Xylella fastidiosa is one of the most important threats to plant health worldwide, causing disease in the Americas on a range of agricultural crops and trees, and recently associated with a critical epidemic affecting olive trees in Europe. A main challenge for the detection of the pathogen and the development of physiological studies is its fastidious growth, as the generation time can vary from 10 to 100 h for some strains. This physiological peculiarity is shared with several human pathogens and is poorly understood. We performed an analysis of the metabolic capabilities of X. fastidiosa through a genome-scale metabolic model of the bacterium. This model was reconstructed and manually curated using experiments and bibliographical evidence. Our study revealed that fastidious growth most probably results from different metabolic specificities such as the absence of highly efficient enzymes or a global inefficiency in virulence factor production. These results support the idea that the fragility of the metabolic network may have been shaped during evolution to lead to the self-limiting behavior of X. fastidiosa. Xylella fastidiosa. Using genome-scale metabolic network reconstruction, modeling, and experimental validation, we explored its metabolic capabilities. Despite genome reduction and slow growth, the pathogen’s metabolic network is complete but strikingly minimalist and lacking in robustness. Most alternative reactions were missing, especially those favoring fast growth, and were replaced by less efficient paths. We also found that the production of some virulence factors imposes a heavy burden on growth. Interestingly, some specific determinants of fastidious growth were also found in other slow-growing pathogens, enriching the view that these metabolic peculiarities are a pathogenicity strategy to remain at a low population level.High proliferation rate and robustness are vital characteristics of bacterial pathogens that successfully colonize their hosts. The observation of drastically slow growth in some pathogens is thus paradoxical and remains unexplained. In this study, we sought to understand the slow (fastidious) growth of the plant pathogen IMPORTANCEXylella fastidiosa is one of the most important threats to plant health worldwide, causing disease in the Americas on a range of agricultural crops and trees, and recently associated with a critical epidemic affecting olive trees in Europe. A main challenge for the detection of the pathogen and the development of physiological studies is its fastidious growth, as the generation time can vary from 10 to 100 h for some strains. This physiological peculiarity is shared with several human pathogens and is poorly understood. We performed an analysis of the metabolic capabilities of X. fastidiosa through a genome-scale metabolic model of the bacterium. This model was reconstructed and manually curated using experiments and bibliographical evidence. Our study revealed that fastidious growth most probably results from different metabolic specificities such as the absence of highly efficient enzymes or a global inefficiency in virulence factor production. These results support the idea that the fragility of the metabolic network may have been shaped during evolution to lead to the self-limiting behavior of X. fastidiosa. Ralstonia solanacearum on several hosts revealed that bacteria acquired both enhanced growth rate and better robustness through enlarged catabolic capacities, showing that these phenotypes are under selective pressure , and forms biofilms , as well as the biosynthesis of all vital compounds    , and Xylifornia)  to estimifornia) . Because10.1128/mSystems.00698-19.5TABLE S1X. fastidiosa CFBP 8418 with a draft network of X. fastidiosa 9a5c. Download Table S1, PDF file, 0.6 MB.Comparison of the reconstructed network of Copyright © 2020 Gerlin et al.2020Gerlin et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00698-19.6TABLE S2X. fastidiosa CFBP 8418 with a draft network of X. fastidiosa Temecula1. Download Table S2, PDF file, 0.6 MB.Comparison of the reconstructed network of Copyright © 2020 Gerlin et al.2020Gerlin et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the X. fastidiosa, E. coli, and R. solanacearum revealed that X. fastidiosa has undergone a strong metabolic reduction, which is associated with its reduced genome size, and illustrated by the decreased numbers of reactions that are specific to X. fastidiosa . Among them, we identified several metabolic pathways related to in planta lifestyle and pathogenicity such as nitrilase (EC 3.5.5) reactions, which detoxify nitriles  and to alternate carbon metabolism reactions, which enable the use of diverse compounds as carbon source, like carbohydrates  or small organic molecules . These findings highlighted that X. fastidiosa metabolism appears limited in terms of metabolic diversity.The comparative analysis revealed a large number of reactions (450) absent only in X. fastidiosa possesses all the vital vitamin and cofactor biosynthetic pathways, it lacks several related pathways that are generally found in fast-growing bacteria. For instance, the couple ubiquinone-8/ubiquinol-8 was the only redox couple from the quinone group found to be synthesized. In contrast to R. solanacearum, no cobalamin (vitamin B12) biosynthesis is possible. Accordingly, X. fastidiosa uses only enzymes with no requirement for cobalamin, which cannot be found in plants . It was thus suggested that the bacterium preferentially uses anaerobic respiration , an enzyme from central and respiratory metabolism composed of four subunits, was also found to have lost a membrane subunit in e domain . We thus2, more than half through the TCA cycle. NADH is also mainly synthesized thanks to the TCA cycle and ATP through oxidative phosphorylation.Our primary analysis of the network structure showed that the core metabolism is complete, and its functionality was confirmed by flux balance analysis (FBA) simulations , accepti37–X. fastidiosa appears to be conserved and functional, although it seems to have lost several pathways compared to other bacteria. These losses do not affect the survival of the bacterium but tend to restrict it to a limited diversity of metabolic behaviors, and probably to a slow and inefficient growth.The core central metabolism from X. fastidiosa metabolism revealed the surprising lack of some enzymes which are generally ubiquitous in bacteria. First, no gene was identified to code for fructose-1,6-bisphosphatase (FBPase) (EC 3.1.3.11), despite intensive homology searches with query sequences from a wide range of organisms. The absence of the corresponding gene in strain CFBP 8418, as well as all the other available genomes of X. fastidiosa, suggested that these bacteria are not able to perform gluconeogenesis. We verified in silico that in our metabolic network, gluconeogenesis was not functional; a precursor such as glutamine could not be converted into glucose with no FBPase. It would imply that X. fastidiosa can grow only in the presence of a hexose or a hexose polymer  and the transaldolase (EC 2.2.1.2). The absence of 6-phosphogluconate dehydrogenase was already reported in other prokaryotes , and itsX. fastidiosa on 190 potential carbon (C) sources using Biolog phenotype microarrays (PMs); 8 can provide relatively fast growth and 18 can provide relatively slow growth at the scale of X. fastidiosa fastidious growth  . The discrepancies observed between experimental and simulation results (growth in vivo but not predicted in silico) were used to correct the network, by adding the missing metabolic reactions for 12 substrates  of the predictions were in accordance with the experimental measurements. The highest predicted growth rate was observed with glycerol, and this was confirmed experimentally.In order to validate the metabolic model predictions, we experimentally assessed the growth of s growth . Growth  and 86) , 44, con sources  by defin10.1128/mSystems.00698-19.7DATA SET S1X. fastidiosa CFBP 8418. Download Data Set S1, XLSX file, 0.2 MB.Metabolic network of Copyright © 2020 Gerlin et al.2020Gerlin et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00698-19.8DATA SET S2Data Set S2, XLSX file, 0.2 MB.Network analysis and modeling. General network composition, biomass objective function, comparative analysis, FBA results, genes and enzymes per reaction distribution, FVA results, efficiency analysis, study of FBPase inefficiency. Download Copyright © 2020 Gerlin et al.2020Gerlin et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00698-19.9DATA SET S3in silico growth assays on different carbon sources, and xylem fluid chemistry. Download Data Set S3, XLSX file, 0.9 MB.Carbon source study: Biolog PM results, Copyright © 2020 Gerlin et al.2020Gerlin et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the in silico the growth on chitin, a polymer composing the insect foregut wall, not present on Biolog PM plates. A recent study showed the necessity of a chitinase (ChiA) for both insect and plant colonization and suggested that chitin is the favored carbon source in the insect environment (X. fastidiosa).We also tested ironment . Our resironment  support X. fastidiosa network, we determined the number of enzymes associated with each reaction in comparison with other reference metabolic models . FVA is an in silico analysis to estimate if alternative flux values, due to an internal or external perturbation, will still sustain an optimal metabolic behavior. We performed FVA using l-glutamine as carbon source and growth as the objective function. Similar analyses were also conducted with E. coli and R. solanacearum (X. fastidiosa than in R. solanacearum (48%) and E. coli (60%). Thus, only a limited proportion of the metabolic reactions can still sustain optimal growth if their fluxes vary. This particular trait confirmed a lack of flexibility in X. fastidiosa metabolism.Fragility and low flexibility of nacearum . As expeR. solanacearum. This other plant pathogen also has a network including virulence proteins and EPS secretion in the model, in contrast to other metabolic models including E. coli. The comparison was thus limited to these two plant pathogens.We finally assessed the efficiency of the network structure for a specific task by determining metabolic yields on proliferation and on virulence factors. Metabolic yields were defined as the maximal proportion of carbon which