potential issue in setting up models for KFP or rKFP: ignoring the reversible fluxes when the reaction is close to equilibrium, as is commonly done, might introduce bias. To check this, we again use a toy system  and, simvice versa. Second, if   in KFP , if the We naturally wonder how likely the change of unlike the case of metabolite removal, the bias increases slowly as the relative pool size changes of different metabolites diverge . We therefore conclude rKFP should be robust to assuming irreversibility and reduced models can be used.In other words, the change in We last note that, like the case of pathway removal discussed in the supplementary text, using the full model in this case and incorporating In addition to the issues on setting up models as discussed in the previous two sections, we have also investigated the experimental design issue of selecting measuring times for a metabolic pathway in (r)KFP. We list below the main conclusions, and leave the derivation details to the supplementary text.First, we note that the problem can be formulated as a classical experimental design one sticness .We choose to adopt an alternative approach for which there is a physical intuition: since the dynamics of the metabolites in a pathway are linear combinations of exponential functions , and in We show that for the first metabolite in a pathway, if a late time has been measured so that its (relative) scale is estimated, the optimal measuring time would be around We also show by using a similar reasoning that for the second metabolite in a pathway the optimal measuring time is around We last note that a typical metabolic network exhibits strong separation of concentration scales. For the example of glycolysis, one source reports that the most abundant metabolite (glucose) is about 35 times more than the second most abundant one and the full range of variation spans over three orders of magnitude In this last section, we apply rKFP to experimental data collected on glycolysis and its two main branching pathways  from cells in normal (5 mM glucose) and glucose-deprived (0.5 mM glucose) media conditions, and estimate the relative changes in the fluxes of the three pathways.We set up three computational models for rKFP, corresponding to three different choices of treating missing data as discussed in earlier sections, and the estimation results of all three models are shown in e.g., The observations support our recommended choice. The shifted green histograms relative to the blue suggest that metabolite removal introduces significant bias, likely because the pool sizes of the missing metabolites change differently from the other metabolites. The flattened red histograms relative to the blue suggest that keeping in the model the missing pathways with no data leaves the estimation greatly underconstrained; on the other hand, their similar averages suggest that the bias introduced by pathway removal is small in this case, likely because the missing pathways have much lower fluxes than the main glycolysis pathway (see the supplementary text) which is consistent with some previous studies  construct the Jacobian of the model, Generating posterior distribution: assuming Gaussian-distributed measurement noise, the measurement of sensitivity analysis or delta method, and is computationally cheap but less accurate; the second one is also known as ensemble methodNote that the last two points constitute the two standard ways of estimating parameter uncertainties: the first one also goes by the name of Experimental procedures of cell culture, metabolite extraction, mass spectrometry, liquid chromatography and data processing follow those in Text S1A file containing three supplementary figures, a derivation of the solution for KFP applied to a linear metabolic pathway, a discussion on applying (r)KFP to metabolic cycles, the description of an analytical approach to studying the effects of removing metabolites and pathways, and effects of pathway removal in KFP and rKFP, and some detailed results on the effects of missing data and selecting measuring times.(PDF)Click here for additional data file.Dataset S113C-labeled relative-quantitation data collected from cells in normal and glucose-deprived media used for the analysis.(CSV)Click here for additional data file.


This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in  The epidermal growth factor receptor (EGFR) signaling cascade is one of the key signaling pathways that are involved in the induction of Epithelial Mesenchymal Transition (EMT) and tumor metastasis. These signaling cascades often affect metabolic fate in tumor cells and control their progression. Here we demonstrate a method to build a mathematical model of the EGFR signaling cascade and use it to study signaling in EMT and how signaling affects metabolism. The model was used to obtain a list of potential signaling and metabolic targets of EMT. These targets may aid in the understanding of the molecular mechanisms that underlie EMT and metastasis. Our results further highlight the heterogeneity of cell models used to study EMT and support the idea of cell specific anti-cancer interventions. Epithelial to mesenchymal transition (EMT) is a developmental process where polarized epithelial cells transition to an invasive mesenchymal-like phenotype through molecular reprogramming that leads to degradation of the extra-cellular matrix (ECM) and the loss of cell polarity. Following recruitment to specific sites at distant locations within the developing embryo, the mesenchymal cells may revert back to the epithelial phenotype by a process known as mesenchymal to epithelial transition (MET), thereby seeding new epithelial tissues . Althoug [EMT is induced by a number of distinct molecular processes . These i ,14.EMT is likely to impact metabolism, but the effects are not as widely studied as cancer metabolism ,16 CanceComputational approaches such as Constraint-based modeling and analysis (COBRA) techniques are very useful in analysis of the complex biological networks like signaling networks –23. PrioIn this study, we built a computational signaling network of EGFR to query how the expression of signaling genes can affect metabolic alterations during EMT in human breast epithelial cells. An EGFR signalling network was reconstructed (EGFR_SN) and was constrained with the transcriptomics data of the breast epithelial cell line D492 and its mesenchymal counterpart D492M  to form in silico predicted increase in flux of the AKT pathway induced higher glycolytic activity in D492 cells. This suggested that there may be an EMT-related decrease in glycolysis in D492M as compared to D492 cells, which was confirmed in vitro by glucose uptake and lactate secretion measurements. Comparative analysis of EGFR signaling networks in three other breast epithelial cell lines showed that regulation of signaling pathways are cell specific and follow no simple trend.The EMT specific signaling network of D492 and D492M enabled us to investigate differential gene expression of EGFR signalling genes. This was subsequently extended to other breast epithelial cell lines and their mesenchymal counterparts. Through extensive literature review, the EGFR_SN network was linked to metabolic genes that were likely to be affected. The differential flux values in EGFR_E and EGFR_M allowed the assessment of how the altered signaling affects metabolic gene expression and metabolism in D492 and D492M cells. Increased flux within the AKT and RAS/MAPK signaling pathways was predicted in D492 as compared to D492M. The In order to capture how altered EGFR signaling is propagated through