Clostridium tyrobutyricum is the most promising microorganism for industrial bio-butyrate production. However, the metabolic driving mechanism for butyrate synthesis was still not profoundly studied.Butyric acid, an essential C4 platform chemical, is widely used in food, pharmaceutical, and animal feed industries. C. tyrobutyricum, which provides a comprehensive and systematic analysis for the butyrate synthesis driving mechanisms. Based on the analysis in silico, an energy conversion system, which couples the proton efflux with butyryl-CoA transformation by two redox loops of ferredoxin, could be the main driving force for butyrate synthesis. For verifying the driving mechanism, a hydrogenase (HydA) expression was perturbed by inducible regulation and knockout. The results showed that HydA deficiency significantly improved the intracellular NADH/NAD+ rate, decreased acetate accumulation (63.6% in serum bottle and 58.1% in bioreactor), and improved the yield of butyrate (26.3% in serum bottle and 34.5% in bioreactor). It was in line with the expectation based on the energy conversion coupling driving mechanism.This study reports a first-generation genome-scale model (GEM) for C. tyrobutyricum and provided a new insight for tuning metabolic flux direction in Clostridium chassis cells.This work show that the first-generation GEM and coupling metabolic analysis effectively promoted in-depth understanding of the metabolic driving mechanism in The online version contains supplementary material available at 10.1186/s13068-022-02169-z. Clostridium tyrobutyricum, a Gram-positive, strictly anaerobic bacterium, is widely regarded as the most promising microbial cell factory for butyric acid production -hydrogenase gene (hydA) was perturbed by inducible regulation and knockout, respectively. The result showed inhibiting HydA expression could significantly decrease the acetate accumulation and improve the intracellular NADH/NAD+ ratio and the butyrate yield. These phenomena agree with the analysis in silico for energy coupling metabolic driving mechanism, and this mechanism will provide a new insight for metabolic engineering C. tyrobutyricum.In the present study, the first-generation GEM for C. tyrobutyricum ATCC 25755, the genome sequence was re-annotated to associate with EC number, and then corresponding reactions were collected based on EC number . Finally, the GEM of iCT583 contained 858 reactions, 798 metabolites, and spanned 583 genes. All reactions were assigned to nine metabolic subsystems  is not bigger, which could attribute to fewer open reading frames (ORFs) in C. tyrobutyricum. However, the ORF coverage rate of iCT583 (18.2%) is better than most of the clostridia GEMs in Table  iCac967.For the GEM construction for C. tyrobutyricum in vivo, some reactions were supplemented, and the refined model could reach corresponding metabolic characteristics in silico , the H2 and acetate production were negatively correlated with butyrate production. Moreover, butyrate production has little impact on the cell growth and CO2 emission. These results indicated that iCT583 could model the coupling metabolic feature in vivo. This coupling characteristic could be used as foundation for in silico analysis of the metabolic flux distribution and the cofactor turnover mode; whereas, at the high butyrate production level, the negative coupling relationship could attribute to the competitive carbon flux and reducing power consumption for butyrate synthesis.For exploring the coupling metabolic relationship in silico, the specific butyrate production rate was set from 0 to 4 mmol/g CDW/h. Then the specific production rates of H2 and CO2 the emission, and proton efflux from intracellular to extracellular . Then the PMF drove ATP synthase to produce ATP for cell growth, which created an anaerobic energy conversion system  to redox potential carried by organic redox couples (such as NADH/NAD+ and reduced/ oxidized ferredoxin), then the turnover of these organic redox couples further converted redox potential to some intermediate metabolites for cell growth and energy currency  [2 and H2 emission). It also explained the coupling relationship between cell growth and butyrate production since energy currency and some intermediate metabolites were necessary for cell growth. Therefore, it was believed that the energy conversion could be an original driving force for butyrate production in C. tyrobutyricum.Based the coupling characteristic in silico, the analysis of the cofactor turnover rates Fig. A, middleredoxin) , 27. In iCT583 construction, The Bcd/EtfAB was considered as flavin-based electron bifurcation (FBEB). In the C. tyrobutyricum ATCC 25755 genome, two bcd genes (CTK_C26200 and CTK_C26360) were found, and the flanking region of CTK_C26360 contained two genes encoding electron transport subunits (etfA and etfB)  in the C. tyrobutyricum ATCC 25755 genome. None of their flanking regions contains HydB and HydC coding gene, which are the necessary subunits for Hyd to perform electron bifurcation [C. tyrobutyricum ATCC 25755 belong to G1-group Hyds, which only use ferredoxin as the electron donor for H2 production [C. tyrobutyricum genome provides solid evidence for the two redox loops of ferredoxin.In the two redox loops of ferredoxin, the reaction mechanism of Bcd/EtfAB (CTR0417) and Hyd (CTR0446) was the critical factor for the coupling metabolic relationship. During utylicum  and C. kkluyveri . In C. krredoxin . Therefourcation . It demooduction . TherefoiCT583. Then the variations in the end-products and cofactor turnover rates were extracted to analyze the corresponding relationships. With an increase in the specific acetate production rate, the specific butyrate production rate and H2 emission rate showed negative and positive coupling relationships, respectively . To further analyze the critical factor affecting acetate metabolism, the specific acetate production rate was set from − 2 to 2 mmol/g DCW/h  in C. tyrobutyricum ATCC 25755 genome. CTK_C05160 is the most likely hydA coding gene, and CTK_C029580 (hyd1) and CTK_C026290 (hyd2) belong to the group B family of monomeric hydrogenases. In previous studies, it has been reported that the hydA gene was impossible hardly to be knockout in the C. acetobutylicum [hydA expression using a theophylline-dependent riboswitch (PfdxE) [C. tyrobutyricum, a flavin mononucleotide-based fluorescent protein (BsFbFP) [C. tyrobutyricum. Therefore, the characteristics of PfdxE perfectly fit the requirements for hydA expression regulation.To verify the energy conversion system and the effects of regulating the electron transfer mode, this study plans to regulate Hyd expression and investigate the effects on the products distribution. As mentioned above, three Hyd coding genes were found in the utylicum , 32. The (PfdxE) . For eva(BsFbFP) , 35 was hydA gene was replaced with PfdxE by using an endogenous CRISPR system [3 as a pH buffer. During the fermentation , further restricted energy conversion. It could be the reason for the slow metabolism feature and activation of lactate synthesis. The activation of lactate synthesis could establish a new balance for cofactor turnover and energy conversion. The unsustained acetate assimilation could attribute to a compensation mechanism within Hyds expression, which can upregulate other Hyds (Hyd1 and Hyd2) for compensation. Thus, the PfdxE-hydA strain still produced 1.34 mM H2 under the non-induced conditions  . The butyrate yield and butyrate titer/acetate titer ratio (BA/AA) were increased by 26.3% (ΔhydA: 0.48 ± 0.02 g/g vs WT: 0.38 ± 0.00 g/g) and 15.7% (ΔhydA: 88.91 ± 1.48%vs. WT: 76.82 ± 0.76%), respectively. The Δhyd1 and Δhyd2 mutants did not show a distinct improvement in the butyrate yield or the BA/AA ratio , proton pump (RnfA-E), or the transportation of undissociated acid products. Therefore, with low pH, proton efflux by RnfA-E can be significantly enhanced by Hyd knockout, and then it led to the change in the distribution of products. Furthermore, in methanogenic archaeon, the energy conservation via hydrogen cycling, which generates proton motive force by coupling intracellular H2 emission and extracellular consumption, was also proposed to illustrate the energy generation mechanism [C. tyrobutyricum . The coupling mode should also have a big difference between methanogenic archaeon and C. tyrobutyricum. Therefore, in future studies, the specific functions and the contributions of different Hyds on PMF need to be further evaluated at the qualitative and quantitative levels.Fermentation performance of these mutants was also evaluated in serum bottles with 60 g/L glucose and 40 g/L CaCOATCC 824 , but it ATCC 824 . Maintaiechanism . These h ΔhydA mutant, this study also investigated the fermentation performance under three different pHs in a bioreactor. At pH 5.5 and 6.0 , and there was a distinct formate accumulation (3.43 ± 0.05 g/L). By evaluating the intracellular NADH/NAD+ ratio at the logarithmic stage of fermentation . Notably, the gap in the NADH/NAD+ ratios between the ΔhydA mutant and the wild type became fewer as the pH increased. In general, intracellular pH was slightly higher than the extracellular pH in the neutralophilic bacterium [C. tyrobutyricum could not efficiently grow under an alkaline environment (pH > 7.0) (data not shown). It indicated that the higher intracellular pH could break through the tolerable pH range of C. tyrobutyricum. Therefore, once extracellular pH increases, a series of regulation for