could be invested in the biosynthesis of a specific macromolecule (virulence factor) or biomass. The obtained efficiency data are presented in X. fastidiosa metabolic network is less efficient for production of EPS than R. solanacearum (2 (+16%). In other words, biosynthesis of this virulence factor has a higher carbon cost in X. fastidiosa and this probably impairs reallocation of carbon fluxes to sustain efficient biomass production.Similar efficiency is observed for biomass production and extracellular protein secretion . Howevernacearum , with anX. fastidiosa. This bacterium appears to possess a minimal but fully functional metabolic network, and this was confirmed experimentally. This is in contrast with several other pathogen or symbiont species which have also undergone reductive evolution with the loss of essential metabolic functions  , which was inferred from our study.The reduction of the metabolic network does not create any auxotrophy for insects) . These en planta . TherebyXylella may be due to an environment poor in nutrients. However, Xylella has a fastidious growth even in nutrient-rich artificial media . In addh weight . TherebyX. fastidiosa metabolism was carried out to examine if robustness could have shaped the evolution of its ancestor. By studying gene essentiality and enzyme distribution, we showed that functional redundancy, one of the main sources of robustness, is substantially missing in the X. fastidiosa metabolic network. This lack of robustness reduces the possibilities for X. fastidiosa to protect itself against perturbations both internal as mutations and external as environmental perturbations. FVA showed that the network lacks flexibility, and as mentioned above, this lack of robustness is also characterized by restricted trophic capabilities. This global fragility certainly makes X. fastidiosa very sensitive to deleterious mutations. The high level of homologous recombination observed in the species  when subjected to environmental or internal perturbation , 49. Gen species , 51 coulSeveral elements were raised by our study .Some peripheral biosynthetic pathways appear to be missing, such as the ones for cobalamin and molybdopterin. It limits the bacterium to cobalamin- and molybdopterin-independent enzymes, usually possessing a lower enzymatic activity , 53. SimX. fastidiosa lacks two key enzymes: fructose-1,6-bisphosphatase (FBPase) for gluconeogenesis and transaldolase in the pentose phosphate pathway. We found that these lacks were not deleterious, suggesting that for FBPase the reaction was processed by a nonidentified route. The emerging concept of underground metabolism provides a reasonable hypothesis: the missing reaction could be achieved through the side activity of another enzyme, which leads to a reduced enzymatic activity    was recomyo-inositol 1-phosphatase (IMPase), could have a bisphosphatase activity on fructose-1,6-bisphosphate. This IMPase enzyme (EC 3.1.3.25) was identified in the X. fastidiosa metabolic network. Although myo-inositol is not a hexose, it is a 6-carbon cyclic molecule with hydroxyl groups, which makes its chemical structure very similar to fructose. An enzyme with dual IMPase/FBPase activity was found in archaea (Mycobacterium tuberculosis (−1 versus 7 s−1)  and FBPase activity in the same enzyme was reported, reinforcing the view that FBPase activity could be achieved by an alternative enzyme, with a lower efficiency –57. No S– archaea  and alsorculosis . It was s 7 s−1) . RemarkaX. fastidiosa strains sequenced to date. This observation suggests that this enzyme was already lost in the Xylella ancestor, whereas this enzyme (EC 3.1.3.11) is broadly conserved in living organisms  (X. fastidiosa.The decreased metabolic efficiency (in terms of yield) was particularly obvious in comparison with another vascular plant pathogen, bacteria , 62. In duction) . Our simduction)  predict Xylella species than in other bacteria. Surprisingly, the predictions from the efficiency analysis indicate that the potential for biomass production by X. fastidiosa is virtually similar to that of R. solanacearum  strain isolated from Spartium junceum in Alata (France)   and provPhenotyping was performed using Biolog phenotype microarray plates PM1 and PM2A according to the manufacturer’s protocol, except that Tween 40 was replaced by Tween 80. An initial optical density (OD) of 0.18 (600 nm) was used for inoculation. Incubation time was 72.5 h. Three independent replicates were performed.negative control + threshold ; otherwise, it was categorized as slow (+). If the results were nonreproducible, the growth was assessed to be undetermined. For six substrates (out of 190), for which growth was assessed unambiguously as positive for two out of the three replicates, growth was referred to (+). The complete results are available in Growth was assumed proportional to respiration and was assessed by calculating the area under the curve (AUC) and the slope of the logarithm on the exponential phase (respiration rate). Growth was considered effective if AUC was >AUChold see . If the LUYA00000000.1)  . The pro00000.1)  was follEscherichia coli K-12 MG1655 , Ralsto(iRP1476 ), Pseudo(iMO1086 ), RalstoMBEL1391 ), and Ba (iYO844 ). X. fash method . The SAMh method  was usedh method , followiThe draft metabolic model was manually curated, evaluating the reactions one by one. The accuracy of each reaction was checked using KEGG , BiGG 7, BioCyc X. fastidiosa genome  (X. fastidiosa subsp. fastidiosa Temecula1 (GenBank accession no. GCA_000007245.1)   and X. f07245.1)  were genE. coli, R. solanacearum, and X. fastidiosa. The metabolic model considered for E. coli was the model iJO1366  methodology was used . The conlication : l-glutaX. fastidiosa and, as a comparison, in R. solanacearum. To avoid bias, all the constraints were removed, and a generic l-glutamine uptake rate of 1 mmol·h−1·g (dry weight [DW])−1 was used. Each of the biological processes (biomass and virulence) was studied separately, and the maximization of their synthesis was used as the objective. Each simulation result gave access to the proportion of carbon lost in the form of CO2, with the exported CO2 rate (R_EX_CO2_e). This allowed us to determine the maximal amount of carbon which could be dedicated to a virulence factor or biomass production. Metabolic yields, and consequently cost of virulence factors, were computed as the ratio between maximal production rate and the uptake rate, normalized by the number of carbons.The structural efficiency of the network was tested in 10.1128/mSystems.00698-19.4TEXT S4Text S4, PDF file, 0.9 MB.Mathematical modeling of metabolism and FlexFlux user guide. Download Copyright © 2020 Gerlin et al.2020Gerlin et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the https://github.com/lgerlin/xfas-metabolic-model.Simulations were performed with the open source tool FlexFlux  and are All data generated or analyzed during this study are included in figures or tables or in the supplemental material. In particular, raw data from Biolog phenotype microarray plates (for carbon substrate phenotyping) are available in X. fastidiosa subsp. multiplex strain CFBP 8418 metabolic model was named Xfm1158. The metabolic model was deposited in BioModels . The main scripts and command lines used for the study are available on GitHub: https://github.com/lgerlin/xfas-metabolic-model.The ioModels  and assitExplore  (https:/


Mycobacterium tuberculosis (Mtb). Using 13C isotopomer analysis, we show that BDQ-treated Mtb redirects central carbon metabolism to induce a metabolically vulnerable state susceptible to genetic disruption of glycolysis and gluconeogenesis. Metabolic flux profiles indicate that BDQ-treated Mtb is dependent on glycolysis for ATP production, operates a bifurcated TCA cycle by increasing flux through the glyoxylate shunt, and requires enzymes of the anaplerotic node and methylcitrate cycle. Targeting oxidative phosphorylation (OXPHOS) with BDQ and simultaneously inhibiting substrate level phosphorylation via genetic disruption of glycolysis leads to rapid sterilization. Our findings provide insight into the metabolic mechanism of BDQ-induced cell death and establish a paradigm for the development of combination therapies that target OXPHOS and glycolysis.The approval of bedaquiline (BDQ) for the treatment of tuberculosis has generated substantial interest in inhibiting energy metabolism as a therapeutic paradigm. However, it is not known precisely how BDQ triggers cell death in  M. tuberculosis upon BDQ treatment and find reliance on glycolysis and synergistic cell death when oxidative phosphorylation is also targeted.Bedaquiline (BDQ) is a known tuberculosis treatment, but the precise mechanism of cell death is unclear. Here, the authors explore the metabolic profiles of  We have shown that a combination of three ETC-targeting compounds  sterilises Mtb cultures within 5 days7. The killing efficiency of this combination is due, at least in part, to increased cellular reductive stress via NADH accumulation and increased oxidative stress from reactive oxygen species (ROS). However, it is unknown whether intracellular ATP depletion leads directly to cell death, what minimum intercellular ATP concentration is required for Mtb viability8 or even when Mtb cells die.The development and clinical approval of bedaquiline (BDQ)Mtb remodels its metabolism to compensate for reduced ATP levels, as shown by proteomic methods9. These metabolic adaptations facilitate redox balance, and proteomic data suggest that ATP production occurs via substrate-level phosphorylation, at least in the short term (~3–4 days)10. However, metabolic fluxes cannot be determined from proteomic data, and it remains to be established whether BDQ-induced death of the bacillus is due solely to ATP depletion, or to subsequent changes in metabolism that occur in response to ATP depletion. Additionally, several studies have shown that BDQ-induced killing may be due to the uncoupling of electron transport, leading to impaired proton motive force or a combination of this futile proton cycling and reduced ATP levels12. However, since BDQ shows delayed bactericidal activity in vitro, it is reasonable to posit that this delayed activity is due to continued, but insufficient, ATP production via substrate-level phosphorylation. Hence, we have proposed glycolysis as a drug target since disruption of both OXPHOS and substrate-level