metabolic pathways in the breast epithelium, we built a constraint based EGFR network, EGFR_SN. To generate EGFR_SN, the EGFR pathway map (Reactome ID: R-HSA-177929) was downloaded from the Reactome database . SeveralFig 2 shows a sub-network of EGFR_SN representing AKT signaling.The resulting reconstructed network EGFR_SN accounts for 182 reactions, 216 genes, 152 reacting species, 11 inhibitors and 2 activators. The 182 reactions were divided between 83 internal reactions and 99 exchange reactions. Exchange reactions were added in order to remove dead-ends in the network and acquire feasibility. The number of exchange reactions added here is large compared to what is typically present in metabolic networks, where exchange reactions are added mainly to allow for accumulation of metabolites and secretion of wastes. Internal reactions represent connections between internal signaling components, while the exchange reactions represent connections of the system boundary with the environment. Microarray gene expression profiles of the human breast epithelial cell line D492 and the mesenchymal like D492M were used to constrain the EGFR_SN network to build an EMT specific signaling model. D492 can be used as a model for studying EMT for which biological data is available: mRNA, micro-RNA, cell phenotypic data, growth curves etc. ,37. D492Fig 3. Differentially regulated (up-regulated and down-regulated) genes in the two cell lines led to two different signaling networks: EGFR_E and EGFR_M for D492 and D492M respectively, as described in the methods section.To derive epithelial and mesenchymal specific signaling networks, gene expression data from D492 and D492M was mapped onto the EGFR_SN network. The pipeline is described in EGFR_E and EGFR_M networks were investigated through flux analysis. Flux distribution in EGFR_E and EGFR_M networks were estimated using random sampling ,40 Fig . Fig 4A We next studied how the flux in the EGFR_M network could be modified so that it became similar to the EGFR_E flux, in order to identify how the EMT process could be reversed. This was achieved by using an optimization algorithm similar to the MOMA algorithm which is frequently used to study perturbations in metabolic networks . The alg. However, among these 22 genes, MAPK1, NRAS, HRAS and EGFR genes were overexpressed in D492 as compared to D492M and the inhibitor PTEN was overexpressed in D492M.The algorithm highlighted five reactions whose bounds needed to be relaxed such that the flux distribution of EGFR_M is close to that of EGFR_E. These included three internal reactions: 1) phosphorylation of the MAP2K dimer by RAF, 2) phosphorylation of MAPKs by MAP2Ks (RAF/MAPK pathway), 3) PIP2 conversion to PIP3 by PIK3 (AKT pathway), and two exchange reactions, belonging to the RAF/MAPK pathway. Through GPRs we identified that there are 22 genes associated with the reactions predicted above MAPK1, NRAS, HRAS and EGFR and down-regulation of PTEN inhibitor in EGFR_M may lead to its transformation into EGFR_E.Based on these predictions and by analysing the flux differences between EGFR_E and EGFR_M, we hypothesize that increased AKT and RAS/MAPK signaling in D492 epithelial cells promotes their transition to the D492M mesenchymal like phenotype. However, after attainment of a mesenchymal state, AKT and RAS/MAPK signaling is reduced and alternative pathways such as CaM signaling gets activated which may be involved in the maintenance of the mesenchymal state. We also hypothesize that up-regulation of in silico predicted targets from our study have previously been implicated to play a role in EMT. The activation of MAPK1 protein has been shown to induce EMT in MCF10 breast epithelial cells by phosphorylation and consequent stabilization of Twist1 [in silico predictions, we made EGFR overexpressing D492M cell line (D492MEGFR) (method section). D492MEGFR cells showed higher phosphorylation of AKT and ERK1/2 , although reversal of mesenchymal to epithelial phenotype was not observed . Thus, we hypothesize that for complete reversal of mesenchymal to epithelial phenotype, the MAPK1, NRAS and HRAS genes may need to be overexpressed in addition to EGFR, along with PTEN inhibition in D492M. Likewise, maintaining expression of the MAPK1, NRAS, HRAS and EGFR in epithelial cells while suppressing the expression of PTEN is expected to inhibit EMT. At present, a number of EGFR inhibitors have been approved by the FDA for cancer treatment. These include Cetuximab and Panitumumab that are monoclonal antibodies against EGFR, and Erlotinib, Gefitinib and Lapatinib which are specific tyrosine kinase inhibitors against EGFR. All these compounds are prescribed against advanced, late-stage or metastatic cancers [PTEN inhibition can help maintain the epithelial phenotype, thereby preventing EMT and metastasis. However, recent findings suggest that loss of PTEN function may promote tumor progression in a mouse model [PTEN in EMT is needed.Interestingly, most of the f Twist1 . In anotf Twist1 , suggestf Twist1 . Furtherf Twist1 . In a ref Twist1 , subsequ cancers ,45. The  cancers . Our prese model  and EMT se model , highligTaken together, these above results demonstrate the EGFR_SN network can be used to study in detail the differences in EGFR signaling between epithelial and mesenchymal cells during EMT and identify gene targets which could possibly be used to hinder or even revert EMT..We next studied how changes in AKT signaling could influence metabolic gene expression in EMT. The AKT pathway was chosen since it has previously been shown to affect various metabolic pathways, e.g. glycolysis and fatty acid metabolism in cancer cells ,48. To rTable 1). Higher expression of glycolytic genes suggested higher glycolytic activity in the D492 cells.The ratio of average flux in each signaling reaction of the AKT pathways in the EGFR_M and EGFR_E network was used to identify differences in relative metabolic gene expression in the D492 and D492M cells. The higher flux observed in the AKT pathway in the EGFR_E network suggested increased expression of genes belonging to glycolysis, fatty acid and purine/pyrimidine metabolism in D492 in comparison to D492M  from the microarray data and in silico predictions were not in agreement. This suggests an alternate level of regulation that metabolic genes may encounter during EMT, in addition to the direct regulation by molecular signaling pathways. For accuracy measure we did not take into account metabolic genes which did not have any detectable expression values (denoted NA). Ultimately, we observed that the expression of most of the metabolic genes directly affected by AKT signaling during EMT was correctly predicted in our D492 model system.However, the gene expression of 2 out of 15 (13.3%) of the metabolic genes  cells [We conclude that this EMT related decrease in aerobic glycolysis appears to be driven by an overall decrease in the expression of glycolytic enzymes due to down-regulated AKT signaling in D492M cells. To the best of our knowledge, this is the first report that demonstrates that C) cells  which deC) cells .in silico analyses of the changes in the expression of AKT signaling genes. Further, we investigated how these changes in AKT signaling, that impact the expression of metabolic genes (Table 1), are propagated through other metabolic pathways during EMT. Based on RECON2 [Table 1). This led to the formation of epithelial metabolic (Met_E) and mesenchymal metabolic (Met_M) networks, specific for AKT signaling regulated metabolism during EMT (described in methods section). Metabolic differences between the Met_E and Met_M models were