Caldicellulosiruptor bescii to produce bio-based chemicals from plant biomass. The model utilizes subsystems-based genome annotation, targeted reconstruction of carbohydrate utilization pathways, and biochemical and physiological experimental validations. Specifically, carbohydrate transport and utilization pathways involving 160 genes and their corresponding functions were incorporated, representing the utilization of C5/C6 monosaccharides, disaccharides, and polysaccharides such as cellulose and xylan. To illustrate its utility, the model predicted that optimal production from biomass-based sugars of the model product, ethanol, was driven by ATP production, redox balancing, and proton translocation, mediated through the interplay of an ATP synthase, a membrane-bound hydrogenase, a bifurcating hydrogenase, and a bifurcating NAD- and NADP-dependent oxidoreductase. These mechanistic insights guided the design and optimization of new engineering strategies for product optimization, which were subsequently tested in the C. bescii model, showing a nearly 2-fold increase in ethanol yields. The C. bescii model provides a useful platform for investigating the potential redox controls that mediate the carbon and energy flows in metabolism and sets the stage for future design of engineering strategies aiming at optimizing the production of ethanol and other bio-based chemicals.Metabolic modeling was used to examine potential bottlenecks that could be encountered for metabolic engineering of the cellulolytic extreme thermophile IMPORTANCE The extremely thermophilic cellulolytic bacterium, Caldicellulosiruptor bescii, degrades plant biomass at high temperatures without any pretreatments and can serve as a strategic platform for industrial applications. The metabolic engineering of C. bescii, however, faces potential bottlenecks in bio-based chemical productions. By simulating the optimal ethanol production, a complex interplay between redox balancing and the carbon and energy flow was revealed using a C. bescii genome-scale metabolic model. New engineering strategies were designed based on an improved mechanistic understanding of the C. bescii metabolism, and the new designs were modeled under different genetic backgrounds to identify optimal strategies. The C. bescii model provided useful insights into the metabolic controls of this organism thereby opening up prospects for optimizing production of a wide range of bio-based chemicals. Global population growth and increasing industrial development have generated a greater demand for energy and resources . HoweverCaldicellulosiruptor bescii is a promising platform microorganism for converting lignocellulose to bio-based chemicals, based on its ability to degrade nonedible plant biomass, both cellulose and hemicellulose, without any enzymatic or chemical pretreatments  of 78 to 80°C and a maximum growth temperature (Tmax) of 90°C. C. bescii utilizes a wide range of simple and complex carbohydrates, both C5- and C6-based  knockout strain, MACB1034, is constructed with the pyrE mutant, providing a stable genetic background, and is used as the parent strain for the ethanol production (Clostridium thermocellum (Topt = 55 to 60°C) was expressed heterogeneously in C. bescii, enabling the engineered C. bescii strain to produce ethanol from crystalline cellulose, albeit at a suboptimal growth temperature of 60°C (Pyrococcus furiosus (Topt = 100°C) and alcohol dehydrogenase (AdhA) from Thermoanaerobacter sp. strain X514 (Topt = 72°C), enabled C. bescii to produce ethanol from crystalline cellulose optimally at 75°C  due to the better compatibility of thermophiles with industrial processing conditions.Genome-scale models (GEMs) provide a systems-level view of the metabolism and are frequently applied to optimize the production of bio-based chemicals in engineered microorganisms. This approach has been used to model thermophilic bacteria that are capable of degrading cellulose or hemicellulose, such as  to 60°C , 18) andt = 70°C ), as welolyticum , which d to 90°C  and, mor to 90°C . This is to 90°C –24. The C. bescii have revealed several unique features in the central carbon and redox metabolism of this organism. Its central carbon metabolism is characterized by the presence of a glyceraldehyde-3-phosphate ferredoxin oxidoreductase (GOR), which uses ferredoxin as an electron carrier for oxidizing glyceraldehyde-3-phosphate (GAP) to 3-phosphoglycerate. The GOR pathway functions in parallel to the canonical GAP dehydrogenase (GAPDH), which uses NAD as the electron carrier (C. bescii metabolism is the fate of the reduced ferredoxin (Fdred) produced by GOR and pyruvate ferredoxin oxidoreductase (POR), since Fdred can be oxidized by three redox related enzyme complexes (red oxidation to H2 production (2ase) that reversibly couples H2 production to energy-yielding Fdred oxidation and energy-requiring NADH oxidation  from Thermoanaerobacter sp. strain X514 (Topt = 60°C). It was hypothesized that this would use Fdred produced from carbohydrate oxidation to increase the production of NADH for NADH-dependent ethanol production; however, the introduction of Rnf_Na had only a marginal effect in enhancing ethanol yields  and complex  carbohydrates. The model was then used to identify metabolic bottlenecks and predict novel engineering strategies for the optimization of bio-based chemicals by resolving constraints in redox balancing.Further enhancement of ethanol production in C. bescii, GEM-iCbes, contains 610 genes, 718 metabolites, and 714 metabolic reactions  and C6 , disaccharides , as well as a variety of plant polysaccharides, including both cellulose and hemicellulose (xylan). Functional roles of extracellular enzymes that contribute to polysaccharide degradation, including 15 glycosyl hydrolases (GHs) and three polysaccharide lyases (PLs), were manually curated and incorporated into the model. The extracellular GHs are responsible for the degradation of cellulose, xylan, mannans, and starch, while the PLs are required for pectin degradation. Uptake of oligo- and monosaccharides involves 18 ABC superfamily transporters encoded by 65 genes. The reconstructed carbohydrate catabolic pathways involve at least 77 presumably cytoplasmic enzymes, including 37 GHs. Specific transcriptional regulators controlling these carbohydrate utilization pathways were excluded from the model; their transcriptional regulons, including DNA-binding sites and carbohydrate utilization genes, and operons are presented in the accompanying paper  and the lactate dehydrogenase knockout strain MACB1034 (Δldh)    as the sole carbon source, using experimental data obtained from the WT strain of C. bescii  no-ethanol, where the ethanol production flux was constrained to zero; (ii) half-maximum, where the ethanol production flux was constrained to 50% of the maximum; and (iii) maximum, where the ethanol production flux was constrained to the maximum of model predictions. Model simulations were performed with the E1M strain  a core-essential set, representing reactions required in all minimal networks of a simulation condition; (ii) a conditionally essential set, representing reactions required in some but not all minimal networks; and (iii) a nonessential set, representing reactions not used in any minimal networks. Convergence analysis of the minimal networks was performed under the no-ethanol, half-maximum, and maximum-ethanol simulations .The minimal metabolism simulations were performed using a modified DSMZ 516 medium with up to 11.8 g/liter cellulose as the sole carbon source, which corresponds to the experimentally measured consumption of cellulose in the E1 strain . Up to 110.1128/mSystems.01351-20.3DATA SET S3C. bescii. Download Data Set S3, XLSX file, 0.4 MB.Model configuration and results summary of the minimal network analysis, which was used for the identification of essential functions for the optimization of ethanol production in Copyright © 2021 Zhang et al.2021Zhang et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2, along with the production of ethanol  no-ethanol, (B) half-maximum, (C) maximum. Download FIG S3, TIF file, 0.8 MB.Convergence analysis of 1,000 minimal network simulations in the Copyright © 2021 Zhang et al.2021Zhang et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.01351-20.10TABLE S1Table S1, PDF file, 0.02 MB.List of abbreviations and full names for the enzymes and metabolites included in Copyright © 2021 Zhang et al.2021Zhang et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 and the generation of other products, such as pyruvate, lactate, and acetoin (1F0 ATP synthase (ATPSYN) and the substrate-level phosphorylation pathway composed of phosphotransacetylase (PTA) and acetate kinase (AckA), were also classified as core-essential when ethanol production approaches the maximum. While the ATPSYN was required for the production of ATP, the substrate-level phosphorylation was mainly used for carbon overflow through the trace production of acetate. Finally, the redox reactions, involving MBH, BF-H2ase, and BF-Nfn, were also required as core-essential functions for maximizing the production of ethanol. According to model predictions, under maximum ethanol production, MBH carried positive metabolic fluxes that couple the oxidation of Fdred with the production of H2. Also, BF-H2ase carried negative metabolic fluxes that take the H2 produced from MBH into the generation of the reduced cofactors NADH and Fdred, while BF-Nfn carries positive fluxes that facilitate the production of NADPH using reducing power from NADH and Fdred  in vitro , where ethanol production was optimized in the two BF-H2ase settings, followed by the simulation of flux variability for every reaction in the C. bescii model   activity, which reversibly converts malate to oxaloacetate coupled to the reduction of NAD to NADH  . In C. bNADH see . Therefotion see , the abs2ase is constrained to the H2-producing direction, a 17% reduction in ethanol yield (from 129 to 107 mM) was observed when the acetate production was blocked, while no influence was observed in the PCK mutant  to the reduction of NAD; this highlights the significance of both ATP production and redox balance in enabling the maximum ethanol production of C. bescii. Further, the deletion of all three redox functions resulted in zero fluxes in both biomass yield and ethanol production, indicating the triple deletion of redox functions would be lethal  Simulation of the E1M strain with BF-H2ase constrained to the H2-producing direction. (C) Simulation following the condition of (B), with the addition of Mrp to the model. (D) Simulation following the condition of (B), with the Rnf_Na replaced by Rnf_H. (E) and (F), Simulation following the condition of (B), with Rnf_Na replaced by the SH1 and SH2 soluble hydrogenases, respectively. Download FIG S4, PDF file, 1.5 MB.Variability of metabolic fluxes with ethanol production varying from zero