phosphorylation would prevent ATP production, leading to the rapid killing of Mtb7. However, reduced ATP levels are not necessarily an indicator of cell death, as anti-TB drugs can kill Mtb without reducing ATP levels13.In response to BDQ, 15. Cell death certainly requires energy16 and is linked to metabolism. For example, in Streptococcus pneumoniae, the conversion of pyruvate (PYR) to acetoin or to acetate, determines cell survival or death, respectively18. Further, it has been shown that central metabolism is involved in antibiotic-induced cell death20. This has led to a “unified model”, which posits that drug-target interactions produce diverse downstream perturbations, leading to the collateral damage of macromolecules, increased stress responses, increased metabolic activity and ultimately to cell death21. Conversely, changes in metabolic activity can be triggered by hypoxia, nutritional deprivation or biofilm formation, and can lead to drug tolerance22. Overall, these studies and others demonstrate that the metabolic status of bacterial cells is crucial in modulating drug susceptibility21. Therefore, a comprehensive understanding of the mechanisms of drug-induced Mtb cell death will help guide therapeutic intervention strategies.While cell death in bacteria has been studied for decades, there are still gaps in our understanding of this process24. The ANA node links glycolysis and gluconeogenesis with the tricarboxylic acid (TCA) cycle and the ETC via a set of anaplerotic and cataplerotic reactions25. Two other important metabolic pathways in bacteria are the glyoxylate shunt and the methylcitrate cycle (MCC), which are also linked to the ANA node27. These pathways are important in Mtb because they are required for the degradation of fatty acids and sterols necessary for survival in host cells. The MCC has an essential role in the degradation and assimilation of the toxic by-product propionyl-CoA and is responsible for the production of toxic intermediates such as methylisocitrate (MIC)25. These reactions are tightly regulated by known effectors, including fatty acids and adenosine phosphate (AXP) species, and contribute to the metabolic flexibility essential for Mtb’s environmental adaptability and pathogenicity29.The phosphoenolpyruvate (PEP)–pyruvate–oxaloacetate node, or anaplerotic (ANA) node, is an important metabolic motif for ATP production via substrate-level phosphorylation and metabolic regulation in bacteriaMtb reprograms its central metabolism to compensate for chemotherapeutic depletion of ATP, and how these adaptations significantly delay cell death. Here, we test the hypothesis that BDQ-mediated inhibition of ATP synthase rewires central metabolism, leading to a metabolic vulnerable state. We then test the hypothesis that BDQ killing can be enhanced by inhibiting glycolysis or gluconeogenesis. The rationale for these hypotheses is twofold: firstly, understanding how ATP derived from OXPHOS and substrate-level phosphorylation interacts with central carbon metabolism may lead to strategies that can disrupt these feedback loops. Secondly, understanding drug-induced cell death, a multifaceted process that cannot be fully elucidated by the interaction of a drug with its target, is essential for developing more accurate anti-TB drug screens. To test these hypotheses, we performed a series of carbon-tracing experiments followed by 13C-metabolic flux analysis to determine how BDQ-treated Mtb remodels its central carbon metabolism. Specifically, we examined carbon distribution in glycolysis, the pentose phosphate pathway (PPP), the TCA cycle, the glyoxylate shunt and the methylcitrate cycle (MCC). We also examine BDQ’s ability to trigger the excretion of TCA metabolites to maintain metabolic homoeostasis. We then identified candidate metabolic pathways that allow Mtb to survive for prolonged periods without OXPHOS, but which ultimately prove toxic. Finally, using a series of genetic mutants in glycolysis/gluconeogenesis, we show that disruption of OXPHOS and glycolysis leads to synergistic killing of Mtb.It is currently unknown how Mtb30, metabolic fluxes cannot be inferred from -omics data. Here we used 13C-isotopomer analysis to measure qualitative changes in the metabolic fluxes of Mtb after exposure to BDQ. Using several 13C tracers combined with an isotopomer model, we comprehensively defined the metabolic profile of BDQ-treated Mtb.Although proteomic, transcriptomic and metabolomic studies provide indirect evidence that BDQ modulates energy metabolism in 13C enrichment of metabolites in the presence of a 13C-labelled substrate is stable over time and independent of the metabolite levels31. For example, at isotopic steady state, α-ketoglutarate (αKG) and glutamate are in complete exchange and have identical mass-distribution vectors, despite having different intracellular levels when grown on 13C6 glucose. Further, we observed the complete exchange of the C5 backbone in the conversion of αKG to glutamate  between metabolites that are in an isotopic steady state, consistent with previous studies32. In contrast, BDQ increased the CID towards the M + 0 isotopologue of glutamate and decreased CID towards all other isotopologues compared to αKG  can be examined, an isotopic steady state occurs when ate Fig.  in Mtb cαKG Fig. . This st13C6 glucose as a substrate, we profiled BDQ-treated Mtb (~16 h at 30× MIC50). Of particular interest is the effect of BDQ on the first irreversible rate-limiting step of glycolysis, the conversion of fructose-6-phosphate (G/F6P) and ATP to fructose-1,6-bisphosphate  and ADP by phosphofructokinase (Pfk)33. Firstly, there was significantly more of the M + 6 species in the G/F6P metabolite pool of BDQ-treated bacilli  and the glycolytic intermediate-derived amino acids histidine (His), glycine (Gly) and valine  , 6-phosphofructokinase (Rv3010c) and aldolase (Rv0363c) 24 h after BDQ treatment reported by Koul et al.al) Fig.  provide al) Fig. , with a al) Fig. . Both R513C6-glucose carbons during BDQ exposure is re-routed through the PPP to avoid the ATP-consuming Pfk step, which allows for the conservation of ATP production by pyruvate kinase (PykA) and maintenance of glycolysis. This leads to the production of reducing equivalents37 and nucleotides, ultimately proceeding towards phosphoenolpyruvate (PEP) and PYR. Hence, inhibition of OXPHOS by BDQ heightens Mtb’s dependency on glycolysis and substrate-level phosphorylation in an attempt to conserve energy and enhance ATP production, consistent with the delayed cidal effect of BDQ on Mtb9.Overall, our data suggest that flux of 25  of PEP and PYR when cultured on ose Fig. . 13C enrBDQ Fig. . In the PYR Fig. . This reMtb Fig. . Importa13C glucose into M + 3 PEP and PYR species (derived from glycolysis and gluconeogenesis) is significantly decreased by BDQ treatment  with a concomitant decrease in PYR  converts PEP to oxaloacetate via a CO2-fixation reaction41.Labelling from ent Fig. . This reent Fig. , along went Fig. . Furtherent Fig. . This isent Fig. . This isPYR Fig. . This co13C2 acetate  and observed that BDQ treatment significantly reduced survival of Mtb Δppdk cells compared to wild-type-treated cells  has been shown to provide an alternate gateway into gluconeogenesislls Fig. .Mtb Δppdk is more susceptible to BDQ compared to wild-type cultures. Since PPDK is essential for the intracellular survival of Mtb, targeting this gene may lead to killing of Mtb and provide drugs that could potentially synergise with BDQ. These results establish the vulnerability of the metabolic routes towards PEP and PYR production  the oxidative branch where isocitrate proceeds to oxaloacetate via αKG, succinate and malate Fig. , (ii) prn41 Fig. .Fig. 3BDMtb cells with glucose or acetate using 13CO2 (NaH13CO3) as the tracer. Figure 13C, which is consistent with CO2 fixation. In the presence of glucose, and especially acetate, we observed more labelled isocitrate, succinate and malate, albeit less αKG during exposure to BDQ , or pyruvate carboxylase (Pca)/malic enzyme (Mez) with pyruvate phosphate dikinase (Ppdk) acting in the anaplerotic direction from either OAA to PEP, or malate/pyruvate to PEP, respectively. However, the 13C-isotopomer profile cannot distinguish between these two scenarios.To investigate the effect of BDQ on gluconeogenesis and the catalytic direction of PEPCK, we cultured BDQ Fig. . The lacBDQ Fig. . Interesrol Fig. , indicat13CO2 cannot proceed beyond PEP (gluconeogenesis) to label G3P and F6P , indicating that the TCA cycle was proceeding via the oxidative branch  and glutamine (Gln) levels as these amino acids play a crucial role in the regulation of energy metabolismose Fig. , which ient Fig. , whereasent Fig. , which iBDQ Fig. . As seenBDQ Fig. , the conBDQ Fig. 46. ThereGln Fig.  followinGln Fig. .Mtb grown on glucose, in acetate-cultured cells, BDQ had little effect on Gln labelling  and is involved in cholesterol and odd-chain fatty acid and amino acid catabolism Fig. 48. Our CCO2 Fig. . NotablyCO2 Fig. , with roCO2 Fig. . In Mtb ent Fig.  and totaent Fig.  were sigent Fig. , with a ent Fig. . In factent Fig. . This suMIC Fig. . OverallMtb contains a MMCoA pathway that is activated by vitamin B12 (B12)  or propionate (odd-chain fatty acid)  Fig. 48,49. We13C propionate as a tracer, we further demonstrated that after BDQ treatment, flux of 13C3 propionate increased directly through the MCC even though there was an overall reduction in the quantity of MIC. This suggests that less propionate is being utilised in fatty acid metabolism, which is used as a sink for propionate and reductants. Overall, this suggests that BDQ induces metabolic poisoning through the accumulation of MIC, thereby enhancing lethality of BDQ.Overall, our stable isotope analyses show that BDQ induces the MCC up to the production of MIC, leading to increased abundance of this toxic metabolite. Using Mtb have shown that stressful environmental conditions, e.g., hypoxia, trigger a compensatory response, leading to secretion of succinate to maintain an energised membrane potential52, and that BDQ activates components of the Dos dormancy regulon53, which is also induced by hypoxia. Also, several studies show that metabolites from central metabolism are excreted via exosomes55. Collectively, these studies suggest a link between metabolic poisoning and secretion of metabolites. Hence, we tested the hypothesis that BDQ-induced stress