identified based on differences in their relative flux span (methods section). Reactions carrying higher flux in Met_E compared to Met_M included reactions that are involved in N-glycan metabolism, Glycolysis, Fatty acid synthesis, Fatty acid oxidation, nucleotide interconversion and pentose phosphate pathway. Reactions carrying higher flux in Met_M involved Glutathione, glycerophospholipid, and inositol phosphate metabolism. Alteration of metabolic pathways, including N-glycan, glutathione metabolism, glycolysis, fatty acid and purine metabolism that we observed from our constructed Met_E and Met_M metabolic networks, have been shown to play an important role in the regulation of EMT [. In conclusion, this method was able to predict metabolic pathways that may be affected downstream upon activation of AKT signaling in breast epithelial cells during EMT. This method extends the approach of associating metabolic phenotype with regulation of signaling pathways. Further, it also suggests the possibility of determining the metabolic regulation in cases that are limited by the availability of metabolomic data or the gene expression data of metabolic genes. Although these predictions are context specific in relation to AKT signaling, the integration of other signaling pathways affected during EMT may give a more coherent picture of altered metabolism.Results from the previous section showed that the metabolic phenotype could be accurately predicted by n RECON2 , we haven RECON2  of metabn RECON2 ,62. In tn of EMT ,60,63,64. Numerical values of the fluxes within AKT, RAS and CaM pathways in HMLE, MCF-7 and MCF-10A are given in supplementary file .We next studied whether a general trend of higher flux in the reactions of the AKT and RAS pathways (downstream of EGFR) was observed in other human breast epithelial cell lines, when compared to their mesenchymal counterparts. We mapped the microarray transcriptomic datasets for the three human breast epithelial cell lines  onto the EGFR_SN network similar to the method used for the D492 cells (methods section). Comparisons between cell lines were done in terms of the ratio between flux in the mesenchymal network vs flux in the corresponding epithelial network . In contrast, the mesenchymal counterparts of MCF7 and MCF10A cells had higher flux in the AKT pathway , suggesting that cells that have undergone EMT may have increased glycolytic activity. This is in agreement with Kondaveeti et al. who have reported an increase in glycolytic activity post-EMT in MCF-7 cells as a result of increased expression of glucose transporters and lactate dehydrogenase [. Thus, no general pattern was observed in the flux distributions between the epithelial and mesenchymal networks for the different breast epithelial cell lines. Induction of EMT by different factors  also seemed to differentially regulate signaling pathways as was evident in the MCF10A and HMLE cells. For example, the induction of EMT in HMLE cells by overexpression of Slug and Twist resulted in different flux patterns in the RAS/MAPK and DAG/IP3 and CaM pathways . Similar effect was seen by Deshiere et al., where they have shown that TGFβ1 treatment and CK2b silencing activate divergent signaling pathways, that ultimately lead to EMT in MCF-10A cells [ Tables.Higher flux through the reactions in the AKT pathway was observed in both the D492 and HMLE epithelial cells, suggesting that the HMLE cells may have similar metabolic phenotype as D492 rogenase . The flu0A cells . FinallyIn summary, we observed that different cell lines may affect different signaling regulation during EMT. Moreover, variation in the methods of EMT induction may dictate differential regulation of the signaling and metabolic cross talk.in vitro testing would be required to confirm these in silico predictions. Thus, in this study we showed that the metabolic phenotype can be predicted in silico using gene expression profiles of EGFR signaling components.Herein we have demonstrated a method to build a stoichiometric model of the EGFR signaling (EGFR_SN) network employing COBRA methods that aids in understanding the differential activation of downstream EGFR signaling pathways during EMT. Epithelial and mesenchymal specific EGFR signaling networks were obtained by integrating microarray transcriptomics data of signaling genes from the D492 breast epithelial and mesenchymal cells with EGFR_SN. The epithelial and mesenchymal networks were used to predict the expression of metabolic genes. The predicted expression values were in agreement with transcriptomics data of metabolic genes as well as biochemical data that demonstrated higher glycolytic activity in D492 epithelial cells. Furthermore, signaling genes leading to reversion to the epithelial phenotype (MET) via up- regulation in the mesenchymal cells were predicted. Additional EGFR_SN is not limited in scope to the investigation of EMT. The signalling network could be used to highlight signaling-metabolic crosstalk in different cell types for which metabolic reconstructions exist  or for din silico predictions. Second, our study of EMT signal transduction is primarily based on transcriptional signatures which may or may not necessarily correlate with the translational output (protein-levels) [The pipeline developed for D492 was used on other cell models representative of breast epithelial cell lines. We assumed that different breast epithelial cells models would have similar signaling patterns and hence could have a general interpretation of regulation of signaling pathways during EMT. In contrast to our hypothesis, the regulation of signaling pathways showed no general pattern and appeared to be a cell-specific phenomenon. Flux values in the AKT pathway from our network, suggest that there may be an EMT related decrease in aerobic glycolysis in both the HMLE and D492 cell lines, while the opposite was observed in the MCF-7 and MCF-10A breast epithelial cells. There are several possible explanations for this disparity. First, the highly complex nature of the regulation of EMT, which may be differentially regulated by the cellular micro environment or EMT inducing factors as seen in our -levels) ,69. This-levels) . The disThe EGFR pathway network was downloaded from the Reactome database which is curated and peer reviewed . The EGFm by r stoichiometric matrix S, where m denotes the number of network components  and r the number of network reactions. Reactions within the network were mass-balanced. The system was assumed to be at steady state, which means that the fluxes v =  satisfy the equations Sv  =  0. The upper and lower bounds of all the internal reactions were set to 1000 and zero, respectively. A lower bound of zero was used since all the reactions are irreversible.The COBRA model was represented by an  was developed that enabled a feasible steady state network, while minimizing uptake/secretion (exchange) of the dead-end molecules. The methodology is similar to the one used by Vardi et al. [The initial COBRA model contained many dead ends and was infeasible due to network gaps . Dead-eni et al. . First, v, the flux values in individual reactions, pj and nj which represent the amount of relaxation of upper and lower bounds for reaction j, respectively and binary variables yj which indicate whether reaction j is relaxed or not. The objective is to minimize the number of reactions that are relaxed  / vE(i) where vM(i) and vE(i) represent the average flux in reaction i for the mesenchymal and epithelial networks, respectively. Reactions which had vM(i) / vE(i) greater than 1 carried higher flux in the mesenchymal network. A