to the predicted maximum at 100 steps. Black (blue) lines with numbers indicate the upper (lower) bounds of fluxes carried out by specific reactions in the designated model. Areas in gray indicate potential flux ranges of the reactions. (A) Simulation of the E1M strain with BF-HCopyright © 2021 Zhang et al.2021Zhang et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C. bescii, E1 (MACB1062) to a modeled E1M strain  was inserted to the E1M strain to help establish a sodium gradient that can be used to drive the production of NADH by Rnf_Na  or NADPH-producing (SH1) was introduced in place of the Rnf_Na in E1M. The SH2 and SH1 hydrogenases have been reported to consume H2 under physiological conditions in hyperthermophilic archaea  of C. bescii into the production of reducing equivalents for the AdhE mediated ethanol production  and 43% (SH2), and the deletion of MBH and BF-H2ase on the genetic background of pyrE and ldh deletions resulted in a 66% reduction in ethanol yield over the engineered SH2 and SH1 designs  that mediate the interconversion of Fdred, NADH, and NADPH. Interestingly, the model indicated that the coupling of MBH and the H2-consuming direction of BF-H2ase is an efficient mechanism for producing NADH, which is required for ethanol synthesis using AdhE. In such a coupling, MBH drives the production of H2 while establishing a proton gradient that can be used by ATPSYN for the production of ATP, and BF-H2ase utilizes the H2 produced by MBH to drive the production of NADH along with a partial recovery of Fdred  introduce a membrane bound sodium/proton antiporter, Mrp, to establish the sodium ion gradient required for Rnf_Na function  and Thermococcus onnurineus (Tmax = 90°C)  , 39. TheC. bescii model through the introduction of soluble hydrogenases, SH1 or SH2 , proton pumping , and redox processes  were highlighted that underlie the mechanisms for ethanol yield optimization. The mechanistic understanding acquired from modeling was also applied for the testing of new engineering designs that can be implemented by future experimental studies. The C. bescii model, as well as the computational procedures described in this study, will provide a solid foundation for guiding future engineering designs for the production of other desirable bioproducts.In summary, a genome-scale metabolic model of C. bescii WT and Δldh strains were grown under anaerobic conditions with 80:20 (vol/vol) nitrogen-carbon dioxide headspace at 75°C with shaking at 150 rpm. Monosaccharide model validation experiments were conducted in sealed 100 ml serum bottles with 50 ml of modified DSM 516 media, termed DG25 or DF25  nitrogen-carbon dioxide at 1.5 liters/h and agitation by dual Rushton stack impellers at 150 rpm. The same modified medium was used with substrate loading (fructose) increased to 100 mM to avoid carbon-limiting growth. Media were prepared at pH 7.2 (room temperature) and allowed to acidify to pH 6.2 (measured at 75°C) during growth, whereupon the pH was maintained at 6.2 by the automated addition of 10% (wt/vol) sodium bicarbonate solution.DF25 see . Cells fDF25 see  with theg for 10 min (Beckman Avanti J-30I JLA 10.500 rotor). Cell pellets were flash frozen with liquid nitrogen and stored at –80°C. The remaining steps of extract preparation were conducted anaerobically in a Coy chamber. Cell pellets were thawed and resuspended in 25 mM morpholinepropanesulfonic acid (pH 7.0), containing 1 mg/ml lysozyme at a ratio of 3 ml/g (wet weight) cells. Resuspended cells were incubated at room temperature for 10 min, followed by three 10-s rounds of sonication . For whole-cell extracts, lysates were washed with excess buffer over a regenerated cellulose centrifugation filter  to remove small molecules including redox cofactors. For cytosolic extracts, lysates were clarified by ultracentrifugation at 100,000 × g for 1 h (Beckman L90K ultracentrifuge 70.1Ti rotor), and then clarified lysates (S100) were also washed as described above. Both whole-cell and cytosolic extracts were sealed anaerobically and stored at –80°C until use.For preparation of whole-cell extracts, 1-liter cultures were grown with 25 mM glucose as described above, to late exponential phase, and harvested by centrifugation at 6,000 × 18 or Bio-Rad Aminex HPX-87H columns). NAD(P)(H)-dependent activities were measured by changes in absorbance at 340 nm (ɛ = 6.22/mM · cm), and methyl viologen (MV)-dependent activities were measured at 600 nm (ɛ = 13.70/mM · cm).Unless otherwise specified, all enzyme assays were performed at 75°C using the S100 fraction in either 3-ml cuvettes (2 ml reaction volume) sealed by silicone stoppers on an Agilent Technologies Cary 100 UV-Vis spectrophotometer with an Agilent Cary series temperature controller or by high-performance liquid chromatography  in total volume of 2 ml. Pyruvate phosphate dikinase (PPDK) assays mimicked PK assays, with AMP and GMP replacing ADP and GDP (no activity observed with GMP). Phosphoglycerate kinase (PGK) was measured discontinuously in the reverse direction with 3-phosphoglycerate and ATP or GTP, measuring nucleotide hydrolysis by HPLC, with minor modifications to the described protocol , in a total volume of 1 ml. After incubation, 0.1 ml of the reaction mixture was removed from the reaction vial at 1-min intervals up to 5 min, cooled on ice to halt the reaction, and injected into the HPLC (hydrosphere C18 column). Nucleoside monophosphate kinase (NMPK) and nucleoside diphosphate kinase (NDPK) were measured discontinuously by HPLC (hydrosphere C18 column) by measuring the appearance and disappearance of ATP, ADP, AMP, GTP, GDP, and GMP after incubation for 5 min at 70°C with cell extracts and MgCl2 in 50 mM phosphate buffer (pH 7) (Pyruvate oxidoreductase (POR), indole-3-pyruvate oxidoreductase (IOR), 2-ketoisovalerate oxidoreductase (VOR), and 2-ketoglutarate oxidoreductase (KGOR) assays were performed as described previously using 1 mM MV, 5 mM pyruvate, 2.5 mM indole-3-pyruvate, 5 mM 2-ketoisovalerate, and 5 mM 2-ketoglutarate substrates, respectively . Malate protocol . Brieflyr (pH 7) , 46.Δldh strain instead of the WT. Malic enzyme (ME) was assayed in both forward and reverse directions as described previously, with both NADH and NADPH . Manual curations were performed by combining homology searches to public databases, such as KEGG  and the lactate dehydrogenase knockout mutant strain MACB1034 (Δldh)  as the sole carbon source, and the WT strain was simulated to examine the growth of C. bescii using cellulose  were calibrated based on the ratios of their relative concentrations, and the simulation of growth using cellulose was performed by optimizing the biomass yield while calibrating the production of carbon products  based on experimental data (see C. bescii (Two strains of 4 (Δldh)  , 50% , and 99.99% (maximum) of the predicted maximum. The selection of 99.99% as a threshold for the maximum ethanol simulation was to account for potential uncertainties in solving linear programming models using the CPLEX optimizer. A convergence test was performed for each set of simulations to confirm that the classification of core-essential, conditionally essential, and nonessential reactions were stabilized after 1,000 random simulations (see fva function for different engineering designs while setting the BF-H2ase as either unconstrained or constrained to the H2-producing condition. For each simulation 100 steps were taken by varying the ethanol production constraints from zero to the maximum, while the flux variability was examined for the key metabolic reactions in the C. bescii model (see A modified DSMZ 516 medium , 14 was ions see . A detaiions see . Flux raodel see .


Ascocoryne sarcoides, was quantified using genome-enabled, stoichiometric modeling. In silico analysis identified available routes to produce these hydrocarbons, including both anabolic- and catabolic-associated strategies, and determined correlations between the type and size of the hydrocarbons and culturing conditions. The analysis quantified the limits of the wild-type metabolic network to produce functionalized hydrocarbons from cellulose-based substrates and identified metabolic engineering targets, including cellobiose phosphorylase (CP) and cytosolic pyruvate dehydrogenase complex (PDHcyt). CP and PDHcyt activity increased the theoretical production limits under anoxic conditions where less energy was extracted from the substrate. The incorporation of both engineering targets resulted in near-complete conservation of substrate electrons in functionalized hydrocarbons. The in silico framework was integrated with in vitro fungal batch growth experiments to support O2 limitation and functionalized hydrocarbon production predictions. The metabolic reconstruction of this endophytic filamentous fungus describes pathways for both specific and general production strategies of 161 functionalized hydrocarbons applicable to many eukaryotic hosts.Functionalized hydrocarbons have various ecological and industrial uses, from signaling molecules and antifungal/antibacterial agents to fuels and specialty chemicals. The potential to produce functionalized hydrocarbons using the cellulolytic, endophytic fungus,  Ascocoryne sarcoides is an endophytic fungus isolated from the Eucryphia cordifolia tree in Chilean Northern Patagonia and has been studied for its capacity to produce functionalized hydrocarbons (FHs) [A. sarcoides produces a variety of FHs hypothesized to play a role in the symbiotic relationship with its host [A. sarcoides also have uses as renewable chemicals, such as biofuels and flavor compounds, made from low-cost substrates, including cellulose and other agricultural wastes [A. sarcoides a candidate for bioprocessing [A. sarcoides has been studied in vitro at the organism level, but there have been minimal molecular-level pathway examinations and no in silico reconstructions of its metabolism [ns (FHs) ,3,4,5,6.ns (FHs) . A. sarcits host ,9. FHs pl wastes ,11,12. Tocessing . The pro8 to C10 FHs, such as 1-octen-3-ol, through the oxidation of double bonds and subsequent redox reactions [4 to C10 FHs through the deamination and decarboxylation of amino acids or their derivatives, including 1-methyl-3-butanol [Production of FHs has been shown to occur via four primary metabolic routes in eukaryotes: traditional fermentation, fatty acid production, linoleic acid degradation, and Ehrlich pathways. Traditional fermentation pathways produce low molecular weight byproducts from central metabolisms, such as ethanol and acetate. Fatty acid biosynthetic machinery can produce many FHs as a function of the initiating molecule, the number of elongation cycles, and endpoint of the final cycle. Examples include octane, octanol, and hexanoic acid . Linolei-butanol . Additio-butanol ,16,17. AAcetyl-CoA is a building block for many FHs and their precursors, such as lipids and some amino acids . The proStoichiometric