leads to metabolite secretion in Mtb. To this end, we characterised the exo-metabolome of BDQ-treated Mtb cultured on glucose or acetate and compared exo-metabolomes to the corresponding intracellular metabolomes.Previous studies in Mtb was cultured on glucose or acetate  using an isotopomer model is the gold standard for quantifying metabolic fluxes in microbial cells56. This holistic integrative approach uses isotope metabolite data to directly measure metabolic fluxes. We developed an isotopomer model of Mtb central carbon metabolism that includes the TCA cycle, glycolysis, PPP and anaplerotic reactions28. For this study, we expanded this model to include the MCC, amino acid degradation pathways and lipid biosynthesis . Importantly, confirming our manual analysis, there is also a change in the flux direction of the PPP, with carbons flowing into glycolysis bypassing the ATP-consuming Pfk reaction  for lipid biosynthesis during BDQ treatment. Carbons are also withdrawn from the Val degradation pathway and the MCC and then fed into the TCA cycle , BDQ-exposed 58.Propionate (PROP) induces gluconeogenesis in BDQ-treated cells, whereby PEP is converted to phosphoglyceric acid (PGA)  and OXPHOS would abolish ATP production, resulting in rapid sterilisation of Mtb. To test this hypothesis, we examined whether perturbation of glycolysis 24 synergises with inhibition of OXPHOS via BDQ. Indeed, we observed that exposure of Mtb deleted for pykA (Mtb ΔpykA) to BDQ sterilised the culture within 5–6 days , the first rate-limiting step in glycolysis59. In contrast to Mtb ΔpykA, we observed no significant effect following BDQ treatment of Mtb ΔpfkA, suggesting that disruption of glycolysis must occur at specific nodes for synergy with BDQ to be observed  of ATP production. Previously, we proposed a model for the rapid and sustained increase in Mtb respiration following BDQ treatment, in which the ETC is used to “burn off” reducing equivalent by-products resulting from ATP production via substrate-level phosphorylation7. Evidence for this model is the strong correlation between the bactericidal activity of ETC-targeting drug combinations and the elevated intracellular NADH:NAD ratios of the treated bacteria9. Our demonstration of increased dependence on substrate-level phosphorylation and the bifurcation of the TCA cycle into the glyoxylate shunt in the presence of BDQ provides further evidence for this model.Glycolysis and OXPHOS, the two major pathway sources of ATP, are essential for the growth and persistence of Mtb. Since OXPHOS is conserved between prokaryotes and eukaryotes, this energy pathway has historically been viewed as an unsuitable target for anti-TB drugs. However, this view has been challenged by the discovery of BDQ, a specific inhibitor of bacterial ATP synthase. Similarly, our findings are likely to stimulate renewed interest in glycolysis/gluconeogenesis as an anti-TB drug target in combination with inhibition of OXPHOS, potentially leading to therapies that reduce TB-treatment times.An important conclusion from this study is that perturbation of both glycolysis/gluconeogenesis and OXPHOS is necessary to rapidly sterilise Mtb ΔpfkA and ΔpykA, suggesting that anaplerotic and cataplerotic pathways trigger different compensatory responses. We reason that here Mtb is bypassing the ATP-consuming Pfk step in glycolysis to restore homoeostatic levels of ATP. Under BDQ treatment, we show that glucose-cultured Mtb favours the anaplerotic reaction for ATP production, leading to a decrease in the total amount of PYR, causing a 10-fold increase in the PEP:PYR ratio  was shown to be highly responsive to ATP levels when exposed to BDQ30, which is not unexpected since GS is ATP-dependent. Asp also plays a role in Glu formation in the form of nitrogen transfer72, and importantly, the conversion from Asp to Asn is also ATP-dependent. Hence, similar to GS30, inhibition of asparagine synthetase is also likely to synergise with BDQ. How can the accumulation of oxaloacetate (using Asp as a proxy)47 upon BDQ exposure be explained? One reason is that to limit the production of NADH, increased levels of oxaloacetate are channelled through the MCC cycle, causing an accumulation of MIC to toxic levels. Indeed, this 60-fold increase in MIC accumulation is almost certainly toxic, and has previously been observed in Mtb strains lacking MIC lyase activity48. The fact that vitamin B12, which stimulates the MMCoA pathway, is unable to remediate BDQ killing of Mtb, suggests that MIC build-up alone is insufficient to mediate BDQ killing. Regardless, we propose that MIC plays a key role in BDQ-mediated Mtb killing.Previous studies suggest that 52. On the other hand, several bacterial metabolomic studies have shown that metabolites from central metabolism are excreted via exosomes55. Regardless, our data suggest a link between metabolic poisoning and extracellular metabolites.Another intriguing finding was the substantial quantities of extracellular malate, fumarate and succinate upon BDQ exposure, which has not been reported to date. These results indicate that BDQ-mediated stress negatively affects membrane bioenergetics, which leads to the electrogenic secretion of metabolites with protons via an unidentified symporter to restore the membrane potential. Hence, BDQ mimics the hypoxia-induced secretion of succinateMtb in response to reduced ATP levels induced by BDQ. It also highlights glycolysis/gluconeogenesis as a potential drug target for combination therapy. Notably, simply targeting any juncture in glycolysis/gluconeogenesis is not adequate to achieve enhanced synergistic killing since Mtb has the capacity to rapidly re-route carbons via alternative pathways such as the PPP. Ultimately, by understanding the multifaceted metabolic mechanisms of Mtb cell death, it may be possible to identify drugs targeting central metabolism that synergise with current anti-TB drugs.Overall, this study provides insight into the mechanisms of metabolic rearrangement of Mycobacterium tuberculosis (Mtb) H37Rv was cultured in Middlebrook 7H9 media (Difco) supplemented with 10% OADC (Difco) and 0.01% Tyloxapol (Sigma-Aldrich) at 37 °C. Mtb H37Rv was obtained from BEI Resources (NR-123). Mtb ΔpykA was supplemented with 30 mM acetate and was a kind gift from Dr. Michael Berney . Mtb ΔpykA was constructed as outlined in Noy et al.24. The Mtb Δppdk and Mtb ΔpfkA mutants were constructed using the strategy outlined in Basu et al. and Phong et al.59. BDQ was used in all experiments at a final concentration of 1.62 μM (CFUs and metabolomics). BDQ MIC50 is equivalent to 54 nM. All 13C-tracer carbon sources were purchased from Sigma-Aldrich.Mtb cultures were grown to an OD600 ~ 0.6 as described above, pelleted and washed twice using Middlebrook 7H9 media (Difco) containing 0.01% Tyloxapol. After the final centrifugation step, the pellets were suspended in 4.5 ml of Middlebrook 7H9 containing the 13C-tracer carbon source  and BDQ (or DMSO as control). Cultures were incubated overnight (~16 h) at 37 °C before sampling (4 ml). Pellets were kept on dry ice until further processing, and a fraction of the culture supernatant was kept for liquid chromatography–mass spectrometry (LC–MS) analysis. The frozen pellets were suspended in 1.8 ml of extraction buffer (2:2:1 methanol:acetonitrile:water) and bacilli lysed via five rounds of bead beating (7000 rpm for 60 s). Both culture supernatant and cell lysate were sterilised using 0.2-μm membrane-spin filters. Protein concentrations of bacterial lysates and culture filtrates were measured using a Micro BCATM Protein Assay Kit (Thermo Scientific), and a Quick StartTM Bradford Kit (Bio-Rad), respectively. Culture supernatants were submitted for organic acid LC–MS analysis without further intervention. Cell lysates were dried under reduced pressure, and the resulting pellets were suspended in water and analysed for organic acid by LC–MS. For amino acid analysis by LC–MS, the suspended cell lysates were diluted with acetonitrile  before analysis.Mtb extracts were separated on a Biorad Aminex HPX-87 column (300 × 7.8 mm), using an aqueous 0.1% formic acid isocratic mobile-phase programme at a flow rate of 400 µl/min and an injection volume of 20 µl. Amino acids were separated on a Waters BEH Amide column  using a gradient-elution programme at a flow rate of 200 µl/min and an injection volume of 1 µl. Two mobile phases were used: mobile-phase A  and mobile-phase B . The gradient programme was as follows: 0–0.2 min, 99% of B; 0.2–24 min, a curvilinear decrease from 99–30% of B; 24–25 min, 30% of B; 25.1–35 min, 99% of B. Both columns were connected to a Dionex Ultimate 3000 UPLC, and the total negative (organic acids) and positive (for amino acids) ion chromatograms (50–750 m/z scan range) were collected using a Thermo Scientific Q Exactive mass spectrometer. The total ion chromatograms of all relevant metabolites, and their respective isotopologues, were visualised and analysed using Skyline V 3.7 . Chromatographic data were analysed using Windows Excel 2016, and statistical analysis and data representation performed using Graph Pad Prism 7.Organic acids from 5 CFU/ml (or an OD600 = 0.001–0.009) in a 96-well microtitre plate and incubated at 37 °C . Control wells consisted of bacteria and growth medium only (7H9). These were treated with the same volume of DMSO  as used in drug-containing wells. For the vitamin B12 Alamar blue assay, vitamin B12 (purchased from Sigma-Aldrich) was used at a final concentration of 20 µg/ml. Plates were incubated at 37 °C for 7 days. On day 7 of the incubation, 30 μl of 0.02% Alamar blue solution  was added and plates were reincubated at 37 °C for 24 h. Bacterial growth leads to a chemical reduction of resazurin sodium salt to resorufin sodium salt (strongly fluorescent) and a colour change from blue to pink. The plate was then read using a plate reader at 570 nm/600 nm.Briefly, two-fold serial dilutions of the drug (10 µl) were inoculated with a suspension of mycobacteria (90 µl) at a cell density of 1028. The isotopomer model includes mass-balance equations constructed based on reaction stoichiometry, carbon atom transitions and reaction reversibility. The equations were constructed for pathways in glycolysis, pentose phosphate pathway, the tricarboxyclic acid cycle, glyoxylate shunt, anaplerotic reactions, methylcitrate cycle, amino acid biosynthesis and degradation. Isotopomer network compartmental