ratio below 1 indicates higher flux in the epithelial network. A literature based survey provided evidence of whether a metabolic gene is positively or negatively regulated by AKT signaling . The prediction of the metabolic gene expression derives from whether EGFR_E or EGFR_M have higher flux in AKT signaling.Flux differences in individual reactions in the mesenchymal and epithelial networks were quantified in terms of fold changes, vj represents the decision variables as before, vE are fixed values representing the mean flux distribution of EGFR_E, obtained from random sampling and ||. || represents the Euclidean norm. The upper and lower bounds for each flux correspond to the values from the EGFR_M network. The algorithm returns a set of reactions in EGFR_M whose bounds can be relaxed in order to obtain a flux distribution that resembles that of EGFR_E.The optimization algorithm of section 4.1 was modified by replacing the objective function (1) byet al. [Table 1) led to the identification of up-regulated reactions in D492 and similarly were determined in D492M. This information was then used to define the upper and lower flux bounds of the affected reactions in the EMT metabolic network to form Met_E  and Met_M  networks . Up- and down-regulation of the Met_E and Met_M models was simulated as described in section 4.4. Specifically, up-regulation in an epithelial metabolic model was simulated by downregulating the corresponding reaction in its mesenchymal counterpart, and vice versa. Flux bounds of the up-regulated metabolic reactions due to AKT signaling in D492 were constrained by an arbitrary factor, one-hundredth of the initial bounds in D492M and vice versa.We constrained RECON 2  using thet al.  to generM(i)/ sE(i) was used to quantify the flux differences between the networks. Here, sM(i) and sE(i) represent the average flux in reaction i for the mesenchymal and epithelial cells, respectively.The flux values through each reaction in both the models were determined through random sampling method and the relative flux span, s4 D492 or D492M cells were cultured in 48-well plates (Costar) in 200μL H14 medium as previously described [−1 for D492 and 0.0161 h−1 for D492M. The glucose and lactate concentrations in the spent medium were measured in an ABL90 blood gas analyser . Glucose uptake and lactate secretion per cell were calculated for each cell line as described in [For proliferation assays and spent medium analysis, 1.5 × 10escribed . Spent mribed in , based oVectors used for viral production were acquired from Addgene, pBABE-EGFR and empty backbone  and were used as provided. Phoenix HEK293 cells were used for retroviral (EGFR) virus production, using Arrestin transfection (Life Technologies). D492M cells were transduced overnight with viral supernatant containing 8 μg/ml Polybrene (Sigma-Aldrich). EGFR and empty backbone cells were selected using 2 μg/ml puromycin (Life Technologies).EGFR , ZEB1  and GAPDH . Experiments were done in triplicates on 7500 Real Time PCR System (Applied Biosystems). EGFR mRNA levels were normalized to GAPDH and relative mRNA differences were calculated using the 2ΔCt method.Total RNA was isolated using TRI-Reagent solution (Ambion) and reverse transcribed using SuperScript IV (Invitrogen). The resulting cDNA was used for Real-Time Quantitative Reverse Transcription PCR, in Maxima Probe/ROX qPCR Master Mix (Thermo Scientific) with primer pairs and probes for Proteins were isolated using RIPA lysis buffer supplemented with protease and phosphatase inhibitors (Life Technologies). For Western blot analysis 5 μg of protein lysates were loaded per lane on NuPage 10% Bis-Tris gels (Life Technologies) in 2-(N-morpholino)ethanesulfonic acid (MES) running buffer (Life Technologies). Samples were denatured using 10% mercaptoethanol at 95°C for 10 minutes before loading. Samples were transferred to Immobilon FL PVDF membranes (Millipore) and blocked in Li-cor blocking buffer for 1 hour. Primary antibodies were incubated overnight at 4°C and secondary IRDye antibodies were incubated at room temperature for 1 hour (Licor). The following primary antibodies were used for Western blotting: Actin antibody , EGF Receptor , Phospho-Akt (Ser473) , Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) , N-Cadherin , E-Cadherin  and Cytokeratin 14 . Near-infrared fluorescence visualization was measured using Odyssey CLx scanner .S1 Dataset(ZIP)Click here for additional data file.S1 File(XLS)Click here for additional data file.S2 File(XLS)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S4 File(XLSX)Click here for additional data file.S1 Methods(DOCX)Click here for additional data file.S2 Methods(DOCX)Click here for additional data file.S1 Fig(A) Western blotting for epithelial markers E-Cadherin and CK14 and mesenchymal marker N-Cadherin. Overexpression of EGFR in D492M does not revert the mesenchymal phenotype towards an epithelial phenotype. D492MEGFR retains N-Cadherin expression and does not gain E-Cadherin or CK14 expression. (B) Real-Time Quantitative Reverse Transcription PCR of the EMT transcription factor ZEB1 in D492, D492MEGFR and D492MEmpty normalized to GAPDH. ZEB1 transcription was not detected in D492 and the transcription level of ZEB1 was unchanged in D492MEGFR compared to D492MEmpty. D492MEGFR retains mesenchymal ZEB1 expression.(TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableER: Microarray Expression data in TWIST, SLUG and SNAIL induced HMLE cells respectively. PE: Proposed Expression. Predictions in agreement with microarray data are highlighted in green and that otherwise are highlighted in orange.(DOCX)Click here for additional data file.S3 TableER: Microarray Expression data in TWIST, SLUG and SNAIL induced HMLE cells respectively. PE: Proposed Expression. Predictions in agreement with microarray data are highlighted in green and that otherwise are highlighted in orange.(DOCX)Click here for additional data file.S4 TableER: Microarray Expression data in TWIST, SLUG and SNAIL induced HMLE cells respectively. PE: Proposed Expression. Predictions in agreement with microarray data are highlighted in green and that otherwise are highlighted in orange.(DOCX)Click here for additional data file.S5 Table“Reference” column lists the studies from which the influence of AKT signaling on the expression of the corresponding metabolic genes was derived. +1 and -1 denotes positive and negative regulation, respectively.(DOCX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