modeling is a computational systems biology approach that can identify ecologically relevant phenotypes and gene targets for optimizing metabolic network function. It does so by accounting for critical central metabolism properties, including enzyme compartmentalization and cellular energy costs for metabolite transport ,25,26,27Fungal endophytes are an important natural component of plant microbiomes and potential consolidated bioprocess catalysts. The presented work is one of the first metabolic reconstructions of an endophytic fungus, with the first study examining production of therapeutic resveratrol , and mapA. sarcoides NRRL 50072 metabolic model was constructed from the genome annotations and transcriptome data available at http://asco.gersteinlab.org (accessed on 30 April 2021) [Thielavia terrestris as a representative filamentous fungus [A. sarcoides NRRL 50072 metabolite studies [2 was required to produce essential fatty acids and, therefore, biomass.An il 2021)  and conss fungus . Protein studies . O2 was The stoichiometric model was compiled as described in the following section using Microsoft EXCEL and converted to appropriate formats using CellNetAnalyzer version 2014.1 ,39. FluxA. sarcoides were influenced by oligosaccharide chain length and acetyl-CoA transport routes. A. sarcoides can utilize cellulose as a carbon substrate [A. sarcoides genome, and native acyl-carnitine transport is not expected to be a major route for acetyl-CoA transport from mitochondria to the cytosol [The nutrient requirements to produce cellular energy and functionalized hydrocarbons (FHs) in ubstrate , which hubstrate . The imp cytosol ,23.A. sarcoides (2 and C3 monomers), and stage of the final reduction cycle. These variations in components and pathways have a combinatorial effect [A. sarcoides  as defined by the Cmoles of carbon source and moles of O2, respectively, consumed to produce a mole of cellular energy . EFMs that minimized carbon and O2 required to produce cellular energy formed a trade-off surface  with chain lengths from C3 to C20 were modeled that represent most FHs reported for A. sarcoides , such as isopentyl derivatives, retained less carbon source carbon  were governed under oxic conditions by the FH degree of reduction. Aromatic Ehrlich pathway derivatives, such as indole-3-acetate and 4-hydroxyphenylacetate, required the least carbon source of any considered FHs under oxic conditions .Simulated production of Ehrlich pathway derivatives indicated an increase in Cmoles of substrate requirements as Ol groups . Aromati2 availability required coproduction of other byproducts, such as ethanol and acetate, to produce the cellular energy required for their production and therefore required more carbon source  the incorporation of a cellobiose phosphorylase (CP) and (2) the incorporation of cytoplasmic pyruvate dehydrogenase (PDHcyt).In silico models provide a framework for predicting network modifications to enhance catalytic function . Two enz2 for all FHs, except for 2-methyl-propanoate and 2-methyl-butanoate, which did not require a net input of energy to produce  glucose bond via sugar phosphorylation using inorganic phosphate. The energy conservation alleviated the cost of activating n-1 glucose molecules using ATP, where n is the number of monomers in the oligosaccharide being depolymerized. CP activity decreased the predicted requirements for carbon source and O produce . This ef produce . The Cmocyt activity was hypothesized to uncouple cytosolic acetyl-CoA production from the energetic transport requirements and provide cytosolic reducing equivalents that increased the yield of fatty acid-derived FHs  Ehrlich pathway-derived FHs are generally more efficient at retaining substrate electrons and Cmoles than fatty acid-derived FHs. However, Ehrlich pathway derivatives were generally less reduced compounds. (2) Aromatic, Ehrlich pathway derived FHs required the least amount of carbon source under aerobic conditions of any FH considered in the study. (3) Contrary to the production of small primary fermentation products, including acetate, ethanol, and lactate, O2 limitation decreased the theoretical yield of FHs due to energy limitations. (4) FHs produced from isopentyl derivatives were less sensitive to O2 limitation than aromatic derivatives.Functionalized hydrocarbons (FHs) produced by the endophytic fungi A. sarcoides (Fatty acid production can account for the most commonly reported FHs from arcoides . The precyt) was predicted to reduce the carbon source requirements of FH production to the theoretical limits. The stoichiometric analysis predicted the addition of CP and PDHcyt activities to FH producing cells could increase FH yields up to 38% on the carbon source (cyt activity reduced the energetic requirement for cytosolic acetyl-CoA, increasing the yield of FHs. The combined impact of these modifications reduced the requirement for carbon sources to produce fatty acid derivatives to 1.5 Cmoles carbon source per Cmole of FH. These modifications and others, such as disrupting acetyl-CoA partitioning between the mitochondria and cytosol, have increased product yields and rates in other genetically modified organisms [Genetic modifications were examined in silico that impacted energy conservation and the transport and compartmentalization of acetyl-CoA. Modification of enzymatic activity (i.e., CP and PDHn source . CP actirganisms ,46.A. sarcoides NRRL 50072 cultures were performed under different O2 partial pressures to further interpret in silico results (2 availability (2 partial pressure decreased, secretion of the primary fermentation products, acetate and ethanol, increased to balance cellular reducing equivalents and cellular energy production. In vitro changes in A. sarcoides growth and excreted products suggest that the transition to fermentative metabolism occurred when the headspace was less than ~10% O2 (2 partial pressures, but the diversity of secreted FHs increased (4-fold lower than theoretical limits, which indicated production was not for catabolic purposes but rather served other biological functions, like perhaps, communication molecules. Feedback mechanisms in metabolism and physiology, such O2 concentration and carbon source, may control the production of specific FHs in vivo [2 partial pressure [2 partial pressure, a common signaling molecule for hyphal growth, 1,3-octadiene, was observed. In contrast, more reduced biomass derivatives, such as Ehrlich pathway derivatives, were observed with decreased O2 partial pressure (In vitro analysis of  results . The in lability . As the  ~10% O2 . The expncreased . All in  in vivo ,48. Prevpressure . This mapressure , suggest2 to produce each FH. The simulations also provide insights into natural conditions that would favor their production and provide targets for metabolic engineering of eukaryotic hosts to increase FH production efficiency.Biological FH synthesis is metabolically costly, yet it is observed in many microorganisms spanning many ecosystems. The current study presents the most extensive stoichiometric analysis of FHs produced by either an endophytic or filamentous fungus. The results provide metabolic blueprints for producing 161 different FHs, quantifying the metabolic costs in terms of cellulose-based carbon source and O


Trypanosoma brucei is a unicellular eukaryote that causes human African trypanosomiasis, also known as sleeping sickness  and glycerol kinase [GK]) for the same substrate (ATP) . When glversus carbon catabolite repression mechanism is mainly an immediate switch to the less preferred carbon source when the preferred one is exhausted. Theoretically, a metabolic contest could be found in any organism provided that 3 conditions are met, i.e., (i) the sequestration of both metabolic pathways in the same subcellular compartment; (ii) the competition for the same substrate between 2 enzymes, one in each pathway; and (iii) an unbalanced activity between the competing enzymes.Metabolic contest is a new concept to describe metabolic choices that resembles the well-characterised catabolic repression or carbon catabolite repression observed in prokaryotes, yeasts, and fungi, albeit based on a completely different molecular mechanism. The mechanisms by which carbon catabolite repression is imposed are quite variable. They follow a general rule, however, with complex sensory systems relying mostly on protein kinases and phosphatases , which iin vivo in the fly is still an open question. The preference of glycerol over glucose is based on the huge overexpression of GK, which could be reduced by at least 90% without affecting the glycerol metabolic flux [T. brucei, GK is encoded by 5 duplicated GK genes, while the genome of almost all other trypanosomatids contains a single GK gene, suggesting that GK gene duplication was positively selected in T. brucei to allow the preference for glycerol. Although, one cannot exclude that the high GK level has other reasons (see next section).The physiological role of glycerol metabolism lic flux . Incidenlic flux . It is aGlycerol has also been shown in vitro to prevent differentiation of early PCF, which expressed the surface protein GPEET, to late PCF, which are GPEET negative . GPEET ii.e., the cytosolic and glycosomal localisation of phosphoglycerate kinase , respectively [T. brucei BSF, as well as in plant-infecting Phytomonas spp., remains the exception in the trypanosomatid world as most of the trypanosomatid parasitic forms deve-loped a “procyclic-like” form of glycolysis, i.e., relative low glycolytic flux and pyruvate being further metabolised in the glycosomes and the mitochondrion [The topology of the central carbon metabolic network is similar between PCF and BSF, with one main exception, ectively . Anotherectively ), with pectively  Fig 1C)i.e., theectively  Fig 1A)i.e., thehondrion ,20.T. brucei life cycle progression to PCF, including down-regulation of HK expression [Because of the intronless and polycistronic expression nature of trypanosome genes, differential expression of glycolytic enzymes should be controlled posttranscriptionally . No RNA-pression .in vitro, so far believed to be exclusively dependent on glucose for growth [in vivo development of BSF. It is to be noted that the low GK/HK activity ratio (2.2) provides a rational explanation for the absence of metabolic contest for ATP between GK and HK, resulting in concomitant consumption of glucose and glycerol when both are present  ,32 compat midgut . The ong