analysis (INCA)57 was used to construct the metabolic model. Carbon isotopologue data generated from isotopic labelling experiments (ILEs) were included in the model. Steady-state mass balances and elementary metabolite unit (EMU) balances were used to simulate the ILEs. Flux estimations were performed using non-linear weighted least-squares fitting approach to determine the flux values that are the most likely description of the labelling data and biomass constraints. A multistart optimisation approach was used for flux estimations with a total number of restarts of 100. Flux distributions with a minimum statistically acceptable sum of squared residuals were considered the best fit.An isotopomer model was constructed as described in Beste et al., 2011, and was extended by adding reactions for the methylcitrate cycle and amino acid degradationMtb, Mtb ΔpykA, Mtb Δppdk and Mtb ΔpfkA with their corresponding complemented strains were diluted to an OD600 of 0.01 in 7H9 media. Cultures were treated with BDQ at 30× MIC50. Following this, 100-µl aliquots of the treated cultures were removed at the indicated time points, serially diluted in phosphate-buffered saline and plated onto 7H10/7H11 OADC agar plates.Mid-log-phase P < 0.05 were considered statistically significant.All data are expressed as the mean ± standard error of the mean (SEM). All statistical analyses were performed on Graph Pad Prism, version 7 . Statistical analyses of the various tests were performed using either a one-way or two-way analysis of variance (ANOVA), with Tukey’s multiple-comparison test. Values of Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling. While temperature impacts the function of all cellular components, it’s hard to rule out how the temperature dependence of cell phenotypes emerged from the dependence of individual components. Here, the authors develop a Bayesian genome scale modelling approach to identify thermal determinants of yeast metabolism. For instance, the model eukaryotic organism Saccharomyces cerevisiae has an optimal growth temperature (OGT) of ~30 °C, whereas a temperature of 42 °C is already lethal to the organism5. Since cell growth fundamentally requires all cellular components to be functional in the temperature window of cell growth, proteins, an abundant group of biomolecules that carry out the majority of catalytic functions and are also highly sensitive to changes in temperature7, are considered to have a critical effect on cell physiology in relation to temperature. However, despite all our knowledge of temperature effects at both the cellular and molecular levels, including recent breakthroughs in temperature-dependent protein folding10 and enzyme kinetics12, the temperature association between proteins and cell physiology is still poorly understood.Temperature is a key environmental and evolutionary factor that shapes the physiology of living cells. Organisms have successfully adapted to survive in diverse temperature ranges13. Examples include the dominant activation barrier and the number of essential proteins to cell growth14 and the activation energy of the growth process and the free energy change of protein denaturation15. These models showed excellent performance when describing the general cell growth rate at various temperatures, however, they could not pinpoint the specific rate-limiting enzymes, nor predict the amount of improvement in growth rate by replacing these enzymes with temperature-insensitive homologs.Multiple studies have attempted to model the temperature effects on cell growth, though with very few proteome-wide parameters18, which are a comprehensive mathematical representation of cellular biochemical reactions19, have been used to model the thermosensitivity of metabolism in Escherichia coli, for instance by associating metabolic reactions with protein structures20 or by modeling protein-folding networks21. It however remains challenging to model more complex, eukaryotic organisms, such as S. cerevisiae, due to their metabolic complexity16 as well as due to the lack of availability of the required enzymatic data22, including high-quality protein structures21. In addition, such GEMs rely on thousands of parameters to describe the temperature effects on protein folding and kinetics16, which have to be empirically or computationally estimated21. This leads to large statistical uncertainties in model parameters and can make the models unreliable, due to inaccurate temperature associations between proteins and cell physiology. Therefore, in order to enable accurate modeling of the temperature dependence of cell metabolism, a key requirement is to develop a modeling approach that resolves the issues with large uncertainties of temperature-related parameters and produces accurate temperature constrained predictions.To this end, genome-scale metabolic models (GEMs)S. cerevisiae, the most widely used industrial organism with readily available thermal experimental data24 and highly sophisticated GEMs25. We first quantify and reduce the large uncertainties in the parameters describing enzyme thermosensitivity using Bayesian statistical learning26 to simulate phenotypic data. We show that the resulting models are capable of reproducing various experimental datasets and provide explicit insight into how yeast metabolism is affected by temperature. Our approach identifies the sterol metabolism as a key factor in the yeast thermal adaptation and predicts the flux-controlling enzymes in superoptimal temperature ranges as potential targets for the future design of thermotolerant yeast strains. We then experimentally validate the predicted most rate-limiting enzyme by replacing it with an ortholog from a known thermotolerant yeast Kluyveromyces marxianus. We hereby demonstrate the power of Bayesian genome-scale modeling for studying complex biological systems.Hence, in the present study, we introduce a Bayesian genome-scale modeling approach to model the temperature effects on cellular metabolism in 16 16 Fig. , (ii) fl16 Fig. . The ecG Tm Fig. , (ii) thCp‡ Fig. , and  to update Prior distributions (P(θ)) of model parameters to Posterior ones (P(θ|D))  or P(θ|D) as a Prior or Posterior etcGEM, respectively. The resulting Posterior etcGEMs provided a more reliable platform to study the thermal dependence of cell metabolism, with an inherent benefit that the uncertainty in the interpretation and prediction from the improved Posterior etcGEMs could also be quantified.To resolve the challenges arising from the uncertainties in the parameter values, we used Bayesian statistical learningD)) Fig. . We refe16 model and the resulting model was termed etcYeast7.6. Enzyme Tm and Topt parameters were either collected from literature or predicted by machine-learning models (“Methods”). The heat capacity change 31 and then applied for all enzymes. As a result, the etcYeast7.6 model was obtained with an expansion of 2292 temperature-associated parameters for a total of 764 metabolic enzymes 4, (ii) anaerobic batch cultivations (n = 8)5, and (iii) fluxes of carbon dioxide (CO2), ethanol, and glucose in chemostat cultivations (n = 6)23 . Another potential source of error was due to assuming the same We observed that etcYeast7.6 predictions made using the initial parameter values could only accurately reproduce the experimental observations when the temperature was lower than 30 °C, while the method failed at temperatures above 30 °C  reduction in variance was observed with 59% (449/764), whereas a significant  change in the mean value was found with merely 26% (200/764). Importantly, we observed that the approach tended to change the enzyme Topt rather than its Tm and Posterior etcGEM performance during the Bayesian approach was from enzyme Topt s   was found between the Posterior mean values and the experimental data  Fig. . This inata Fig. . Furtherues Fig. .Posterior etcGEMs capable of describing various experimental observations  are on average 20 °C higher than enzyme Topts collected from BRENDA35  with a mean melting temperature below 42 °C were present   . At first, at the lethal temperature of 42 °C, only a small improvement in growth rate (from 0.01 to 0.06 h−1) was predicted and no significant growth difference was detected between the wildtype and the strain with kmERG1 . Among all the enzymes in the model, the squalene epoxidase ERG1 displayed order of magnitude higher median flux sensitivity coefficient than other enzymes, indicating that it is the most flux-controlling enzyme at 40 °C Fig.  and abov−1 Fig. . We, thekcat and denaturation temperature independent) in the order of decrescent flux sensitivity coefficient values in each of the Posterior etcGEMs. Interestingly, in the case of recovering the cell growth rate to 0.2 h−1, we found an agreement among all Posterior etcGEMs that ten enzymes are required to be fully functional at 42 °C  enzymes were each predicted by more than 10% of the Posterior etcGEMs  for 707 S. cerevisiae proteins revealed a Tm distribution with a mean value of 52 °C and a minimum of 40 °C7, which suggests that protein denaturation alone might not be sufficient to explain the decline of yeast cell growth between 30 °C (OGT) and 42 °C . An alternative explanation is provided by the evidence of a significant increase of NGAM observed with yeast cells grown in anaerobic chemostat cultivations at high temperatures (33–40 °C) compared to ones grown at low temperatures (5–31 °C)5, which suggests an imbalance in cellular energy allocation in the superoptimal temperature range. Quantitative assessment using our modeling approach revealed that impaired cell growth is caused by a combination of decreased kcat values, increased NGAM costs, and protein denaturation  the flux through each reaction cannot exceed the capacity of its catalytic enzyme: E]i is the concentration of enzyme i; (2) the total enzyme amount is constrained by the experimental measurement: kcat were considered, [E]i in the first constraint should be [E]N,i, which is the concentration of individual active enzymes. [E]i in the second constraint should be The central concepts of an enzyme constrained modelrms Fig. . In addikcat values can be described with an expanded Arrhenius equation (macromolecular rate theory), by including a nonzero heat-capacity change  or a denatured state (U), and an equilibrium state was assumed: N ↔ U. TherebyE]t,i is the concentration of enzyme Gu(T) is the free energy