intracellular metabolism and physiological states change will form a new homeostasis PMF which was lower than that before pH increase. It meant that electrochemical potential from PMF could be not efficiently converted to redox potential. It is a kind of PMF dissipation in nature. The dissipation of PMF could further impair energy conversion and weaken the metabolic driving force for butyrate production. Essentially, activation of lactate production at higher pH was a metabolic characteristic under a status with low-energy conversion. When the ΔhydA mutant with pH 6.5, the formate accumulation could be attributed to the synergistic weakening both in a redox loop of ferredoxin (between Hyd and Pfor) and the PMF, which caused activation of pyruvate formate-lyase to compensate acetyl-CoA synthesis for cell growth. Therefore, the high-level intracellular NADH/NAD+ ratio in the ΔhydA mutant is certainly consistent with the status of in silico analysis, and the gap of NADH/NAD+ ratios between the ΔhydA mutant and the wild type becoming smaller with the pH also provide evidence for the coupling relationship between PMF and NADH turnover rate. Also, these results demonstrated that the energy conversion coupling driving mechanism is reasonable.For determining the effects of pH on the metabolic characteristic ine Table. . Furtheracterium . For maiC. tyrobutyricum ATCC 25755 (iCT583) and this model was capable of matching experimental growth and modeling the coupling metabolic behavior. Through this model, two redox loops of ferredoxin, which coupled the proton efflux with the processes of crotonyl-CoA reduction and pyruvate oxidation, were found to be the essential mechanisms of energy conversion coupling metabolic process. In this process, the electron transfer triggered by the redox reaction is converted into PFM, which supports energy supply for the basic process of cell activities. Then, the reaction products of the redox reaction as intermediate metabolites are further converted into end-products to maintain cellular homeostasis. Actually, for the C. tyrobutyricum, the products that human beings need were never the aim of cell lifecycle, but energy conversion for the reproduction. However, for biotransformation, efficient accumulation of desired products is the goal pursued by metabolic optimization. It will be an inevitable contradiction, unless the biotransformation is in the cell-free system. To address this challenge, deep understanding of metabolic driving mechanisms will be meaningful and useful for redesigning metabolism. This work revealed the energy conversion coupling driving mechanism for butyrate synthesis in C. tyrobutyricum, which can reasonably explain metabolic characteristics and phenomena in butyrate fermentation. Tuning this energy conversion system by regulating Hyd expression can significantly affect the distribution of fermentation products and metabolic characteristics. It demonstrated that the metabolic driving mechanism is an important factor for regulating metabolic flux. This insight can be further applied to metabolic optimization and develop C. tyrobutyricum as chassis cells for other chemicals and biofuel. In addition, this coupling driving mechanism also could be referenced for analyzing other Clostridium species, in which products synthesis was coupled with cellular growth.Overall, this work provided the first GEM for C. tyrobutyricum ATCC 25755 was obtained from the work of Joungmin et al. [The genome sequence of n et al. . The COBn et al. . Gurobi,n et al. , and a sC. tyrobutyricum ATCC 25755 at 37 °C under anaerobic conditions. The seed culture was cultured in a CGM medium for fermentation, and the composition is described in a previous study [600 = ~2.0 in 100-mL serum bottles containing 50 mL of liquid CGM, at 37 °C. For batch fermentation in serum bottles, the fermentation medium was based on the CGM medium supplemented with 60 g/L glucose and 40 g/L CaCO3 (for buffering pH changes). Escherichia coli Top10 was used as a cloning host for general plasmid construction. E. coli CA434 was used as the donor strain for conjugation [E. coli were cultivated in Luria Broth (LB) medium at 37 °C (30 μg/mL chloramphenicol was supplied when necessary). Colonies of recombinant C. tyrobutyricum strains were screened on RCM plates (RCM medium with 20 g/L agar) supplemented with 250 μg/mL D-cycloserine and 30 μg/mL thiamphenicol.Reinforced Clostridial Medium (RCM)  was used to activate us study . The seeAll plasmids and primers used in this study are shown in Additional file 600 value.The batch fermentation kinetics were studied in a 5-L bioreactor  with 1 L of CGM medium. Before inoculation, nitrogen gas was sparged in the bioreactor for ~ 30 min to reach anaerobic conditions. A 50 mL of the seed culture (5%) was used to inoculate the fermentation medium. After inoculation, the fermentation medium was agitated at 150 rpm and 37 °C, and the pH value was maintained using a 30% (v/v) ammonia solution. Samples were taken regularly to analyze the butyrate, acetate, residual sugar concentrations and the ODFor detecting fluorescence intensity, the cells at the late logarithmic stage were collected by centrifuging at 5000 × g for 3 min, washed twice with PBS, and resuspended in the same-volume PBS buffer. Fluorescence intensity measurements were performed by a plate reader , with 150 μL volume for each sample in a 96-well plate. The wavelength excited was at 488 nm, and the wavelength captured was at 535 nm.600 value to a dry cell weight (DCW), which was converted as 1 OD600 = 0.30 ± 0.04 g DCW/L. The H2 and CO2 concentrations were measured by gas chromatography  and the chromatographic conditions based on a previous protocol [The glucose, butyrate and acetate concentrations were measured by HPLC  using chromatographic conditions based on a previous protocol . The celprotocol .+ analysis, the wild type and ΔhydA mutant cells were collected after fermentation for 18 h in a bioreactor. The concentrations of NADH and NAD+ were measured using a NAD(H) content assay kit . The NADH/NAD+ ratios were calculated from the concentration of NADH divided by the concentration of NAD+.For intracellular NADH and NADTotal RNA was extracted using a bacteria RNA kit  and was reverse transcribed into cDNA using HiScript III RT SuperMix . A relative quantitative PCR (qPCR) assay was performed using qPCR Master Mix . The specific primers used for qPCR are listed in Additional file Additional file 1: Table S1 Comparison of cell growth of C. tyrobutyricum in silico and in vivoa. Table S2 Bacterial strains and plasmids used in this study. Table S3 Primers used in this study.Additional file 2: Figure S1 Compared the arrangement of Bcd-EtfAB complex in the chromosome. bcd, butyryl-CoA dehydrogenase; etfA, electron transfer flavoprotein, alpha subunit; etfB, electron transfer flavoprotein, beta subunit; hbd, 3-hydroxybutyryl-CoA dehydrogenase; crt, 3-hydroxybutyryl-CoA dehydratase; rex, redox-sensitive transcriptional regulator. Figure S2 CTR0446 (Hyd) and CTR0447 (RnfA-E) constraining simulation. In this process, FBA was used as the analysis method, and biomass synthesis reaction was set as the objective function. The reaction flux of CTR0446 and CTR0447 were constrained to increase gradually from 0 mmol/g DCW/h to 10 mmol/g DCW/h, respectively, then, the specific product rates of main productions were collected from every simulation result. Figure S3 Characterization of theophylline riboswitches in C. tyrobutyricum. The values represent averages for three replicates and error bars represent standard errors of these experiments. . Figure S4 Confirmation of replacement of the hydA promotor to theophylline-dependent inducible expression part. (A) Schematic of PCR verified method for PfdxE-hydA strain. Primers were marked as the red and blue arrows in (A), respectively, and the primer annealed site of hydA-test-F and hp-test-R were located at the flanks of the upper and lower homologous arms. (B)The agarose gel electrophoresis results for validating the PfdxE-hydA mutant. The 1 lane represented the PCR product of amplified using primers hydA-test-F and PfdxE-R; the 2 lane represented the PCR product amplified using primers PfdxE-F and hp-test-R. Figure S5 Confirmation of knockout hydA gene. (A) Schematic of PCR verified method for ΔhydA strain. Primers were marked as the red arrow in (A), and the primers annealed site of T3-test-F and T3-test-R were located at the flanks of the upper and lower homologous arms. (B) The agarose gel electrophoresis results for PCR products. Figure S6 Confirmation of knockout hyd1 gene. (A) Schematic of PCR verified method for Δhyd1 strain. Primers were marked as the red arrow in (A), and the primers annealed site of T5-test-F and T5-test-R were located at the flanks of the upper and lower homologous arms. (B) The agarose gel electrophoresis results of PCR products. Figure S7 Confirmation of knockout hyd2 gene. (A) Schematic of PCR verified method for Δhyd2 strain. Primers were marked as the red arrow in (A), and the primers annealed site of T6-test-F and T6-test-R were located at the flanks of the upper and lower homologous arms. (B) The agarose gel electrophoresis results of PCR products. Figure S8 Fermentation characteristic evaluation in serum bottles with CaCO3 as pH buffer. (A), (B), (C) and (D) represent wild-type strain, hydA deficient strain, hyd1 deficient strain, and hyd2 deficient strain, respectively. Figure S9. The phylogenetic tree of C. tyrobutyricum with relative organisms.