Here we test its applicability for EFM enumeration.Elementary flux mode (EFM) analysis is a well-established, yet computationally challenging approach to characterize metabolic networks. Standard algorithms require huge amounts of memory and lack scalability which limits their application to single servers and consequently limits a comprehensive analysis to medium-scale networks. Recently, Avis et al. developed mplrs—a parallel version of the lexicographic reverse search (lrs) algorithm, which, in principle, enables an EFM analysis on high-performance computing environments  and thus offers new possibilities for the unbiased analysis of substantially larger metabolic models via EFM analyses. EFMlrs is an open-source program that comes together with a designated workflow and can be easily installed via pip.The online version contains supplementary material available at 10.1186/s12859-021-04417-9. Arguably one of the most successful approaches in systems biotechnology and metabolic engineering are constraint-based methods (CBMs). These methods reconstruct  metabolic networks from genetic information and combine it with steady-state analysis to predict phenotypes from genotypes . The sucIn contrast, EFM analysis allows an unbiased characterization of a metabolic network, as it describes all feasible steady-state phenotypes in terms of elementary pathways, so-called EFMs, without the necessity of an optimality criterion . EFMs arHowever, this definition of EFMs allows only two homogeneous inequality constraints - the steady-state assumption and the sign restrictions on the rates of irreversible reactions. Thus, a more general definition that also allows the incorporation of other inhomogeneous linear constraints, such as upper and lower reaction bounds, was needed. In 2007 Urbanczik et al. presented such a concept for the first time and expanded the definition of EFMs by introducing elementary flux vectors (EFVs) . AlthougIn further course we use the definition as proposed by Klamt et al. in 2017. It is an equivalent but more general definition of EFMs that also includes EFVs, by specifying EFMs as convex-conformally non-decomposable pathways  in a metHowever, the enumeration of EFM/Vs is challenging, as the numbers of EFM/Vs grow combinatorially with the size of the metabolic network , which eMathematically, the enumeration of EFM/Vs in metabolic networks is a vertex enumeration problem in convex polyhedra. There are essentially two approaches to solve this problem: (i) reverse search , and . Thus, we can understand any flux distribution as a superposition of elementary metabolic units.Both, EFMs and EFVs can be computed with mplrs  using eiThe most commonly used method for the complete enumeration of EFMs in metabolic networks is the double description method (DDM).N and iteratively converts it to binary form. For each reaction (i.e. in each row of the matrix) every column with a negative value at this reaction is replaced by all possible combinations of pairs of columns such that their conic sum is zero  which for single core as well as for parallelized implementations, has to be available on one single server. Although, the required memory can be stored in an out-of-core memory instead as well, this approach is not computationally performative and was therefore disregarded. Hence, the RAM requirements of the DDM can easily exhaust even state-of-the-art systems [During the iteration phase many candidate EFMs (i.e. columns in the matrix) are constructed that are not present in the final list of EFMs. For instance, the intermediate EFMs mentclass2pt{minim systems . AdditioThrough parallelization and optimization techniques efmtool , 24 is omplrs  is an imsimple tetrahedron. A polyhedron is regarded as simple or non-degenerate if each vertex lies on exactly l hyper-planes and therefore has a unique base. For such polyhedra, lrs is very efficient as the resulting spanning forest has a single component such that each vertex is produced once [simple polyhedra, the run time only depends on the input data [Figure ced once . For theput data . Yet, thmaster, a consumer and multiple workers. The master process handles the input data and runs in a main loop that consists of distributing sub-problems to the workers and receiving unfinished sub-problems from them. The master process starts by choosing an initial worker which it sends the initial sub-problem to. This worker then sends unfinished sub-problems back to the master as soon as its budget is exhausted or the problem is solved. The budget defines the maximum number of nodes that can be visited by a worker. It is dynamically calculated by mplrs depending on the number of available threads and the current size of the sub-problem list. As soon as the initial worker sends back unfinished sub-problems, the master starts distributing these sub-problems to all workers available. Each worker receives one sub-problem and only solves this assigned task. Unsolved sub-problems are sent back to the master and solved ones are sent to the consumer process which collects all results received from the workers and builds the output stream [However, in 2017 Avis et al. presented an improved and parallel implementation of lrs —the mplrtrue minimum basis. Since this would increase memory requirements and negatively impact performance it was not included in the current version of mplrs. Hence, during EFV enumeration duplicates can occur [Duplicates are avoided as only solutions with a lexicographically minimum basis are printed. However, uniqueness can only be guaranteed when the polyhedron is a pointed cone with the origin as the only vertex an occur .real parallelization requires at least 4 processes, since two processes are always occupied by master and consumer. This way of balancing the load gives three main advantages: (i) workers can solve their assigned sub-problems independently, (ii) the required amount of RAM is distributed equally over a cluster and (iii) it’s possible to stop and later continue calculations from the last calculated result at any time [Regarding lrs’s load balancing strategy, it becomes clear that any time .These properties make mplrs  highly sThe main purpose of EFMlrs is the pre- and post-processing of metabolic models for enumeration of EFM/Vs on HPC clusters via mplrs . In addiCOBRApy, NumPy, SymPy and Pandas) are used for parsing sbml models, numerical computation and data processing. EFMlrs is part of the python package index and can therefore be easily installed via pip. The complete python code, further documentation as well as a tutorial are available on GitHub (http://github.com/BeeAnka/EFMlrs). Additionally, detailed documentation about the structure of the code and the functions used can be accessed on https://efmlrs.readthedocs.io.EFMlrs is an open-source program, implemented in python 3 under the GPLv3 license, supported on Linux and macOS. Common python libraries  per thread. Thus, total memory consumption scales linearly with the number of threads and is—in contrast to the efmtool —no longeH-representation. For use with efmtool, the input matrices needed to be transformed into a flux cone  mplrs was by far the fastest. The difference becomes most evident for the perm10 model. Here the DDM based tools needed a bit more than 3 h, while mplrs was able to solve it in less than a minute. Additionally, looking at the average amount of RAM needed for calculating one model, it shows that the fast performance of DDM-based methods is payed in memory. On average, polco needed 153.5 GB, efmtool 116.2 GB and mplrs 0.2 GB of RAM per model. Our results are well in line with conventional wisdom that mplrs is faster for non-degenerate models, whereas DDM-based methods, like efmtool or polco, perform better in highly degenerate cases [Figure te cases , but reqP, as in  cone . Hence, we got 22 sub-models. The smallest  that mplrs  and efmtVI-syn3A  will havminimal genome cell this astonishing number of EFMs can be found? To clarify this question, we made further efforts to analyze the biggest set of EFMs still process-able  opens the doors to HPC systems for EFM/V analysis via mplrs.EFMlrs is the first program that gives users the ability to enumerate EFM/Vs in metabolic models on HPC clusters via mplrs . It can Additional file 1: More detailed information on EFMlrs compression algorithms incl. detailed description, pseudo-code snippets and comparison of different geometric formulations.


Vitreoscilla haemoglobin (VHb) in different organisms are revised. The use of VHb to reduce overflow metabolism is proposed and studied through flux balance analysis in E. coli at a fixed maximum substrate and oxygen uptake rates. Simulations showed that the presence of VHb increases the growth rate, while decreasing acetate production, in line with the experimental measurements. Therefore, aerobic VHb expression is considered a potential tool to reduce overflow metabolism in cells.Overflow metabolism is a phenomenon extended in nature, ranging from microbial to cancer cells. Accumulation of overflow metabolites pose a challenge for large-scale bioprocesses. Yet, the causes of overflow metabolism are not fully clarified. In this work, the underlying mechanisms, reasons and consequences of overflow metabolism in different organisms have been summarized. The reported effect of aerobic expression of  This phenomenon was already observed for yeasts by Louis Pasteur in 1861 [Fast growing cells often display incomplete oxidation of the carbon source, even if oxygen is present in non-limiting amounts. As a result, partially oxidized molecules, rather than CO in 1861  and norm in 1861 . Traditi in 1861 .The biotechnological production of molecules requires the transformation of a substrate (most commonly glucose) into the desired molecule. Because these are autocatalytic processes, the amount of product that can be synthesized depends on the amount of cells in the culture. Therefore, a standard procedure to maximize the amount of synthesized product is to attain an elevated amount of cells in the culture. These so-called high cell-density cultures require elevated amounts of carbon source that lead to overflow metabolism.w/w) of the carbon source (glucose) consumed [Examples of overflow metabolites excreted by organisms of biotechnological relevance are shown in consumed .Diverse strategies have been applied to reduce overflow metabolism in different organisms, including genetic interventions  and the Sq) [Sq, the substrate can be fully oxidized via the tricarboxylic acid cycle (TCA) and NADH regenerated by the electron transport chain (Sq is accompanied by an increased oxygen consumption rate (Oq2). After a certain threshold value of glucose uptake , overflow metabolites start to accumulate. This coincides with a maximum rate of oxygen consumption . Above this respiratory capacity, Sq can continue to increase, with the concomitant synthesis of overflow metabolites  patterns in relation to Sq [A relatively straightforward explanation of overflow metabolism considers that a metabolic imbalance between catabolism and anabolism occurs at high glucose uptake rates (qS) . At low rt chain . Increasres, see ). NADH rres, see . Enzyme on to qS .Sq < S,critq in fed-batch cultures avoids overflow metabolism and allows attaining high cell-densities. This principle has been successfully applied to a variety of