difference between the denatured state and the native state and can be expressed asHu(T) and ΔSu(T) are the enthalpy and entropy changes between the denatured and native states at temperature T. It has been found that convergence temperatures Hu and ΔSu, respectively60. At such temperatures, the ΔHu and ΔSu converge to a common value of ΔH* and ΔS*. Thereby,Since there is not enough detailed information regarding the heat-induced denaturation process of yeast proteins, a simple two-state model denaturation was assumed as in many other studiesGu(T) need to be determined in order to model the temperature dependence of enzyme activities, and they can be associated with six unknown parameters: H*, ΔS*, and ΔCp,u for ΔGu(T).In summary, the values of H*, ΔS,*and ΔCp,u) for each enzyme, indirect measurements have to be used to approximate the larger set of thermal parameters. As there are six free variables in the system, six different equations are required to solve for those parameters.Tm:At the protein melting temperature Topt, the enzyme activity is maximized:At the enzyme optimal temperature r = kcat[E]N;in which kcat at the enzyme optimal temperature Topt is known:31.r2 = 0.998, Pearson’s correlation) between ΔH* and ΔS* of 116 proteins from Sawle et al.41 , if the distance between simulated data D is less than a given threshold PosteriorP(θ|D) when P(θ) is very different from P(θ|D), the acceptance rate would be very low and thus this approach becomes computationally expensive to generate a population of Approximate Bayesian computation16, the Tm (melting temperature) and T90 (the temperature at which 90% of the protein is in the denatured state) for 266 yeast proteins have been reported previously7. For enzymes lacking an experimentally measured Tm, a melting temperature of 51.9 °C (the average of existing Tms of 707 yeast proteins) was assumed. In the original paper7, the 95% confidence interval was reported for peptides measured in the experiments and the average standard error was estimated at 3.4 °C. This same value was used as the uncertainty measure for the experimentally determined Tms, since the standard error for protein Tm was not available. The Tm of the 266 enzymes was then described with a normal distribution N, in which Tm,i is the experimentally measured melting temperature of protein i. For enzymes that use the mean Tm of 707 proteins7 as Tm estimation, the corresponding uncertainty is described as the standard deviation of the 707 Tms, equaling 5.9 °C. Thereby, a normal distribution N was used.Among the 764 enzymes included in ecYeast7.6Topt values of all enzymes in this study were calculated using a previously developed machine-learning method Tome v1.0 (https://github.com/EngqvistLab/Tome)22, which predicts enzyme Topt based on primary sequences. This model has a coefficient of determination (R2 score) of 0.5 on the test dataset. Root-mean-squared error (RMSE) of the prediction was then estimated withfi is the predicted value and yi is the observed true value of enzyme i. Then each one of these predicted Topts was described with a normal distribution N.31. Given that 33, a standard variance of 2.0 was selected from testing a wide range of values because it covers a broad range of N was subsequently used to describe the 5. Since the experimental data only covers the temperature range of between 5 and 40 °C, any NGAM for temperatures lower than 5 °C was set to the value at 5 °C and for those higher than 40 °C was set to the value at 40 °C. The Eq.  was estimated from experimental data5 as 70.17 mmol ATP/gdw. Other parameters were unchanged. For the simulation of fluxes at aerobic chemostat conditions, with the same model settings as aerobic batch conditions, the simulation was carried out by first fixing the growth rate to a given dilution rate (0.1 h−1) and minimizing the glucose uptake rate. Then the glucose uptake rate was fixed to the simulated value multiplied by a factor of 1.001 (for simulation purposes). Finally, the total enzyme usage was minimized (same as used in ref. 16). To get the flux sensitivity coefficient of an enzyme at a given temperature, the kcat of all reactions associated with this enzyme were perturbed by a factor of (1 + δ). Then the maximal growth rates were simulated before (u) and after (up) perturbation. Finally, the flux sensitivity coefficient of enzyme i was calculated as μ and up are maximal specific growth rate before and after perturbation. δ of 10 was used in this study.At a given temperature, first the 4 and anaerobic5 batch cultivations at different temperatures, and glucose, carbon dioxide, and ethanol flux values at different temperatures measured in chemostat cultivations with a dilution rate of 0.1 h−1 23. The distance function was designed as follows: first, the coefficient of determination (R2) between simulated and experimental data was calculated for each of the above conditions. Then the average R2 across these three conditions multiplied by −1 was used to represent the distance The observed data used in this study was the maximal specific growth rate in aerobicPrior and Posterior was carried out by Welch’s t test62. The significance test for reduced variance was carried out by the one-tailed F-test. p values were adjusted with the correction63 using a family-wise error rate of 0.01. The significance cutoff was set to 0.01 , validation (10%), and test (10%) datasets. A random forest regressor with 1000 estimators was used. The train and validation datasets were used to optimize the hyperparameter. The obtained model could explain in total 23% the variance in the test dataset. The feature importance scores were extracted directly from the obtained model.Totally, 2292 parameters of 21504 parameter sets were used as the input feature matrix and the average 64. All the genetic manipulations were conducted based on the CRISPR/cas9 system. The codon-optimized kmERG1 were ordered from GenScript (Supplementary Data K. marxianus (KmERG1) was integrated to replace the native ERG1 (ScERG1) using CRISPR/cas9, yielding HL01. All the design and construction of the plasmid follows the previously described method64. The gRNA cassette for target gene scERG1 was obtained using the single-stranded oligos gRNA-ERG1-F/gRNA-ERG1-R, followed by assembling with the linearized backbone plasmid pMEL10, the single gRNA plasmid was constructed by Gibson assembly. The repair fragment containing kmERG1 with round 60 bp overlap was amplified by primers kmEGR1-scERG1up-F/kmEGR1-scERG1dn-R using codon-optimized kmERG1 as a template. Then the repair fragment and single gRNA plasmid were co-transformed into IMX58. All the strains and primers used in this study were listed in Supplementary Tables The background strain we used in this study was IMX581 derived from CEN.PK113-5D, which contains an integrated Cas9 expression cassette controlled by TEFp promoterS. cerevisiae IMX581 and HL01. Five single colonies of each strain were selected and precultured in YPD media at 30 °C, and cells were then transferred to flasks in 20 mL YPD media to reach 0.1 initial OD600 cultured at 40 ± 0.5 °C, 200 rpm. After that, the cells were transferred into fresh YPD media every 24 h with 0.1 initial OD600 and cultivated at 40 ± 0.5 °C, 200 rpm.The thermotolerance was tested and compared between Further information on research design is available in the Supplementary informationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1


To reduce fossil resource use, renewable carbon sources including biomass and CO2 itself need to be used. The (bio)catalysts of choice that are able to convert these carbon sources into valuable chemicals often have to be tailored to meet the industrial requirements in titer, rate, and yield, and, hence, ultimately, in cost. While exciting examples exist, from vitamins to plastic monomers and bioplastics, the metabolic engineering of such biocatalysts is still time and cost consuming. With the improvement of biological tools and ideas for standardization, creating, and/or building new whole-cell biocatalysts becomes an ever more rapid task.In times of ever-increasing demand for chemicals and the subsequent increase in COEscherichia coli strains [Here, we focused on metabolic engineering and synthetic biology that are driven by flux and/or metabolome approaches. Fluxes of all intracellular biochemical reaction steps are the ultimate outcome of genetic and environmental alterations. We are convinced that quantitative approaches in metabolite analysis will help to reduce the time required to establish an efficient whole-cell biocatalyst. In this Special Issue, Laviña, et al. demonstrated how metabolomics was utilized to characterize adaptive laboratory evolution  strains . With th strains . By comp13C flux redistribution has been performed on the first reported CRISPR-based genome-edited Synechococcus elongatusn to improve the co-production of succinate and ethylene [E. coli, flux balance analysis has been shown to be useful in pathway interpretation regarding formaldehyde condensation with tetrahydrofolate [13C labeling dynamics concerning l-histidine production with engineered Corynebacterium glutamicum [Additionally, the intracellular fluxes show advantages in decipher redox cofactor imbalances, futile cycles, and the use of alternative pathways. Thermodynamically feasible reaction conditions can not only explain the phenotype observed but may also lead to genetic targets for further strain improvement and to new biochemical network designs. Therefore, the common approaches to study intracellular fluxes were also exemplified in this Special Issue. Specifically, metabolic profiling in combination with rofolate . Finallyutamicum . TogetheS-methylmalonyl-CoA, for the heterologous production of polyketides in E. coli [An essential group of metabolites that always draws a lot of attention is thioesters. At least one-third of all cellular carbon is typically metabolized through a thioester of coenzyme A (CoA) and they are involved in 5% of all enzymatic reactions . From th E. coli .Nevertheless, enzyme assembly, an interesting but also challenging strategy to control metabolic flux, was also highlighted in this Special Issue. In this approach, enzymes, substrates, or metabolites were ligand-bound or physically sequestrated into isolated compartments that bring them closer, and consequently, enhance the flux of a metabolic pathway. Xueqin Lv et al. have introduced recent studies on scaffold-free strategies, synthetic artificial scaffolds, and physical compartments for enzyme assembly or pathway sequestration. Moreover, the authors also discuss further the potential applications and challenges of compartmentalized metabolic flux control .Saccharomyces cerevisiae [In recent years, deep learning has experienced exciting progress in influencing human life, and science was no exception. Our Special Issue covered two interesting topics showing how machine learning can support mass data processing in spectrometry-based metabolomics  and promrevisiae . Applica


WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic ( Sex‐specific, whole‐body human metabolic models were developed and constrained with physiological, dietary, and metabolomic data. They recapitulate known whole‐body metabolic functions and enable mechanistic exploration of host‐microbiome co‐metabolism. Moleculet al, et al, et al, The constraint‐based reconstruction and analysis approach Palsson,  generatein silico molecular‐level description of human metabolism is available  reaction identifiers . The literature was also reviewed for the absence of cellular organelles in the different organs  as well as a MATLAB  Live Script  enabling the reproducibility of all presented simulations.The conversion of a reconstruction into a model involves the imposition of condition‐specific constraints . We defined an organ to be metabolically non‐essential P = 0.0003 and P = 0.0001 for Harvetta and Harvey, respectively; Fig To further assess the predictive capacity of the WBM models, we simulated 57 inborn errors of metabolism (IEMs) by deleting the corresponding reaction(s) in all organs having the known defective gene(s) . We then predicted and compared for each IEM the known biomarkers in either blood, urine, or CSF. Across the 57 IEMs, a total of 215 out of 252 (85.3%) biofluid specific biomarkers were qualitatively predicted correctly by Harvetta and 214 out of 252 (84.9%) by Harvey Fig . We compin vivo  varies between individuals, depending on their age, sex, weight, height, and body composition. Phenomenological methods   = 15.06, P = 1.10e‐4)  , P = 0.90). In contrast, the WBM prediction added predictive value to the Mifflin‐St Jeor equation , P = 0.025). The last result implies that the WBM models are significantly superior to the Mifflin‐St Jeor equations in terms of prediction accuracy. Taken together, the results demonstrate that the WBM models provide good and replicable estimations of BMRs that improve on standard phenomenological methods. However, the analyses also highlight the need for personalized parameterization of the WBM models for a given simulation and that the use of further parameters is likely to improve the prediction fit. Equally, confounding factors, beyond age and sex, should be considered and validation in a larger cohort will be required.An independent, external data set, composed of age, weight, height, fat‐free body mass, and BMR data from six female and 16 male athletes , which corresponds to 11.8 mole ATP/day/person with the molecular weight of ATP being 507.18 g/mole (Dataset EV2: 3.2.2.7). A lower bound on a heart demand for hydrolysis of ATP (VMH ID: Heart_DM_atp_c_) was set to 6 mole ATP/day/person to account for the energy required for pumping blood through the body while allowing the remained to be consumed by supporting heart metabolic reactions. Despite the lack of comparable measurements of activity‐induced ATP consumption rates, we set these parameters on brain and heart ATP hydrolysis because each organ contributes to the overall energy consumption in a whole‐body model. After accounting for whole‐body maintenance and energy demand due to brain and heart activity, the basal metabolic flux distribution predicted that the remaining energy that the WBM models could extract from the dietary input was 515.7 kcal for Harvetta and 502.5 kcal for Harvey. These values correspond to approximately 20% of the average European dietary input  used for the WBM simulations. This result is consistent with a 15–30% allocation of dietary input toward physical activity , the liver (“gut‐liver‐axis”), and the colon   WBM models. In the large‐intestinal lumen of these personalized myWBM models, gut microbes can take up dietary metabolites after they transit from the small intestinal lumen where small intestinal enterocytes may also absorb them. Metabolites produced by gut microbes can be taken up by colonocytes or excreted via the feces.To constrain Harvetta and Harvey in a gut microbial context, we used published shotgun metagenomics and clinical data on 149 healthy individuals provided by the Human Microbiome Project (HMP) Consortium . Alistipes and Bacteroides sp. were predicted to produce the majority of L‐tryptophan, a precursor of serotonin. Finally, L‐tyrosine, a precursor of dopamine, was predicted to be produced by a variety of species belonging to the Actinobacteria, Firmicutes, and Proteobacteria phyla. Taken together, the modeling results predicted that gut microbes have the molecular mechanisms that may influence host brain metabolism directly by synthesizing neurotransmitters and indirectly by providing the amino acid precursors to be further metabolized by human enzymes. The predicted maximum secretion potential, as well as the contributing strains, depended on each individual's microbiome composition. Thus, the WBM models can be used to generate novel mechanistic hypothesis as to how the gut microbiota might influence brain metabolism  = 852.21, P = 1.56e‐80, see Clostridium scindens, Blautia hydrogenotrophica, and the well‐known pathobiont Clostridium difficile  = 953.32, P = 8.8e‐84, Microbially produced butyrate serves as the main energy source for the colonocyte  = 85.37, P = 3.547e‐25, Clostridioides genus abundances, substrate availability was limiting for p‐cresol production and subsequent sulfonation  microbiome contributes to inter‐individual variation in disease development and progression  approach allows the integration of physiological parameters and quantitative metabolomics data to restrict organ uptake rates to physiologically relevant values and thereby limit the achievable intra‐organ metabolic flux states. Our computational approach is tractable, as it relies either on solutions to linear or quadratic optimization problems. In contrast, hybrid modeling approaches, such as the ones integrating physiology‐based pharmacokinetic modeling with genome‐scale models .In this part, we describe the reconstruction approach and the information used to generate the WBM reconstructions, Harvey and Harvetta. The conversion of the reconstructions into condition‐specific WBM metabolic models is then described Statistical analyses details can be found at the end of the Materials and Methods section. Simulation‐relevant information and methods can be found in the Dataset EV2 . In Recon3D, many of the enzyme‐catalyzed reactions or transport reactions are associated with the corresponding gene(s) that encode the protein(s). These so‐called gene‐protein‐reaction associations (GPRs) are represented through Boolean rules  for isozymes or protein complexes, respectively. The reconstruction of Recon3D consists of 13,543 reactions, 4,140 unique metabolites, and 3,288 genes. The Recon3D publication also contained a flux and stoichiometrically consistent global metabolic model (Recon3D model) accounting for 10,600 reactions, 5,835 non‐unique metabolites , and 1,882 unique genes. Hence, we used the Recon3D model, rather than Recon3D as starting point, as we wished the WBM reconstructions, and their derived models to be also flux and stoichiometrically consistent. From the Recon3D model, we removed from all reactions, and metabolites, involved in protein and drug metabolism, as these pathways were beyond the anticipated scope of the first version of the WBM reconstructions. We then removed flux inconsistent reactions  using the COBRA toolbox , and the cerebrospinal fluid receives metabolites from the brain, finally draining into the circulatory blood compartment Fig . The lunTo ensure that metabolites found in different biofluids based on metabolomics data (see below) would also be present in the WBM reconstructions, we added demand reactions for all those metabolites to the corresponding biofluid compartments of the draft meta‐reconstructions. By doing so, we enforced that at least one organ could produce a given metabolite, or that it would be taken up from the diet (and the lumen).1.3et al, et al, et al, et al, et al, We defined a core reaction set containing all reactions, which should be present in the WBM reconstructions based on literature and omics data evidence. For each organ, the core reaction set was expanded to include organ‐specific (i) protein information from the human proteome map (HPM) . For reactions that were found to be absent in an organ, we set the corresponding lower and upper bounds to 0 in the draft meta‐reconstructions, which corresponds to removing them from the meta‐reconstructions. These absent reactions were then removed from the meta‐reconstructions by the algorithm as they were not flux‐consistent.1.3.1et al, The HPM resource  major metabolic pathway, (ii) product formed, and (iii) cellular location. The reaction content of Recon3D* was classified into 427 metabolic units when only the major metabolic pathway was considered, and the cellular compartments ignored. When the whole metabolic unit, along with all its components, was taken into account, 5,637 metabolic units resulted. Usage of the metabolic units greatly accelerated the reconstruction process as they account for individual metabolite‐specific pathways as well as key enzymes of the biochemical pathways, in the same way that they are frequently reported and referred to in the biochemical literature. This literature information was translated into the occurrence and non‐occurrence of metabolic units. Additionally, we noted tasks that an organ can carry out  or the inability to carry out a particular task , leading to the formulation of organ‐specific metabolic objectives.We also collected information on the pathways/reactions that are absent across organs . Primaryhttp://www.vmh.life).Brief descriptions of the organ‐specific metabolism for those organs that we curated extensively for their metabolic content can be found at the Virtual Metabolic Human database , the least information could be found. We enabled