All organisms on the Earth are composed of one or more cells, making cellularity a fundamental organizing principle of life. Cellular organization as we see it today is complex, requiring the metabolic maintenance of membranes and their constituents as well as genetic control over the timing and coordination of cell division. Despite this complexity, phylogenetic evidence suggests that metabolic pathways for membrane phospholipid biosynthesis had evolved by the time of the last universal common ancestor of life . Some coPrebiotic chemistry experiments suggest that the materials required to make membranes would have been present prior to the origin of life. Specifically, abiotic membranes or protocells can form from naturally occurring or otherwise prebiotically plausible lipids . ProtoceThe RNA World hypothesis proposes that the current tripartite genetic system composed of DNA, RNA and proteins was preceded by a stage in which functional RNAs including RNA enzymes, or ribozymes, were encoded by RNA genes . WhetherA parallel line of theoretical and computational research suggests that natural selection would have favoured cells or protocells during the early evolution of life because of their ability to stabilize genomes. For example, if the genetic material of early life forms was encoded on small, single-gene chromosomes rather than larger multigene chromosomes, these gene-sized chromosomes may have replicated at different rates, disturbing the balance of gene products. Protocell encapsulation could have mitigated inter-gene conflict and ensured that at least some progeny received the correct complement of genes after protocell division . Recent et al. [Taken together, these and related lines of research show that different selection pressures favouring genome integrity also promote the evolution of protocell encapsulation or cellular organization. These simulations, however, all assume that some kind of heritable genes, such as those imagined by the RNA World hypothesis, already exist, and that evolution is acting on those genes as units of selection. A recent publication by Nunes Palmeira et al.  is uniqu2 can enter a protocell wherein it is fixed to form the two-carbon compound acetate, a process that is plausible in certain potential origin of life settings, namely iron sulfur minerals and alkaline hydrothermal vent systems. The fixed carbon can then be converted through various protometabolic pathways into prebiotic amino acids, sugars or fatty acids, or it can be consumed in order to store chemical potential energy in the form of phosphorylated acetate. As fatty acids accumulate, these are incorporated into membranes, which leads to protocell growth and division. One subset of the amino acids in this protometabolic network can facilitate the formation of iron–sulfur clusters, an important enzyme cofactor that catalyses redox reactions across a range of metabolic pathways. The other subset of amino acids may be combined with sugar and energy to generate nucleotides.In their model, CO2, FMN, coenzyme A, S-adenosyl methionine and UDP-glucose.An important feature of this model is that nucleotides are treated as catalysts rather than components of a genetic polymer such as RNA. Many reactions in modern metabolism, especially thermodynamically unfavourable reactions, are catalysed by cofactors that are composed of nucleotides or nucleotide derivatives. ATP, for example, is both the adenosine nucleotide used in RNA synthesis and the universal currency of chemical potential energy used throughout metabolism by every organism. NADH, which is similarly used throughout metabolism as a carrier of redox potential energy, is a dinucleotide composed of an adenosine nucleotide and a nicotinamide nucleotide. Other examples of nucleotide- or nucleoside-based cofactors include NADPH, FADHThe prevalence of nucleotide cofactors in metabolism was described several decades ago as evidence for an RNA World, the idea being that nucleotide cofactors are remnants of ribozyme active sites in which the surrounding scaffold has since been replaced by protein enzymes . Nunes PWhen nucleotides predominantly catalyse fatty acid synthesis, this has a counterintuitive long-term effect of undermining protocell growth and division. Though fatty acid synthesis should lead directly to protocell growth and division, diverting the metabolic flux toward fatty acid synthesis also decreases the rates of synthesis of other products that would otherwise feed back on the protometabolic network. Specifically, fatty acids represent an endpoint in the network and thus do not contribute to the synthesis of nucleotides from amino acids, sugars and energy. They also do not contribute to increased carbon fixation, i.e. the input into the protometabolic network. Therefore, when metabolic flux is diverted to fatty acid synthesis, the rates of metabolism ultimately slow down overall and lead to decreased, rather than increased, protocell growth and division.Alternatively, protocell growth and division is enhanced when nucleotide catalysis creates a positive feedback loop within the protometabolic network. This is best achieved when nucleotides catalyse carbon fixation, thereby increasing the input of reactants into the entire protometabolic network. Protocell growth and division can also be increased through nucleotide catalysis of amino acids and energy currency. In both of these cases, the increased production of amino acids or energy currency has the ultimate effect of increasing carbon fixation. So even though, in this scenario, the nucleotides catalyse downstream pathways in the protometabolic network, this results in a subsequent acceleration of input into the entire network.The authors find that the protometabolic network is most successful when nucleotide catalysis is general and flux remains balanced through all branches of the network. This result is especially satisfying because the sort of general catalysis that promotes protocell growth and division in the model is similar to the general catalysis that we see performed by the nucleotide cofactors that drive metabolism today. Nucleotide cofactors like ATP and NAD are used by many enzymes across many different metabolic pathways. As in the protometabolic network model, these cofactors in modern metabolism are essential in carbon fixation as well as the downstream synthesis of biomolecules. As Nunes Palmeira and colleagues point out, nucleotide cofactors even catalyse their own biosynthesis; that is, ATP (a purine nucleotide) is an essential component of purine biosynthesis pathways.This protometabolic network, though theoretical, is also supported by recent research in prebiotic chemistry. For example, a recently discovered abiotic reaction network synthesizes amino acids and other metabolites from small organic compounds that themselves can be synthesized through abiotic carbon fixation . DespiteAs the authors point out, in a protometabolic network catalysed by nucleotides, random polymers of RNA or peptides may be able to bind and stabilize nucleotide cofactors, enhancing their catalytic functions. Protocells that incorporate these kinds of polymers may grow and divide faster as rates of carbon fixation and general flux increase through the protometabolic network. Experimental evidence supports such a scenario, demonstrating that non-enzymatic, template-directed copying of RNA can occur within protocells  and thatbona fide cellular organization in order to stabilize those genomes [The model described by Nunes Palmeira and colleagues thus depicts a first step in establishing the deep relationship between genetic inheritance and cellular organization in early evolutionary history. Protocells were previously shown to have been stabilized by nucleobases . Here, t genomes –10. This


Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem.We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts.We show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions.Our results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes. Metabolism generates energy from food supply, provides building blocks for macromolecular biosynthesis, and plays a pivotal role within cellular functions. It is regulated by a sophisticated web of interactions that respond to the overall physio-pathological state. In turn, several metabolites control signal transduction and epigenetic pathways. As a result, metabolism is in a privileged position to integrate metabolic, genetic, epigenetic, and environmental signals, so that each physio-pathological condition can be associated with a specific metabolic state , 2.Metabolic networks represent powerful instruments to study metabolism and cell physiology  under different conditions , 4. SuchS of dimension M is the number of metabolites and R is the number of reactions. In a COBRA model, a steady-state condition is imposed, that is, the total production of any metabolite must equal the total amount of its consumption. Hence, any possible metabolic flux configuration is represented by a vector The starting point of COBRA modeling is the information embedded in the metabolic network, which can be represented with a stoichiometric matrix is (FBA) , which cis (FBA) . In a nuis (FBA) .Incorporation of the gene expression data into the objective function is most typically used when the aim is to reconstruct a tissue-specific metabolic network. For example, the INIT algorithm seeks for a flux distribution that maximizes the flux through highly expressed reactions while minimizing the flux through lowly expressed reactions , 12. TheIncorporation of gene expression data into flux constraints is instead more typically used when the aim is to obtain a plausible flux distribution of the system under study, without necessarily delivering a specific model. This is the case when one wants to characterize the flux distribution of single-cells within a mixed cell population.This latter kind of integration is generally based on Gene Protein Reaction (GPR) associations, which describe the relationship between genes and reactions and among genes possibly associated with the same reaction. More in detail, the GPRs represent logical formulas describing how gene products concur to catalyze a given reaction, using Boolean operators. The AND operator is used when distinct genes encode different sub-units of the same enzyme, whereas the OR operator is used for genes encoding isoforms of the same enzyme. Starting from the mRNA abundances of the genes partaking in a specific reaction, current approaches evaluate the GPR of the reaction by using either Boolean , or contThe integration of bulk transcriptomics data into metabolic networks based on RAS-derived constraints has been widely explored, however only a few works , 15 haveIn , the proin toto feasibility problems due to constraining essential reactions to zero. Moreover, they did not focus on unsupervised clustering of single cells, but they performed differential reaction expression analysis between pathogenic and non-pathogenic Th17 cells.Wagner et al.  used a dAlthough the use of denoising strategies to improve cluster analysis of scRNA-seq datasets has been widely explored and debated , 19, to In this work, we explored the possibility of applying denoising strategies as a systematic way to remove false zero RAS values and to obtain a more realistic clustering of single-cell flux distributions. To this aim, we selected three public scRNA-seq datasets, for which prior information about the expected clustering was available. We studied the effect of applying denoising either on the read counts matrix or after the RAS computation . Finally, we compared the results of three different state-of-the-art denoising methods, which represent the best compromise between accuracy, usability and efficiency, according to , namely We computed the RAS values from the normalized read counts (TPM) for the three selected datasets using the ENGRO2 metabolic network model. For each dataset, we retained only the metabolic genes included in the ENGRO2 model . Note that not all the genes in the model have an expression value in the TPM matrix. Indeed, there is a percentage of coverage that varies between We investigated the level of sparsity of RAS matrices. Table To investigate the distribution of the zeros across the cells, we reported in Fig. rentiate .Such a high number of zeros RAS values could represent a problem when one wants to use them to constrain the upper bounds of the associated fluxes in the metabolic network, as per Eqs. entclass1pt{minimaTo analyze more in detail such problem, in Fig. We evaluated the effect of applying three denoising strategies on the three RAS matrices, namely MAGIC, ENHANCE, and SAVER. At the first instance, we applied such strategies to the read counts matrix. In this case, the zero values were removed from the read counts matrix before the RAS computation, taking into account all the gene profiles of the cells. In Table Because denoising was not always sufficient to remove the totality of zero RAS values from essential reactions, we explored the possibility of applying the denoising strategies on the RAS matrix directly, taking into account the RAS profile of the cells. In Table To investigate whether the use of a denoising strategy allows uncovering also important reaction-reaction relationships, we analyzed the general level of correlation between the RASs of arbitrary pairs of reactions, as well as the correlation between specific pairs of reactions for which we expected a good correlation.In Fig. As a specific example, Fig. To evaluate the efficiency of using the fluxes predicted by FBA as features for cluster analysis, we compared the clustering results with those obtained when using the read counts as features.a priori knowledge , each cell will divide again. Mass accumulation takes place continuously during the cell cycle. Recent studies indicate that the rate of cell mass accumulation is higher for larger cells, approximating - with some deviations - an exponential rate . As a reHence, we investigated how the use of a denoising strategy affects the possible relationship between biomass synthesis rate and cell cycle phase, for the datasets including proliferative cells . Figure To further analyze this aspect, we reported the same UMAPs of Fig. Our preliminary results indicate that the MAGIC algorithm represents a good denoising strategy to remove possible false zero values from the RAS matrix, whereas ENHANCE and SAVER showed some limitations with respect to the quality of the flux cluster analysis (SAVER) and in removing all zero values from the essential reactions (ENHANCE). However, our conclusions might not always hold, because we used the default parameters for each denoiser, while one can expect an improvement of the results if parameters are differently tuned.ad hoc denoising algorithm.Our results also suggest the need to develop denoising strategies specific for the RAS matrix. On the one hand, applying current denoising strategies on the read counts matrix might affect the evaluation of the GPRs since the entries of the read counts matrix are modified based on the comparison of the entire transcriptomics profile of the cells, which involves a large number of genes not directly connected with metabolism. On the other hand, applying current strategies on the RAS matrix rather than on the read counts matrix does not seem to significantly improve results. Moreover, the assumptions of denoising algorithms conceived for read counts, such as the assumption of negative binomial distribution of SAVER, do not necessary hold for RAS values. Hence, as further work, we intend to explore the possibility of incorporating metabolic constraints, such as the correlation between the activity of reactions in a linear chain, into Remarkably, we observed a good correlation between the optimized biomass synthesis rate calculated by FBA and the cell cycle position deduced from the single-cell expression of signature cell cycle genes. To our knowledge, this is the first time that such correlation has been assessed, and this result may have important implications.Although recent data correlating metabolism to cell cycle progression are accumulating in a model eukaryotic organism  the correlation between the expression of reactions within the same linear metabolic pathway, ii) the clustering of cells into expected sub-populations; iii) the correlation between cell cycle phase and biomass synthesis rate.The application of methods to denoise scRNA-seq data is currently debated because of the possible generation of false-positive values and its non-obvious usefulness for downstream analyses . In factGSE110949 [SE110949  ComposedE-GEOD-86618 [OD-86618  ComposedGSE118056 [SE118056  ComposedThe dataset transcripts are identified by Ensembl ID (E-GEOD-86618) or Gene Symbol (GSE110949 and GSE118056). For all these datasets, cluster analysis on the read counts matrix were already performed in the original papers and a strong separation between the transcriptomics profiles of the cells was observed, which is consistent with specific a priori knowledge.In this work, we considered three public scRNA-seq datasets: Before the computation of the RAS matrix, some pre-processing operations was necessary on the read counts matrix. More in detail, we filtered out cells with less than 2000 detected genes and genes not expressed in at least 3 cells. Then, we normalized each cell by total counts over all genes so that every cell has the same total count after normalization, that is, the Transcript Per Kilobase Milion (TPM) normalization.score_genes_cell_cycle of Scanpy toolkit [On the transcriptomics data, we estimated the cell cycle phase, using the function  toolkit  which, gMAGIC [MAGIC  is a PytENHANCE [ENHANCE  is a PytSAVER [SAVER  is an R-For the parameters of the various denoising algorithm, we used the default values and we tested their application both on the read counts matrix and the RAS matrix.To mitigate the presence of false zero values in the transcriptomics data, we considered three different denoising methods: To properly consider the role of denoising in the capability of a simulated metabolic model to achieve a metabolic function, we decided to use the recently published metabolic network model ENGRO2 . ENGRO2 a priori, hence the need for a cluster analysis.The choice of using a core model is motivated by the likely presence of thermodynamically infeasible loops in genome-wide metabolic network reconstructions, such as the latest Recon3D  model, wThe availability of nutrients  significantly affects the metabolic fluxes. Since the exact medium composition is mostly unknown even for common in vitro protocols and in vivo models, we decided to consider a rich in silico medium, as done in , where ar, as a preliminary step, the maximum and minimum flux through such reaction, based on extracellular flux constraints, must be determined specifically for each reaction. To this aim, we performed a Flux Variability Analysis (FVA). FVA [In order to properly limit the flux of an internal reaction VA). FVA , 37 is aessential reactions for In order to compute the metabolic fluxes, we used FBA , which sR reactions involved in a metabolic network, the RAS matrix consists of a C is the total number of cells. The entries of the matrix are obtained substituting the mRNA abundances in the corresponding GPRs. Then, the logical expressions are solved by taking the minimum transcript level value when multiple genes are joined by an AND operator, and the sum of their values when multiple genes are joined by an OR operator [g for cell c, and j. The RAS related to reactions with the OR operator is computed using the following formula:Starting from a RNA-seq count matrix and a list of operator , 38. Morj might carry, obtained by FVA (Eq. j vary between We created a population of metabolic models, starting from the ENGRO2 metabolic network, and computed, for each cell, specific constraints on internal fluxes following an approach similar to , 15. In For a comparison, we considered also the possibility of applying an strategy . This ststrategy  also takThe final result of the computational steps described above consists in a matrix of single-cell optimal fluxes Cluster analysis on the optimal fluxes  toolkit . For thelgorithm  to clustWe repeated a similar workflow for the optimal fluxes Additional file 1. Cell-cycle effect on flux cluster analysis.


Saccharomyces cerevisiae generally consumes glucose to produce ethanol accompanied by the main by-products of glycerol, acetic acid, and lactic acid. The minimization of the formation of by-products in S. cerevisiae was an effective way to improve the economic viability of the bioethanol industry. In this study, S. cerevisiae GPD2, FPS1, ADH2, and DLD3 genes were knocked out by the Clustered Regularly Interspaced Short Palindromic Repeats Cas9 (CRISPR-Cas9) approach. The mechanism of gene deletion affecting ethanol metabolism was further elucidated based on metabolic flux and transcriptomics approaches.S. cerevisiae with gene deletion of GPD2, FPS1, ADH2, and DLD3 was constructed by the CRISPR-Cas9 approach. The ethanol content of engineered S. cerevisiae GPD2 Delta FPS1 Delta ADH2 Delta DLD3 Delta increased by 18.58% with the decrease of glycerol, acetic acid, and lactic acid contents by 22.32, 8.87, and 16.82%, respectively. The metabolic flux analysis indicated that the carbon flux rethanol in engineered strain increased from 60.969 to 63.379. The sequencing-based RNA-Seq transcriptomics represented 472 differential expression genes (DEGs) were identified in engineered S. cerevisiae, in which 195 and 277 genes were significantly up-regulated and down-regulated, respectively. The enriched pathways of up-regulated genes were mainly involved in the energy metabolism of carbohydrates, while the down-regulated genes were mainly enriched in acid metabolic pathways.The engineered S. cerevisiae increased with the decrease of the by-products including glycerol, acetic acid, and lactic acid. The deletion of genes GPD2, FPS1, ADH2, and DLD3 resulted in the redirection of carbon flux.The yield of ethanol in engineered The online version contains supplementary material available at 10.1186/s12934-022-01885-3. Saccharomyces cerevisiae is widely applied in the chemical industry, beverages, bioethanol, pharmaceuticals, and cosmetics [S. cerevisiae, 5% of carbon source is converted into glycerol as a byproduct [Ethanol mainly produced by osmetics . 90–95% osmetics . Ethanolosmetics –6. In thyproduct . Althougyproduct .S. cerevisiae, which catalyze the reduction of DHAP to glycerol-3-phosphate by synergetic catalysis approach [GPD2 affects the carbon metabolic pathway of glycerol formation by regulating redox potential.The glycerol formation can be effectively regulated by knocking out the glycerol formation pathway, preventing glycerol secretion, and regulating redox balance. Therefore, the redirection of carbon flow and the influence of the intracellular redox potential pathway will affect glycerol formation and ethanol production. Glycerol is produced from dihydroxyacetone phosphate (DHAP) in the presence of glycerol-3-phosphate dehydrogenase and glycerol-3-phosphatase phosphatase. NAD+ dependent glycerol-3-phosphate dehydrogenase has two isoforms of Gpd1 and Gpd2 in approach , 10. Redapproach . The maiapproach . Thus, GS. cerevisiae, ethanol is produced from pyruvate via the following two steps: decarboxylation of pyruvate to acetaldehyde by pyruvate decarboxylase; acetaldehyde is further reduced to ethanol by alcohol dehydrogenase (Adh). ADH2 encoding for alcohol dehydrogenase II catalyzes ethanol oxidation toward acetaldehyde [ADH2 deletion is involved in the increased demand for NAD+ regeneration in the carbon metabolism. Both cofactor removal and consumption reduction could result in the metabolic cofactor imbalance [S. cerevisiae through the protein channel Fps1 regulated by extracellular osmolarity [S. cerevisiae fps1Δ mutant exhibits the intracellular accumulation of glycerol. The glycerol accumulation triggers other regulatory systems for the reduction of glycerol biosynthesis, which then results in an increase of ethanol yield [d-lactate dehydrogenase 3 (Dld3) in S. cerevisiae is involved in the conversion of d-lactate to pyruvate [In aldehyde . Thus, daldehyde . In addimbalance , 16. Glymolarity , 18. Fpsmolarity . S. cereol yield . In addipyruvate .S. cerevisiae GPD2, FPS1, ADH2, and DLD3 to modify the metabolic pathway in this study  approach was used to knock out GPD2, FPS1, ADH2, and DLD3 in S. cerevisiae were successively knocked out by the CRISPR-Cas9 approach. Each deletion of the gene was confirmed by PCR and sequencing identification. The S. cerevisiae mutant with GPD2, FPS1, ADH2, and DLD3 deletion was named S. cerevisiae SCGFAD. S. cerevisiae was inoculated into YPD liquid medium containing 50 g/L glucose. The growth of S. cerevisiae was determined by measuring the absorbance at the wavelength of 600 nm . Thus, S. cerevisiae SCGFAD with GPD2, FPS1, ADH2, and DLD3 deletion exhibited a higher ethanol production rate compared with the wild-type strain by modifying the metabolic pathway.The ethanol production and glucose consumption in 8 h Fig. . During S. cerevisiae during the fermentation for ethanol production. In this study, the contents of glycerol, acetic acid, and lactic acid during fermentation were determined to investigate the effect of gene deletion on the by-product formation .Glycerol, acetic acid, and lactic acid were the by-products of ion Fig. . During iae Fig. A. The coS. cerevisiae SCGFAD on acetic acid and lactic acid content was also investigated compared with the wild-type strain . In addition, after fermentation for 72 h, the lactic acid content in the fermentation broth from S. cerevisiae SCGFAD was 6.28 mg/L, which was 16.82% lower than that of wild-type S. cerevisiae (7.55 mg/L). These results indicated that the deletion of the four genes of GPD2, FPS1, ADH2, and DLD3 resulted in the decrease of glycerol, acetate, and lactate contents in S. cerevisiae.The effect of gene deletion in ain Fig. B. The coS. cerevisiae, metabolites were in the logarithmic stage when the fermentation time reached 18–30 h. Three-time points of 18 h, 24 h, and 30 h were selected as sampling points to measure the concentrations of glucose, ethanol, glycerol, lactic acid, acetic acid, succinic acid, and biomass. Metabolites and their contents from the wild-type and engineered S. cerevisiae under three different fermentation stages were recorded in Table According to the fermentation characteristics of S. cerevisiae. Based on the hypothesis principle of no change in the composition of S. cerevisiae cells during fermentation, the demand coefficients of cell precursor in a unit of mmol/g DCW (dry cell weight) were rm1 = 2.515 rm, rm2 = 0.606 rm, rm3 = 0.601 rm, rm4 = 0.007 rm, rm5 = 0.528 rm, rm6 = 1.756 rm, rm7 = 0.876 rm, rm8 = 1.159 rm, rm9 = 0.834 rm, rmCO2 = 2.610 rm, where rm represented the increase rate of cell biomass (g/h).According to the metabolic flux model and the chemical reaction equations of the metabolites  of the glycolytic pathway was dramatically higher than the flux r2(16.6121/6.5337) of the pentose phosphate pathway, which indicated that glycolysis was the main pathway of glucose carbon metabolism in S. cerevisiae. Metabolic flux distribution showed that deletion of GPD2 for glycerol synthesis and FPS1 for transport of glycerol resulted in a decrease in rglycerol from 3.7300 to 1.9220. The carbon flux r5 for the glycerol synthesis pathway decreased dramatically from 3.8525 to 2.0411. Carbon flux redistribution resulted in carbon flux r4 for the catalysis of dihydroxyacetone phosphate to glyceraldehyde-3-phosphate increased from 42.1628 to 46.2194. The increase of carbon fluxes r6, r7, r8, and r9 for the conversion of glyceraldehyde-3-phosphate to acetaldehyde contributed to reducing glycerol production and increasing ethanol yield. In addition, DLD3 deletion decreased the carbon flux r11 from 0.6211 to 0.6011 in the reaction node of acetaldehyde and acetate. The co-knockout of four genes increased the carbon flux rethanol from 60.969 to 63.379, which caused the increase in ethanol yield.Each flux data was normalized based on the glucose consumption rate (C·mol/ (L·h)). The metabolic flux distribution of lts Fig. . The fluS. cerevisiae mRNA Library was performed by the following steps including accurate quantification of total RNA, mRNA purification and fragmentation, synthesis and purification of the double-stranded cDNA, end repair and dA tail addition, ligation, purification of ligation products, fragment size sorting, and library amplification. After Illumina Hiseq™ sequencing, the data were used to perform the analysis of gene expression differences. A total of 472 DEGs from S. cerevisiae SCGFAD were identified compared with wild-type S. cerevisiae  by the U.S. Food and Drug Administration. However, several by-products of glycerol, yeast biomass, carbon dioxide, and other minor products during yeast fermentation lowered the total ethanol yield. The ethanol yield was increased by 12% by deleting GPD1 and GPD2 to completely abolish yeast’s glycerol formation. However, the growth of S. cerevisiae with the double mutations of GPD1 and GPD2 was severely affected [ADH2 plays a crucial role in the conversion of ethanol to acetaldehyde in S. cerevisiae [FPS1 caused the increase of ethanol by 10% [GPD2 and FPS1 reduced the content of glycerol and organic acids [GPD2, FPS1, ADH2, and DLD3 were deleted to construct engineered S. cerevisiae with an increase of ethanol yield by 18.58%. In brief, this study provided a genetic modification strategy for the improvement of ethanol yield in S. cerevisiae by regulating the carbon flux distribution and inhibiting the by-product formation on the basis of a comprehensive analysis of ethanol metabolism.Rational metabolic engineering was used to redirect the metabolic flow in ormation . S. cereaffected . ADH2 plrevisiae . The dell by 10% . The preic acids , 26. DueS. cerevisiae [ethanol from 60.969 to 63.379, which meant more carbon flux in ethanol production in engineered S. cerevisiae. KEGG enrichment analysis showed the enriched pathways of up-regulated genes were mainly involved in the energy metabolism of carbohydrates, while the down-regulated genes were mainly enriched in acid metabolic pathways. The transcriptomics analysis provided large information on gene expression and function differences in engineered S. cerevisiae due to gene deletion.The metabolic flux and sequencing-based RNA-Seq transcriptomics were effective methods to elaborate the molecular mechanism of arabinose fermentation in engineered revisiae . In thisS. cerevisiae GPD2 delta FPS1 delta ADH2 delta mutant, and S. cerevisiae GPD2 delta FPS1 delta ADH2 delta DLD3 delta mutant were 7.55, 6.59, and 6.28 mg/L, respectively. Correspondingly, the ethanol concentrations of the above three strains were 19.64, 23.12, and 23.29 g/L, respectively. The increased amount of ethanol (0.17 g/L) was much higher than the decreased amount of lactic acid (0.31 mg/L) in S. cerevisiae mutant with four-gene deletion compared with three-gene deletion. Thus, DLD3 deletion could result in the redirection of other metabolisms related to ethanol production. This study further indicated that DLD3 deletion caused the decrease of the carbon flux r11 from 0.6211 to 0.6011 in the reaction node of acetaldehyde and acetate. However, the influence degree and regulation mechanism of DLD3 deletion on other carbon metabolism pathways need to be further clarified.In this study, the lactic acid contents of the wild-type strain, S. cerevisiae GPD2, FPS1, ADH2, and DLD3 involving ethanol production were knocked out by the CRISPR-Cas9 approach. The gene deletion in engineered S. cerevisiae caused the increase of ethanol content by 18.58%. In addition, the contents of glycerol, acetic acid, and lactic acid decreased by 22.32, 8.87, and 16.82%, respectively. The carbon flux rethanol in engineered strain increased from 60.969 to 63.379, which represented more carbon flux in ethanol production. In addition, 472 DEGs from S. cerevisiae SCGFAD were identified, in which 195 and 277 genes were significantly up-regulated and down-regulated, respectively. KEGG enrichment analysis indicated the enriched pathways of up-regulated genes mainly were energy metabolism processes, and amino acid and nucleotide sugar metabolism, while the down-regulated genes were mainly enriched in acid metabolic pathways. The engineered S. cerevisiae strain would be applied in bioethanol industry for high-level ethanol production with less formation of by-products.Four genes of S. cerevisiae S288c. S. cerevisiae GPD2 Delta FPS1 Delta ADH2 Delta DLD3 Delta mutant (SCGFAD) was constructed by knocking out GPD2, FPS1, ADH2, and DLD3 genes of wild-type S. cerevisiae. Cas9-NTC and gRNA-trp-HYB were from Addgene Company . GPD2-gRNA, FPS1-gRNA, ADH2-gRNA, and DLD3-gRNA plasmids carrying hygromycin B (HyB) resistance gene were amplified from gRNA-trp-HYB with the prepared primers. These plasmids expressing 20-bp gRNA were used to recognize the target gene loci of S. cerevisiae. The four pairs of primers were designed using the Weblink http://chopchop.cbu.uib.no/ online search system for 20 bp of gRNA sequences  containing 20 g/L of glucose was prepared to culture S. cerevisiae for cell proliferation at 30 °C and 200 rpm. When the OD600nm of S. cerevisiae reached 1, 1 mL of broth was inoculated into a 250-mL conical flask loaded with 100 mL of YPD containing 50 g/L glucose for further fermentation under the conditions of 30 °C and 200 rpm. The solid medium YPDN was prepared by the addition of 80 μg/mL NTC in YPD solid medium to screen the hypothetical transformants with Cas9-NTC integration. In addition, YPDNH medium was prepared by addition of 300 μg/mL HyB and 80 μg/mL NTC in YPD solid medium to screen the hypothetical transformants with the co-integration of Cas9-NTC and gRNA plasmid.The wild-type strain used for gene knockout in this study was Saccharomyces cerevisiae GPD2, FPS1, ADH2, and DLD3 were sequentially knocked out by the CRISPR-Cas9 technology. The genetically engineered S. cerevisiae with GPD2, FPS1, ADH2, and DLD3 deletion (SCGFAD) was constructed  method was used to measure the contents of ethanol and by-products. The parameters for the measurement of glucose, ethanol, and glycerol contents were a mobile phase of 0.01 mol/L Hc column . In addic column .Saccharomyces cerevisiae cerevisiae metabolic network model was constructed according to the method established by the previous report [S. cerevisiae was constructed according to the above requirements. The cell precursor demand coefficients using unit of mmol/g DCW (cell dry weight) were referred to the previous report [S. cerevisiae cell was in a pseudo-steady state. The unknown intracellular reaction was calculated by the measured metabolite consumption or production rates. The stoichiometric model was calculated based on the carbon balance of the compound with the rate unit of C·mol/ (L·h). All the calculations were performed by Matlab Software [s report . The sels report . The corSoftware .Construction of the cDNA library was performed via the following steps: (1) RNA extraction, mRNA purification, and double-stranded cDNA synthesis. The double-stranded cDNA was synthesized after mRNA was purified from total RNA. The purified cDNA samples were subjected to end A tail addition and linker ligation reactions ; (2) theq < 20), and then the clean data were obtained [q value < 0.5 and fold differences |FoldChange|> 2 were defined as significantly different genes.The original image data file processed by Illumina Hiseq™ was analyzed by CASAVA . After the conversion into the original sequencing sequence, Trimmomatic was used to remove sequences with N bases, linker sequences in reads, low-quality sequences (obtained , 37. Theobtained . The resobtained . Accordiobtained , 41. GenAdditional file 1. Reaction equations of metabolites in S. cerevisiae.Additional file 2. S. cerevisiae DEGs.