microbial and animal cells [Sq by genetic manipulation has also been a successful strategy to reduce overflow metabolism in E. coli [μ) is also affected. The activity of the tricarboxylic acid cycle (TCA) in E. coli was increased to better cope with the high glycolytic flux at elevated Sq, which decreased acetate formation [+ regeneration rate. For instance, Vemuri and co-workers expressed a water-forming heterologous dehydrogenase in E. coli and S. cerevisiae [Maintaining the cells growing at al cells . Decreas E. coli ,23,24,25 E. coli . Althougormation . Anotherrevisiae ,28. Desprevisiae  proposedThe approach of metabolic imbalance can partially explain the causes of overflow metabolism and inspire some genetic interventions to reduce it. However, other hypotheses have been proposed from different perspectives, as explained below.E. coli growing at different μ. They found that as μ increased, the abundance of proteins related to carbohydrate transport and metabolism lowered, while those related to translation increased. In order to quantitatively describe the effects on the proteome, the authors defined the investment of translational capacity as the “protein expression cost” (defined as the product of the protein concentration multiplied by its length in amino acids). The expression cost of the ATP synthase and NADH dehydrogenase I relative to the total proteome increased proportionally to μ and reached a plateau coincident with the shift to overflow metabolism. Although the authors did not clearly link these results with the control of overflow metabolism, they suggested that E. coli shifts to a more economic protein usage. Due to the high demand of protein resource for the respiratory chain , different mechanisms for energy production are preferred. This hypothesis was analysed in detail by Basan and co-workers using E. coli as a model organism [600nm/h for fermentation, while for respiration it was approx. 390 mM ATP/A600nm/h. Therefore, the authors proposed that overflow metabolism is a programmed global response of the cells to cope with the proteome demands for energy generation. Further modelling and flux balance analysis have been applied to predict overflow metabolism in E. coli [E. coli and S. cerevisiae coupled to proteomic analysis to successfully predict the start of overflow metabolism in relation to μ, Sq, or ATP rate. Interestingly, they found that the differences between energy yields of respiration and fermentation were much larger for E. coli than for S. cerevisiae. Proteome allocation coupled with dynamic flux balance analysis and adjustable maintenance energy level allowed good prediction of growth overflow metabolism and recombinant protein production of engineered E. coli strains [Peebo and co-workers  analysedorganism . The aut E. coli . Chen an E. coli  also mod strains .Lactococcus lactis [Clostridium ljungdahlii [E. coli [E. coli strains have a superior performance for plasmid DNA vaccines production in batch and fed-batch mode [The hypothesis of proteome allocation as the origin of overflow metabolism has also been evaluated for s lactis  and Closngdahlii . Proteom[E. coli . Proteomtch mode . HoweverE. coli including the constraint for the concentration of enzymes, named “FBA with Molecular Crowding” (FBAwMC) was introduced by Beg and co-workers [μ. Although overflow metabolism was also predicted, the model estimated a lesser excretion of acetate than the experimentally determined. FBAwMC was refined and combined with enzyme activity measurements to analyze metabolic shifts from low to high μ in E. coli [Sq values were estimated with good accuracy. According to their calculations, the mitochondria contribute 5 times more to molecular crowding than glycolytic enzymes and 50 times more than lactate dehydrogenase. Van Hoek and co-workers used FBAwMC to study the metabolism of L. lactis and S. cerevisiae yielding interesting results [It has been suggested that cells have evolved to maintain the enzyme-protein levels at the minimum level compatible with function. The volume occupied by proteins in the cell is 20–30% of the cell volume . This large proportion may limit the diffusion and solubility of molecules in the cell due to the viscosity of the remaining unbound water . Therefo-workers . This mo E. coli . Vazquez E. coli  applied  results . In agreE. coli was introduced as an additional constraint in FBA [Sq and oxygen availability was well simulated. However, the S,critq obtained did not agree with the experimental data. This could be probably due to the fact that other important molecules, such as ATPase were not considered. In a more comprehensive study, Szenk et al. [μ-dependent surface-to-volume ratio, as well as the physical size and of the proteins involved in respiration and fermentation, to evaluate whether pure respiration could be limited by membrane crowding at fast μ. According to their calculation, the surface efficiency (given in ATP/s/nm2) for aerobic respiration is three, while it reaches a value of 15 for acetate fermentation. Furthermore, they found that the percentage of the membrane occupied by the electron transport chain component increases with the growth rate, and plateau at a value of ca. 8%, coincident with the onset of overflow metabolism. Moreover, the authors linked the surface efficiency with the phosphate/oxygen  ratio. P/O ratio decreases when the surface efficiency increases (fermentation), and the growth rates at which overflow metabolism triggers are higher at lower P/O ratios.Important elements of the energy production cellular machinery are located in the membranes. Therefore, molecular crowding of the membrane can be a limiting factor to shift from purely aerobic to aerobic-fermentative energy production. For example, crowding of the cytochromes in the membrane of t in FBA . The welk et al.  integratVitreoscilla stercoraria (VHb) is a single domain haemoglobin that exists as a dimeric protein of two identical subunits with a mass of 15.7 Da each [+ transmembranally, instead of protons [offk = 78 mM−1 s−1) [Oq2 and μ and biomass formation , reaching 70% of the respiration rate of the wild type strain [+/O ratio, the transmembrane ΔpH, and the ATP content of VHb-expressing E. coli cells were 1.5, 1.6 and 2 times greater, respectively, than the corresponding values in non-expressing cells [E. coli cells displayed higher oxidative activity than non-expressing cells, as indicated by the Redox Sensor Green fluorescence [The haemoglobin of the aerobic bacteria  Da each . Initial protons . VHb disM−1 s−1) . In consiews see ,50,51,52e strain , and is e strain . The H+/ng cells . VHb-exprescence .E. coli. In contrast, Ramandeep and co-workers [E. coli, and that 57% of the VHb was localized within 0.1 μm of the inner membrane. Using immunofluorescence microscopy, Juarez and co-workers found that VHb is distributed in the cytoplasm and the membranes of organelles in CHO cells [E. coli [Halomonas bluephagenesis. This improved cell formation and poly (3-hydroxybutyrate) production under oxygen limitation [H. bluephagenesis, particularly cytochromes and beta subunit of the ATP synthase. Therefore, the presence of VHb affects respiratory efficiency not only by increasing oxygen transport, but also by increasing a higher amount of enzymes of the respiratory chain. This contributes to a better understanding of the enhanced respiratory capacity of cells expressing VHb.Cellular localization of VHb has also been investigated, due to its relationship with the respiration proteins. Khosla and Bailey , using f-workers , using iHO cells . Because[E. coli . Approximitation . MoreoveE. coli and CHO cells, In comparison with the relatively abundant information on the use of VHb for improving bioprocesses under oxygen limitation, the application of VHb for aerobic cultures is scarce. +/NADH ratio is key to the metabolic differences between the metabolic switches: redox balance is one of the factors leading to overflow metabolism [As explained before, overflow metabolism has been associated with fast growth, since under fast growth energy is generated via fermentation instead of respiration. In addition to bacteria and fungi many other organisms—mammalian cells, plants—use respiration at low glucose uptake rates and aerobic fermentation at high glucose uptake rates . It was tabolism ,44,74. Atabolism .E. coli and S. cerevisiae [Using a network-based approach a recent study suggested that there is an upper limit to the Gibbs energy release rate of revisiae . Due to E. coli it was suggested that overflow metabolism should be considered a reversible process and be universal including ethanol consumption by S. cerevisiae and lactate by mammalian cells [E. coli and found that the efficiency of ATP synthesis is the key driver.Through a kinetic model of an cells . Mori etan cells  combinedE. coli enhanced by 5-fold the content of the cytochrome bo and by 1.5 –fold the content of the cytochrome bd in cells of E. coli [+/e− = 2). In contrast, an H+/e− ratio of 1 is obtained by cytochrome bd [Fifteen models of overflow metabolism have been reviewed recently . In addi E. coli . Cytochrhrome bd . TherefoE. coli genome scale model [Here, to investigate the effect of P/O ratio on the overflow metabolism, flux balance analysis (FBA) was carried out using le model  in COBRAle model . Followile model , the P/OE. coli varies between 0.67 and 2.67 [E. coli, namely Sq = −11 mmol g−1h−1 [Oq2 = −8 mmol g−1h−1 values were implemented for wild type (Sq = −11 mmol g−1h−1, maximum O2 uptake rate of Oq2 = −15 mmol g−1h−1 [Sq values, 25% higher O2 uptake rates were set due to the experimental measurements [Oq2 was selected to cover from relatively low to maximum capacity of the cell. To see the effect of P/O ratio on overflow metabolism, runs for varying P/O were carried out. At low P/O ratio values, acetate production rate was higher, in agreement with the finding that decreased P/O ratios yielded in higher rate of acetate production [O2 is at its maximum value (COq2 decreases after P/O ratio of around 2.Theoretically P/O ratio in and 2.67 . For conl g−1h−1  and qO2 ild type . For thel g−1h−1  were useurements . The valoduction . In all oduction . When qOum value  in the pOq2, growth rate is maximized at stable µ below P/O ratio of 1. For P/O ratio > 1 maximum growth rate increases with increasing P/O ratio. bo-type and bd-type cytochrome oxidases replace each other for P/O ratio below 1 and above 1.Oq2 was around 20% higher [Oq2 (In the presence of VHb, it was observed that % higher  compared% higher  and evenher [qO2 . This imThese simulations may help to understand how VHb could contribute to reduce production of overflow metabolites. Considerable investigations are still needed to better understand the physiological changes caused by aerobic VHb expression. Nevertheless, the current available information point to VHb technology as a strong ally to improve aerobic bioprocesses.


Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens that can manipulate host cell environment for adaptation, evading immune defences, virulence, and pathogenesis of TB infection. Host-pathogen interaction is important to establish infection and it involves a complex set of processes. Metabolic cross talk between the host and pathogen is a facet of TB infection and has been an important topic of research where there is growing interest in developing therapies and drugs that target these interactions and metabolism of the pathogen in the host. Mtb scavenges multiple nutrient sources from the host and has adapted its metabolism to survive in the intracellular niche. Advancements in systems-based omic technologies have been successful to unravel host-pathogen interactions in TB. In this review we discuss the application and usefulness of omics in TB research that provides promising interventions for developing anti-TB therapies.Tuberculosis (TB) is a devastating infectious disease that kills over a million people every year. There is an increasing burden of multi drug resistance (MDR) and extensively drug resistance (XDR) TB. New and improved therapies are urgently needed to overcome the limitations of current treatment. The causative agent,  Mycobacterium tuberculosis (Mtb) and it kills over a million people every year. In 2016, WHO estimated around 1.7 billion people were reported to have latent TB infection (Tuberculosis (TB) remains a global pandemic and the biggest infectious killer despite being a preventable and curable disease . This confection . MDR andnfection . This ranfection , 5. Thesnfection . We needin vivo and in vitro TB models has enabled identification of the metabolic characteristics of Mtb within the intracellular niche. It is now well-established that Mtb uses multiple host metabolites such as lipids and amino acids as nutrient sources during infection  facilitates the genomic and transcriptomic research of mycobacteria. The genomic or transcriptomic analysis focuses on the variation of genomic features, such as gene expression level, DNA sequencing or regulatory elements annotation, which enables the identification of essential genetic or regulatory targets under certain conditions –18. Resiinst Mtb . Esterhuinst Mtb . A recenpatients .in vitro and within the host. Discovery proteomic analyses by Rosenkrands et al. (M. bovis BCG (Chicago strain). Chicago and Copenhagen BCG strains exhibited highly similar proteomes with only three identified variants while Mtb H37Rv and Erdman strains had 18 variants between them , nuclear magnetic resonance (NMR) and LC-MS are three most widely used analytical platforms for metabolomics. Depending on the purpose of the study, metabolites are analysed either by untargeted or unbiased methods/platforms, mainly used for qualitative analysis or by targeted analyses involving SRM or multiple reaction monitoring (MRM). Metabolism is at the heart of survival, growth, and virulence of Mtb, and other pathogens inside the human host cells. Metabolism of immune cells is equally important in driving a variety of cellular and immunological responses during infection and therefore, in recent years, immunometabolism has emerged as a rapidly growing area with the motive to develop new diagnostics and therapeutics for TB. The advancements in analytical tools and platforms have also allowed metabolomics and metabolic investigations at single cell and subcellular (cellular microcompartment) levels  levels , 55. In in vitro and in vivo animal models. Activity-based metabolomic profiling which uses cellular metabolome as the platform using protein and time-dependent production and consumption of small molecules, assigned functional and structural characteristics of Rv1692 as a glycerol 3-phosphate phosphatase in glycerophospholipid metabolism , lysophosphatidylinositol (18:0) and acylphosphati-dylinositolmannoside (Ac1PIM1) elevated in plasma of patients with active pulmonary TB . Another13C tracer studies have re-assigned identity and functionality of Mtb enzymes and elaborated its central carbon metabolic (CCM) pathways such as Rv1248c as 2-hydroxy-3-oxoadipate synthase catalysing C-C bond formation between α-ketoglutarate and glyoxylate , PEPCK, malic enzyme (MEZ), and PPDK in gluconeogenesis, propionate and cholesterol detoxification and lipid synthesis   . Agapovay (SIMS)  performen source . 15N-isoMetabolism in a biological system involves numerous complex biochemical processes. Each biochemical reaction involves consumption and production of metabolites or chemical species. Metabolic flux through a reaction defines the rate at which a substrate is utilized and is the accurate measure of the activity of a pathway in the network. Metabolomics provides a measure of the pool sizes of the metabolites that can indicate active pathways but cannot measure the fluxes through the pathways. Therefore, measuring metabolic fluxes is the way to confirm network activity and function, and define the metabolic phenotype of a system.in silico modelling comprises of stoichiometric, mass, charge and energy balanced reactions and is used for prediction of steady-state phenotypes. The models include an objective function, typically a biomass equation. Flux distributions through the network are computationally optimized to maximize biomass function and growth rate to predict phenotypes. The accuracy of these predictions needs to be further validated using experimental approaches. These genome scale models are powerful as these platforms can be used to integrate transcriptomic, proteomic, and metabolic data to provide a holistic view of an organism’s metabolic and biochemical network in various environmental and nutritional conditions, stress and disease which is otherwise very challenging to derive from experimental and previously discussed omic platforms alone. Such metabolic modelling has been applied to study metabolic flexibility of Mtb and to predict mutant phenotypes  and GSMN-BCG (M. bovis BCG) models , flux variability analysis (FVA) and gene essentially predictions . Constraenotypes –78. GSMNenotypes . DPA usil status . Metabol) models . Lofthouriations . Genome riations . Rienksmriations , 77. Tilriations  unified riations . Recentlriations  conducteriations . Althougriations . The pre2, and biomass production rates , which is an amalgamation of computational and experimental techniques, provides precise quantification of metabolic fluxes in the network. The general workflow of steady state MFA is elaborated in on rates . Metabolon rates , Isotopoon rates , WUFlux on rates  are avai13C-MFA, which uses 13C-isotopically labelled substrates has been used to quantify intracellular central carbon metabolic fluxes of Mtb in vitro and in human THP-1 macrophages. 13C-MFA of Mtb in vitro cultures cultivated at slow and fast growth rate using 13C-glycerol as the labelled carbon substrate identified a novel pathway for pyruvate dissimilation through glyoxylate shunt and anaplerotic reactions and demonstrated the ability of Mtb to fix CO2 for synthesizing biomass , a computational tool that probed the uptake and utilisation of carbon sources including a mixture of amino acids, C1 and C2 compounds by Mtb from human THP-1 macrophages  to identify the nitrogen sources acquired by Mtb from the host cell and their intracellular assimilation  grant, BB/V010611/1 United Kingdom. The funders have no role in preparation of this work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA.Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation  Moreover, approaches from the constraint-based modeling framework have been employed to integrate heterogeneous high-throughput data, including: gene expression levels7, proteome abundances9, and metabolite concentrations15.Constraint-based modeling of genome-scale metabolic models have been used to identify patterns in steady-state flux distributions, pointing at design principles of metabolic networks16. A metabolic network is represented by a stoichiometric matrix, 17. As a result, Flux Balance Analysis (FBA), as the prominent approach on which the constraint-based framework rests, can provide predictions about steady-state flux distributions with the assumption that the biological system optimizes a particular task (e.g. maximizing cellular growth)17. However, fluxes directly depend on the concentration of enzymes and concentration of metabolites, leading to scenarios in which predictions of FBA do not consider constraints due to metabolite concentrations.Genome-scale metabolic models provide a mathematical representation of all documented biochemical reactions that interconvert nutrients from the environment into extracted products and biomass18. This approach introduces additional constraints to ensure that the resulting steady-state flux distribution respects the laws of thermodynamics, thus restricting the space of feasible flux distributions. More specifically, TMFA allows a flux through a reaction only if associated change of Gibbs free energy 19. The value of One approach that accounts for the effect of metabolic concentrations on metabolic fluxes is Thermodynamic Metabolic Flux Analysis (TMFA)We note that 21, and later, the contribution of pseudoisomeric groups22. However, for many metabolites 23, nor can be estimated by using the group contribution methods and extensions thereof22. The group contribution method is reported to not be applicable in the case of organic–inorganic complexes as well as for a small number but often encountered organic substructures21. As a result, the standard Gibbs free energy for more than a third of reactions in the entire KEGG database are missing since 22. Moreover, in the most recent version of the ModelSEED database, more than half of the included metabolites have unspecified 24.This approach has been extended to consider the contribution of different chemical groups, termed group contribution method19. Hence, To overcome the challenge in TMFA, Henry et al. introduced the idea of determining a linear combination of reactions with undetermined 18. This is ensured by the following constraints:25 to model reduction27, the procedure of reaction lumping has not been fully specified. We would like to note that the reaction lumping does not consider removal of reactions while retaining key functional properties, as applied in stoichiometric techniques for model reduction with application of thermodynamic constraints28 or without them30.Let the reactions be partitioned into two classes, Here we introduce and precisely formulate an approach for reaction lumping and provide an efficient implementation that is applicable with genome-scale metabolic models. The proposed formulation of the approach identifies a maximal subset of metabolites that can be eliminated by lumping, leading to automation of this step in the application of the constraint-based approaches based on TMFA.The calculation of standard Gibbs free energy, R of the stoichiometric matrix Here, all reversible reactions are split into two irreversible reactions. If metabolites with unknown mentclass2pt{minimmentclass2pt{minimThe resulting lumped reaction for a group, 31. We make sure that every reaction involving the metabolite The formulation in Eq.  correspoon (Eqs.  and 9)))31. We mon (Eqs. ). ClearlR of reactions to be lumped is specified by the user. In the tests we conduct, we exclude biomass and exchange reactions from R since the biomass reaction is synthetic and the exchange reactions are often poorly supported with evidence. Exclusion of the biomass reaction from the set R was done to prevent an infeasible lumped reaction that would lead to blocking of the biomass reaction . If . If 9). The group lumping fails to find a lumped reaction if the feasible space of the linear program is empty, i.e. there exists no linear combination that can eliminate the metabolites with unknown mentclass2pt{minimThe sequential implementation starts each iteration with a single metabolite mentclass2pt{minimE. While the linear combination y = E, it also involves metabolite G. Similarly to E, no linear combination eliminates metabolite H while constraining metabolite Note that in solving the linear program in Eqs. –9), we , we 9), The combined implementation applies first the group and then sequential implementation on each of the groups of metabolites resulting from the partition. Therefore, it ensures maximizing the number of metabolites with unknown mentclass2pt{minimE. coli the range for Glycerophosphoglycerol (g3pg), Sn-Glycero-3-phosphoethanolamine (g3pe), and water in the cytosol had to be relaxed to 1 µM –32. We then determined the variability of We implemented TMFA by allowing the concentrations to range between 1 µM and 20 mM. In the case of 33), requiring MATLAB’s solver ‘intlinprog’ as it outperformed other solvers within a benchmark showcase34. All options in the solver were left to default apart from ‘MaxTime’ which was set to 120 s . The time limit had no influence on the outcome as it never caused a premature stop. The statistical analysis was conducted in R (v. 4.0.2) requiring the packages ggplot235, ggnewscale36, gridExtra37, RColorBrewer38, mdthemes39 and cowplot40. Figures 41. All computations were done on a Desktop PC with AMD Ryzen 5 processor (6 × 3.60 GHz) and 32 GB DDR4 RAM.The lumping procedure was implemented in MATLAB v 9.6.03, requir18 do not specify how to systematically find such combination of reactions that lead to elimination of metabolites of unknown 42.Our proposed lumping procedure is designed to identify lumped reactions for any model with incomplete thermodynamic data to enforce stricter thermodynamic constraints, see Eqs. –4). As . As 4). E. coli43, B. subtillis44, and H. sapiens27. For every model, each reversible reaction is split into two irreversible reactions. The genome-scale model E. coli iJR904 contains 1303 reactions and 756 metabolites of which 38 and 26 are not annotated with B. subtilis iBsu1103 contains 2774 reactions and 1381 metabolites, of which 2025 and 434 are of unknown H. sapiens redHuman compromises 11,139 reactions and 4521 metabolites of which 3145 and 1032 are of unknown We applied the combined lumping procedure with the genome-scale models of three organisms actions “”. The coeaction “”. The mofashion “”. A recentation “”.By applying the proposed lumping procedure, we were able to investigate the extent to which metabolites with unknown entclass1pt{minimaurs Fig. a. In theons Fig. b. SimilaA lumping procedure that naively attempts to eliminate every metabolite of unknown tes see “”. The grmentclass2pt{minimThe combined procedure for the iBsu1103 model required solving 1006 linear program in 196 s. The linear program which took the least amount of time required 0.09 s, while the slowest required 14.31 s Table .Table 2PDespite the better performance (in time) for the sequential procedure on the iBsu1103 model, the potential of the combined implementation was demonstrated on the case of the redHuman model. Here, the combined procedure required solving 2650 linear programs in 2659 s. The fastest linear program took 0.19 s, while the slowest required 13.59 s. The lumping based only on the sequential procedure required 4500 linear programs, with time that ranged from 0.19 to 0.9 s. In comparison to the combined procedure, the application of only the sequential took 772 s longer and required solving 1850 more linear programs  metabolites with missing The application of the combined procedure on the two models demonstrated that the group implementation provides speed-up due to a reduced number of linear programs to solve. In addition, our results showed that the group procedure adds marginal increase in running time, in case it needs to be followed by the sequential procedure.E. coli and B. subtilis (see “To demonstrate the advantages of using the models that consider lumped reactions, based on the proposed procedures, we implemented TMFA for the models of lis see “”. We theE. coli’s model, we found that the lumping changed the number of irreversible reactions from 353 in the original model to 350 in the one that also considered the lumped reactions to impose thermodynamic constraints (Table E. coli model with lumped reactions include: UAG2E, DAPabc, and ACMAMUT (see Supplementary Figure In the case of B. subtilis’ model, we observed that there is no change in the number of irreversible and reversible reactions with and without consideration of lumped reaction (Table In the case of on Table . Howeveron Table . Therefo45, leaving the flux solution space less constrained. Here, we proposed an algorithm which fills the gap between the introduction of the idea of reaction lumping and the TMFA approach that imposes special constraints to the lumped reactions. Our procedure starts with the identification of groups of metabolites with unknown The idea of reaction lumping has been introduced to provide additional constraints for reactions with unknown In general, the linear program in the group implementation takes a longer time to solve due to the larger number of constraints it includes. Although the sequential procedure starts with smaller linear programs, requiring shorter time to be solved, the iterative process leads to an increasing number of such program that include a larger number of both constraints and variables. The total duration, thus, depends on the particularities of the analyzed model. In our comparative analysis we presented a case when the combined procedure performs worse than the naive sequential implementation applied alone. In this case, only few groups of metabolites with unknown 42. Our proposed group lumping procedure investigates whether such reactions can be constrained with thermodynamics. The shown decrease of reactions with unknown The recently published toolbox to conduct TMFA, called matTFA, states that any reaction involving metabolites with unknown 18. The reproducibility of our findings is further guaranteed by the provided implementation of the proposed procedure and accessibility of all data, following the FAIR principles. The implementation is general to allow the identification of lumped reactions in any metabolic model complying with the input specifications. Therefore, the systematic way for identification of lumped reactions by the proposed combined procedure has the potential to further propel the applications of TMFA, due to the increasing availability of quantitative metabolomics data46. Indeed, our application of TMFA with and without consideration of lumped reactions in two genome-scale models shows that the lumping procedure provides for more constrained predictions of metabolic phenotypes.Our comparative analysis on three genome-scale models that differ in terms of size and complexity of the modelled metabolic pathways shows that the lumping procedure is time-efficient, systematic, and results on reproducible findings. The proposed combined procedure clearly defines how lumped reactions are formed and ensures that The provided procedure allows some flexibility with respect to the choice of reactions to be lumped. In our analysis, we did not consider synthetic and exchange reactions in the lumping, and future efforts will be dedicated to the consideration of these cases. In addition, follow-up studies will be dedicated to investigative the effect of alternative solution in the detection of lumped reactions on the possibility to maximize the number or metabolites with unknown Supplementary Information


When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation—known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells. Metabolically active organelles compete for cytosolic space and resources during metabolism rewiring. Here, the authors develop a computational model of yeast metabolism and resource allocation to predict condition- and compartment-specific proteome constraints that govern metabolic strategies. The associated reprogramming of metabolism between such metabolic strategies is of key interest in biotechnology and biomedical research.Macromolecular synthesis and energy conservation by metabolism underlies cellular maintenance, growth and fitness. Unicellular organisms such as yeasts generally display a great variety of metabolic strategies that lead to competitive fitness across conditions2. The same phenomenon is known as the Crabtree effect in Saccharomyces cerevisiae (Baker’s yeast)3. At sugar limitation yeast respires glucose completely to CO2; at sugar excess it displays respirofermentative metabolism, where respiration is combined with ethanol formation . The extent to which these two metabolic strategies are used can be titrated in a glucose-limited chemostat: at a specific critical dilution (=growth) rate, ethanol formation starts and increases linearly with growth rate4. Other microorganisms show similar behaviour5: for example, E. coli produces acetate at higher growth rates at the expense of respiration6.One well-known example is overflow metabolism in which under aerobic conditions not all substrate is fully oxidised but secreted as by-products. In cancer cells, it is referred to as the Warburg effect: enhanced glycolytic activity with lactate as byproduct at the expense of respiration10. In essence, it is based on the catalytic benefits of proteins and their associated costs11. These costs comprise competition for resources such as building blocks, energy and synthesis machineries, and for space in cellular compartments. Two key features of this resource allocation paradigm can explain metabolic adaptations. First, cellular compartments can become full when they are fully occupied with  active proteins, such that an increase in one protein has to come at the expense of another. This was postulated as a phenomenological rule based on experimental observations12, but also follows naturally from growth-rate maximisation13. Second, cells allocate their limited resources for protein synthesis according to their demands15. Consequently, fractions of needed proteins vary with growth rate within compartments whose protein content is bounded, and this can lead to active proteome constraints related to full compartments.In the last decade, a theoretical framework has been developed that can explain why cells shift metabolic strategies upon environmental or gene-expression perturbations7; this becomes important at fast growth when biosynthesis and ribosome demands are high and thus require large proteome fractions. Earlier work suggests that the proteome-constrained resource allocation paradigm, which was largely developed for E. coli, may also be a powerful perspective for regulation of eukaryal yeast metabolism, such as ribosome biosynthesis16, and growth on different sugars17. However, a key feature of the metabolism of a eukaryal cell is the presence of metabolically active organelles, most prominently mitochondria. Each organelle introduces two new compartments (intra-organellar space and membrane), and how these compartments impact adaptation of metabolism, and which compartments become limiting under different conditions, is an open question.Within this framework, the onset of overflow metabolism was explained by the smaller protein cost of generating ATP through fermentation than respiratory pathwaysSaccharomyces cerevisiae, a comprehensive, quantitative, data set in which growth rate is systematically varied and both fluxes and protein expression levels are measured, which are needed to validate resource allocation predictions, are still rare . Here, we present such data sets and, in parallel developed, detailed and comprehensive, compartmentalised and quantitative model of metabolism and protein synthesis of yeast. The model can compute the costs and benefits of protein expression and translocation; It can be used to interpret or predict experimentally determined changes in growth rate,  protein expression and metabolic fluxes as a result of growth rate optimisation through resource allocation into different, compartmentalised, proteome fractions. Comparison of the model predictions with the data gives unprecedented insight into our physiological understanding of this important model organism.Moreover, despite the wealth of experimental data on 19 metabolic genome-scale metabolic model of yeast (GEM) by coupling metabolic fluxes to the synthesis of the catalysing enzyme and added constraints on protein concentrations, expressed as protein fractions of the total proteome  proteins. The UP fraction is cytosolic, has an average amino acid composition and is added to always maintain a constant protein density in the cytosol. It has a minimum expression level estimated from the experimental proteomics data . We also included our data from batch cultures on glucose (growth rates 0.37–0.39 h−1) and on trehalose; Trehalose is a disaccharide of two glucose molecules, hydrolysed extracelullarly29, thus providing slow release of glucose that supports low growth rates.We subsequently used the model to analyse yeast’s physiological response to different levels of glucose availability. Traditional Flux Balance Analysis computes continuous chemostat cultures by minimising glucose uptake rate at fixed growth (=dilution) rate30. The resulting relationship between growth rate and residual glucose concentration fit experimental data very well  conditions. We conclude that the model can adequately predict the changes in metabolic fluxes when the growth rate is varied through the availability of glucose.As in the chemostat, the specific growth rate is equal to the dilution rate, the maximal feasible growth rate