the secretion of mucin degradative products, glycans, and ethanolamine into the large‐intestinal lumen from the colon  biomass_reaction, (ii) biomass_maintenance, and (iii) biomass_maintenance_noTrTr. The biomass_reaction is the general biomass reaction as in Recon3D* model, biomass_ maintenance is same as biomass_reaction except for the nuclear deoxynucleotides, and biomass_maintenance_noTrTr, where _noTrTr stands for no transcription and translation, is devoid of amino acids, nuclear deoxynucleotides, and cellular deoxynucleotides except for adenosine‐triphosphate.et al, et al, et al, The biomass reaction was retained only for tissues/organs known to possess regenerative capacity, i.e., liver , or calculated using phenomenological methods (see Dataset EV2: 3.2 for more details)  and the storage of metabolites in the various organs was enabled .Second, a fasting condition was defined, in which all dietary uptake reactions were closed (lower and upper bound were set to zero) but access to stored metabolites from the different organs was enabled .Using these four differently setup meta‐reconstructions along with the corresponding  core reaction sets and the fastCore algorithm, we generated four flux‐consistent subnetworks. We then removed the demand reactions for the biofluid metabolites, defined all reactions present in each subnetwork to be the core reaction set for the given setup, and repeated the extraction of flux‐consistent subsets from the meta‐reconstructions. By doing so, we also enforced that at least one reaction could either transport or excrete a given biofluid metabolite or that it is catabolized in at least one organ.Finally, we joined the fasting and the feeding subnetworks for each sex. The rationale for having the feeding and the fasting condition is that the human body can fast overnight, and thus the WBM reconstructions should capture this capability regarding catabolic as well as anabolic metabolic reactions. Note that the WBM reconstructions are not able to starve, as this would require the degradation of muscle proteins, which we did not explicitly capture in Recon3D*, and thus in the WBM reconstructions.The fastCore adds a minimal number of additional reactions to the core reaction sets to form the flux consistent, compact subnetwork. Hence, the added reactions represent hypotheses of which reactions and pathways would be needed to make the subnetworks flux consistent, given a set of core reactions. It does not mean that the proposed solution is biologically relevant. Consequently, after the generation of the male and female draft WBM reconstructions, we manually inspected that the added reactions were consistent with the current knowledge about organ‐specific metabolism. The core reactions and the absence of organ‐specific reactions were updated based on literature evidence, and the subnetwork generation was repeated.et al, As one example, we encountered genes/proteins that were present in an organ‐specific manner as per the human proteome data set . We also added transport reactions for all metabolites present in the extracellular space of the organs to the corresponding biofluids. For instance, certain metabolites were present in the CSF compartment due to brain and spinal cord metabolism but no corresponding transport reactions from CSF [csf] to blood circulation [bc] were included by fastCore. Hence, we added those reactions as the entire CSF drains into the blood circulation. Again, the reason that such reactions were not included into the reconstructions by fastCore as it returns a most compact subnetwork.Moreover, WBM model predictions were compared with organ‐specific data and known whole‐body metabolic functions , amino acid cycle (Cahill cycle)). In this step, we added missing reactions to the sex‐specific draft WBM reconstructions. During the entire reconstruction process, we performed the same quality control and assurance tests as defined for genome‐scale metabolic reconstructions .To ensure reproducibility of the presented work, and also to reduce the number of supplemental tables, we provide a MATLAB  live script along with a PSCM toolbox, which is an extension to the COBRA Toolbox , if not stated differently.2.1.2S matrix), where the columns correspond to reactions (variables) and the rows correspond to the metabolites. If a metabolite (j) participates in a reaction (i), the stoichiometric coefficient is entered in the corresponding cell . Consequently, each row represents the mass‐balance equation for a given metabolite (dx/dt).The conversion of a metabolic reconstruction into a condition‐specific model includes the transformation of the biochemical reaction list into a computable, mathematical matrix format  and systems boundaries, in the form of so‐called exchange reactions Palsson, . The conet al, In flux balance analysis  minus maximal possible flux value for a reaction i : fluxSpani = .We performed flux variability analysis . First, we tested for secretion or production of metabolites when all the exchanges and sinks were closed (see Simulation details). We shall refer to this test as leak test. Thereafter, the models were tested for carrying non‐zero fluxes for reactions known to be required for important metabolic functions. We refer to these latter tests as metabolic function tests (see Simulation details).2.2.1.1et al, The WBM reconstructions were tested for thermodynamically infeasible loops that could generate metabolites or compounds when no mass enters the model. Such a test was done in two steps. Firstly, all the exchange, sink, and demand reactions were constrained to zero for the lower bound. Then, all the exchange reactions were optimized to check whether the model was carrying any non‐zero flux. After that, the biomass was optimized to check whether the model was carrying any zero flux. Secondly, a demand reaction for each compartment‐specific metabolite in the model was created and optimized. The basic purpose of running such a leak test was to check whether the model can generate anything from nothing. In case any of the demand or exchange reactions carry a non‐zero flux, the respective reaction was optimized while minimizing the Euclidian norm  or more. In order to perform efficient flux variability analysis on the WBM models with acceptable solution times, it is recommended to run the optimization problems on all cores, each spawning 2 threads.et al, In addition to appropriate hardware, appropriately tuned software was important. For large‐scale models, poorly performing code and an untuned solver are bottlenecks. As the optimization solver accounts for most of the simulation time, parameter tuning together with an appropriate interface are critical. The Tomlab interface is recommended when interfacing the industrial‐quality CPLEX solver from MATLAB. Some analyses that were computing intensive, such as flux variability analysis, were run from MATLAB, but the solver is interfaced directly through a compiled MEX interface written in C. The newly developed Julia language has proven itself to be a valuable alternative in launching a multi core flux variability analysis  and in silico prediction (three categories: increased/inconclusive/decreased) as the predictor. Finally, respecting potential dependencies of predictions coming from the same IEM, the agreement percentages per IEM was calculated. For example, if four of five biomarkers in a certain IEM were correctly predicted, the agreement percentage would be 80%. The models (Harvey/Harvetta vs Recon3D*) were then tested against each other using a non‐parametric sign test with agreement percentages per IEM as the response variable. This test effectively provided statistical inference on the question of whether Harvey/Harvetta showed higher accuracy in predicting biomarkers per IEM in comparison to Recon3D* in a majority of IEMs. All statistical calculations were performed in STATA 14/MP.To analyze the prediction accuracies and compare them across models and IEMs, the predictions for each metabolite were categorized  according to the simulation results (Table EV16). Then, the agreement in percentage with the 4.2et al, et al, et al, To validate the WBM‐based BMR predictions, we calculated the Pearson correlations and corresponding R‐squared values of the WBM predictions when regressing them on the empirically measured BMRs from (Prentice 4.3To analyze the relation between microbiome traits and fluxes in the organ‐specific reactions, we calculated the maximal fluxes through the liver sulphotransferase, the liver alcohol dehydrogenase, and the colonic carboxylic acid:CoA ligase. Then, we performed OLS regressions with the fluxes as the dependent variable and the Bacteroidetes/Clostridia ratio as the predictor, allowing for non‐linear transformations of the predictor using multivariable fractional polynomials. Besides the classical parametric test on the fit of the regression models, we used two non‐parametrical methods to assess the significance of the regression models in sensitivity analyses. First, we used bootstrapping using 4,000 replications to determine p‐values. Second, we used quantile regression as an outlier robust method. The full results can be found in IT, RMTF, and SS conceived the study. IT, SS, MKA, and AH contributed to the reconstructions. RMTF and LH contributed tools and methods. IT and RMTF performed the simulations. IT, AH, and RMTF analyzed the data. IT, JH, and AH performed the statistical analysis. IT, RMTF, SS, JH, and AH wrote the manuscript. All authors edited the manuscript.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Table EV4Click here for additional data file.Table EV5Click here for additional data file.Table EV6Click here for additional data file.Table EV7Click here for additional data file.Table EV8Click here for additional data file.Table EV9Click here for additional data file.Table EV10Click here for additional data file.Table EV11Click here for additional data file.Table EV12Click here for additional data file.Table EV13Click here for additional data file.Table EV14Click here for additional data file.Table EV15Click here for additional data file.Table EV16Click here for additional data file.Table EV17Click here for additional data file.Table EV18Click here for additional data file.Table EV19Click here for additional data file.Table EV20Click here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Review Process FileClick here for additional data file.