Acetobacter species play an import role during cocoa fermentation. However, Acetobacter ghanensis and Acetobacter senegalensis are outcompeted during fermentation of the cocoa pulp-bean mass, whereas Acetobacter pasteurianus prevails. In this paper, an in silico approach aimed at delivering some insights into the possible metabolic adaptations of A. ghanensis LMG 23848T and A. senegalensis 108B, two candidate starter culture strains for cocoa fermentation processes, by reconstructing genome-scale metabolic models (GEMs). Therefore, genome sequence data of a selection of strains of Acetobacter species were used to perform a comparative genomic analysis. Combining the predicted orthologous groups of protein-encoding genes from the Acetobacter genomes with gene-reaction rules of GEMs from two reference bacteria, namely a previously manually curated model of A. pasteurianus 386B (iAp386B454) and two manually curated models of Escherichia coli (EcoCyc and iJO1366), allowed to predict the set of reactions present in A. ghanensis LMG 23848T and A. senegalensis 108B. The predicted metabolic network was manually curated using genome re-annotation data, followed by the reconstruction of species-specific GEMs. This approach additionally revealed possible differences concerning the carbon core metabolism and redox metabolism among Acetobacter species, pointing to a hitherto unexplored metabolic diversity. More specifically, the presence or absence of reactions related to citrate catabolism and the glyoxylate cycle for assimilation of C2 compounds provided not only new insights into cocoa fermentation but also interesting guidelines for future research. In general, the A. ghanensis LMG 23848T and A. senegalensis 108B GEMs, reconstructed in a semi-automated way, provided a proof-of-concept toward accelerated formation of GEMs of candidate functional starter cultures for food fermentation processes. D-sorbitol into L-sorbose  are Gram-negative, obligately aerobic bacteria that incompletely oxidize a range of alcohols and reducing sugars for energy production. The region-and stereo-selectivity of these oxidation reactions has led to numerous biotechnological applications, most notably the conversion of -sorbose . The genentation . For thesis 108B . To gainsis 108B . Howeverprevails .Acetobacter species is growth on ethanol as the sole carbon source. Therefore, a defined medium containing ethanol as the sole carbon source and ammonium as the sole nitrogen source is often used  provides a well-defined data structure of the metabolic network inferred from a bacterial genome . Next toA. ghanensis LMG 23848T and A. senegalensis 108B, taking advantage of a comparative genomic analysis that encompassed a selection of 36 strains belonging to 27 species of the Acetobacter genus and a previously reconstructed and manually curated GEM of A. pasteurianus 386B 2SO4, 5.0 g/l; KH2PO4, 1.375 g/l; MgSO4.7H2O, 0.5 g/l; ethylenediaminetetraacetic acid (EDTA), 15.0 mg/l; ZnSO4.7H2O, 4.5 mg/l; CoSO4.7H2O, 0.35 mg/l; MnCl2.4H2O, 1.0 mg/l; CuSO4.5H2O, 0.3 mg/l; CaCl2.2H2O, 4.5 mg/l; FeSO4.7H2O, 3.0 mg/l; MoO3, 0.24 mg/l; H3BO3, 1.0 mg/l; and KI, 0.1 mg/l, dissolved in 30 mM citrate buffer (pH 6.0). The final pH of the medium was adjusted to 5.0 with 0.1 M NaOH. A filter-sterilised vitamin mixture was added after heat sterilisation of the medium at 121°C for 20 min. The final vitamin concentrations were: biotin, 0.0005 mg/l; calcium pantothenate, 0.01 mg/l; nicotinic acid, 0.01 mg/l; myo-inositol, 0.25 mg/l; thiamine-HCl, 0.01 mg/l; pyridoxine-HCl, 0.01 mg/l; and para-aminobenzoic acid, 0.002 mg/l. Eight different carbon sources, namely glucose, fructose, mannitol, citric acid, glycerol, lactic acid, ethanol, and acetic acid (as sodium acetate), were used at a final concentration of 30 mM to assess if they could sustain growth of the three Acetobacter strains investigated. The pH of the citric acid, lactic acid, and sodium acetate stock solutions was set to 5.0.Growth experiments were performed in a modified defined medium . This meAcetobacter strains were grown overnight at 30°C in a standard incubator (160 rpm) in a modified yeast extract-glucose-mannitol (mYGM) medium 2HPO4, 1.0 g/l; KH2PO4, 1.0 g/l; and Na3C6H5O7, 1.0 g/l. The final pH of the medium was adjusted to 5.5 with 0.1 M NaOH. The overnight culture was centrifuged  and washed with a filter-sterilized saline solution . Then, the cells were resuspended in sterile saline solution and inoculated in 2 ml of the defined medium mentioned above at an optical density at 600 nm (OD600) of 0.01, in duplicate, in test tubes with a total volume of 20 ml. The three Acetobacter strains were allowed to grow at 30°C for 48 h and 120 h. A threshold value for the OD600 of 0.1 was used to identify whether or not the strains had grown.For inoculum build-up, the three ) medium , which cAcetobacter strains were grown overnight at 30°C in a semi-defined medium (pH 5.5), which contained: lactic acid, 5.0 g/l; sodium acetate, 10.0 g/l; granulated yeast extract, 5.0 g/l; MgSO4.7H2O, 1.0 g/l; NH4H2PO4, 20.0 g/l; and K2HPO4, 10.0 g/l (600 of 0.1 was used to identify whether or not the strains had grown.A similar growth experiment was performed in a modified defined medium with 30 mM phosphate buffer (pH 6.0) in triplicate. In this case, for inoculum build-up, the three 10.0 g/l . A thresA. ghanensis LMG 23848T and A. senegalensis 108B genomes were originally annotated using the bacterial genome annotation system GenDB v2.2  and A. senegalensis 108B . The amino acid sequences of each set of protein-encoding genes were annotated using a combination of tools, namely BlastKOALA from the KEGG database , the genome sequence of 96 strains of Acetobacter species was available in the NCBI RefSeq database. All those genome sequences were retrieved, as well as the genome sequences of Gluconobacter oxydans 621H, Komagataeibacter nataicola RZS01, and Escherichia coli str. K-12 substr. MG1655 (further referred to as E. coli). The latter three strains were used as outgroup. Next, the Acetobacter genomes were ranked according to their assembly quality level, based on information in the NCBI RefSeq database. For each Acetobacter species, the genome with the highest assembly quality level was selected, and in the case that the genome was not from the type strain, the type strain genome was also selected. Next, OrthoFinder  associations, linking the different entities, possibly with their stoichiometry . Howevereletions . In the eletions .A. ghanensis LMG 23848T and A. senegalensis 108B, the manually curated GEM of A. pasteurianus 386B  and parsimonious FBA with biomass formation as the objective function were performed to examine the flux distributions of the newly constructed GEMs under simulated in vitro growth conditions. The total allowable carbon consumption flux was set to 60 c-mmol per gram of cell dry mass per h.Further manual curation was performed for the iAg23848 and iAs108B GEMs in a semi-automated way. First, GEM gap filling was performed using iAp386B454 as a template. Hereto, the GEM gap filling method of CobraPy was used , which cE. coli GEM as reference. For E. coli, two curated reconstruction sources were leveraged, namely the EcoCyc Pathway/Genome Database embedded in Pathway Tools version 21.5, and the iJO1366 GEM of the BIGG database  as A. nitrogenifigens, Acetobacter sp. DsW_54 (fruit fly) as A. fabarum, Acetobacter sp. JWB (fruit fly) as A. pomorum, Acetobacter sp. BCRC 14118 (African vinegar) as A. ascendens (formerly identified as A. pasteurianus), and Acetobacter sp. B6 (South Korean vinegar) as A. pasteurianus.Based on all predicted orthogroups, OrthoFinder inferred a rooted species tree . The outA. pasteurianus iAp386B454 GEM reference to evaluate the iAp386B454 GEM reaction presence or absence in 11 high-quality Acetobacter genomes. The most frequent missing reactions in the central metabolism, which has been published before for A. pasteurianus 386B , phosphoglucomutase , ornithine cyclodeaminase , a putative periplasmic mannitol oxidation reaction , ribose 5-phosphate isomerase , and fructokinase . Both phosphate acetyltransferase (EC 2.3.1.8) and acetate kinase (EC 2.7.2.1) were missing in A. aceti TMW2.1153.The predicted orthogroups were used in combination with the nus 386B , were th+ symporter was missing in A. aceti TMW2.1153, the genome contained genes encoding a quinone-dependent (EC 1.1.5.12) and cytochrome c-dependent (EC 1.1.2.4) D-lactate dehydrogenase. This strain was originally isolated from a water kefir and other A. aceti strains have been typically found in an ethanol-rich habitat, examples including vinegar. The absence of the lactate:H+ symporter in the A. aceti TMW2.1153 genome might indicate genome evolution toward excluding lactate as a substrate, other means of lactate transport in the cell, or, possibly, the erroneous prediction of absence of a gene encoding this function. In contrast, characteristic reactions for the Acetobacter genus, including the membrane-bound pyrroloquinoline quinone (PQQ)-dependent ethanol dehydrogenase (EC 1.1.5.5) and succinyl-CoA:acetate CoA-transferase (EC 2.8.3.18) were found in all high-quality Acetobacter genomes considered.Although the lactate:HA. ghanensis LMG 23848T and A. senegalensis 108B. GEM compositions before and after curation-based gap filling are shown in A. pasteurianus 386B gene identifiers in the iAp386B454 GEM, which were not present in the A. pasteurianus 386B NCBI RefSeq genome version used for the OrthoFinder analysis. The 10 reactions associated to these genes were computationally filtered and manually checked to see whether the presumed orthologs were present in the A. ghanensis LMG 23848T and A. senegalensis 108B genomes, and if so, the reactions and associated gene-reaction rules were manually added to their respective GEMs. This was the case for D-glucose:ubiquinone oxidoreductase (EC 1.1.5.2), phosphoenolpyruvate carboxylase (EC 4.1.1.31), and 3-isopropylmalate dehydratase (EC 4.2.1.33), both for A. ghanensis LMG 23848T and A. senegalensis 108B.Combining the predicted orthogroups and the iAp386B454 GEM allowed to reconstruct GEMs for A. ghanensis LMG 23848T, a quinone-dependent D-lactate dehydrogenase (EC 1.1.5.12) was absent in the genome, but a cytochrome c-dependent enzyme (EC 1.1.2.4) was identified. No acetolactate decarboxylase (EC 4.1.1.5) was found in the A. ghanensis LMG 23848T genome. Instead, a gene encoding diacetyl reductase (EC 1.1.1.303) was present. Finally, no quinone-dependent glycerol 3-phosphate dehydrogenase (EC 1.1.5.3) was found. Whereas for A. senegalensis 108B no proton-translocating NAD(P)+ transhydrogenase (EC 7.1.1.1) was found in the genome, a soluble NAD(P)+ transhydrogenase (EC 1.6.1.1) was present. Additionally, no gene could be found encoding gluconokinase (EC 2.7.1.12), possibly explaining why A. senegalensis 108B could not grow under defined medium conditions with glucose as the sole carbon source. The fact that citrate could sustain growth of this strain paved the way to find a genomic cause. Indeed, a putative citrate:H+ symporter and a gene cluster encoding citrate lyase was found in the genome, as previously reported  and A. malorum CECT 7742 (strawberry vinegar). Finally, the A. senegalensis 108B genome encoded a phosphoenolpyruvate carboxykinase (EC 4.1.1.49), which was not found in A. pasteurianus 386B, thus providing another anaplerotic link between the TCA cycle and the gluconeogenesis pathway in A. senegalensis 108B.For reported . The genA. ghanensis LMG 23848T and A. senegalensis 108B were used to fill the reaction gaps in these models using the iAp386B454 GEM as a reaction repository. The FBA gap filling results indicated that 17 iAp386B454 GEM reactions were needed to obtain in silico growth of A. ghanensis LMG 23848T with glucose as the sole carbon source. For A. senegalensis 108B, 10 iAp386B454 GEM reactions were necessary when growth was required with lactic acid as the sole carbon source. Manual curation allowed the addition of all but two reactions for A. ghanensis LMG 23848T, for which no genomic evidence could be found. These reactions were the ones catalyzed by asparagine synthase (EC 6.3.5.4) and ornithine cyclodeaminase (EC 4.3.1.12). Both are involved in amino acid biosynthesis, the first being necessary for L-asparagine biosynthesis and the latter responsible for L-proline biosynthesis. The gene encoding the alternative L-proline biosynthesis enzyme NAD(P)-dependent pyrroline-5-carboxylate reductase (EC 1.5.1.2) was annotated with an internal stop codon in the NCBI RefSeq annotation. Other biosynthesis pathways for these amino acids may exist in A. ghanensis LMG 23848T, since in vitro growth was obtained under defined medium conditions without the addition of amino acids. Conversely, for A. senegalensis 108B, genomic evidence was found for all gap filling results. However, this evidence had to be retrieved from genes in the NCBI RefSeq annotation, for which no amino acid sequence was available  or genes only annotated in the GenDB or PATRIC genome annotation versions.The results of the growth experiments for L-proline and L-asparagine were additionally available, in which case the predicted specific growth rate was 0.70 h−1 and the consumption of both amino acids was balanced by the protein biosynthesis reaction. The flux distribution revealed obligatory NADPH oxidation by the proton-translocating NAD(P)+ transhydrogenase. However, genome annotation analysis did not support the presence of this reaction, since the A. ghanensis LMG 23848T genome missed the 𝛽-subunit of this enzyme. Growth of A. ghanensis LMG 23848T under defined medium conditions with glucose was only found when a citrate buffer was used. Allowing additional in silico consumption of citrate showed that constraining the proton-translocating NAD(P)+ transhydrogenase did not prevent growth, resulting in the prediction of a specific growth rate of 0.58 h−1 with the glutamate synthase reaction (EC 1.4.1.13) as the largest NADPH consumer.Flux balance analysis with the iAg23848 GEM on glucose as the sole carbon source did not result in growth, unless in silico growth, suggesting that this model contained all reactions necessary for biomass formation. FBA with lactic acid resulted in a specific growth rate of 0.86 h−1. The soluble NAD(P)+ transhydrogenase produced NADPH, whereas phosphoenolpyruvate carboxykinase supplied all necessary phosphoenolpyruvate. FBA with citric acid resulted in a specific growth rate of 0.75 h−1. Most of the citric acid consumed was directly used in the TCA cycle, whereas only 12% was consumed by citrate lyase. No acetate was produced by the model; all intracellular acetate produced was consumed by succinyl-CoA:acetate CoA-transferase.The iAs108B GEM was solved with FBA under two growth conditions, namely with lactic acid or with citric acid as the sole carbon sources. No additional nutrients had to be added to obtain Acetobacter genomes examined. Presence/absence was compared to experimental data related to the growth of the strains in a defined medium containing ethanol as the sole carbon source and obtained for the type strains of a number of Acetobacter species  had at the time of analysis no genome sequenced, two species for which a genome sequence was available (A. aceti and A. nitrogenifigens) encoded the glyoxylate cycle, and finally, three species with a genome sequence available  did not encode the glyoxylate cycle. The latter three species could thus correspond to false positive experimental results based on growth with ethanol as the sole carbon source that were obtained formerly , NADH dehydrogenase type II (EC 1.6.5.9), cytochrome 3bo oxidase (EC 7.1.1.3), and cytochrome bd oxidase (EC 7.1.1.7). Concerning NADPH metabolism, E. coli contains both a proton-translocating NAD(P)+ transhydrogenase and a soluble NAD(P)+ transhydrogenase. From the comparative genomic analysis, it was apparent that, at least for the high-quality Acetobacter genomes, the presence of these enzymes was mutually exclusive. An interesting exception was A. aceti, for which both reactions were predicted to be present.The enzymes involved in the aerobic respiratory chain as known for A. ghanensis LMG 23848T and A. senegalensis 108B, two candidate starter culture strains for cocoa fermentation  was probably compensated by the presence of a cytochrome c-dependent enzyme (EC 1.1.2.4), which probably allowed lactate consumption. However, this may be an explanation for the lower lactate consumption rate of A. ghanensis LMG 23848T, compared to A. pasteurianus 386B, under cocoa pulp-simulating conditions  was present, as is the case in lactic acid bacteria + transhydrogenase may represent an important and hitherto not sufficiently explored adaptation to the use of different carbon sources and their influence on the redox metabolism of Acetobacter species  or absence  of the glyoxylate cycle enzymes represented an interesting case where genomic and experimental evidence go hand in hand. Alternative pathways have been described for the assimilation of C2 compounds in microorganisms, namely the ethylmalonyl-CoA pathway and the methylaspartate cycle . RP was the recipient of a PhD Fellowship strategic basic research of the Research Foundation Flanders .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Escherichia coli as an ideal microbial factory has been engineered to biosynthesize diols from carbon sources. Here, we comprehensively summarized the biosynthetic pathways of diols from renewable biomass in E. coli and discussed the metabolic-engineering strategies that could enhance the production of diols, including the optimization of biosynthetic pathways, improvement of cofactor supplementation, and reprogramming of the metabolic network. We then investigated the dynamic regulation by multiple control modules to balance the growth and production, so as to direct carbon sources for diol production. Finally, we proposed the challenges in the diol-biosynthesis process and suggested some potential methods to improve the diol-producing ability of the host.As bulk chemicals, diols have wide applications in many fields, such as clothing, biofuels, food, surfactant and cosmetics. The traditional chemical synthesis of diols consumes numerous non-renewable energy resources and leads to environmental pollution. Green biosynthesis has emerged as an alternative method to produce diols.  Fossil resources such as oil, petroleum and natural gas are the primary fuels and important chemical raw materials . HoweverTo date, diol production mainly relies on chemical synthesis from non-renewable energy resources . For exaE. coli has a clear genetic background and a mature gene-manipulation toolbox even though various microorganisms such as Klebsiella pneumoniae, Bacillus licheniformis or Bacillus amyloliquefaciens could naturally produce diols. Metabolic engineering is an efficient tool to enhance the ability of the chassis to synthesize diols, because it could endow microorganisms with the heterologous synthesis of compounds and improve the production of different compounds. To produce biofuels or other valuable chemicals, numerous biosynthetic pathways have been designed and constructed in different chassis. Here, this review summarizes the construction of biosynthetic pathways of representative diols, and analyzes the current metabolic-engineering strategies and their effects on optimizing diol production. Moreover, we consider the unsolved difficulties in promoting the production and suggest many potential methods to further optimize and enhance the productivity of diols in E. coli.As an ideal microbial cell factory, Glucose, a cheap sugar with an extensive value in use, is the preferred carbon source for the biosynthetic production of diols. Glucose is gradually decomposed into a series of intermediates with the catalysis of enzymes by glycolysis and the TCA cycle. The generated intermediates in each step are the important precursors for the production of different diols. The biosynthetic pathways of the representative diols based on the order of the intermediate formation and the length of carbon chains are summarized in 3-Phosphoglycerate (3PG) is the origin of EG production. Under the successive catalysis of SerA, SerC and SerB, serine is generated from 3PG. Serine could be catalyzed into hydroxy-pyruvate by serine-glyoxylate aminotransferase AGT, or ethanolamine by serine decarboxylase SDC. These two intermediates could be transformed to glycolaldehyde via the catalysis of α-keto acid decarboxylase MDLC, or ethanolamine oxidase AAO. In the end, glycolaldehyde is oxidized into EG under the expression of YqhD or FucO. The whole process produces one molecule of NADH and consumes one molecule of NAD(P)H.R)-1,2-PDO is generated. In contrast, for the synthesis of (S)-1,2-PDO by direct fermentation from glucose, the biosynthetic design is based on the proven pathway from organic acids to diols [S-Lactate-CoA could be generated from pyruvate or acetyl CoA. (S)-1,2-PDO is biosynthesized under the catalysis of aldehyde dehydrogenase PdcD and alcohol dehydrogenase MmsB [For 1,2-PDO biosynthesis, dihydroxyacetone phosphate (DHAP) is converted from fructose-1,6-phosphate . Especially, DHAP and glyceraldehyde 3-Phosphate (G3P) could be mutually transformed. DHAP is catalyzed into methylglyoxal (MG) by MG synthase. After two reduction reactions,  by glutamate dehydrogenase GDH and OHB decarboxylase PDC. Similarly, OHB can also be transformed into 3-HPA by PDC. Finally, 1,3-PDO is synthesized from 3-HPA by an aldehyde reductase YqhD.Both malate and oxaloacetate can be gradually converted to 1,3-PDO. Malate can be gradually transformed into 2-keto-4-hydroxybutyrate (OHB) under the catalysis of malate kinase LysC, the malate semialdehyde dehydrogenase Asd, malate semialdehyde reductase Ssr and DHB dehydrogenase LldD, respectively. At the same time, meso-2,3-BDO, -2,3-BDO and -2,3-BDO. As for 2,3-BDO biosynthesis, three key enzymes are involved: α-acetolactate synthase ALS, α-acetolactate decarboxylase ALDC, and BDO dehydrogenase BDH, also known as acetoin reductase. In the first step, two molecules of pyruvate are transformed into one molecule of acetolactate via acetolactate synthase ALS, and then ALDC catalyzes acetolactate to R-acetoin. -2,3-BDO or meso-2,3-BDO could be obtained by the catalysis of BDO dehydrogenase with the consumption of one molecule of NAD(P)H. For the formation of -2,3-BDO, acetolactate is spontaneously catalyzed to diacetyl in the presence of oxygen, and diacetyl is further transformed to S-acetoin by diacetyl reductase DAR. -2,3-BDO is generated by -BDO dehydrogenase BDH. Additionally, as an intermediate, S-acetoin could be transformed to meso-2,3-BDO under the catalysis of glycerol dehydrogenase encoded by dhaD.Commonly, 2,3-BDO contains three isomeric forms: 1,4-BDO is biosynthesized from succinate. Succinate is converted to succinyl-CoA by succinyl-CoA synthetase SCS. Next, succinyl-CoA is reduced to 4-hydroxybutyrate (4-HB) in two successive steps, which need the catalysis of CoA-dependent succinate semialdehyde dehydrogenase SucD and 4-hydroxybutyrate dehydrogenase 4HBd. Subsequently, 4-HB-CoA transferase catalyzes 4-HB into 4-HB CoA. 4-HB CoA is reduced to 4-hydroxybutyraldehyde by 4-HB-CoA reductase. Finally, 1,4-BDO is produced by alcohol dehydrogenase. This bio-pathway consumes one molecule of ATP and four molecules of NAD(P)H.E. coli by combining the endogenous and exogenous pathways. However, the current yield and productivity cannot satisfy the demands of industrialization. Based on this urgent purpose, various metabolic-engineering strategies including optimization of the biosynthetic pathways of diols, optimization of cofactor supplementation, and reprogramming of the metabolic network have been designed and established. The concrete details are summarized in Currently, the biosynthetic pathways of diols from glucose have been realized in L-homoserine and the supply of cofactor CoA, respectively. However, the deamination rate of L-homoserine and the amount of CoA in the host restrict the production of 1,3-PDO and 1,4-PDO. For this reason, Wang et al. redesigned the diol pathways based on the natural metabolic pathways of amino acids (aspartate or glutamate) in E. coli. The productive process was approximately divided into three parts: the synthesis of amino acids, the degradation of amino acids, and the formation of diols. A carboxylic-acid reductase Car, from Mycobacterium marinum was chosen to reduce noncognate ω-hydroxylic acids (ω-HAs). With the overexpression of Car and YqhD, ω-HAs were degraded into diols [The pathways of 1,3-PDO and 1,4-PDO rely on the formation of to diols . Howeverto diols .Due to the existence of endogenous pathways, diols can be easily synthesized from amino acids. Hence, amino-acid overproducers are indispensable for diol production. Various tools for the screening and selection of amino-acid overproducers, such as an amino-acid-biosensing system based on rare-codon or codon-extension strategies have been established ,29,30. ZE. coli could produce diols via the exogenous pathways, there are still quite a few deficiencies such as a low efficiency of substrate conversion and poor chassis adaptation. Furthermore, the microbial production of diols is subject to carbon loading and end-product inhibition by diols and other end-products [−1 to 1.2 g L−1 [S)-1,2-PDO, Zhu et al. integrated the bio-pathway into the genome, and the strain was engineered to delete the genes responsible for undesired lactate utilization, which could avoid the generation of D-lactic acid and the additional consumption of glucose in other branches of metabolic pathways [Besides the redesign of the synthetic pathways, the knockout of the competing pathways in the host is also crucial for the elevation of diol biosynthesis, which could affect the direction of the metabolic flux. Nowadays, bulk commercial chemicals need a “factory” in order to be overproduced from a common carbon resource . As a noproducts . To avoiproducts . Similar.2 g L−1 . In ordepathways . With thpathways . TherefoR, R)-2,3-BDO. They are encoded by bdhA from Bacillus subtilis, adh from Clostridium beijerinckii, or adh from Thermoanaerobium brockii, respectively. All of the selected BDHs have strict stereospecificity. By introducing them into the pathway, the titers of -2,3-BDO significantly increased by 5.8, 5.1, and 6.1 g L−1, respectively, from 40 g L−1 glucose. All of the enantiomers’ purities could reach 99% [lac gene. The purity of the product was higher than 99% and the yield was up to 30.5 g L−1 [S)-1,2-PDO production, it can be catalyzed from L-lactaldehyde by FucO, YqhD or MmsB. To ensure the purity of the product, Zhu et al. tested the specific activity of these three enzymes in vivo, and finally proved that MmsB could efficiently transform the intermediate into (S)-1,2-PDO [Except for the optimization of the host, highly active enzymes are also critical for constructing efficient diol biosynthetic pathways. The modification and optimization of the enzymes could hoist the production capability of chassis and the yield of products. Because of the multiple stereo forms of some diols, their substrate specificity is considered for the selection of exogenous enzymes. As a diol with three isomers, the insufficient purity of 2,3-BDO would emerge. The enzyme that plays a decisive role in 2,3-BDO configuration is BDO dehydrogenase BDH. Consequently, Yan et al. chose three different BDHs to synthesize , which decreased the inhibition by NADH to enhance the flux of the TCA cycle [L-homoserine, Chen et al. established a mutant library of Gdh through silico mutagenesis. By docking screening, GdhK92V/T195S was obtained that could catalyze L-homoserine to 4-hydroxy-2-ketobutyrate with high activity [E89Q/Δ452−466. At the same time, the citrate synthase GltAR163L and PPC were overexpressed to increase the carbon flux of glutamate. The 1,4-BDO titer of the host increased by 45.6% [Moreover, engineering the enzymes themselves could further improve the yield of diols. Yim et al. modified two enzymes in the biosynthetic process of 1,4-BDO. Because the pyruvate dehydrogenase of neumonia . There wneumonia . AdditioCA cycle . For 1,3activity . In addiactivity . As for by 45.6% . Similarby 45.6% .Pseudomonas butanovora, is an activated transcription factor whose signal molecules are C2–C5 alcohols. When BmoR combines with the signal molecules, it can induce the transcription of the σ54-dependent bmoP promoter [bmoP promoter as a signal representation. The concentration of diols is positively correlated with the amount of GFP expression [Except for deleting the competing pathways or engineering the key enzymes to obtain overproducers, the common method is random mutagenesis for strains such as atmospheric and room-temperature plasma (ARTP) mutagenesis or N-methyl-N′-nitro-N-nitrosoguanidine (NTG)-induced mutagenesis . Therefopromoter . The grepression . ConsequE. coli using the kinetic modeling framework ORACLE  and confirmed the potential strategies to increase the production. Moreover, ORACLE provided the indispensable enzymes that could control 1,4-BDO production and influence the change of enzymes to intracellular states such as cell growth, byproduct formation or redox state [At the same time, with the rapid development of the artificial design, computer simulation could directly help to analyze the related information about the metabolic network of the chassis by integrating metabolomics, proteomics, and transcriptomics. It could improve the production of desired chemicals, identify the enzymes required in the synthetic process, or even design a pathway that has not been reported. Especially, the biosynthetic pathway of 1,4-BDO was established based on the BNICE algorithm, and the host strain was engineered by the OptKnock algorithm. This computer-simulation tool could help confirm the optimized target in metabolic engineering by the analysis of genome models . Andreozox state . Hence, pntAB, which encodes a membrane-bound proton-translocating transhydrogenase [12-dependent coenzymes are also required in the biosynthetic process. However, E. coli cannot naturally synthesize vitamin B12 and its cost is expensive, suggesting the vitamin-B12-dependent pathways are not suitable for diol production in industrialization [12, Yun et al. produced 1,3-PDO by overexpressing the vitamin-B12-independent enzyme GDHt and its activity factor from Clostridium butyricum [12 and the products could be efficiently accumulated [Besides the above-mentioned strategies, the optimization of cofactor supplementation is also beneficial for diol production. Numerous NAD(P)H molecules would be consumed during the production process, and the introduction of exogenous pathways or the overexpression of enzymes would also break the balance of NAD(P)H because of the existence of the competing routes. Therefore, sufficient supplementation and the balance of NAD(P)H are essential for stable diol biosynthesis. To solve this problem, numerous studies in past years have balanced the recycling of NAD(P)H by modifying the pathways. Guo et al. maintained the balance of NADPH by coupling 1,3-PDO biosynthesis with the synthetic pathway of methyl butane, since excessive NADPH from methyl-butane production could be utilized to produce 1,3-PDO. Especially, the volatility of methyl butane is beneficial to 1,3-PDO separation and purification after fermentation. With the optimization of the host and fermentation conditions, the yield increased by 3.3–4.3 folds . Liu et rogenase . A novelrogenase . Besideslization ,47. To rumulated .rpoD expression and its genotype. The yield reached 38.6 g L−1 at 62 h, which was increased by 2.3-fold compared with the wild type [Furthermore, the engineering of RNA polymerase (RNAP) for reprogramming the metabolic network could further promote the conversion of carbon sources to products. An ideal productive factory should provide maximal resources for production. However, most microorganisms focus on their growth rather than production; the optimization of synthetic pathways cannot fundamentally change the model of resource allocation. As a leader of the gene regulatory system, RNAP controls the proportion of resources allocated to growth and production . Numerouild type . At the ild type ,53.The establishment of the mutagenesis library of RNAP is an optionable method to obtain an ideal RNAP mutant. To screen the ideal RNAP mutant, a high-throughput screening method needs to be developed. Nowadays, FACS is widely used in various industries including enzyme modification, strain screening and component optimization ,55,56. FKlebsiella [Citrobacter [Clostridium [Lactobacillus [E. coli. For instance, Tong et al. firstly introduced the 1,3-PDO pathway from K. pneumoniae into E. coil. After glycerol was transported into the cell, it was gradually dehydrated and oxidized to 1,3-PDO. However, the pathway from glycerol depended on vitamin B12, and the accumulated 3-HPA inhibited the activity of GDHt, since the yield of 1,3-PDO was still at a low level [Except for glucose, other carbon sources such as glycerol, xylose and starch could also be utilized for diol production , Table 2ebsiella ,58, Citrrobacter ,60, Closstridium  or Lactobacillus ,63 couldow level .D-xylose for EG production in E. coli. This pathway avoided the high pressure and temperature that are required in the chemical synthesis, and also prevented the side reactions from non-specific carbon–carbon bond cleavages in the hydrogenolysis of xylitol [Xylose, a kind of pentose, is one of the raw materials of lignocellulose, in which the content is approximately 20% . With th xylitol . Subsequ xylitol .E. coli cannot utilize starch as the only carbon source. Recently, the global yield of starch has reached 41 million tons [E. coli such as itaconic acid [Streptococcus bovis NRIC 1535 by the cell-surface display and introducing the reported synthetic enzymes to the host [Various monosaccharides are used to produce diols. However, due to the lack of the ability to assimilate oligosaccharides and polysaccharides, ion tons . Reportenic acid  and polynic acid . Based othe host .Saccharomyces cerevisiae [B. licheniformis BL1 [Methylotuvimicrobium alcaliphilum 20Z [Lactobacillus diolivorans [E. coli is being rapidly propelled. Sathesh-Prabu et al. used the hydrolysate of empty palm-fruit bunches to produce 2,3-BDO by introducing a glucose-induced system from Pseudomonas putida, which could be further applied to the production from other lignocelluloses [Compared with fossil fuels, sugar or traditional starch is an environmental-friendly raw material and is convenient for fermentation. However, with the increasing prominence of population and environmental issues, more and more attention has been paid to diol production from industrial and agricultural by-products and waste as the basic raw materials. At the same time, how to efficiently convert diversified wastes into diols has become a research focus in this field. To date, multiple studies have focused on finding alternative sources for diol production, such as using cellobiose in revisiae , soy hydrmis BL1 , methaneilum 20Z  or agro-livorans . Based olluloses . Erian elluloses . As a polluloses . Additiolluloses . It has lluloses ,94. TherThe dynamic regulation of metabolic pathways in microorganisms is an efficient way to maintain the balance of growth and production, which can autonomously regulate gene expression and metabolic flux, as well as detect environmental and intracellular signals. On the contrary, static regulation might lead to an imbalance of the metabolic flux, overaccumulation of intermediates and waste of carbon sources. Nevertheless, in order to improve the productivity, most modifications are designed to direct most carbon sources to production, regardless of growth requirements. As a result, the nutrients for growth would be insufficient, and cell homeostasis would be destroyed. A strategy needs to be established to achieve a dynamic balance between production and growth so that diols can continue to be synthesized with high efficiency. At the same time, the fermentation of chemicals relies on exogenous inducers such as IPTG. However, three factors cannot be ignored: the irreversible induction, the toxicity, and the high cost of inducers. The ultimate purpose of dynamic regulation is to achieve autonomous control of the flux. This tool could be constructed by redesigning promoters or biosensors that respond to metabolites or regulate the transcription of metabolism, or a quorum-sensing system that depends on cell concentrations and group level. Nowadays, dynamic regulation has been applied to produce different compounds in diverse chassis ,96. For P. putida KT2440 [zwf1P system; (2) the xylose-induced system, XutR-xutAP; (3) the LA-induced system, LvaR-lvaAP; (4) the 3-HP/LA-induced system, HpdR-hpdHP; (5) the 3HP-induced system, MmsR-mmsAP. The first system has been successfully applied to the production of 2,3-BDO in E. coli [For the monitoring of carbon sources, substrate-induced promoter systems have been established for the regulation of gene expression in a KT2440 . The ind E. coli . As a moE. coli could allow for the diol biosynthesis from carbon sources. In addition, a series of metabolic-engineering strategies have been developed and adopted into engineered E. coli to enhance diol production, such as the optimization of biosynthetic pathways, the improvement of cofactor supplementation, and reprogramming of the metabolic network. Unfortunately, there are still several limitations for diol production. The first that should be mentioned is that a high concentration of diols would generate toxicity to the chassis. Enhancing the diol tolerance is crucial, such as via the direct evolution of the chassis by continuous culture under selective pressure, or via acceleration of the cell-membrane synthesis in order to increase resistance to diols. The second limitation is the downstream processing of diols. Because of the high boiling point and strong hydrophilicity, the separation and purification of diols are relatively difficult after fermentation [2 and another 1/4 was from glycogen, which is the storage energy for cyanobacteria [E. coli might be an alternative strategy for the production of diols. In this system, cyanobacteria and E. coli would be responsible for supplying the carbon source from CO2 and for converting the carbon source into diols, respectively. Additionally, E. coli has been engineered to generate carbon sources from CO2 [E. coli in order to realize diol production from CO2 in one host.Recently, the biosynthesis of diols has drawn much attention because of its environmental friendliness and high efficiency. The introduction of artificial pathways into entation . Recent entation ,100. In entation . For insbacteria . Hence, from CO2 . In the


They revealed that the major differences in cancers are associated with lipid, TCA cycle, and carbohydrate metabolism. Thorough integration of these types with multiomics highlighted little association with genetic alterations but a strong association with markers of epithelial–mesenchymal transition. Our analysis indicates that in absence of variations imposed by the microenvironment, cancer cells adopt distinct metabolic programs which serve as vulnerabilities for therapy.Cancer cells reprogram their metabolism to support growth and invasion. While previous work has highlighted how single altered reactions and pathways can drive tumorigenesis, it remains unclear how individual changes propagate at the network level and eventually determine global metabolic activity. To characterize the metabolic lifestyle of cancer cells across pathways and genotypes, we profiled the intracellular metabolome of 180 pan‐cancer cell lines grown in identical conditions. For each cell line, we estimated activity for 49 pathways spanning the entirety of the metabolic network. Upon clustering, we discovered a convergence into only two major metabolic types. These were functionally confirmed by  Metabolomics and computational analysis of 180 cell lines grown under the same condition reveals the activity of over 45 metabolic pathways and identifies two major metabolic types. Beyond the seminal example of aerobic glycolysis  and far from saturation , we assessed whether differential genes were overrepresented in known TF‐targets , and the epithelial marker E‐cadherin was higher in type 2 . Microscopy analysis also highlighted the expected morphology differences. Type 1 cells featured spindle‐like shapes resembling fibroblasts, whereas type 2 portraited rounded regular shapes, consistent with EMT progression.Several of the significant associations pointed to an increase of metastasis‐related processes, that is, EMT. To directly test this hypothesis, we used the EMT score proposed by Rajapakse et al . The scoet al . We valiet al . In lineTo further substantiate the association to EMT, we have compared the expression of EMT genes across all cell lines . Given the experimental design in which a single substrate is labeled, the FC of each detectable metabolite informs on the fraction of carbon that originated from either glucose or glutamine. To highlight differences in carbon fluxes between type 1 and type 2, we computed the difference in FC between the averages of the two types .The association analysis provided novel leads on the regulatory differences that characterize the main metabolic types, but does not draw robust hypotheses on nutrient utilization or nutrient fluxes. To directly assess differences in pathway usage between the major metabolic types, we used an untargeted ypes Fig . This al13C]glucose, the vast majority of pathways of primary metabolism exhibited higher 13C‐labeling in type 2 cells  and phosphatidylglycerols  contents were higher in type 2  and sphingomyelins  were higher in type 1, consistent with the spindle‐like morphology that requires increased membrane surface. Ether phosphatidylcholines and triacylglycerols were characterized by remarkable within‐type shifts  and ether phosphatidylcholines . Lower saturation decreases tight packing of acyl chains and, in turn, increases membrane fluidity . In the data related to the second tracer [U‐13C]glutamine, the labeling enrichment was lower, in the range of 10–20%  in type 2 cells, which was not coupled to higher lipid content. The remaining fraction of nonlabeled lipids resulted from other carbon sources, which could be from direct lipid uptake.The resulting data revealed striking differences between the two metabolic types. This is shown exemplarily for TAG 50:1 grown on  glucose , [U‐13C] glutamine , or naturally labeled growth medium. At 48 h, we extracted metabolites as described and analyzed on Agilent 6546 Q‐TOF instrument . Mass spectra were recorded from 50 to 1,050 m/z in 4 GHz HighRes, negative ionization mode. Annotation was done by matching the measured mass of the ions with reference compounds derived from the Human Metabolome Database (HMDB 4.0), taking labeling patterns of potential metabolites into consideration. For each measured metabolite, mass distribution vector and fractional contribution have been computed using formulas described in the following review . Three replicates of each cell line were grown for 48 h in either growth medium with either [U‐13C] glucose, and [U‐13C] glutamine medium. At 48 h, internal standard  was added to all cell lines to enable the quantification of lipid species. Lipid extraction was performed using 50:50 (v/v) methanol/isopropanol for 1 h at −20°C. Untargeted lipidomics was performed by LC–MS on a Thermo Fisher Q‐Exactive HF‐X mass spectrometer . For liquid chromatography, we used a 30 mm Waters ACQUITY UPLC BEH C18 column (cat. no. 186002352) and a 7 min gradient from 15% buffer B (90% (v/v) isopropanol, 10% acetonitrile, 10 mM of ammonium acetate) and 85% buffer A  to 99% Buffer B. Mass spectra were recorded from 150 to 2,000 m/z in positive ionization mode, recording MS1 and MS2  spectra. Lipidomics data processing for nonlabeled samples was done using Compound Discoverer 3.1 . Lipids were annotated with MS2 information. Lipids from each class were quantified using class‐specific internal standards.Cells were grown in the same three conditions as above: naturally labeled, [U‐13C‐isotopomer traces were extracted as ion chromatograms from labeled samples based on accurate mass and retention time. Related mass isotopomers were integrated with identical boundaries and normalized to unity to obtain labeling fractions.For labeled lipids, we adopted a targeted data extraction. The most abundant representatives for each lipid class were selected in naturally labeled samples. All et al  leading‐edge analysis (Subramanian et al, Gene dependencies were obtained from Tsherniak et al  and the l (et al . Gene seSarah Cherkaoui: Conceptualization; data curation; formal analysis; validation; investigation; visualization; writing – original draft; writing – review and editing. Stephan Durot: Validation. Jenna Bradley: Investigation. Susan Critchlow: Supervision; writing – original draft. Sebastien Dubuis: Formal analysis; investigation. Mauro Miguel Masiero: Validation; investigation. Rebekka Wegmann: Formal analysis; validation. Berend Snijder: Supervision. Alaa Othman: Validation; methodology. Claus Bendtsen: Resources; supervision; writing – review and editing. Nicola Zamboni: Supervision; writing – original draft; project administration; writing – review and editing.CRediT author contributions listed above, the contributions in detail are:In addition to the SCh and NZ designed the study. JB and SCr carried out the original cell lines experiments. SeD performed metabolomic measurements. SCh performed the bioinformatic analysis. StD and SCh performed follow‐up cell line experiments, labeling, and lipidomic. AO assisted lipidomic measurements and analyzed lipidomic data. NZ supported labeling analysis. RW and SCh performed microscopy measurement and analysis. MMM evaluated drug response. CB and BS supervised parts of the study. NZ supervised the whole study. SCh and NZ wrote the manuscript. All authors reviewed and approved the final manuscript.The authors declare that they have no conflict of interest.Appendix S1Click here for additional data file.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.PDF+Click here for additional data file.


Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism  Bacillus subtilis is a model organism of choice for the industrial production of various valuable compounds, such as biopolymers and proteins, due to its excellent capacity for protein secretion, good growth characteristics, distinct endogenous metabolism, and robustness in industrial fermentation [B. subtilis have been successfully applied to guide the production of riboflavin [B. subtilis GEM was published in 2008 [entation . Genome-boflavin , isobutaboflavin , 2,3-butboflavin . The fir in 2008 , and sev in 2008 ,7,8, whiEscherichia coli [Saccharomyces cerevisiae [Aspergillus niger [Corynebacterium glutamicum [B. subtilis [B. subtilis (ec_iYO844) only integrated enzyme kinetic parameters for 17 reactions located in the central carbon metabolism using the GECKO method, but this model allowed more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to the GEM [By contrast, enzyme-constrained models (ecModels) introduce enzyme kinetic information into a GEM, thus reflecting the protein resource limitation faced during cell growth, enabling them to identify the rate-limiting enzymes in the pathway and further guide rational metabolic engineering. As a consequence, ecModels have been successfully applied to guide the production of L-lysine , poly-glhia coli ,12,15, Srevisiae , Aspergius niger , Corynebutamicum  and B. ssubtilis . The fir the GEM .B. subtilis (ecBSU1), using an updated ECMpy workflow [B. subtilis on different carbon sources, simulate the overflow metabolism, and explain the trade-off between biomass yield and enzyme usage efficiency. Finally, we predicted the target genes of B. subtilis for enhancing the production of industrial chemicals , and the predictions were in good agreement with the literature.In this study, we first systematically updated the iBsu1147 model through GPR update, biomass reaction standardization, etc., after which we established a comprehensive collection of parameters that affect the prediction accuracy of the ecModel , and constructed the first genome-scale ecModel of workflow . Subsequkcat and molecular weight (MW) of an enzyme affect the prediction accuracy of the ecModel [kcat, and the GPR relationship affects the accuracy of MW calculation. Accordingly, we systematically corrected the EC number and GPR relationships. We used the GPRuler tool [R (Revised iBsu1147).The model iBsu1147, which has the most reactions and genes, was selected as the initial model for the integration of enzymatic constraints. Since the iBsu1147 model was released in 2013, we performed quality control on the model, covering substrate utilization, redox balance, energy balance, biomass reaction standardization, and mass balance. Our previous results shown that the  ecModel . For exaler tool  and protler tool  for detaler tool  IDs  and j-th protein (g_num represents proteins expressed in the whole proteome).The molecular weight (MW) of each enzyme was downloaded from the UniProt database according to the gene ID, and the quantitative subunit information was obtained by parsing the ‘Interaction information’ in UniProt . For exae BRENDA  and SABIe BRENDA  databasehe PAXdb  databaseR following the ECMpy workflow and named ecBSU1 , so that each reaction only has one corresponding enzyme. Next, we calculated the MW of each enzyme. For reactions catalyzed by enzyme complexes, we used the total sum of proteins in the complex (j is the number of jth subunits in the complex). Finally, a new enzymatic constraint  and AutoPACMEN was a direct 10-fold expansion without an automated process. In contrast, ECMpy identify the most likely wrong parameters and corrected them through an automatic process based on the cost of enzymes. The process includes the following steps: First, we calculated the enzyme cost of each reaction in the pathway with biomass maximization as the objective , as described in GECKO 2.0 [To improve the agreement of model predictions with experimental data, the enzyme-constrained model required further adjustment of the original ive    , scipy, kcat data in the workflow, ultimately affecting the prediction accuracy of the ecModel. Consequently, we replaced the old EC numbers in the model based on BRENDA, updating a total of 38 reactions , the addition of 4 new reactions , and the modification of GPR relationships for 5 reactions .EC numbers affect the extraction and assignment of eactions . MW is aeactions . For exaBSU31360 and BSU31370′, which has 74% sequence similarity. By further searching the BioCyc database for verification, we found that the protein encoded by BSU31370 is a bifunctional enzyme that catalyzes two reactions , whereas the protein encoded by BSU31360 catalyzes only the NADPH-dependent butanol dehydrogenase reaction. They are more likely to be two isoenzymes, and therefore the correct GPR relationship for BTS should be ‘BSU31360 or BSU31370′. Finally, 1736 reactions, 1459 metabolites, and 1155 genes were included in iBsu1147R.We also observed that there were 58 reactions in the model for which the “and” relationships were not identified by the GPRuler tool, and only 9 reactions had more than 25% similarity . By searR model in terms of substrate utilization, redox balance, energy balance, biomass reaction standardization, and mass balance, we found that all these aspects led to abnormalities in the metabolic pathways generated by the simulation. The boundary of 6 reactions was modified in terms of substrate utilization . For example, experiments have shown that B. subtilis can grow using malate [After running quality control of the iBsu1147g malate , so the g malate  to avoidR model and found that the original molecular mass of the biomass was 1.025 g/mmol, and H+ was missing in the right side of the biomass equation (the coefficient was 105) produced by the hydrolysis of ATP for growth-associated maintenance energy. After correction, the molar mass of the biomass became 0.92 g/mmol. Subsequently, we examined the precursor metabolites and found that the molar masses of protein, DNA, and RNA were 0.86 g/mmol, 0.95 g/mmol, and 0.95 g/mmol, respectively. We normalized the coefficients so that the molar masses of all components were 1 g/mmol, and the molar masses of the biomass also became 1 g/mmol. The details of all the modified reactions are listed in In addition, the molar mass of biomass and its components  was 1 g/mmol, and deviations from this value will result in errors in the calculated specific growth rate. Using the BiomassMW algorithm , we examR contains different IDs for metabolites and reactions, including KEGG IDs and ModelSEED IDs. To meet the input requirements of AutoPACMEN, we converted the KEGG IDs of 1007 metabolites and 785 reactions, as well as the ModelSEED IDs of 265 metabolites and 542 reactions into BiGG IDs. In addition, we kept the original IDs for reactions and metabolites that were not included in the BiGG database.Finally, we found that iBsu1147catk values ” with the highest measurement coverage and evaluation score, and calculated f = 0.588 g enzyme/g protein according to Eq. 1. Finally, the initial B. subtilis ecModel (ecBSU1) contained 1155 genes, 1459 metabolites, 3307 reactions, and 2331 enzyme kinetic parameters, with a total enzyme bound of 0.165 g enzymes/gDW.We used AutoPACMEN to match 2331 6.17 s−1 B. In tot6.17 s−1 C. The enkcat values , as was reported for ecYeast7 [catk correction. We found that the maximal growth rate calculated by ecBSU1 with glucose as substrate was 0.092 h−1 , as 17 reactions with catk values were unidirectional, resulting in its ability to simulate growth using only glucose as a substrate  close to the experimental value [−1 h−1 acetate and 3.93 mmol gDW−1 h−1 pyruvate). The difference in overflow products is mainly due to the limitations of the current ecModel, as the optimization process of the model only supports the pathway that generates the lowest enzyme cost, so that the current ecModel will only generate one overflow product.Further in-depth analysis using the ecModel revealed that the addition of the enzyme kinetic constraint information allowed ecModel to simulate the overflow of by-products from cells at high substrate concentrations. For example, the ecBSU1 results showed that it severe overflow metabolism would occur when utilizing malate at 26.51 mmol gDWal value . HoweverB. subtilis based on the enzyme cost of reactions. We selected 10 products for analysis from 51 commercial chemicals produced using the B. subtilis platform summarized in the literature [−1 (10% of the maximum) and performed reaction enzyme cost calculations with each of these 10 products as the objective function, respectively. Subsequently, a literature search was performed to validate the top 15 reactions in terms of enzyme cost in each pathway.GEMs play a guiding role in predicting metabolic engineering targets. In this work, we predicted potential target genes for the synthesis of several important chemicals  in terature . The proribA, ru5p__D_c --> db4p_c + for_c + h_c) and carbamoylphosphate synthetase , respectively  caralyzes a limiting step for the synthesis of the important precursor dihydrofolate (DHF), and co-overexpression of folC, pabB, folE, and yciA resulted in an additional 66.8-fold improvement of the 5-methyltetrahydrofolate titer, which reached 960.27 μg/L [We found that most of the predicted potential targets for the 10 products have been reported in the literature . Among tectively . Experimp to 25% . In addither 31% . For 5-m.27 μg/L . TherefoDuring the construction of traditional GEMs, not much attention is paid to whether the GPR relationship is “and” or “or”, but the correctness of these two relationships directly affects the simulation accuracy of ecModels. In this study, we systematically examined and corrected the GPR relationships in iBSU1147 by combining the GPRuler tool and protein homology. In addition, we systematically updated the EC numbers, carbon source utilization pathways, biomass reactions, mass balance, energy balance, and redox balance in the iBsu1147 model. This quality checking and correction process has far-reaching implications for improving the quality of GEMs and the construction of high-quality ecModels.R, we constructed ecBSU1 based on the ECMpy approach, in which 2331 of 3307 reactions were integrated with enzyme constraints according to AutoPACMEN, and the kcat coverage reached 76.4% after excluding 254 exchange reactions. The coverage of enzyme parameters was much higher than in ec_iYO844, which integrated only 17 reactions  located in the central carbon metabolism with the addition of catk values. However, enzyme kinetic data are sparse, and measured turnover rates are normally available for only a small fraction of metabolic reactions even in well-studied organisms [catk data for most of the reactions in the model, only 163 reactions had catk values derived from B. subtilis.Using iBsu1147rganisms . Therefokcat correction for 28 reactions. The ecBSU1 corrects the problem that the growth rate of GEMs increases indefinitely with the increase of the carbon source utilization rate. Next, ecBSU1 and iBsu1147R were, respectively, used to simulate the growth rate of B. subtilis on different substrates, and the results showed that the accuracy of ecBSU1 was much better than that of iBsu1147R at the high growth rate stage with enzyme constraints. In addition, the overflow metabolism of B. subtilis was explored using ecBSU1, which showed a physiological state of overflow metabolism in the presence of excess substrates, and demonstrated a trade-off between biomass yield and enzyme usage efficiency.Due to the diversity and incomplete coverage of enzyme parameters in the database, the initial ecModel was over-constrained, but it was able to accurately predict cellular phenotypes after B. subtilis for the synthesis of several commercial chemicals, and the reactions with the highest enzyme consumption were identified as metabolic engineering targets, which was in good agreement with the literature. This provides a new strategy and theoretical basis for metabolic engineering.Thus, ecBSU1 can be used to guide the rational design of microbial cell factories from a new perspective. The simulation results of GEMs usually only contain reaction fluxes, which cannot distinguish the physiological characteristics of each reaction. By contrast, ecBSU1 combines the kinetic characteristics of each reaction based on fluxes, thus demonstrating the enzyme consumption of each reaction, which can assist us to locate the kinetic bottlenecks of different metabolic states. We simulated the enzyme consumption of kcat prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences [kcat values for more than 300 yeast species. In addition, the integration of enzyme constraints greatly improves the predictive power of GEMs and brings the model predictions closer to the experimental measurements, but the biological system is too complex and to be fully described by enzymatic constraints alone. Therefore, it is necessary to integrate more biological data into novel composite constraints, which can include data on thermodynamics [Currently, ecBSU1 has a typical limitation also found in other ecModels, as the implementation of the enzyme abundance constraint is highly dependent on precise kinetic parameters and abundance data for each enzyme , both ofequences . Using tdynamics ,46 and rdynamics , or consdynamics .B. subtilis on a genome-wide scale, which significantly improved its metabolic phenotype prediction ability. The resulting model can be used to explain metabolic overflow phenomena and predict metabolic engineering targets for the synthesis of commercial chemicals in B. subtilis. This study has guiding significance for the rational design of microbial cell factories and provides an important integrated metabolic network model of B. subtilis. Finally, it also offers insights for the improvement of GEMs of other strains, so that the role of such models in the development of synthetic biology can be broadened in the future.This work integrated enzymatic constraints into the GEM of


Synechocystis sp. PCC 6803. ED-P consists of two reactions, the first catalyzed by 6-phosphogluconate dehydratase (EDD), the second by keto3-deoxygluconate-6-phosphate aldolase/4-hydroxy-2-oxoglutarate aldolase (EDA). ED-P was previously concluded to be a widespread (∼92%) pathway among cyanobacteria, but current bioinformatic analysis estimated the occurrence of ED-P to be either scarce (∼1%) or uncommon (∼47%), depending if dihydroxy-acid dehydratase (ilvD) also functions as EDD (currently assumed). Thus, the biochemical characterization of ilvD is a task pending to resolve this uncertainty. Next, we have provided new insights into several single and double glycolytic mutants based on kinetic model of central carbon metabolism of Synechocystis. The model predicted that silencing 6-phosphogluconate dehydrogenase (gnd) could be coupled with ∼90% down-regulation of G6P-dehydrogenase, also limiting the metabolic flux via ED-P. Furthermore, our metabolic flux estimation implied that growth impairment linked to silenced EDA under mixotrophic conditions is not caused by diminished carbon flux via ED-P but rather by a missing mechanism related to the role of EDA in metabolism. We proposed two possible, mutually non-exclusive explanations: (i) Δeda leads to disrupted carbon catabolite repression, regulated by 2-keto3-deoxygluconate-6-phosphate (ED-P intermediate), and (ii) EDA catalyzes the interconversion between glyoxylate and 4-hydroxy-2-oxoglutarate + pyruvate in the proximity of TCA cycle, possibly effecting the levels of 2-oxoglutarate under Δeda. We have also proposed a new pathway from EDA toward proline, which could explain the proline accumulation under Δeda. In addition, the presented in silico method provides an alternative to 13C metabolic flux analysis for marginal metabolic pathways around/below the threshold of ultrasensitive LC-MS. Finally, our in silico analysis provided alternative explanations for the role of ED-P in Synechocystis while identifying some severe uncertainties.The Entner-Doudoroff pathway (ED-P) was established in 2016 as the fourth glycolytic pathway in  Synechocystis sp. PCC 6803 (Synechocystis) is a model cyanobacterial organism with complex carbon metabolism, reported to contain all known glycolytic routes found so far in cyanobacteria: (i) Embden-Meyerhof-Parnas pathway (EMP-P), (ii) oxidative pentose phosphate pathway (OPP-P), (iii) lately characterized , producing 2-keto3-deoxygluconate-6-phosphate (KDPG). The second reaction is catalyzed by keto3-deoxygluconate-6-phosphate aldolase , producing pyruvate and glyceraldehyde 3-phosphate (Synechocystis (Sulfolobus solfataricus (Entner-Doudoroff pathway (ED-P) is the shortest glycolytic route, consisting of only two reactions. The first one is catalyzed by 6-phosphogluconate dehydratase , both under autotrophic and mixotrophic conditions, up to 18% and 57%  6-phosphogluconate dehydrogenase (GND) for OPP pathway, (ii) phosphoketolase (PKET) for PKET pathway, (iii) keto3-deoxygluconate-6-phosphate aldolase (EDA) for ED pathway and (iv) phosphofructokinase (PFK) for upper EMP pathway. The raw data for the occurrence of the marker enzymes were collected from UniProt 1 and lat to date . Along w to date , the gen13C labeling data for autotrophic growth . The routine for parameter estimation was a hybrid genetic algorithm. The model versions for tested growth conditions  with simplified carbohydrate and biomass synthesis (weighted sum of sink reactions). These metabolic reactions were coupled with simplified light reactions, Ci and glucose uptake as the primary input parameters. This model is an updated version of the previous model employed for the analysis of PKET pathway . The curSynechocystis without ED-P  with a single set of parameters; mixotrophic growth conditions are verified separately to respective fluxomic data. The justification and broader description of this part of our Methodology can be found in our previous work , which represented blocked ED-P. After a confirmation that the metabolic fluxes available via ED-P cannot explain the reported level of growth impairment under mixotrophic conditions, we have incorporated a full version of ED-P (EDA and EDD) and tested the impact of accumulation and possible excretion of KDPG (intermediate of ED-P) as well as other scenarios under autotrophic conditions. Since all these scenarios are based on carbon availability which plays a marginal role under mixotrophic conditions, we proposed that the missing link, allowing us to explain the experimental data, is the signaling role of KDPG (carbon catabolite repression). We did not perform any simulations based on the signaling role of KDPG as the growth impairment caused by Δeda provides a sufficient description only of its impact, but new experimental data are needed to determine the kinetics of the signaling process. The current version of the model assumes a very low level of KDPG for WT, based on reported undetectable levels  toward EDD in E.coli (k 12) or Pseudomonas while ilvD from E. coli and Pseudomonas showed between 55 and 65% similarity with ilvD from Synechocystis, respectively. Currently, there are only four cyanobacteria annotated with a native (single functioning) EDD  EDD . This faing) EDD  (4.2.1.9ing) EDD  among cySynechocystis, there is no mention of gluconate as a substrate for ilvD in recent biochemical analysis (zwf (glucose-6-phosphate dehydrogenase)  . At thatogenase) , but no for Δzwf  or (ii) Prochlorococcus does not contain either PKET or PFK  followed the reported mean growth impacts (around –2%) (pket (double mutant of PKET1 and PKET2), no experimental data are available, but previously reported 11% growth impairment for Δpket1  leads to a massive 43% growth increase under AC-auto and a significant carbon flux redistribution within the central carbon metabolism (gnd), which is in disagreement with previously reported statistically insignificant impact (around + 2%) in comparison to WT . We denoted this scenario as Δgnd* (asterisk differentiates from simple in silico gnd mutant discussed above) and the flux values and growth impact for Δgnd* are shown in zwf as a consequence of Δgnd was reported previously for Gluconobacter oxydans  conditions. Simulated Δund –2%) , see Tabr Δpket1  is in liof PKET1 . Additiotabolism , Δgnd, won to WT . The pre oxydans , which sfor ED-P . When weply Δgnd   due to the above predicted and discussed the real impact of Δgnd*, which was greatly limiting the flux via ED-P  other metabolic functions of EDA, which are further discussed in the next section. However, an experimental verification for the role of 6-phosphogluconate accumulation, level of KDPG and preferably also fluxomic data for these mutants are needed.Finally, the model failed to match the reported (∼60%) growth impairment  for Δedanditions . If we c species  and its hocystis . Other cpket and for most of the double mutants, it is difficult to discuss the results of simulations. However, the highest growth impairment caused by ΔedaΔpfk  (WT)  (WT) . Since E-P) (WT)  and ED-P-P) (WT) , or the  too low . Thus, oeda, which can shed new light on the role of EDA. However, the model is able to mimic only the last day of the time-series experiment under both conditions .Besides the reported mean growth impairment, there are also time-series data , Makowk for Δedanditions  day 7. Wnditions , day 4. ∼50-fold , day 2 a∼50-fold . Since t AC-mixo  but is c AC-mixo . Since tvia ED-P has been so far unknown due to difficulties in measuring labeled metabolites produced by ED-P, i.e., pyruvate and glyceraldehyde-phosphate, as more prominent metabolic pathways  produce them in higher quantities. By looking at metabolic fluxes estimated by our model, the flux via ED-P is slightly higher under mixotrophic conditions  itself could not explain the sudden boost in growth impairment on day 4 (presumably the day of depleted glycogen). The required increase in GND activity  is less than 2% of flux via glyceraldehyde-3-phosphate dehydrogenase , EDA should catalyze the interconversion of 4-hydroxy-2-oxoglutarate and pyruvate from glyoxylate (KEGG), see Synechocystis , from proline to pyruvate and glyoxylate, producing two molecules of NAPDH  a minor flux via ED-P, as suggested in our own flux estimation, ii) significant flux under certain conditions, such as fluctuating light  ED-P is missing in Synechocystis. Furthermore, Δeda was reported to deactivate OPP shunt  behind the temporal component of growth impairment under both discussed conditions, to metabolic plasticity of the central carbon metabolism of Synechocystis. Therefore, a thorough experimental and theoretical analysis is required to shed more light on the depths of Synechocystis metabolism.Finally, a new labeling experiment has been published recently. This study  comparedng light , which mng light , otherwing light ; we notePP shunt , which dThe original contributions presented in the study are included in the article/JJ and AB jointly discussed ideas and concepts and wrote the manuscript. JJ was done modeling. AB was done bioinformatic analysis. Both authors contributed to the article and approved the submitted version.


Despite the fact that antimicrobial resistance is an increasing health concern, the pace of production of new drugs is slow due to the high cost and uncertain success of the process. The development of high-throughput technologies has allowed the integration of biological data into detailed genome-scale models of multiple organisms. Such models can be exploited by means of computational methods to identify system vulnerabilities such as chokepoint reactions and essential reactions. These vulnerabilities are appealing drug targets that can lead to novel drug developments. However, the current approach to compute these vulnerabilities is only based on topological data and ignores the dynamic information of the model. This can lead to misidentified drug targets.This work computes flux constraints that are consistent with a certain growth rate of the modelled organism, and integrates the computed flux constraints into the model to improve the detection of vulnerabilities. By exploiting these flux constraints, we are able to obtain a directionality of the reactions of metabolism consistent with a given growth rate of the model, and consequently, a more realistic detection of vulnerabilities can be performed. Several sets of reactions that are system vulnerabilities are defined and the relationships among them are studied. The approach for the detection of these vulnerabilities has been implemented in the Python tool CONTRABASS. Such tool, for which an online web server has also been implemented, computes flux constraints and generates a report with the detected vulnerabilities.https://github.com/openCONTRABASS/CONTRABASS under GPL-3.0 License. An online web server is available at http://contrabass.unizar.es.CONTRABASS is available as an open source Python package at Bioinformatics online.A glossary of terms are available at  Antimicrobial resistance (AMR) occurs when bacteria, viruses, fungi and parasites evolve to become unresponsive to conventional antibiotics, thus threatening our ability to treat common infections and increasing the risk of spread of severe illnesses. The emergence of multidrug-resistant bacteria (MDR) is particularly alarming, as they can cause infections untreatable with existing antibiotics. According to the World Health Organization, AMR is one of the primary global public health threats of humanity due to its high increasing rate  of metabolism have been reconstructed for more than 6000 organisms including bacteria, archaea and eukaryotes by either automatic or manual reconstruction  and micrDespite the number of GEMs developed recently, most of these models lack genetic information, which could hamper the model analysis based on gene essentiality and calls for the design of computational methods that exploit as much as possible the available biological information. Here, we focus on the computation of critical reactions in metabolic networks such as essential reactions, dead reactions, and chokepoints  has been developed.In most GEMs, unknown flux bounds are given default flux bounds, e.g. Plasmodium falciparum model. Finally, Section 3 reviews the implemented tool CONTRABASS and Section 4 concludes the article.The remainder of the article is organized as follows: Sections 2.1 and 2.2 recall preliminary concepts and definitions such as constraint-based models and essential reactions. Structural definitions of constraint-based models and flux-dependent definitions are recalled in Sections 2.3 and 2.4, respectively. In Section 2.5, growth-dependent definitions are introduced. Section 2.6 is devoted to dead reactions, blocked reactions and their relationship. Section 2.7 shows how the integration of dynamic constraints affects the computation of vulnerabilities in Definition 2.1.constraint-based model . For example, the reaction m is a reactant and is consumed when r occurs.if m is a product and is produced when r occurs.if m is neither consumed nor produced when r occurs.if Example 2.1.r7 models the reaction The Petri net in Constraint-based models can be expressed as bipartite graphs with two types of nodes representing reactions and metabolites. Hence, constraint-based models can be represented graphically as Petri nets , where pFlux balance analysis (FBA)  for FBA is:Let rg be the reaction that models growth (or biomass production) in a constraint-based model. Without loss of generality, it will be assumed that Let μmax.The solution of the above LPP  will be Definition 2.2.essential reaction if the solution of the following LPP: or to the depletion of a metabolite  .Example 2.5.rg models growth, reactions r1, r2 are essential reactions, and thus, they are also EROG. If one, or both reactions, r3, r4 are forced to have flux equal to 0, metabolite m, which is essential for growth, can only be produced through a non-optimal path. Then, the model will not be able to achieve optimal growth. Therefore, these reactions are EROG.In the Petri net of Special attention is given to the set of reactions ERμmax be the maximum growth calculated by FBA   is a matd by FBA . FVA is The computation of the flux bounds by means of FVA , can be r. If the flux bounds L, U of the constrained-based model are replaced by Let ting FVA  on a conGiven Example 2.6.rg in z in rg is equal to 1 and the rest of components of z are 0. In order to obtain growth-dependent sets, the flux bounds computed by FVA with γ = 1 are assigned to the reactions and the net in γ can be considered). In this new net, r2, r3, r5, r6 and r7 are dead reactions, i.e. r4, which was a reversible reaction in Let us assume that Similarly, given r3 is the only flux-dependent consumer of metabolite mb and, thus, is a flux-dependent chokepoint reaction, i.e. r3 becomes a dead reaction, i.e. In the model of r4 is not the only producer or consumer of any metabolite in the model of r2, r3 and r5 become dead reactions and reaction r4 becomes the only consumer of ma and the only producer of mc, consequently, r4 becomes a growth-dependent chokepoint reaction On the other hand, reaction Given their importance in model tuning and drug discovery, the following sections focus on dead reactions, essential reactions and chokepoint reactions.μmax. Such a set coincides with the set of blocked reactions. Recall that a reaction Definition 2.11., the amount of md would decrease(increase) indefinitely, which contradicts the steady-state constraint, therefore the only possible steady-state flux for r6 is In the Petri net in This subsection explores the relationship between dead reactions and blocked reactions, and shows that the set of growth-dependent dead reactions is the same for any suboptimal state, i.e. for any growth strictly lower than the maximum growth In 0.A reaction with null maximum and minimum feasible flux is a blocked reaction. Note that this procedure is equivalent to computing FVA, see r, i.e. the interval Lemma 2.1.2such that Proof.Given s of FVA . Notice Interestingly, the set of growth-dependent dead reactions, Lemma 2.2.2such that Proof. Let Then, the set of growth-dependent dead reactions cannot decrease with γ:Lemma 2.3.2such that r becomes dead when γa is increased to γb. Then, given that the constraints in b is further increased by Proof. The convex set of possible solutions defined by the constraints in Let us now show that the set of growth-dependent dead reactions cannot increase with γ in suboptimal states, i.e. with Theorem 2.4.From Lemmas 2.2 and 2.3, the following theorem can be derived straightforwardly:Corollary 2.5.1 can be strictly greater than 1 is strictly greater than Notice that DRThus, in particular, the set of blocked reactions coincides with the set of dead reactions in suboptimal states.P.falciparum (iAM-Pf480)  (In addition to the above, through the interactive HTML report users can also access the data available in the model, this is, reactions, genes and metabolites along with their databases identifiers if available; explore the reaction sets defined in this article; and also explore the intersection of different sets of critical reactions.https://contrabass.readthedocs.io. CONTRABASS makes use of the Python toolbox COBRApy (https://github.com/openCONTRABASS/CONTRABASS and https://github.com/openCONTRABASS, respectively. All the code is released under GPL-3.0 License.The documentation of the tool is available at The development of new drugs is an expensive and costly process. This process needs to be streamlined in order to face current health threats as those posed by MDR. In this work, computational methods to identify critical reactions, i.e. vulnerabilities, in the metabolism of microorganisms are proposed. When applied on models of pathogenic bacteria, such critical reactions are potential drug targets.The developed methods classify the reactions of the models in different sets, e.g. essential reactions, chokepoint reactions, blocked reactions, etc., according to their role in the metabolism. In order to exploit the information available in the model as much as possible, flux- and growth-dependent sets of reactions have been defined. Such sets capture the role of the reactions as a function of the growth rate of the organism. Thus, it is possible to unveil how the cell uses its reactions to achieve its growth. The relationship among these sets has been explored and it has been shown that the set of dead reactions keeps constant for any non-optimal growth rate. Moreover, the computation of other flux- and growth-dependent sets, such as dead-end metabolites and dead reactions, can help in the identification of potential shortcomings of the model. In order to facilitate their use, an online web server and a command line tool implementing the discussed methods have been made publicly available.