MYC oncogene is a potent driver of growth and proliferation but also sensitises cells to apoptosis, which limits its oncogenic potential. MYC induces several biosynthetic programmes and primary cells overexpressing MYC are highly sensitive to glutamine withdrawal suggesting that MYC-induced sensitisation to apoptosis may be due to imbalance of metabolic/energetic supply and demand. Here we show that MYC elevates global transcription and translation, even in the absence of glutamine, revealing metabolic demand without corresponding supply. Glutamine withdrawal from MRC-5 fibroblasts depletes key tricarboxylic acid (TCA) cycle metabolites and, in combination with MYC activation, leads to AMP accumulation and nucleotide catabolism indicative of energetic stress. Further analyses reveal that glutamine supports viability through TCA cycle energetics rather than asparagine biosynthesis and that TCA cycle inhibition confers tumour suppression on MYC-driven lymphoma in vivo. In summary, glutamine supports the viability of MYC-overexpressing cells through an energetic rather than a biosynthetic mechanism.The  MYC activation can sensitise cells to apoptosis upon glutamine withdrawal. Here the authors show that MYC activation enhances global transcription and translation that creates a metabolic demand, while glutamine limitation causes a metabolic demand and supply imbalance through loss of TCA energetics and thus, sensitises cells to apoptosis. MYC oncogene is a potent driver of growth and proliferation, and these oncogenic attributes drive its amplification or overexpression in many human cancers1. Deregulation of MYC also drives activation of tumour suppressive pathways, most notably sensitisation to apoptosis2 and activation of the p53 pathway resulting in restriction of cell cycle4. Deregulation of MYC sensitises cells to multiple apoptotic triggers in vitro, including withdrawal of serum2, treatment of cells with death receptor ligands such as TNF5 or TRAIL6 and withdrawal of glutamine7. MYC-induced sensitisation to apoptosis is abrogated in most systems upon expression of antiapoptotic members of the BCL-2 family, such as BCL-2 or BCL-xL8. Similarly, many systems show a requirement for one or both of the proapoptotic BCL-2 family members BAX or BAK1 for the apoptosis induced by MYC11, although the molecular mechanism is not thought to involve direct transcriptional activation of these genes.The 12 and that these target genes mediate both its tumorigenic and tumour suppressive functions. More recently, two studies provided comprehensive analyses of MYC-induced gene targets and promoter occupancy in various contexts14, allowing unparalleled insight into the regulation of gene expression afforded by MYC. A contrasting model holds that MYC activation acts to amplify the existing transcriptional programme of the cell16 and such a model would be hard to reconcile with one in which the apoptotic programme of MYC was based upon specific gene targets. Regardless of which model is correct, no convincing MYC target genes have thus far been identified that carry out its proapoptotic function.Two conceptual models of MYC-induced transcription have been proposed. The longest-standing model is that MYC activates a specific set of target genes, estimated to represent over 10% of the mammalian genome under conditions of MYC overexpression20, which impacts upon the total cellular content of RNA as well as transcriptional and translational load. This potent deregulation of global transcription requires the provision of abundant nucleotide triphosphates (NTPs). This, in turn, creates considerable demand for the energy and metabolic precursors required to feed the pentose phosphate pathway, pathways of purine and pyrimidine nucleotide biosynthesis and synthesis and function of the translational machinery. Indeed, several groups have reported MYC-induced activation of genes in the pentose phosphate and nucleotide biosynthesis pathways in vitro26 and in vivo in D. melanogaster27 and in mouse tumours28.In addition to activation of RNA polymerase II-directed transcription, MYC activates transcription directed by both RNA polymerases I and III29, we identified nucleotide catabolism as an unexpected consequence of MYC activation. Nucleotide catabolism is known to occur in multiple tissues under conditions of hypoxia, particularly in muscle when metabolic demand exceeds supply33 and this led us to hypothesise that MYC may elicit high metabolic demand and that apoptosis may occur when metabolic supply does not meet that demand. Here, we report that MYC drives a striking increase in global transcription and translation and accordingly diverts glycolytic carbon from 3-phosphoglyceraldehyde to ribose 5-phosphate via reversal of the non-oxidative pentose phosphate pathway. This high demand for nucleotides provides a platform for the glutamine-dependence of cells harbouring deregulated MYC34 and we therefore investigated this dependence in the context of metabolic supply and demand. Withdrawal of glutamine in MYC-overexpressing primary human MRC-5 fibroblasts led to a near-complete loss of key TCA cycle metabolites and a striking increase in AMP, ADP and nucleoside/nucleobase levels and activation of AMP kinase, indicating energetic stress due to lack of TCA cycle activity. Withdrawal of serum, which also caused depletion of TCA cycle metabolites, triggered apoptosis in these cells, as did pharmacological inhibition of the TCA cycle enzyme oxoglutarate dehydrogenase (OGDH). Indeed, the latter also elicited tumour suppression in MYC-driven lymphoma in vivo. Perturbations of mitochondrial energetic supply and cellular metabolic demand also revealed an inverse relationship between energetic status and apoptosis. Finally, we showed that asparagine supplementation rescued these cells from apoptosis but so did the knockdown of asparagine synthetase (ASNS), indicating that exogenous asparagine protects from apoptosis through product inhibition of ASNS activity rather than through enabling protein synthesis, as previously suggested34. Taken together, our results demonstrate that MYC-induced sensitisation to apoptosis occurs through an imbalance between metabolic demand and mitochondrial energy supply.Using untargeted stable isotope tracing of glucose and glutamineT2  in the presence of 4-hydroxytamoxifen (4-OHT) are highly sensitive to glutamine withdrawal and undergo apoptosis, as measured by annexin V/propidium iodide staining  for these experiments because they are genetically normal, and all tumour suppressive and homeostatic pathways are therefore intact. MRC-5 fibroblasts expressing MYC-ERing Fig. . We confing Fig. . We alsoing Fig. , as prevT2, MYC activation strongly enhanced global transcription in cells under normal growth conditions . Thus MYC can drive high and continued demand for glutamine and glutamine-derived metabolites for biosynthetic and energetic purposes even when glutamine is withdrawn.Protein translation exerts demand for glutamine as a biosynthetic precursor and energy source to fuel the GTP-dependent reactions of polypeptide chain elongation. Promotion of global translation by MYC has previously been reportedome Fig. . WithdraT2 that were deprived of cystine, although this was slower than the response to glutamine deprivation and only slightly accelerated by MYC activation. Withdrawal of the other amino acids did not lead to apoptosis in either cell type as previously reported7 nor did glutamine withdrawal from cells transduced with the pM6P vector, most likely because of homeostatic attenuation of biosynthetic processes in response to the withdrawal of an essential nutrient. We therefore conclude that glutamine withdrawal does not trigger apoptosis in MYC-expressing cells through impairment of translation. Important cytoprotective metabolic fates of glutamine and cystine are into the biosynthesis of glutathione, which acts to limit ROS-mediated damage and apoptosis. Treatment of cells with 20 μM l-buthionine sulfoximine (BSO), a potent inhibitor of glutathione synthesis, led to a more striking drop in glutathione levels than glutamine withdrawal , nucleobases  and nucleotide-containing lipid precursors . These data indicate that the main metabolic change elicited by MYC activation is a reprogramming of nucleotide metabolism. In particular, they reveal an unexpected MYC-dependent induction of nucleotide catabolism in addition to induction of nucleotide biosynthesis.Having established high MYC-driven metabolic demand, we wished to interrogate metabolite supply in more detail and we chose untargeted stable isotope approaches to examine the usage of two key nutrients, glucose and glutamine, upon MYC activation. Cells transduced with pM6P or pM6P-MYC-ER15N-glycine labelling experiments revealed that withdrawal of glutamine inhibited de novo production of adenine nucleotides and adenosine  upon glutamine withdrawal, and this was significantly higher in cells transduced with pM6P-MYC-ERT2   to visualise metabolic flux through the pentose phosphate pathway. MYC activation led to a striking increase in 6-phosphogluconate levels and label incorporation (m + 6) from 13C6-glucose in the presence of 6-AN, revealing high MYC-driven flux through the pathway  pulse of way Fig. . We obseway Fig. . In contway Fig. . A dramaway Fig. . The isoway Fig. .31 and in the exercise of muscle, particularly under conditions of ischaemia33. These are states in which energetic supply cannot meet demand, and we therefore examined whether MYC might promote nucleotide catabolism through high energy consumption, particularly when energetic supply is limiting . We found that cellular ATP levels were slightly lower upon glutamine withdrawal, and these were further reduced by MYC activation  in a reaction that also releases hydrogen peroxide. The rapid clearance of hydrogen peroxide by peroxidases and catalases renders this reaction irreversible under physiological conditions, and this reaction therefore leads to the loss of purines from the cellular metabolic pool. We observed xanthine accumulation only under conditions of MYC activation and glutamine withdrawal  essentially abrogated MYC expression whilst a second (sh1) gave partial knockdown  and m + 3 (reversed non-oxidative PPP) isotopologues in the parental line transfected with non-silencing shRNA control. Knockdown of MYC led to a reduction in the contribution of both pathways to AMP synthesis, with the sh2 shRNA leading to striking depletion of AMP, ADP and ATP  led to a reduction in expression of MYC  into the adenine nucleotides  withdrawal and also a reduction in levels of fumarate and malate  is that they interconvert adenine nucleotides in a reversible reaction whereby two ADP molecules are converted into one ATP and one AMP molecule. Under conditions of low energy charge (e.g. through extensive hydrolysis of ATP to ADP or lack of ATP production), AKs can act as a source of ATP as well as generating AMP which activates AMP kinase, resulting in cellular energy conservation  sensitises cells to apoptosis through a homeostatic mechanism, including or regardless of the primary transcriptional targets. Here we present data that support a homeostatic metabolic model for MYC-induced sensitisation to apoptosis. MYC induces metabolic demand for nucleotides that is strong enough to divert carbon away from glycolysis into a reversed PPP to support the production of ribose 5-phosphate and nucleotide biosynthesis. Our short-term stable isotope tracing studies with 13C6-glucose not only show enhanced flux through the oxidative arm of the PPP, but also demonstrate flux from glyceraldehyde 3-phosphate to ribose 5-phosphate through reversal of the non-oxidative arm of the PPP. This pathway consists of two bidirectional enzymes, transaldolase and transketolase, that catalyse reactions whose direction is determined by substrate & product concentrations. Thus, reversal of this pathway is a clear indicator of high demand for ribose 5-phosphate driven by MYC. It is also a striking example of how MYC can reprogramme cellular metabolism through a homeostatic mechanism separately from its direct transcriptional activation of metabolic target genes43.MYC requires its transactivation domain to induce apoptosisThe energy crisis that is driven by MYC activation and withdrawal of glutamine does not manifest as a major reduction in ATP levels, but rather as an accumulation of lower-energy nucleotides, nucleosides and bases. Within the nucleotide pools, this remodelling is most likely mediated by the adenylate kinases (with the exception of AK2) that catalyse the reversible conversion of 2 × ADP into ATP and AMP, allowing for the maintenance of ATP concentration whilst accumulating lower-energy species. Here we report that AK2 catalyses the opposite reaction, resulting in ADP production from AMP and ATP. This observation, whilst initially unexpected, is in fact predictable due to the localisation of AK2 to the intermembrane space. The adenine nucleotide translocase (ANT) on the inner mitochondrial membrane provides ATP to the intermembrane space whilst withdrawing ADP from this environment, promoting the production of ADP from ATP and AMP by AK2, particularly under conditions where cytosolic AMP is elevated. Through the generation of ADP, AK2 can therefore fuel mitochondrial ATP production and act to increase cellular energy charge. This non-canonical direction of AK2 catalysis is not only consistent with substrate/product availability but also with the physiological function of the gene family in the conservation of cellular energy charge.33 and hypoxia31 remain obscure. We were unable to identify conditions under which inhibition of nucleotide catabolism was detrimental to cell survival . This may, in large part, be due to redundancy in the catabolic pathways since there are eight currently identified nucleotidases in addition to the nine adenylate kinases that remodel adenine nucleotides in different cellular compartments. A plausible explanation for the catabolism and production of hypoxanthine and xanthine in such cases would be that accumulation of AMP leads to the promotion of nucleotidase activity purely based upon substrate concentration. Such a model would allow for catabolism of AMP and therefore continued activity of the AKs, enabling continued conversion of ADP to ATP in turn. Irrespective of potential benefit to cellular homeostasis, the products of nucleotide catabolism are diagnostic of the shortfall between energetic supply and demand in this context, as they are in others33.The exact reasons for induction of nucleotide catabolism in our experiments, and indeed upon muscle activity34 since there is a remaining requirement for glutamine to support multiple metabolic pathways. Our apoptotic and energetic data, supported by measurements of very high levels of asparagine, indicate that asparagine supplementation rescues from apoptosis through product inhibition of ASNS. Although ASNS activity consumes ATP, it seems unlikely that this could have a major impact on cellular energetics. We propose that a more likely explanation for this is that ASNS removes aspartate from the malate aspartate shuttle and that, in the absence of anaplerosis from glutamine, this has a detrimental impact on mitochondrial energetics.Re-addition of glutamine rescued Myc-overexpressing cells from the apoptotic phenotype and enabled their proliferation, consistent with a homeostatic mechanism based on metabolic supply and demand rather than triggering of an irreversible commitment to apoptosis and cell cycle arrest. In contrast, the rescue from apoptosis afforded by asparagine is not accompanied by proliferation44 and the dependence of MYC expression upon glutamine availability in activated T cells also suggests that glutamine metabolism could be targeted in cancer45. Unfortunately, however, the therapeutic efficacy of glutaminase inhibition in Myc-driven tumours is rarely observed in vivo. Indeed, metabolism of glutamine via amidotransferases and altered utilisation of glucose-derived carbon have recently been identified as compensatory mechanisms in Myc-driven liver tumours46. This illustrates how metabolic redundancy can promote bypass of inhibition of single metabolic nodes and suggests that synthetic lethal approaches targeting multiple pathways are likely to be required. Since the reaction catalysed by OGDH has less redundancy than that catalysed by GLS, we targeted this enzyme in place of GLS inhibition. Genetic approaches cannot be used to recapitulate the metabolic impact of glutamine withdrawal because they lack the requisite rapidity of onset and so we turned to a small molecule inhibitor of OGDH, the lipoate analogue CPI-613. The mechanism of action of CPI-613 is likely to be complex, and it does not inhibit OGDH in all cases40, although we confirmed that it did so in our cells. Indeed, CPI-613 recapitulated the rapid apoptosis elicited by glutamine withdrawal in MYC-overexpressing cells in vitro and achieved tumour suppression in MYC-driven lymphoma in vivo. The development of more specific OGDH inhibitors may therefore be a desirable approach to therapeutic targeting of MYC-overexpressing tumours.Inhibition of glutaminase  was initially proposed as a promising strategy for targeting of MYC-driven tumours47.Our data also indicate that targeting AK2 could be an attractive option for cancer therapy since it should preferentially target cells that exhibit high demand for energy. Indeed, one can envisage a scenario where alternating regimes are applied, based upon inhibition of nucleotide biosynthesis (leading to selection for tumour cells that elevate biosynthetic capacity and therefore elevate metabolic demand) followed by inhibition of the TCA cycle and AK2 (leading to tumour suppression due to lack of energetic support for the excessive metabolic demand). Such ‘metronomic’ delivery of chemotherapy has been effectively demonstrated in a mouse model of melanoma53. BAX has been shown to require the mitochondrial ATPase for its apoptotic function54 and to localise to mitochondria in cardiomyocytes in response to ischaemia and AMPK activation55. The common denominator in most of these studies is the involvement of mitochondrial nucleotide metabolism in the regulation of apoptosis by the BCL-2 family, and our model of MYC-induced sensitisation to apoptosis through energetic demand continues that theme.The robust impact of the BCL-2 family upon MYC-induced apoptosis indicates that common molecular mechanisms are most likely shared between the two. BCL-2 and BCL-xL can protect mitochondria from damage due to reactive oxygen species (ROS), mitochondrial membrane potential (MMP) decline and impaired nucleotide exchange and respiratory controlIn summary, many oncogenes promote growth through activation of various signalling pathways or through transcriptional regulation of fundamental cellular programmes. The deregulation of these growth-promoting programmes inevitably has homeostatic consequences for the cell in terms of biosynthetic and energetic supply and demand. We propose that an increased understanding of these secondary homeostatic responses will open up new possibilities for therapeutic intervention.T2 and selected with blasticidin (5 μg/ml). BCL-xL was expressed with pMSCV-based retrovirus. Cells were cultured in DMEM/10% FCS. Compounds for treatment were obtained from Sigma unless otherwise stated. 4-hydroxytamoxifen (200 nM) was used to activate the MYC-ERT2 and was present in all vector control samples as well. For siRNA experiments, DharmaFect 1 transfection reagent was used to deliver Dharmacon SMARTpool siRNA mix by reverse transfection in six-well plates at 30 nM final concentration. The day after transfection, assays were carried out as described below.MRC-5 human fibroblasts  were transduced with retroviral vector pM6P-blast  or pM6P-blast-MYC-ER2) and 5 μl annexin V-FITC (BD Bioscience). Immediately prior to analysis, propidium iodide was added. Data were acquired using a BD Accuri C6 flow cytometer and analysed using FlowJo v9.6.2, reporting single- and double-positive populations as apoptotic. The gating strategy is provided in Extended Data 1a.Cells were seeded in 6-well plates and on the following day, 4-OHT was added to all wells. After a further 24-h incubation, glutamine was withdrawn (where stated), and cells were incubated overnight. For determination of apoptosis, cells were trypsinized and collected into FACS tubes. Pellets were washed with PBS and resuspended in 100 μl annexin V buffer  was added and apoptosis was measured using an Incucyte live cell imager . Results were obtained as percent apoptotic area based upon the percentage of cell area (determined by phase contrast microscopy) that stained positive for the caspase 3/7 reagent.® RNA Alexa Fluor® 488 HCS Assay (ThermoFisher) was used for the determination of transcription. Cells were plated in 96-well plates, treated as described, incubated with 5-ethynyl uridine (EU) for half an hour before fixation and permeabilization. Alexa Fluor® azide was added for EU detection. Cells were then washed with Click-iT® reaction rinse buffer and counterstained by DAPI. All the images and analysis were done with the ImageXpress Micro XLS Widefield High-Content Analysis System .A Click-iT13C6,15N2-lysine, cell pellets were extracted with 8 M urea. DTT was added to 10 mM and samples were incubated at RT for an hour. Iodoacetamide was then added to 25 mM and incubated at RT for an hour and DTT was then added to final concentration of 25 mM. Samples were incubated overnight at 37*C with lysC (estimated ratio of lysC to the protein of 1:50 to 1:200) and peptides were acidified by addition of TFA to 0.5%. An estimated 10 μg of the resulting peptide solution was loaded onto an activated (20 μl methanol), equilibrated (100 μl 0.1% TFA) C18 StAGE tip and washed with 100 μl 0.1% TFA. The bound peptides were eluted into a Protein LoBind 1.5-ml tube (Eppendorf) with 20 μL 80% acetonitrile, 0.1% TFA and concentrated to less than 4 μl in a vacuum concentrator. The final volume was adjusted to 6 μl with 0.1% TFA. Online LC was performed using a Dionex RSLC Nano HPLC. Following the C18 clean-up, 5 μg peptides were injected onto a C18 packed emitter and eluted over a gradient of 2–80% ACN in 120 minutes, with 0.1% acetic acid throughout. Eluting peptides were ionised at +2 kV before data-dependent analysis on a Thermo Q-Exactive Plus. MS1 was acquired with mz range 300–1650 and resolution 70,000, and top 12 ions were selected for fragmentation with normalised collision energy of 26, and an exclusion window of 30 s. MS2 were collected with resolution 17,500. The AGC targets for MS1 and MS2 were 3e6 and 5e4, respectively, and all spectra were acquired with 1 microscan and without lockmass. Data were analysed using MaxQuant (v 1.5.8.3)56 in conjunction with UniProt FASTA database (UP000005640_9606.fasta (Human) 2017_05 release), with match between runs (MS/MS not required), LFQ with 1 peptide required, and statistical analyses performed in R58.Following labelling of cells for 6 hours with Thr172 , anti-AMPK , anti-β-actin , and for Fig. Cells were lysed in Cell Lysis Buffer (New England Biolabs) supplemented with Complete Protease Inhibitors (Roche) and subjected to SDS-PAGE, and transfer to Immobilon-P membrane (Merck-Millipore). After blocking in the blocking buffer (TBST + 5% non-fat milk), primary and HRP-conjugated secondary antibodies  were applied in the blocking buffer and antigens were revealed with SuperSignal West Pico chemoluminescent reagent (ThermoFisher). Antibody to AK2 (HPA018479) was from Cambridge Biosciences. Antibody to c-MYC (clone Y69—ab32072) was from Abcam. Anti-phospho-AMPKRNA was extracted using an RNEasy Mini kit (Qiagen) and reverse transcribed using qScript cDNA Supermix (Quanta Biosciences) according to the manufacturer’s protocol. qPCR was performed on a StepOne RT-PCR system (ThermoFisher) using Sybr Select Master Mix (Life Technologies) and the following oligonucleotides: AK2 (tggtagtggagctcattgaga and catttctgcctgcctcaca), ASNS (aaaggtggcagatcatattg and atatgacttcatccagagcc).Metabolites were extracted from six-well plates by washing individual wells with ice-cold PBS and adding of cold extraction buffer . Extracts were clarified and stored at −80 °C until required.59.LC-MS was carried out using a 100 mm × 4.6 mm ZIC-pHILIC column (Merck-Millipore) using a Thermo Ultimate 3000 HPLC inline with a Q-Exactive mass spectrometer. A 32 min gradient was developed over the column from 10% buffer A (20 mM ammonium carbonate), 90% buffer B (acetonitrile) to 95% buffer A, 5% buffer B. 10 μl of metabolite extract was applied to the column equilibrated in 5% buffer A, 95% buffer B. Q-Exactive data were acquired in negative mode , and standard ESI source and spectrometer settings were applied . Metabolites were identified based upon m/z values and retention time matching to standards. Quantitation of metabolites and their isotopologs was carried out using AssayR version 0.1.46 Raji cells were injected subcutaneously into the right flank of 8- to 10-week-old male NOD-SCID IL2Rγ-null (NSG) mice. After 2 days, the animals were treated daily with intraperitoneal injections of either vehicle control or 20 mg/kg CPI-613. Tumour weight and engraftment were analysed when animals were euthanized because burden became large (approaching 1.2 cm in any dimension—no mice exceeded these limits) or when animals showed signs of poor health. All mice were maintained in a standard SPF facility . These animal studies have been regulated under the Animals (Scientific Procedures) Act 1986 Amendment Regulations 2012 following ethical review by the University of Cambridge Animal Welfare and Ethical Review Body (AWERB-PPL number P846C00DB). HALO digital pathology software (Indica Labs) was used to analyse the percentage of tumour using a random forest tissue classifier in samples from haematoxylin counterstained formalin-fixed, paraffin-embedded (FFPE) sections. This analysis was carried out by the Histopath Facility at the Cancer Research UK Cambridge Institute.In all, 1 × 1060. P values are from T tests comparing means of lower and upper tertiles of MYC expression.A compendium of 2999 breast tumours from 17 Affymetrix microarray studies was integrated as described previouslyP values ≤ 0.05 were considered to be significant. In the case of X13CMS analysis, P values were calculated using the Student’s T test as part of the X13CMS workflow in R. Experiments were performed a minimum of three times, with the exception of the X13CMS and xenograft analyses that were carried out once.GraphPad Prism software (Version 9.3.1) was used for statistical analyses unless otherwise indicated in the figure legends. Statistical tests are indicated in the figure legends throughout. For all experiments, Further information on research design is available in the Supplementary InformationReporting Summary


The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders.  •Metagenomic analysis highlights key common bacterial species across metabolic diseases•Metabolic models showed higher levels of acetate produced by disease enriched bacteria•Reaction analysis revealed increases in the glyoxylate and dicarboxylate pathway•Metabolomics and modeling analysis showed the potential role of tartrate metabolism Microbiome; Systems biology; Metabolomics; Omics; Metabolomics In recent years we have seen significant advances in elucidating the importance of the gut microbiome in human health and disease . MicrobiDespite a large number of gut metagenomic studies in metabolic diseases, the contribution of specific microbes to host metabolism during metabolic disease, systematic analysis, and modeling has not been mechanistically studied. In particular, little is known about the specific impact of microbe-microbe metabolic interactions. Recent advances in the metabolic modeling and reconstruction of genome-scale metabolic models (GEMs) have enabled the study of species-specific metabolisms and metabolic interactions within microbial communities . These cwww.microbiomeatlas.com  . CompariClostridium phoceensis was not consistent across the two diseases, being decreased in patients with ACVD but increased in patients with T2D.For a deeper insight at the species level, we identified significant MSPs within patients from all diseases (FDR <0.01) , and S3.C. phoceensis identified above. The selected models contained on average 1,717 reactions, 1,684 metabolites, and 1,022 genes with an average growth rate of 0.89 h−1 , using the MIGRENE Toolbox . We ran The community models for individuals in the Swedish T2D cohort showed a common theme of lower production of key metabolites in the T2D communities. We observed on average the T2D community models predicted lower secretion of acetate and butyrate A, BCAAs The community modeling for individuals in ACVD cohort predicted higher production of the butyrate and acetate, and the tyrosine and histidine in the diseased models D–4F, whiThese findings are relevant as they highlight how the community behaves differently from the individual models. For example, the production of acetate which was shown to be highly secreted by the individual models increased in disease now shows lower production from the T2D community models when compared to the control community models. Also, in the ACVD community models, we observed the BCAA production differs from that predicted in the individual modeling.To evaluate the importance of the microbiome tartrate and glyoxylate cycle in obesity, we investigated the plasma level of metabolites and gut metagenome from publicly available data on the longitudinal Swedish wellness study . Plasma We performed association analysis between the reaction abundance profile and metabolomics using multivariate association analysis. This metabolic analysis showed the association between reactions from the glyoxylate and dicarboxylate pathway and plasma metabolites in obese individuals of this cohort compared to non-obese ones. We used plasma metabolite profiles and BMI to find associations between the abundance of glyoxylate and dicarboxylate pathways in the gut using a multivariate random-effects model (MVEM). Focusing on reactions relating to glyoxylate metabolism we found associations between 14 reactions and 45 plasma metabolites belonging to carnitines, fatty acids and lipids, amino acids, steroids, and organic acids. +] in particular had five positive associations with proline, ketoleucine, histidine, glutamate, and alanine. Other reactions which were positively associated with alanine plasma levels were tartrate decarboxylase and tartrate dehydratase along with four other enzymes . Other aTo investigate if these microbial reactions were also associated with BMI, we split the Swedish cohort by individual patients’ BMI and compared the two subgroups. We ran statistical tests (Wilcoxon rank-sum test) to ascertain if the reactions were correlated with one group or the other. Of the 14 reactions, 7 showed a significant positive association with BMI, including tartrate dehydratase, tartrate decarboxylase, and malate dehydrogenase. 4 reactions , malate dehydrogenase  (NADP+), malate dehydrogenase [NAD(P)+], tartrate decarboxylase) were significantly increased in abundance in samples from obese participants compared to other non-obese participants  for anaerobic growth +] and tartrate dehydratase both produce oxaloacetate while malate dehydrogenase  produces pyruvate . Both oxFrom these findings, we would hypothesis that an increased abundance of tartrate metabolism is not beneficial for health. This is based on the reactions showing an increase across all three of our diseases, also the fact that the gut microbiome metabolizes this metabolite. Tartrate is fermented in the colon to SCFAs , we saw This analysis can be used to unravel the association between impaired gut microbiomes and metabolic disorders. We have identified tartrate metabolism in the microbiome as a significant pathway with high potential to impact on each of these metabolic syndromes. As well as this giving a potential new biomarker, it also suggests potential novel intervention targets for disease. Further research into the metabolism of tartrate in the gut environment is needed to understand the direct impact this compound has on gut microbes and host health. It is clear that the gut microbiome is a key factor in maintaining health and our highlighted novel areas for research give areas to focus on for both disease understanding and biomarker discovery for obesity and its co-morbidities.There are a number of limitations to our study. Firstly, the cohorts chosen to represent the metabolic diseases are from different geographical regions which will have its own impact and implications on the gut microbiome profile. There are also limitations within the modeling of the bacteria. There are several bacterial species in the gut , while wsaeed.shoaie@kcl.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by Saeed Shoaie  and Sequence Read Archive databases. All data was produced from different next generation sequencing (NGS) platforms. We used a quantitative metagenomic profiling software METEOR  for qualThe models were constructed based on the Kegg Ontology annotation of the genes in the gut catalogue. The KBase reference model was used for mapping the KOs of the genes to the reactions  using fuhttps://github.com/sysbiomelab/MIGRENE) with the objective function to optimize biomass. Model growth rates and exchange reaction flux were determined by FBA for selected significant MSPs  toolbox  in MATLAant MSPs . The simReaction abundance was calculated based on the absence or presence of reactions in the respective MSP models . The reaPersonalised community models were reconstructed for all Swedish samples. Due to computational power limitations the models were made with 10 MSPs per community. The top-ranked 10 bacteria for each sample were chosen for the community models. The models were created using function “MakeCommunity” in MIGRENE toolbox . The objAll statistical analysis was done using R software. Different abundance of taxonomic signatures were tested using the two-tailed Wilcoxon rank-sum test in controls and patients. p values were adjusted for false discovery rate by the Benjamini-Hochberg correction (FDR). Significant MSPs were chosen based on 2 steps: (1) the MSP’s FDR score, FDR<0.01, (2) distinctly different median values in the patients and controls, one equal to 0 and one equal to a real positive number. We estimated the MSP enrichment or depletion in diseases by comparing the average abundance in case samples, and the average abundance in control samples per MSP. The output identified each MSP as either enriched in disease or enriched in control.taRifx, Maaslin2 and car.The initial analysis for the associations between the plasma metabolite levels and reaction abundancies within the gut microbiome was done previously , using a