The conserved target of rapamycin complex 1 (TORC1) integrates nutrient signals to orchestrate cell growth and proliferation. Leucine availability is conveyed to control TORC1 activity via the leu-tRNA synthetase/EGOC-GTPase module in yeast and mammals, but the mechanisms sensing leucine remain only partially understood. We show here that both leucine and its α-ketoacid metabolite, α-ketoisocaproate, effectively activate the yeast TORC1 kinase via both EGOC GTPase-dependent and -independent mechanisms. Leucine and α-ketoisocaproate are interconverted by ubiquitous branched-chain aminotransferases (BCAT), which in yeast are represented by the mitochondrial and cytosolic enzymes Bat1 and Bat2, respectively. BCAT yeast mutants exhibit severely compromised TORC1 activity, which is partially restored by expression of Bat1 active site mutants, implicating both catalytic and structural roles of BCATs in TORC1 control. We find that Bat1 interacts with branched-chain amino acid metabolic enzymes and, in a leucine-dependent fashion, with the tricarboxylic acid (TCA)-cycle enzyme aconitase. BCAT mutation perturbed TCA-cycle intermediate levels, consistent with a TCA-cycle block, and resulted in low ATP levels, activation of AMPK, and TORC1 inhibition. We propose the biosynthetic capacity of BCAT and its role in forming multicomplex metabolons connecting branched-chain amino acids and TCA-cycle metabolism governs TCA-cycle flux to activate TORC1 signaling. Because mammalian mitochondrial BCAT is known to form a supramolecular branched-chain α-keto acid dehydrogenase enzyme complex that links leucine metabolism to the TCA-cycle, these findings establish a precedent for understanding TORC1 signaling in mammals. In all organisms from yeasts to mammals the target of rapamycin TORC1 pathway controls growth in response to nutrients such as leucine, but the leucine sensing mechanisms are only partially characterized. We show that both leucine and its α-ketoacid metabolite, α-ketoisocaproate, are similarly capable of activating TORC1 kinase via EGOC GTPase-dependent and -independent mechanisms. Activation of TORC1 by leucine or α-ketoisocaproate is only partially mediated via EGOC-GTPase. Leucine and α-ketoisocaproate are interconverted by ubiquitous branched-chain aminotransferases (BCAT). Disruption of BCAT caused reduced TORC1 activity, which was partially restored by expression of BCAT active site mutants, arguing for both structural and catalytic roles of BCAT in TORC1 control. We find BCAT interacts with several branched-chain amino acid metabolic enzymes, and in a leucine-dependent fashion with the tricarboxylic acid (TCA)-cycle enzyme aconitase. Both aconitase mutation or TCA-cycle inhibition impaired TORC1 activity. Mutation of BCAT resulted in a TCA-cycle intermediate profile consistent with a TCA-cycle block, low ATP levels, activation of AMPK, and TORC1 inhibition. Our results suggest a model whereby BCAT coordinates leucine and TCA cycle metabolism to control TORC1 signaling. Taken together, our findings forge key insights into how the TORC1 signaling cascade senses nutrients to control cell growth. Exit from G0Complex (EGOC) GTPase subunits Gtr1 and Gtr2 in Saccharomyces cerevisiae  [Seh1-associated) complex and its mammalian ortholog GATOR activate the GTPase activity of, and thereby inhibit, the EGO and Rag GTPase complexes [The Target of Rapamycin Complex 1 (TORC1) is functionally and structurally conserved throughout eukaryotes and senses and responds to nutrients to promote cell growth and inhibit catabolic processes such as autophagy. Amino acids, particularly the branched-chain amino acid leucine, control TORC1 activity by affecting the nucleotide binding status of the n cells) . Under lomplexes –6. Conveomplexes ), and thomplexes –10. Amonomplexes , 12, whiomplexes , 14. In omplexes –19. Furtomplexes –23.Despite considerable focus, the mechanisms by which leucine and other nutrient signals are transduced to control TORC1 activity remain incompletely defined. Plausible mechanisms through which leucine could be sensed are as follows. First, the leucine signal may be elicited via an enzyme for which leucine is a substrate. Candidates in yeast include: 1) the key controllers of leucine metabolism, the BCATs ; 2) aminHere we investigated roles for leucine metabolites and metabolic enzymes in the control of TORC1 activity. We show that KIC is capable of stimulating TORC1 activity following leucine starvation. TORC1 stimulation by leucine or KIC is only partially reduced by leucyl-tRNA synthetase inhibition or EGOC disruption, suggesting EGOC-independent and -dependent routes of TORC1 regulation. Our studies indicate that the BCATs Bat1 and Bat2 are critical to activate TORC1 signaling. We provide evidence that Bat1 governs TCA-cycle flux via both its enzymatic activity and by signaling leucine and KIC availability through formation of a supramolecular complex with the key TCA-cycle enzyme Aco1. A mammalian BCAT-GDH metabolon is known to connect α -ketoglutarate and glutamate production that could fuel the TCA-cycle , 39, andFig 1A) are capable of stimulating TORC1 activity following leucine starvation . Surprisingly, we found that the leucine α-ketoacid KIC was as effective as leucine in activating TORC1 . The other BCAAs isoleucine and valine, and their respective α-ketoacids, α-keto-β-methylvalerate and α-ketoisovalerate, all failed to stimulate TORC1 activity, as monitored by phosphorylation of Sch9-Thr737. Leucine is reversibly metabolized to KIC via the BCATs Bat1  and Bat2 (cytoplasmic), in a reaction coupled to the transamination of α-ketoglutarate to glutamate . We observed very modest TORC1 stimulation by dimethyl α-ketoglutarate , but not by glutamate addition. KIC is further metabolized via Thi3/Aro10 to isovaleraldehyde (IVA) and isoamyl alcohol (IAA), both of which also failed to stimulate TORC1 activity. Furthermore, thi3 aro10 mutants were not rapamycin hypersensitive  and showed wild-type levels of TORC1 activity . Therefore, conversion of KIC to IVA or IAA was not required for stimulation of TORC1 activity.To elucidate the mechanisms via which leucine controls TORC1 activity, we investigated if any products of branched-chain amino acid (BCAA) metabolism . In contrast, we observed a striking ~73% reduction in TORC1 activity in the bat1 bat2 mutant compared to the WT . However, leucine and KIC were still similarly effective in stimulating TORC1  in the bat1 bat2 mutant . Thus the ability of KIC to stimulate TORC1 is, in part, unrelated to its conversion back to leucine. When combinations of leucine or KIC with glutamine, d-KG, or glutamate were added to leucine-starved cells they did not stimulate TORC1 activity above KIC or leucine addition alone, and in contrast to the WT, no stimulation by d-KG was observed . Moreover, we found that the bat1 bat2 mutant was hypersensitive to rapamycin and recovered less well from rapamycin-induced growth arrest than the WT and bat1 or bat2 single mutants . Taken together, these results support a role for BCATs in promoting robust TORC1 activity.To determine if KIC stimulation of TORC1 following leucine starvation is biologically relevant and not merely due to transamination back to leucine, we tested the ability of KIC to stimulate TORC1 activity in TOR1-LM allele, which contains a mutation in the kinase domain (Tor1L2134M) rendering TORC1 independent of upstream activation [bat1 bat2 TORC1-signaling defects. Expression of TOR1-LM partially suppressed the rapamycin hypersensitivity and defects in recovery from rapamycin-mediated growth arrest of the bat1 bat2 mutants . Thus, these results support a role for BCAT upstream of TORC1.To further substantiate a role for BCATs in control of TORC1 activity, we investigated whether the tivation , would sGTR1GTPGTR2GDP-locked alleles that activate TORC1 independent of leucine levels partially suppressed the rapamycin hypersensitivity and recovery defects of bat1 bat2 mutants . Stimulation of Sch9 phosphorylation by KIC and leucine addition to leucine-starved cells was similarly only partially reduced by gtr1 or ego1 mutations . These results support that the BCATs and their metabolites leucine and KIC control and stimulate TORC1 activity via EGOC and EGOC-independent inputs.We also investigated if BCAT control and KIC stimulation of TORC1 activity requires EGOC. Expression of Fig 2C). These results support that similar to leucine, KIC stimulates TORC1 activity partially via the LeuRS. Remarkably, the reduced stimulation of TORC1 activity elicited by leucine and KIC addition to leucine-starved bat1 bat2 cells was nearly blocked by DHBB . These findings support a model in which both BCATs and the LeuRS-EGOC module contribute independently to activate TORC1.The editing activity of leucyl-tRNA synthetase (LeuRS) is implicated in sensing and signaling leucine availability to TORC1 in yeast, acting upstream of the EGOC . Exposurleu2 mutation and incubation in media lacking leucine. Under these conditions cells accumulate the intermediate 3-isopropylmalate (3-IPM), which is subsequently converted to 3-IPM methyl ester, an invasive growth signaling molecule [leu4 leu9, leu1, or leu2 mutants, which each accumulate different pathway intermediates .In our experiments, leucine starvation is achieved via a molecule . HoweverFig 3A) caused by BCAT mutation, could potentially affect TORC1 signaling. We found that ilv2 and ilv3 mutants, which are BCAA auxotrophs, were rapamycin-sensitive, but less hypersensitive than bat1 bat2 mutants . Similarly, TORC1 activity was reduced in ilv2 mutants, but not to the low level observed for bat1 bat2 mutants . Furthermore, ilv2 bat1 bat2, and ilv3 bat1 bat2 triple mutants, which cannot accumulate isoleucine and valine α-ketoacids and their degradation products, were as rapamycin-hypersensitive as bat1 bat2 double mutants . These results support that the effects of BCAT mutation in perturbing TORC1 activity are not mediated by BCAA intermediate accumulation in the bat1 bat2 mutant, and that there are both BCAA biosynthesis-dependent and -independent roles for BCATs in TORC1 signaling.Isoleucine and valine auxotrophy, or elevation of the respective isoleucine and valine α -ketoacids and their fusel alcohol degradation products (BAT1 (BAT1K219R/A) and BAT2 (BAT2K202R/A) to eliminate BCAT activity. BAT1K219R/A and BAT2K202A were as stably expressed as the WT BAT1 and BAT2 respectively  . Loss of BCAT activity of plasmid-expressed BAT1K219R/A was confirmed by an inability to utilize KIC to supplement the leucine auxotrophy , and an inability to complement the bat1 bat2 strain leucine, isoleucine and valine auxotrophy . Surprisingly, the BAT1K219R/A alleles, but not the equivalent BAT2K202R/A alleles, were nearly as effective as WT BAT1 or BAT2 at partially suppressing the rapamycin recovery and TORC1 activity defects of the bat1 bat2 mutant . Taken together, these results suggest that both Bat1 and Bat2 contribute to TORC1 signaling, likely through their BCAT biosynthetic role, and that in addition Bat1 plays a non-enzymatic role.To further test whether a BCAT enzymatic role is required to activate TORC1 signaling, we mutated the conserved pyridoxal phosphate-binding site  of BAT1 bat1 mutant strain and immunoprecipitated, and Bat1-coimmunoprecipitated proteins were identified by mass-spectrometry. Specific proteins identified in the Bat1-FLAG immunoprecipitates (which were absent in control immunoprecipitates from FLAG-untagged cells) were, like Bat1, mitochondrial proteins. Interestingly, these Bat1-interacting proteins included BCAA biosynthetic and metabolic enzymes  and TCA-cycle enzymes including pyruvate dehydrogenase subunits (Lat1 and Pdb1) and aconitase (Aco1) .We next tested the model that Bat1 plays a structural, non-enzymatic role mediated via protein-protein interactions in controlling TORC1. We employed a proteomics approach to identify Bat1-interacting proteins. To this end, Bat1-FLAG was expressed in the S2A and S2B Fig). Because we observed a low signal for Lat1-GFP by Western blot analysis, only Aco1-GFP and Pdb1-GFP interactions were tested . We were unable to demonstrate binding between Bat1-FLAG and Pdb1-GFP; thus this interaction may be weak, consistent with a low number of spectral reads identified by mass spectrometry . Remarkably, Aco1-GFP readily coimmunoprecipitated with Bat1-FLAG under conditions that promote robust TORC1 activity (leucine or KIC addition), while the interaction was disrupted by leucine starvation . The Bat1-Aco1 interaction was not perturbed by rapamycin treatment, consistent with leucine or KIC triggering this interaction upstream of TORC1. Furthermore, Bat1K219R-FLAG retained the ability to interact with Aco1-GFP, indicating that the interaction does not depend on pyridoxal binding or catalysis but does depend on the presence of substrate (leucine or KIC) binding .Our proteomic results support the intriguing model that Bat1 may comprise a central component of a metabolon linking BCAA biosynthesis to energy metabolism via the TCA-cycle. To test this model, we sought to validate the Bat1-FLAG interactions using GFP-tagged TCA-cycle pyruvate dehydrogenase and aconitase alleles. First, we confirmed that Lat1-GFP, Pdb1-GFP, and Aco1-GFP were correctly localized to the mitochondria, and had wild type function with respect to growth on respiratory carbon sources (MATa) and Aco1  interact with each other. Consistent with Bat1 and Aco1 interacting or occurring in close proximity to each other (within a ~7 nM distance), we observed a robust fluorescent signal that co-localized with the mitochondria when diploid cells expressing both VC-Bat1 and Aco1-VN were grown in leucine-replete media . The signal was noticeably brighter than when control cells expressing either Aco1-VC or VC-Bat1 individually were visualized under identical conditions . Moreover, the signal was reduced following two hours of leucine starvation, further supporting that the Bat1-Aco1 interaction is dependent on leucine .We also employed a bimolecular fluorescence complementation (BiFC) approach , 43 to tbat1 bat2 strains. Samples were prepared from control, rapamycin-treated, leucine-starved, and leucine-, KIC-, glutamate- or d-KG-addition to leucine-starved cells. We found that in general, the levels of most amino acids detected were higher in the WT compared with bat1 bat2 mutant cells in most conditions examined, in particular upon rapamycin treatment, which blocks translation (S4 Table). Because bat1 bat2 mutation confers valine, leucine, and isoleucine auxotrophy, these amino acids were supplemented in the growth media; however, they are not metabolized in the bat1 bat2 strain and thus their levels were increased in this mutant compared to the WT under most conditions tested. Interestingly, levels of acetyl CoA and the organic acids pyruvate and citrate were higher in the WT and lower in the bat1 bat2 mutant. In contrast, succinate, fumarate, and malate were lower in the WT and higher in the bat1 bat2 mutant. These results are consistent with a model in which the lack of BCAT results in a block in TCA-cycle flow at the entry point of the pyruvate dehydrogenase product acetyl CoA and near the point of Aco1 action. In accord with this interpretation, leucine starvation resulted in acetyl CoA and citrate accumulation concomitant with decreased levels of all of the other TCA-cycle intermediates in the WT strain. Therefore, BCATs are required to maintain metabolite homeostasis and TCA-cycle flux; likely, at least in part through their BCAA biosynthetic role, and potentially via interactions that affect activity of TCA-cycle enzymes, such as Aco1.To determine whether Bat1 interaction with TCA-cycle enzymes is physiologically relevant, we analyzed the levels of TCA-cycle organic acid, amino acid, and acetyl CoA metabolites in the WT and pda1, pdb1, lat1, lpd1) or aconitase (aco1) on TORC1-related phenotypes. Compared with the WT, all mutants were rapamycin hypersensitive and had impaired recovery from rapamycin-induced growth arrest . Furthermore, the pda1 pdb1 mutant also had reduced TORC1 activity under all conditions tested; for example, Sch9 phosphorylation levels were reduced to 50% of WT in cells grown under control conditions . The aco1 mutant also had substantially reduced TORC1 activity (15–20% of WT levels) following leucine or KIC addition to leucine-starved cells. Mutation of ACO1 renders cells auxotrophic for glutamate [Fig 6C).We next tested if inhibition of TCA-cycle flux perturbs TORC1 signaling. First, we determined the effects of disruption of genes encoding Bat1-interacting pyruvate dehydrogenase subunits . TORC1 activity was then assessed for WT cells in which inhibitors were added during control-growth conditions and during leucine starvation 30 min prior to addition of leucine or KIC. Sodium (meta)arsenite and 2DG markedly reduced levels of TORC1 activity under all conditions tested . To determine if TCA-cycle flux controls TORC1 via increased flux through the electron transport chain, we also assessed the effect of electron transport chain inhibitors  on TORC1 activity. Antimycin A only inhibited growth in non-fermentable carbon sources (ethanol glycerol medium), while rotenone had little effect on growth , consistent with a rotenone-insensitive NADH:Q6 oxidoreductase identified previously in yeast [Fig 6D) indicating that flux through the TCA-cycle, but not the respiratory chain, controls TORC1 signaling under our experimental conditions.We also assessed the effect of inhibitors of glycolysis  and the TCA-cycle (sodium (meta)arsenite) on TORC1 activity. Both 2DG and sodium (meta)arsenite inhibited growth at the concentrations tested , addition of acetate, which increases cytosolic acetyl CoA levels [S1C Fig).We considered three hypotheses to explain the mechanism by which the integration between BCATs and TCA-cycle fluxes could influence TORC1 signaling. First, we reasoned that acetyl CoA could affect TORC1 activity by fueling the TCA-cycle. In mammalian cells, KIC can be converted to acetyl CoA via branched-chain ketoacid dehydrogenase complex (BCKDC) activity. However, no BCKDC enzymes or activity have been identified in yeast. Although acetyl CoA levels decreased upon leucine starvation . However, glutamine did not elevate TORC1 activity in either the pda1 pdb1  or bat1 bat2  mutants when supplemented in the same conditions. These results suggest that while glutamine provides a contribution, there must be additional inputs.Second, TCA-cycle fluxes may signal to TORC1 by affecting glutamine levels, which in turn activates TORC1 independently of the EGOC . Glutamibat1 bat2 mutants growing in leucine-replete conditions had reduced ATP levels compared with the WT . We tested if the observed perturbations in ATP levels were sufficient to foster activation of Snf1 phosphorylation at residue Thr210 by employing a phospho-specific antibody against mammalian AMPK Thr172 that effectively cross-reacts with phospho-Snf1 Thr210 [bat1 bat2 cells compared with the WT under all conditions tested in glucose-containing media  and a moderate elevation of Snf1 phosphorylation in the WT strain upon leucine starvation . Consistent with a TORC1-inhibitory role of Snf1, mutation of SNF1 led to rapamycin resistance compared with the WT . Conversely, mutation of the Glc7 protein phosphatase regulator REG1, which promotes Snf1 dephosphorylation [Fig 7C), confirming earlier reports [snf1 disruption partially suppressed the rapamycin sensitivity and recovery  and TORC1 activity defects  of the bat1 bat2 mutant strain. These results support a model whereby BCATs control TCA-cycle flux and thereby high ATP levels to promote robust TORC1 activity. BCAT disruption leads to an elevated ADP:ATP ratio, which in turn results in Snf1 activation and TORC1 inhibition .Third, reduced TCA-cycle flux should result in increased AMP:ATP or ADP:ATP ratios, which in turn trigger TORC1 inhibition by a cascade involving activation of the AMP-activated kinase (AMPK) Snf1  via Thr21 Thr210 . Interesrylation , resulte reports , 51. MorFig 2C), reported to act upstream of EGOC or Rag GTPases to signal leucine availability to TORC1 [The amino acid leucine is a potent inducer of both yeast and mammalian TORC1 activity, but the mechanisms by which regulation occurs are not fully understood. Here, we demonstrate that, similar to leucine, KIC is able to activate TORC1 via an EGOC-dependent mechanism. In addition leucine and KIC, in combination with BCAT, play novel EGOC-independent roles in activating TORC1 signaling. Consistent with an EGOC-dependent role, we provide evidence that KIC is sensed by the leucyl-tRNA synthetase (LeuRS) in a mechanism analogous to leucine and leucinol sensing . Strikingly, we find the interaction of Bat1 and Aco1 is disrupted by leucine starvation, and reestablished following either leucine or KIC readdition . Although the physiological effects of Bat1 interaction on Aco1 activity remain to be determined, we show that BCAT mutants have perturbed TCA-cycle intermediate levels consistent with a block in the TCA-cycle pathway at the step of pyruvate and acetyl-CoA incorporation. Taken together, we propose that BCATs may affect TORC1 activity by controlling TCA-cycle flux via physical interactions with Aco1 and possibly PDH, and by contributing metabolites to fuel the cycle.We present evidence for both enzymatic and structural roles for BCATs in controlling TORC1 activity. Mitochondrial Bat1 is preferentially expressed during the logarithmic phase of growth when energy is produced by glycolysis while cytosolic Bat2 is expressed during stationary growth phase when energy is obtained via respiration , 53. GivFig 5), catalyzes an analogous set of reactions as the BCKDC, except with pyruvate instead of α-keto acids as substrates [Multiprotein complexes consisting of mitochondrial BCAT (BCATm) and TCA-relevant enzymes, as reported here for yeast Bat1, have also been identified in mammals , 54. BCAbstrates . BCATm-pbstrates , 39, 54,bstrates . Interesbstrates , 57–59, Fig 7E). First, increasing α-ketoglutarate levels may sustain glutamine production, which in turn activates TORC1 activity in an EGOC-independent fashion [aco1 mutant , it failed to stimulate TORC1 activity in the bat1 bat2 mutant , suggesting another TORC1-inhibitory mechanism occurs in this strain. This BCAT-dependent mechanism likely involves a role for maintenance of ATP levels to sustain TORC1 activity. Previously, mTORC1 was proposed to act directly as an ATP sensor by virtue of its unusually high Km for ATP (>1 mM) [We propose two mechanisms via which Bat1 stimulation of TCA-cycle flux controls TORC1 activity independently of the EGOC   compared (>1 mM) , althoug (>1 mM) . A high  (>1 mM) –68. In m (>1 mM) . We find (>1 mM) –72. Rece (>1 mM) , 21. Upo (>1 mM) . Our res (>1 mM) , likely  (>1 mM) . HoweverAs in yeast, both the cytoplasmic BCATc and mitochondrial BCATm isoenzymes are present in mammals, differentially expressed, and control different aspects of leucine metabolism. BCATc expression is restricted to selected neurons but induced in regulatory T-cells, skin grafts, proliferating embryonic and cancer cells –78 and mMedia consisted of Yeast Extract Peptone Dextrose (YPD), Synthetic Complete (SC), Synthetic Dextrose (SD), Synthetic Ethanol Glycerol (SEG) or synthetic medium with amino acids and supplements omitted or added to complement auxotrophies or select for plasmid maintenance , 84. SynS1 Table. To construct strains employed in the BiFC assay, the N-terminal of Bat1 was fused with the N-terminal-half-Venus (VN) (under the control of the mid-strength CET1 promoter) in BY4741 (MATa), and the C-terminal of Aco1 was fused with the C-terminal-half-Venus in BY4742 (MATα). Following PCR-confirmation, strains were crossed to generate a diploid co-expressing both fusion proteins, or control diploid strains expressing either fusion individually.Strains used in this study are derived from BY4742 or BY4741  and are S2 and , respectively. To construct pPC02, the BAT1 gene was PCR-amplified from BY4742 genomic DNA using oligonucleotides that contained XbaI and BamHI sites (MPDC01 and MPDC02) and the XbaI/BamHI-digested PCR product was cloned into the XbaI/BamHI-digested vector, p416ADH. In an analogous fashion, BAT2, amplified using oligonucleotides MPDC13 and MPDC14, was cloned in p416ADH to produce plasmid pJK51. Construction of the BAT1K219R and BAT1K219A alleles was performed as follows using mutagenic oligonucleotides. The 5’ and 3’ ends of BAT1K219R were amplified using oligonucleotide pairs MPDC01+JK46 and MPDC02+JK44, and of BAT1K219A using MPDC01+JK47, MPDC02+JK45, respectively. Based on the complementarity of oligonucleotides JK44 with JK46, and JK45 with JK47, purified PCR products were used as templates in an overlap PCR reaction (oligonucleotides MPDC01+MPDC02). The final overlap BAT1K219R and BAT1K219A-containing PCR products were XbaI/BamHI-digested and cloned into XbaI/BamHI-digested p416ADH to yield pJK12 and pJK15, respectively. Similarly, the 5’ and 3’ ends of the BAT2K202A and BAT2K202R alleles were amplified using oligonucleotide pairs MPDC13+JK372 and MPDC14+JK371, and MPDC13+JK370 and MPDC14+JK369, respectively. Fusion products amplified using oligonucleotides MPDC13+MPDC14 were cloned into p416ADH to create pJK48 (BAT2K202A) and pJK50 BAT2K202R. To construct the FLAG-tagged BAT1 and BAT1K219R plasmids pJK47 and pJK59, BAT1-FLAG and BAT1K219R-FLAG were PCR-amplified from genomic or pJK12 DNA using oligonucleotides MPDC01+JK376, and XbaI/BamHI-digested products were ligated into XbaI/BamHI-digested p416ADH. All plasmids were confirmed by restriction digest analysis and sequencing of the insert.Plasmids and oligonucleotides used in this study are listed in BiFC Venus signal images were taken using a standard fluorescein isothiocyanate filter with identical exposure settings for each strain and condition, and processed using identical levels of image contrast using ImageJ software. To visualize mitochondria, 100 nM MitoTracker Red CMXRos (Molecular Probes) was added to cells for the final hour of incubation. Twice-washed cells were imaged using a Zeiss Axioskop 2 Plus microscope and AxioVision 4.6 image acquisition software.Protein extract preparation, Sch9 phosphorylation, and FLAG coimmunoprecipitation assays were performed as described previously . WesternProtein mass spectrometry was performed by the Duke University Medical Center Proteomics and Metabolomics Core Facility, as follows. The eluents from FLAG-affinity immunoprecipitates were subjected to a 3 min SDS-PAGE separation on an Invitrogen NuPAGE 4–12% gel for desalting purposes and stained with colloidal Coomassie stain. Gel bands were excised and subjected to in-gel reduction with 5 mM dithiothreitol and alkylation with 10 mM iodoacetamide. Trypsin digestion  was allowed to proceed overnight at 37°C. Following peptide extraction, peptides were vacuum centrifuged to dryness and resuspended in 12 μl of 1% TFA/2% acetonitrile. LC/MS/MS was performed on 2 μl of each sample using a nanoAcquity UPLC system (Waters Corp) coupled to a Thermo QExactive Plus high-resolution accurate mass tandem mass spectrometer (Thermo) via a nanoelectrospray ionization source. Briefly, the sample was first trapped on a Symmetry C18 300 mm × 180 mm trapping column (5 μl/min at 99.9/0.1% v/v water/acetonitrile), after which the analytical separation was performed using a 1.7 μm Acquity BEH130 C18 75 mm × 250 mm column (Waters Corp) using a 90 min gradient of 5 to 40% acetonitrile with 0.1% formic acid at a flow rate of 400 nL/min with a column temperature of 55°C. Data collection on the QExactive Plus mass spectrometer was performed in a data-dependent acquisition (DDA) mode of acquisition with a r = 70,000 (at m/z 200) full MS scan from m/z 375–1600 with a target AGC value of 1e6 ions followed by 10 MS/MS scans at r-17,500 (at m/z 200) at a target AGC value of 5e4 ions. A 20s dynamic exclusion was employed to increase depth of coverage.Raw LC-MS/MS data files were processed in Proteome Discoverer and then submitted to independent Mascot searches (Matrix Science) employing the SwissProt database (Yeast taxonomy) containing both forward and reverse entries of each protein. Search tolerances were 5 ppm for precursor ions and 0.02 Da for product ions using trypsin specificity with up to two missed cleavages. Carbamidomethylation (+57.0214 Da on C) was set as a fixed modification, whereas oxidation (+15.9949 Da on M) and deamidation (+0.98 Da on NQ) were dynamic modifications. All searched spectra were imported into Scaffold  and scoring thresholds were set to achieve a protein false discovery rate of 1.0% using the PeptideProphet algorithm.600nm units of cells grown to OD600nm~0.8 in SD+ile+leu+val+gln, and subjected to various treatments. Cells were filtered on 1.2 μm nitrocellulose filters (Millipore), washed twice with 5 ml sterile dH20, resuspended in 1 ml ice-cold methanol, and then pellets were dried on a Speed-vac at room temperature. Pellets were resuspended in ice-cold methanol or 50% acetonitrile/0.3% formic acid for analysis of amino and organic acids, respectively. ATP and acetyl CoA were extracted from 8 OD600nm units of cells as described previously [Amino acids and organic acids were extracted from 8 ODeviously , with moeviously , and pelMetabolomic analyses were performed by the Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, as follows. Amino acids, organic acids, and acetyl CoA were analyzed using stable isotope dilution techniques. Amino acid measurements were made by flow injection tandem mass spectrometry using sample preparation methods described previously , 90. TheATP concentration was determined using the ATP Colorimetric/Fluorometric Assay Kit (Sigma) from extracts resuspended in 100 μl ATP Extract Buffer, as recommended by the manufacturer.S1 Fig(a) Sch9 phosphorylation was determined in triplicate for strains that were cultured in SC+gln with treatments and methodology described in Fig 1. (b) A partial depiction of the leucine biosynthetic pathway is shown. (c) Acetate addition does not stimulate TORC1 activity of the WT following leucine starvation. Sch9 phosphorylation was determined in triplicate as in Fig 1 for strains grown in SC-his-ura-lys+gln, rapa treatment, leucine starvation and readdition of leucine or potassium acetate (in the absence of leucine) at the concentrations indicated for 1 hr following 2 hr leucine starvation.(TIF)Click here for additional data file.S2 Figa) Mitochondrial localization of GFP-tagged Aco1, Pdb1, and Lat1 was visualized microscopically following growth of strains in SC medium to which 100 nM MitoTracker Red CMXRos (Molecular Probes) was added for the final hour of incubation. Cells were washed twice and imaged using a Zeiss Axioskop 2 Plus microscope and AxioVision 4.6 image acquisition software. (b) Five-fold serially diluted cultures were plated onto SC and S-ethanol glycerol media and incubated for the times indicated. (c) Cell lysates from strains expressing ACO1-GFP, LAT1-GFP, PDB1-GFP, and/or BAT1-FLAG were prepared and subjected to western blot analysis using anti-FLAG (for Bat1) and anti-GFP  antibodies.((TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableAverage metabolite levels from cell extracts prepared from cultures grown in SD+ile leu val gln (T0 control), rapamycin treatment , 2 hr leucine starvation (-Leu), and addition for 1 hr following leucine starvation of leucine, glutamine, or d-KG, as indicated. Extracts were prepared in triplicate for amino acids and organic acids, and duplicate for acetyl CoA.(DOCX)Click here for additional data file.