that the model predicted can be directly compared to the experimental data Fig. . The  by providing different sugars, i.e., trehalose, galactose, maltose and glucose during batch cultivation. Ethanol production was already observed on galactose, already at a growth rate of 0.16 h−1 so at a much lower growth rate than the critical growth rate of 0.28 h−1 under glucose-limited growth : a lower sugar uptake capacity alone would be identical to lowering saturation of the transporter as was done for glucose  were required to achieve good fit.We had to decrease the maximal galactose uptake rate by a factor of 2.5 compared to glucose. Furthermore, an increase in minimal UP fraction was needed, to 0.49 g/g protein. To fit all fluxes optimally, we also required additional energetic costs , we find evidence for proportional relationships between protein and flux for high-flux carrying, pathway-grouped, proteins as a function of growth rate Fig. . This is2 fluxes, for both the model and the experimental data  no longer an active constraint in our model Fig. . Thus, iThird, in the case of nutrient uptake limitation, there appears to be excess proteome space that could be filled with anticipatory proteins or heterologous enzymes at no cost in fitness. Even though the composition of such excess proteome space cannot be predicted with our model, we were able to predict metabolic fluxes very well: in this nutrient-limited regime metabolic efficiency (ATP per glucose), not proteome efficiency (ATP per protein), determines the best growth rate strategy. This explains why Flux Balance Analysis applied to only the metabolic network has been so successful, but only under nutrient-limited conditions.49, such a regime is characterised by hierarchical regulation with absence of metabolic regulation, that is, all changes in flux are brought about by changes in enzyme levels, not their degree of saturation. For glycolysis and amino acid metabolism, the average saturation, estimated as the ratio of the predicted minimal enzyme level to the expressed enzyme level, at maximal growth rate is around 0.5, incidently the level predicted as theoretical optimum for specific reaction rate50. In contrast, when growth is limited by glucose availability, the degree of saturation varies and the model suggests a mixture of hierarchical and metabolic regulation, as previously observed in chemostats as well51.Finally, we found linear or even proportional relationships between growth rate and flux, and between flux and enzyme levels in a sugar excess (batch culture) regime. In terms of regulation analysisTo conclude, we present a mechanistic, compartmentalised, model of an eukaryal organism in full detail, which can act as a valuable, computable, knowledge base. We show how it can be used to compute protein costs and identify active growth-limiting constraints, and how it can be combined with quantitative flux and proteomics data to provide unprecedented insight into cellular physiology. Finally, we show that also in eukaryal cells, metabolic strategies can be understood on the basis of growth rate optimisation under nutrient and proteome constraints. What remains to be understood is how the cell’s signalling and regulatory networks manage to implement these  proteome allocation strategies.Saccharomyces cerevisiae strain CEN.PK 113-7D52. The stocks used for the experiments were grown in 500 mL shake flask containing 100 mL of YPD medium . The culture was grown up to early stationary phase and 1 mL aliquots were stored in 20% (v/v) of glycerol at −80 °C. For chemostats, pre-cultures were grown in 500 mL shake flasks containing 100 mL of synthetic medium, the pH was set to 6.0 with 2 M KOH and the medium was supplemented with 20 g L−1 of D-glucose53. Shake flasks with medium were inoculated with the 1 mL frozen stocks of the strain and the cultivations were performed in an orbital shaker at 200 rpm at 30 °C. Pre-cultures for batches with translation inhibitors were performed using a similar approach, whereas for batches with different carbon sources the pre-cultures were made with the respective carbon sources instead of D-glucose.The strain used for this study was −1 in two independent replicate cultures. Growth rates were controlled by modifying the inflow rate on each experiment. Synthetic medium according to Verduyn53 supplemented with 7.5 g L−1 of glucose and 0.25 g L–1 Pluronic 6100 PE antifoaming agent was supplied to the bioreactor from a 20 L continuously mixed reservoir vessel. Cultures were sparged with dried air at a flow rate of 700 mL min−1 and stirred at 800 rpm. The pH of the cultures was maintained at 5.0 by automatic addition of 2 M KOH. If, after at least six volume changes, the cultures dry cell weight concentration and carbon dioxide production ratediffered less than 2% over two consecutive volume changes the cultures were considered to be in steady state. For cultures with dilution rates of 0.27, 0.3, 0.32 and 0.34 h−1, cultures were first maintained at a dilution rate of 0.2 h−1 for 15 h (3 volume changes) prior to increasing the specific dilution rate to said values.Chemostat cultivations were performed in 2 L bioreactors  with a working volume of 1.0 L, the dilution rates used in this study were 0.2, 0.23, 0.27, 0.3, 0.32 and 0.34 h53, the medium was supplemented with 20 g L−1 final concentrations of the carbon sources, either D-trehalose, D-galactose, D-maltose or D-glucose (Sigma Aldrich). The bioreactors were inoculated with 100 mL of yeast shake flask cultures, exponentially growing on the specific carbon source. The final OD660 of all pre-cultures was 4. Cultivations were performed at 30 °C, the pH was kept at 5.0 by automatic addition of 2 M KOH. The working volume of the bioreactors was 1.4 L in 2 L bioreactors . The cultures were stirred at 8000 rpm and sparged with a flow rate of 700 mL min−1 of dried air. Oxygen levels were kept above 40% of the initial saturation level as measured with Clark electrode .Batch cultivations (two independent replicate cultures) were performed using synthetic medium−1 of D-glucose and were supplemented with different concentrations of cycloheximide with the aim of reaching specific growth rates. In total five growth rates were studied, being 0.06, 0.12, 0.2, 0.32 and 0.41 h−1 .Batch cultivations (two independent replicate cultures) with the translation inhibitor cycloheximide were performed as for the batches with different carbon sources, except that all the batch cultures ran on 20 g LCultures dry weight was measured by filtering 20 mL of culture, the sample was filtered in pre-dried and pre-weight membrane filters with a pore size of 0.45 µm (Gelman Science), the filter was washed with demineralised water, subsequently, it was dried in a microwave  and the final weight was measured, the difference being the dry weight in the culture sample.54, whereas samples from batch cultures were centrifuged  and a refractive-index detector (Agilent G1362A).For the measurement of organic acids and residual carbon source concentrations, supernatants of the cultures were used. For carbon-limited chemostat cultures, the samples were directly quenched with cold steel beads and filteredThe exhaust gas from batch cultures was cooled down with a condenser (2 °C) and dried with a PermaPure Dryer  before online analysis of carbon dioxide and oxygen with a Rosemount NGA 2000 Analyser .2CO3 and trehalose and glycogen were extracted using boiling water or alkaline/acid extraction, respectively56. D-glucose released from trehalose and glycogen were measured with a D-glucose assay kit (K-GLUC Megazyme), two biological replicates and three technical replicates were analysed per condition.1 mL of culture was taken from the chemostats and directly added to 5 mL of cold methanol (−40 °C). The sample was mixed and centrifuged , after which the filter was washed with cold TCA 5%. The cells were resuspended in 3 mL of TCA 5% and centrifuged for 15 min at 4 °C at 300058. The absorbance of the supernatant was measured at 510 nm, for calibration lyophilized bovine serum albumin  was used. Two biological replicates and three technical replicates were analysed per condition.For the batches with CHX, culture volumes corresponding to 50 mg of DCW were centrifuged, washed twice with cold demineralised sterile water and divided into two aliquots of 5 mL. 2 mL of the aliquot (containing 10 mg DW) was mixed with 1 mL of 3 M NaOH and incubated at 100 °C for 10 min. The final mix was diluted and processed following the copper-sulfate based methodAliquots of 20 mL of culture from chemostats and batches with different carbon sources were centrifuged . Lysis was performed in 250 µl lysis buffer, 50 mM ammonium bicarbonate with cOmplete protease inhibitor cocktail , using a Minilys Personal Tissue Homogenizer , at maximum speed for 15 cycles of 30 s with a 1 min rest on ice between each cycle.Frozen cell pellets were thawed on ice before transfer to PrecellysTM Coomassie Plus Bradford Assay Kit .Lysed material was centrifuged for 10 min 13,000TM SF surfactant  at 80 °C for 10 min, reduced with 4 mM dithiothreitol  at 60 °C for 10 min and subsequently alkylated with 14 mM iodoacetamide  at room temperature for 30 min. Proteins were digested with 2 μg Trypsin Gold, Mass Spectrometry Grade  at 37 °C for 4 h before a top-up of a further 2 μg trypsin and incubation at 37 °C overnight. Digests were acidified by addition of trifluoroacetic acid  to a final concentration of 0.5% (v/v) and incubated at 37 °C for 45 min before centrifugation at 13,000Protein (100 μg) from each sample was treated with 0.05% (w/v) RapiGestThe sample running order was randomised using a random number generator (Random.org).TM 3000 RSLCnano system (ThermoFisher Scientific) coupled to a Q Exactive™ HF Hybrid Quadrupole-Orbitrap™ Mass Spectrometer. Protein digests (1 ug of each) were loaded onto a trapping column  using 0.1% (v/v) trifluoroacetic acid, 2% (v/v) acetonitrile in water at a flow rate of 12 µL min-1 for 7 min.Samples were analysed using an UltiMate−1. The column was then washed at 1% A: 99% B for 8 min, and re-equilibrated to starting conditions. The nano-liquid chromatograph was operated under the control of Dionex Chromatography MS Link 2.14.The peptides were eluted onto the analytical column  at 40 °C using a linear gradient of 120 min shallow gradient rising from 8% (v/v) acetonitrile/0.1% (v/v) formic acid  to 30% (v/v) acetonitrile/0.1% (v/v) formic acid at a flow rate of 300 nL min6, and the maximum injection time was 100 ms. The 16 most intense precursor ions with charge states of 2–5 were selected for MS/MS with an isolation window of 2 m/z units. Product ion spectra were recorded between m/z 200–2000 at a mass resolution of 30,000 . For MS/MS, the automatic gain control target was set to 1e5, and the maximum injection time was 45 ms. Higher-energy collisional dissociation was performed to fragment the selected precursor ions using a normalised collision energy of 30%. Dynamic exclusion was set to 30 s.The nano-electrospray ionisation source was operated in positive polarity under the control of QExactive HF Tune (version 2.5.0.2042), with a spray voltage of 2.1 kV and a capillary temperature of 250 °C. The mass spectrometer was operated in data-dependent acquisition mode. Full MS survey scans between m/z 300–2000 were acquired at a mass resolution of 60,000 . For MS, the automatic gain control target was set to 3e59. The search parameters were set as follows: label free experiment with default settings; cleaving enzyme trypsin with two missed cleavages; Orbitrap instrument with default parameters; variable modifications: oxidation (M) and Acetyl (protein N-term); first search as default; in global parameters, the software was directed to the FASTA file; for advanced identification ‘Match between runs’ was checked; for protein quantification we only used unique, unmodified peptides. All other MaxQuant settings were kept as default. The false discovery rate (FDR) for accepted peptide spectrum matches and protein matches was set to 1%. The CEN.PK113-7D Yeast FASTA file was downloaded from the Saccharomyces Genome Database (SGD) (https://downloads.yeastgenome.org/sequence/strains/CEN.PK/CEN.PK113-7D/CEN.PK113-7D_Delft_2012_AEHG00000000/).The resulting raw data files generated by XCalibur (version 3.1) were processed using MaxQuant software (version 1.6.0.16)60 in the R environment (version 3.3.3) to obtain differential expression fold changes with associated p values, along with normalised LFQ and intensity values61. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE62 partner repository with the dataset identifier PXD030003.The resulting MaxQuant output was then analysed using the MSstats package (version 3.5.6)Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary