The aim of this experiment was to investigate the conditions for increasing CoQ10 production. At present, microbial fermentation is the main production method of CoQ10, and the production process of microbial CoQ10 metabolism control fermentation is very critical. Metabolic flux is one of the most important determinants of cell physiology in metabolic engineering. Metabolic flux analysis (MFA) is used to estimate the intracellular flux in metabolic networks. In this experiment, Rhodobacter sphaeroides was used as the research object to analyze the effects of aqueous ammonia (NH3·H2O) and calcium carbonate (CaCO3) on the metabolic flux of CoQ10. When CaCO3 was used to adjust the pH, the yield of CoQ10 was 274.43 mg·L−1 (8.71 mg·g−1 DCW), which was higher than that of NH3·H2O adjustment. The results indicated that when CaCO3 was used to adjust pH, more glucose-6-phosphate (G6P) entered the pentose phosphate (HMP) pathway and produced more NADPH, which enhanced the synthesis of CoQ10. At the chorismic acid node, more metabolic fluxes were involved in the synthesis of p-hydroxybenzoic acid , enhancing the anabolic flow of CoQ10. In addition, Ca2+ produced by the reaction of CaCO3 with organic acids promotes the synthesis of CoQ10. In summary, the use of CaCO3 adjustment is more favorable for the synthesis of CoQ10 by R. sphaeroides than NH3·H2O adjustment. The migration of metabolic flux caused by the perturbation of culture conditions was analyzed to compare the changes in the distribution of intracellular metabolic fluxes for the synthesis of CoQ10. Thus, the main nodes of the metabolic network were identified as G6P and chorismic acid. This provides a theoretical basis for the modification of genes related to the CoQ10 synthesis pathway.Coenzyme Q The number of isoprene units on the side chains of CoQn varies from organism to organism. The CoQn molecule in the human body contains 10 isoprene units, namely CoQ10. CoQ10 was discovered by Frederick Crane and colleagues in 1957 [10 is the only lipid-soluble antioxidant synthesized endogenously in the human body [10 . The culture media included agar slant culture medium 2SO4, 4.2 g·L− 1 NaCl, 8.75 g·L− 1 MgSO4, 1.5 g·L− 1 KH2PO4, 1.2 g·L− 1 A), seed culture medium 2SO4, 4.2 g·L− 1 NaCl, 8.75 g·L− 1 MgSO4, 1.5 g·L− 1 KH2PO4, 1.2 g·L− 1 A), and fermentation culture medium 2SO4, 2.5 g·L− 1 NaCl, 12 g·L− 1 MgSO4, 0.8 g·L− 1 KH2PO4, 0.1 g·L− 1 MnSO4, 1.6 g·L− 1 A, 0.1 g·L− 1 B).The R. sphaeroides cultured on agar plates for 6 days were picked out and transferred into the primary seed medium (50 mL/250 mL), and incubated at 32 °C with shaking at 220 rpm for 24 h. Then, they were transferred into the secondary seed medium (350 mL/2500 mL) at 10% inoculum and incubated at 32 °C with shaking at 220 rpm for 22 h. Finally, the secondary seed medium was inoculated at 15% inoculum into a 10 L fermenter. The inoculated fermenter was filled with 7 L. The temperature was 34 °C, the stirring speed was 300 rpm, the aeration rate was 3 L·min− 1, and the tank pressure was 0.04 MPa. The pH was adjusted by adding CaCO3 and NH3·H2O during the fermentation process. Samples were taken at 3 h intervals to determine various parameters.Two colonies of A bioanalytical sensor  was used for analysis. The fermentation broth was diluted by a certain multiple and then filtered. Twenty-five microliters of filtrate was taken for injection determination. The determination was performed three times in parallel and the average value was taken.4 rpm, 10 min), and the supernatant was removed. After washing with distilled water, the samples were centrifuged again, and the supernatant was removed. The organism in the centrifuge tube was dried in an oven at 100 °C to a constant weight. For fermentation broth containing CaCO3, a drop of 6 mol·L− 1 HCl solution was added to dissolve the residual CaCO3 in the fermentation broth before centrifugation.Five milliliters of the fermentation broth was centrifuged . The chromatographic column was Hypersil ODS-SP . The mobile phase consisted of anhydrous methanol and anhydrous ethanol in a ratio of 65: 35 (v/v). The detection wavelength was set at 275 nm. The column temperature was 30 °C, the injection volume was 20 µL, the flow rate was 1.1 mL·min− 1, and the elution time was 15 min. The calculation formula of CoQ10 concentration in the sample is:The filtrate was collected for the determination of CoQ10 in the test sample (mg·L− 1); co is the concentration of CoQ10 in the standard sample (mg·L− 1); K is the dilution multiple of the test sample; S is the peak area of the test sample (mAU·s); So is the peak area of the standard sample (mAU·s); V is the injection volume of the test sample (µL); and Vo is the injection volume of the standard sample (µL).where c is the concentration of CoQ− 1 CH3COONa solution (pH 6.5) and 80% acetonitrile solution, respectively. The detection wavelength was set at 254 nm. The column temperature was 40 °C, the injection volume was 20 µL, and the flow rate was 1 ml·min− 1. Gradient elution was performed.The concentration of free amino acids in the fermentation broth was determined by HPLC. A Venusil-AA amino acid analysis column was used. Mobile phases A and B were 0.1 mol·L− 1 KH2PO4 buffer (pH adjusted to 2.8 by H3PO4) and acetonitrile solution, respectively, and were injected into the column at a ratio of 95: 5 (v/v). The detection wavelength was set at 215 nm. The column temperature was 25 °C, the injection volume was 20 µL, and the flow rate was 1 ml·min− 1.The concentration of organic acids in the fermentation broth was determined by HPLC . The chr10 by R. sphaeroides belongs to growth-coupled fermentation. Therefore, when analyzing the metabolic flux of CoQ10 synthesis, the carbon source consumed by the growth of the bacterium cannot be ignored, and the synthesis pathway of the basic cellular components should be considered. The main cellular components and their contents refer to the experimental data measured by Saheed lmam [R. sphaeroides under aerobic conditions are shown in Table 18:1) has the highest content, accounting for 85%. Stearic acid (C18:0), soft fatty acid (C16:0), and palmitoleic acid (C16:1) accounted for 9%, 5%, and 1%, respectively.The synthesis of CoQeed lmam . The fraR. sphaeroides uses glucose as the sole carbon source, the glyoxylate cycle pathway is largely inoperative and is not considered in this metabolic network. The intermediate reactions without branching points are simplified into a reaction equation. For intermediate metabolites with branching points, the rate of production was equal to the rate of consumption, and the net accumulation was 0, assuming that these metabolites were in the proposed steady state.Since It is assumed that NADPH produced by the HMP pathway and the TCA pathway were not oxidatively phosphorylated but were all used for biosynthesis. The fermentation supernatant of 39–42 h in the late logarithmic growth phase of the organism was analyzed for composition. The results showed that the supernatant basically did not contain free amino acids. The content of organic acid metabolic byproducts such as formic acid, acetic acid, propionic acid, butyric acid, lactic acid, pyruvic acid, and citric acid was also very low. Therefore, the influence of these organic acids on metabolic flux can be ignored in the metabolic network studied.Metabolic flux analysis is a metabolic network chemometric used to describe the conversion of substrates into metabolites and cellular composition. The distribution of carbon flow under the condition of carbon source limitation is generally considered. The equilibrium equation of each metabolite can be expressed as:met is the metabolite concentration; rmet is the net rate of metabolite formation; and u is the dilution factor of the culture medium.where Xmet/dt = 0), it follows that: rmet = 0.Since most of the intermediate metabolites have low intracellular concentrations, the dilution effect can be negligible. According to the proposed steady-state assumption that the concentration of intermediate metabolites is in equilibrium  were divided by the rate of glucose consumption (r1), and the result was the respective values of r1 to r22  that can damage cell membranes [10 has antioxidant properties, it can alleviate the damage caused by ROS on cell membranes. With the increase in Ca2+ concentration, the formation of intracellular ROS was stimulated, which promoted the synthesis of CoQ10 and increased the yield of CoQ10.The organic acids produced during fermentation react with CaCOembranes . Since C2+ has been proven to increase the yield of CoQ10. Whether different concentrations of Ca2+ can all achieve this effect is an aspect worthy of future research, and perhaps the best Ca2+ concentration can be found. Ca2+ is an activator of many enzymes, and the hypothesis that increased CoQ10 yield may also be due to its activation of the activity of related enzymes in the synthesis pathway can also be explored.A certain amount of Ca3 was used to adjust the pH, the metabolic flux from G6P to Ru5P was 70.5, and the metabolic flux from G6P to F6P was 27.9 . The metabolic flux from G6P to F6P migrates due to the addition of CaCO3 during fermentation or the effect of the reduction of NH4+ on fermentation. Some of the metabolic streams passed through the HMP pathway before entering the EMP pathway. This process has a completely different meaning than that of F6P, which is directly converted to F6P by the EMP pathway. Although the loss of carbon source material flux is small, the former provides more reducing power NADPH. Since the synthesis of isoprene pyrophosphate, the precursor of the CoQ10 side chain, requires NADPH to provide reducing power, 10 mol NADPH is directly involved for every 1 mol CoQ10 synthesis. Then, G6P enters the EMP pathway through the HMP pathway to provide NADPH for the synthesis of CoQ10.G6P is the common starting metabolite of both the EMP pathway and the HMP pathway. When CaCO7.9 Fig. . When NH3 and NH3·H2O, had almost no effect on the biosynthesis of the bacterium during the synthesis of CoQ10 by R. sphaeroides. The fermentation times used for conditioning with CaCO3 and NH3·H2O were 50 h and 45 h, respectively , which was higher than that of NH3·H2O conditioning of 226.53 mg·L-1 (7.55 mg·g-1 DCW). The CoQ10 synthesis rates under the two pH adjustments were basically the same within 24 h from the beginning of fermentation. However, between 24 and 42 h, the rate of CoQ10 synthesis under NH3·H2O regulation was faster than that under CaCO3 regulation. The accelerated synthesis of CoQ10 in this process produces more organic acids. As an alkaline substance, NH3·H2O with high solubility can quickly resist the pH drop caused by organic acids, while CaCO3 with poor solubility reacts slowly. Therefore, NH3·H2O can better regulate the pH in this stage, which leads to a faster rate of CoQ10 synthesis. After 42 h of fermentation, the rate of NH3·H2O-regulated CoQ10 synthesis slowed down, whereas the CaCO3-regulated one was still able to maintain a high rate of growth. The reason was mainly that NH3·H2O was consumed in large quantities in the early stage, the concentration decreased dramatically, and the pH-regulating ability was weakened.The two pH regulators, CaCO3 was more favorable for the synthesis of CoQ10 by R. sphaeroides compared with NH3·H2O adjustment. This study provides a reference for researchers in the selection of pH regulators. In addition, the main nodes of the metabolic network were identified as G6P and chorismic acid. This provides a theoretical basis for the modification of genes related to the CoQ10 synthesis pathway.In summary, pH adjustment with CaCO10, an increasing number of researchers are investigating it. Its potential functions are constantly being developed and explored. Microbial fermentation as a production method will be the choice of more people in the future, and one of its most important goals is to maximize the yield of the desired fermentation product. The method is not only the well-known improvement and selection of strains, but also the analysis of the culture environment favorable to the synthesis of products according to the metabolic network of the microorganism, as used in this article. The application of metabolic engineering principles to improve the yield of microbial fermentation products has become a hot research topic.Due to the high value of CoQ


Alzheimer’s disease (AD) is a typical progressive neurodegenerative disorder, and with multiple possible pathogenesis. Among them, coumarin derivatives could be used as potential drugs as monoamine oxidase-B (MAO-B) inhibitors. Our lab has designed and synthesized coumarin derivatives based on MAO-B. In this study, we used nuclear magnetic resonance (NMR)-based metabolomics to accelerate the pharmacodynamic evaluation of candidate drugs for coumarin derivative research and development. We detailed alterations in the metabolic profiles of nerve cells with various coumarin derivatives. In total, we identified 58 metabolites and calculated their relative concentrations in U251 cells. In the meantime, the outcomes of multivariate statistical analysis showed that when twelve coumarin compounds were treated with U251cells, the metabolic phenotypes were distinct. In the treatment of different coumarin derivatives, there several metabolic pathways changed, including aminoacyl-tRNA biosynthesis, D-glutamine and D-glutamate metabolism, glycine, serine and threonine metabolism, taurine and hypotaurine metabolism, arginine biosynthesis, alanine, aspartate and glutamate metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glutathione metabolism and valine, leucine and isoleucine biosynthesis. Our work documented how our coumarin derivatives affected the metabolic phenotype of nerve cells in vitro. We believe that these NMR-based metabolomics might accelerate the process of drug research in vitro and in vivo. Metabolism is the central role of systems biology, and is also amplified relative to changes in the DNA, RNA and enzyme activities . Since tFor accelerating drug discovery, increased techniques are being used for high-throughput screening. These techniques mainly contain silico tests  and biocAlzheimer’s disease (AD) is a typical progressive neurodegenerative disorder accounting for up to 75% of all dementia cases . There a2PO4·2H2O and K2HPO4·3H2O  were purchased from J&K Scientific Ltd. . D2O (purity 99.9%) and sodium 3-(trimethylsilyl) propionate-2,2,3,3-d4 (TMSP) were bought from Cambridge Isotope Laboratories, Inc. . The twelve coumarin derivatives were synthesized and purified in our lab. The detailed synthesis steps have been published previously  pulse sequence, the water suppression irradiation was led to the supersaturation stage with 3 s relaxation delay and 120 ms mixing time. The detailed acquisition parameters were described in our previous works [Before metabolite extraction, about 1 × 10 article ,36. The http://www.hmdb.ca/; access on 17 August 2022) to verify the metabolite NMR spectrum. These targeted metabolites were quantified from complex 1H NMR spectra by using an Automated Quantification Algorithm (AQuA) [p values by CV-ANOVA and fold changes were also calculated between treatment groups and the control group for assessing the statistical significance of differential metabolites. These four parameters were used in the enhanced volcano plots for visualizing the differential metabolites [The Chenomx NMR Suit  was applied for identifying the metabolites from the NMR spectra of samples. Then, we used the HMDB database (URL: m (AQuA) . The prim (AQuA)  and hierm (AQuA)  were perm (AQuA) , and them (AQuA) . In the m (AQuA)  and the m (AQuA)  were useabolites ,44.www.metaboanalyst.ca/; access on 17 August 2022) [Based on the different metabolites, metabolic pathway analysis was used to identify significantly disturbed pathways associated with the treatment groups. These works were performed on the Pathway Analysis module from the web-sever of MetaboAnalyst 4.0 (URL: st 2022) .50 of these twelve coumarin derivatives was 50–100 μM , HMDB database  and Chenomx NMR Suite for assigning the metabolites from the NMR spectra, we identified 58 metabolites for quantitative calculation in MATLAB  with AQuA  > 0.25. The differential metabolites are located at the upper-left and upper-right areas of the volcano plot, with larger circle shapes and gradual warm colors. According to For analyzing the differential metabolites, the four dimensional enhanced volcano plots were used for data visualization . The VIPBased on the differential metabolites, we identified significantly disturbed metabolic pathways in the treatment with different coumarin derivatives . For theIn the past few decades, the study of selective MAO-B inhibitors has been continually increasing because of their critical role in regulating synaptic function and monoamine metabolism . HoweverAccording to metabolic pathway analysis, we found that the aminoacyl-tRNA biosynthesis was the most frequent when the U251 cell was in the different MAO-B inhibitors. Aminoacyl-tRNA biosynthesis is the core metabolic pathway of organisms, and the aminoacyl-tRNA synthetases (ARSs) are universally expressed enzymes accountable for charging tRNAs with their cognate amino acids, which is crucial for the first step of protein synthesis ,50. TherIn addition, two metabolic pathways were perturbed for the U251 cell in the treatment of coumarin derivatives, including taurine and hypo-taurine metabolism and glutathione metabolism. These two perturbed metabolic pathways were involved with oxidative stress. Huang et al. found that the perturbed metabolic pathway of taurine and hypo-taurine metabolism could lead to oxidative stress injury in neurons, and worsening dementia by using metabolomics . The gluThe described pathways are linked to nerve cell function related to aminoacyl-tRNA biosynthesis, glutamine and glutamate metabolism, one-carbon metabolism and oxidative stress. Compared with traditional drug screening, the NMR-based metabolomics support the findings of the metabolic pathways associated with drug action. Our study reported the effect of our coumarin derivatives on nerve cells in vitro through their effect on the metabolic phenotype. We believe that these NMR-based metabolomics might accelerate the process of drug research in vitro and in vivo.


Escherichia coli genome‐scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision–recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene‐protein‐reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high‐throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.The  Escherichia coli genome‐scale metabolic model flux balance analysis (FBA) growth prediction accuracy is quantified using published experimental data assaying mutant fitness across different carbon sources, revealing improved practices for model accuracy evaluation and sources of prediction inaccuracy. Escherichia coli K‐12 MG1655 genome‐scale metabolic model (GEM) represents one of the most well‐established compendia of knowledge on a single organism's cellular metabolism. This model maps genotype to metabolic phenotype and can be used to mechanistically simulate E. coli growth under various gene knockouts and/or environmental chemical perturbations. The E. coli GEM was one of the first GEMs to be analyzed  . We highlight robust metrics for quantification of model accuracy, identify important adjustments to the representation of the data in the simulation of the model, highlight key areas for ongoing model refinement, and use a machine learning framework to suggest specific fluxes associated with incorrect model predictions.In this work, we provide a critical assessment of E. coli GEM by comparing model predictions to previously published experimental data  . Several key areas contributing to poor performance were apparent upon visualization and analysis of the model predictions  had weak negative fitness after five generations, but strong negative fitness after 12 generations . This suggests that carry‐over alone could not maintain the observed growth in these mutants. A separate study—using the Keio collection of individual gene knockout mutants across 30 carbon sources—reported that knockouts of these genes in the biotin and tetrahydrofolate pathways were not essential when assayed on solid medium (where diverse neighboring colonies could in principle cross‐feed metabolites) but were essential when grown in individual liquid cultures  were implicated Fig . Throughet al, et al, et al, et al, Another set of genes that were observed to contribute to false‐positive predictions were genes involved in isoenzyme gene‐protein‐reaction mappings (where there is an “or” relationship in the Boolean mapping of genes to a reaction). Eight different isoenzymes mapping to 10 different reactions were among the lowest fitness genes for which the model simulated a growth phenotype  and/or excluding all isoenzymes from the data (genes with “or” statement in any associated gene‐protein‐reaction mapping)  Fig . Notablying) Fig . The excet al, The dataset utilized here assays 25 different carbon sources, providing insight across diverse carbon utilization pathways. We explored the carbon source‐specific accuracy of the corrected iML1515 model by calculating the precision–recall AUC for each separate carbon source Fig . We obseet al, Genome‐scale metabolic modeling provides additional insight beyond a prediction of growth/no‐growth. Each simulation, where a growth phenotype is predicted, simultaneously predicts the metabolic flux through every reaction in the network. We sought to use this flux information to gain deeper insight into model accuracy. We began by calculating the metabolic fluxes for each simulation using parsimonious flux balance analysis  transport either L‐proline or L‐glutamate from the periplasm to cytoplasm along with a hydrogen ion. High negative flux through these reactions (transporting amino acids and hydrogen ions from the cytoplasm to the periplasm) was associated with incorrect model predictions. The flux through both of these reactions was also clustered (strongly covaried across simulations) with a sodium ion symporter that carried the opposite flux transporting the amino acid and a sodium ion back into the cytoplasm. Thus, the net flux of these reaction clusters is the export of hydrogen ions from cytoplasm to periplasm and import of sodium ions. To further address the hydrogen ion flux, we resimulated flux balance analysis growth predictions while fixing the hydrogen ion flux (ranging between 0 and 10)  Fig . Fixing There were also several reactions involved at branch points in central carbon metabolism that were implicated in the SHAP feature importance analysis. Increased flux through pyruvate dehydrogenase, directing pyruvate to the TCA cycle, was associated with correct model predictions  is cross‐fed rather than tetrahydrofolate. Next, while we believe the vitamin/cofactor predictions can be explained by the cross‐feeding and carry‐over hypotheses, other examples of false‐negative predictions may alternatively be due to missing or unknown biosynthetic reactions that need to be gap‐filled. For the false‐positive predictions, corrected by re‐assignment of reaction reversibility and isoenzyme mapping, it is possible that corrections to alternative reactions further up in the pathways of interest could correct these errors. Additionally, in our machine learning analysis, we used flux profiles from parsimonious flux balance analysis. Alternative methods for simulating metabolic flux in knockout strains have been shown to provide more accurate predictions of metabolic flux  were used to represent the fitness, as preliminary analyses indicated that these scores corresponded more closely with model predictions. Genes were matched to the E. coli GEM through their “sysName,” which corresponds to their BiGG database identifiers  if the model already had the corresponding transport and exchange reactions. The lower bound of these exchange reactions were set to −1,000 to allow unlimited uptake of these metabolites. A correction was made by changing the reversibility of a reaction in the L‐serine biosynthesis pathway . This reaction was made irreversible by setting the lower bound equal to 0. Isoenzyme gene‐protein‐reaction mappings were corrected for several isoenzymes . These genes were identified to be leading to false‐positive predictions. Each gene‐protein‐reaction mapping was changed such that each of these genes was solely responsible for any reaction where they had previously been mapped through an or relationship with another gene.Several corrections were made to the analysis of the iML1515 model to improve accuracy. Vitamins/cofactors were added to the simulation environment. In total, five vitamins/cofactors were added first individually and then all together. These metabolites were either added to the simulation environment either directly to the cytoplasmic compartment  or to the extracellular compartment . A sliding threshold on the fitness value was then used to generate a precision–recall curve. The positive class was set to simulated essentiality (no‐growth phenotype). The area under this precision–recall curve was used to quantify model accuracy. Precision–recall curves were calculated using sklearn.metrics.pre_rec and area under the curve was calculated using sklearn.metrics.auc.Several alternative accuracy metrics were calculated for comparisons with the area under a precision–recall curve metric. The area under the receiver operating characteristic curve was calculated using sklearn.metrics.roc_auc_score, balanced accuracy was calculated as the arithmetic mean of the true‐positive rate and true‐negative rate, and the overall accuracy was calculated as the sum of true positives and true negatives divided by the total number of samples.A null model was used to improve the interpretation of model accuracy results. This null model assumed that all additional genes in a subsequent model were nonfunctional . This analysis was performed by adding null data points to each model until the total number of experiments (number of genes multiplied by number of carbon sources) matched that of the subsequent model. The accuracy was then calculated, with each metric, while including these null data points.+, and the E. coli Biomass reactions. Pyruvate was also a hub node but was left in the network due to its role as a carbon source in this analysis. This hub‐less network was then used to calculate the carbon source gene distance. The distance was calculated as the number of reactions traversed from the extracellular carbon source to the nearest reaction that was essentially encoded by the gene (a gene knockout removed the reaction). For example, distance of 0 corresponded to an essential reaction directly involving the extracellular carbon source, while distance of 1 corresponded to an essential reaction involving any metabolite connected to a reaction of distance 0. Distances on the metabolic network were calculated using the scipy.csgraph.shortest_path method implementation of Dijkstra's algorithm.The distance between carbon sources and genes was calculated using the genome‐scale metabolic model. The model was converted to a bipartite graph with metabolites and reactions as nodes. An edge was placed between any metabolite and reaction where that metabolite was a reactant or product for that reaction (nonzero stoichiometry), and an adjacency matrix was constructed for the metabolic network. From this adjacency matrix, high degree “hub” nodes were removed. Hub nodes were defined as any node with more than 50 connections and consisted of central metabolites such as hydrogen and water, cofactors such as ATP and NAD−7 were discarded form the analysis . Fluxes were clustered into groups of fluxes with covariation greater than 0.99 across samples (leaving 172 flux clusters). One representative flux from each cluster was used for the ML input. ML accuracy was assessed by 100 repeated train/test splits. For each train/test, split a random subset of samples from 80% of the carbon sources and 80% of the genes was selected as the training set. Cross‐validated accuracy was assessed on the full set of test samples as well as a smaller set of samples with no overlapping carbon sources or genes in the training set. Model performance was explored for varying number of leaves in the ML model, an important tuning parameter for overfitting. In the final model, five leaves were used, which balanced full cross‐validation with new carbon/gene cross‐validation. Other parameters were set to the lightgbm classifier default values . Feature importance was calculated using Shapley additive explanations (SHAP) through shap.TreeExplainer  was conducted to classify simulations with biomass flux into false positives  or true positives . Note that we also ran the analysis with a fitness threshold of −1 and found our main results to be robust to this choice; overall, there was a Spearman's rank correlation of 0.93 between feature importance calculations with the −2 or −1 threshold. A lightgbm classifier was used for machine learning. Flux vectors, quantifying the simulated flux across all reactions in the metabolic model , were used as the input for the ML algorithm. Fluxes with variance across samples less than 10David B Bernstein: Conceptualization; data curation; software; formal analysis; supervision; validation; visualization; methodology; writing – original draft; writing – review and editing. Batu Akkas: Data curation; software; formal analysis; visualization; writing – review and editing. Morgan N Price: Data curation; supervision; validation; methodology; writing – review and editing. Adam P Arkin: Conceptualization; supervision; funding acquisition; methodology; project administration; writing – review and editing.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Review Process FileClick here for additional data file.PDF+Click here for additional data file.


Trichuris muris. The model demonstrates the metabolic features of T. muris and allows the prediction of metabolic steps essential for its survival. Specifically, that Thioredoxin Reductase (TrxR) enzyme is essential, a prediction we validate in vitro with the drug auranofin. Furthermore, our observation that the T. muris genome lacks gsr-1 encoding Glutathione Reductase (GR) but has GR activity that can be inhibited by auranofin indicates a mechanism for the reduction of glutathione by the TrxR enzyme in T. muris. In addition, iTMU798 predicts seven essential amino acids that cannot be synthesised by T. muris, a prediction we validate for the amino acid tryptophan. Overall, iTMU798 is as a powerful tool to study not only the T. muris metabolism but also other Trichuris spp. in understanding host parasite interactions and the rationale design of new intervention strategies.Genome-scale metabolic models are widely used to enhance our understanding of metabolic features of organisms, host-pathogen interactions and to identify therapeutics for diseases. Here we present iTMU798, the genome-scale metabolic model of the mouse whipworm  Trichuris muris and use it to identify novel metabolic pathways and predict critical enzymes and essential metabolites for worm survival.In this work, Bay et al describe the construction of the first genome-scale metabolic model for the parasitic whipworm,  Human trichuriasis caused by Trichuris trichiura, also known as whipworm, is one of the most prevalent soil-transmitted helminth infections and it is often modelled in laboratories in mice using Trichuris muris because of its ease of use and similarity to the T. trichiura infection2.Soil-transmitted helminths infect more than 24% of the population in the world and cause morbidity with a variety of symptoms such as diarrhoea, abdominal pain, malnutrition, impaired growth and physical development3. Since bacteria and parasitic worms cohabit the mammalian intestine, they interact with each other as well as interacting with their hosts. Host-helminth-microbiota interactions are still not fully understood due to their complexity, even though many studies have been conducted to reveal the three-way interaction in terms of host immunity, bacterial composition/diversity, and parasite morbidity. The association between T. muris and bacteria begins in early stages of the infection in mice with egg hatching4 and continues across the developmental stages7. In mono-colonised mice, both Escherichia coli and Bacteroides thetaiotaomicron can provide the necessary minimal metabolic requirements of T. muris for its fitness, development, and successful infection8.The mammalian intestine provides a niche for both microorganisms (microbiota) and soil-transmitted helminths (macrobiota)T. muris infection has been studied extensively over a number of years and has informed much of our knowledge on the Th1 and Th2 immune responses11. However, relatively little is known about the detailed biology and biochemistry of the parasite because of the inability of producing mutant T. muris strains and challenges for the growth and development of whipworm in vitro. Therefore, in silico model predictions using a constraint-based model of T. muris are key to understand the helminth’s metabolic capabilities and overcome these challenges and facilitate a deeper understanding of the relationship between parasite, microbiota and host.The immune response in acute and chronic 13. GSMMs have been applied to Caenorhabditis elegans to reveal ageing mechanisms, the contribution of nutrients to its metabolism15, and the importance of its interactions with bacteria during its development16. A compartmentalised GSMM of Brugia malayi, a tissue dwelling helminth, is another nematode metabolic model that has been useful in understanding the relationship between the parasite and its bacterial endosymbiont17.Genome-scale metabolic models (GSMMs) reveal the metabolism of an organism in detail by making computational predictions that help us understand metabolic properties of an organism. GSMMs can be used to study host-bacteria metabolic interactions as well as the molecular mechanism of diseases and to identify drug targets for them, as they represent the full range of known information about a species’ metabolic reactionsT. muris. Using this manually curated GSMM, we predicted and demonstrated the amino acid requirement for T. muris. We also predicted essential enzymes for T. muris survival and validated one of the model’s predictions with a set of biochemical assays and in vitro experiments. This GSSM reveals the mechanism of the glutathione reduction by thioredoxin reductase in T. muris and will be an essential tool in deciphering the complex physiological interactions between the parasite, its host and the host microbiota and opens up the approach being applicable to multiple host-parasite systems. Finally, the GSSM offers the opportunity to identify potential therapeutic targets based on rational examination of parasite metabolism.In this study, we present the reconstruction of the GSMM for T. muris was developed through the evaluation of a large amount of information in a variety of databases and the incorporation of the data retrieved from these databases using the COBRApy tool. The GSMMs of C. elegans 19 were used as template for the reconstruction of the T. muris genome-scale metabolic model due to the lack of bibliomic information on its metabolism.The GSMM of T. muris and C. elegans using the BioMart tool based on the annotated genome of T. muris stored in WormBase Parasite22. Following the homology search, we used WormJam as the first reference model to build a draft model for T. muris as WormJam18 is a consensus metabolic model of previously published C. elegans models. We found the enzymatic reactions associated with the ortholog genes in WormJam and developed a draft metabolic network with them. Transport and exchange reactions were also added to the draft model of T. muris. In total, 1622 reactions out of 3633 WormJam reactions were added to the draft metabolic model of T. muris. WormJam has four compartments including nucleus, cytosol, mitochondrion, and extracellular space. However, we removed the nucleus compartment from the draft model because of the lack of information about T. muris nuclear localisation. Additional enzymes that are found based on T. muris orthologs with Trichuris spp. and C. elegans were searched in KEGG and Rhea to find additional reactions and added to the draft model  are shared with those predicted by iTMU798  consists of 798 genes, 510 enzymes, 1219 reactions, and 930 metabolites in 58 metabolic pathways across three compartments: cytosol, mitochondria, and extracellular space 28 is shown in Fig. The iTMU798 predicted 107 essential genes, of which 11 do not T. muris survival, we performed FBA simulations in which we blocked each amino acid uptake reaction ensuring there is no unexpected metabolite production or consumption (steady state) as constraints in iTMU798. FBA following the amino acid restriction indicated that 14 amino acids in the metabolic model are likely to be produced within the system since constraining their uptake from extracellular compartment did not change the biomass production in iTMU798  is one of the most common tools to predict the cellular growth rate of an organism based on its biomass production using mathematical representation of reactions and metabolites in GSMMs. As a result, it calculates each reaction’s flux within their given boundaries in the metabolic network. To predict the impact of amino acid uptake for the T. muris genome.We showed that the restriction of tryptophan, arginine, phenylalanine, threonine, asparagine, histidine, and lysine uptake from the extracellular space blocked the biomass formation in iTMU798 completely. iTMU798 shows that these 7 amino acids are provided to the system extracellularly and genes responsible for their biosynthesis are not present in the T. muris biomass maximisation  knocking out the essential gene predicted from the model, (ii) inhibiting the enzyme encoded by the essential gene and observe if they impact the growth experimentally. Since making mutant strains of l-seryl-tRNA(Sec) by l-seryl-tRNA(Sec) kinase (PTSK). Hydrogen selenide as a result of the reduction of selenite by thioredoxin reductase-1 (TrxR-1) is used as a precursor for the selenophosphate biosynthesis by selenophosphate synthase (SPS2). O-phosphoseryl-tRNA(Sec) selenium transferase (SecS) converts O-phospho-L-seryl-tRNA(Sec) by using selenophosphate to L-selenocysteinyl-tRNA(Sec) for selenoprotein biosynthesis  that are involved in these 5 reactions for selenoprotein biosynthesis are predicted as essential following single gene deletions based on the biomass production as an objective for iTMU798. In addition to its crucial role to synthesise selenocysteine in the selenocompound metabolism, TrxR-1 also plays a key role in the redox homeostasis of many organisms to protect them against reactive oxygen species (ROS) by reducing oxidised thioredoxin, which serves as an electron donor  with serine in the aminoacyl-tRNA biosynthesis pathway is phosphorylated to O-phospho-sis Fig. . T. murinor Fig. .S. mansoni30 and B. malayi31 by auranofin, an FDA-approved drug used for rheumatoid arthritis, has been previously shown. Therefore, to validate the importance of TrxR for T. muris survival as predicted by the metabolic model, we chose auranofin as an inhibitor of Tm-TrxR. We measured the activity of Tm-TrxR using the whole protein extract from adult worms. Addition of auranofin at both 10 µM and 1 µM showed a significant inhibition of Tm-TrxR activity  in S. mansoni can function like GR by reducing the oxidised glutathione (GSSG). TGR in S. mansoni is a multifunctional enzyme that acts like both TrxR and GR due to the Glutaredoxin domain (Grx) at its N-terminus34. Although TrxR in D. melanogaster does not contain the Grx domain in its structure, it reduces oxidised glutathione using reduced thioredoxin as a substrate33. Even though we could not find trxr-1 in the genome of any Trichuris spp. and the protein domain analysis on InterPro shows that T. muris TrxR-1 does not contain a Grx domain, T. muris shows GR activity  is another enzyme that regulates the redox homeostasis. The inhibition of TrxR-1 can be compensated by GR in ity Fig. . Moreovefin Fig. .T. muris. First, we treated L1 larvae with auranofin in vitro. The data showed that more than 50% of larvae incubated with 1 µM and 0.1 µM auranofin were paralysed at 30 h whereas 0.01 µM auranofin did not exhibit a significant effect compared to the DMSO-treated group  of auranofin are effective at killing L1 larvae in vitro, we also treated ent Fig. . Even thent Fig. . Furtherent Fig. .T. muris does not have enzymes for their biosynthesis  CAs or in media without any amino acids and egg production was measured  was tested 16%, 23% and 65%, respectively19. Further comparisons show that while 82.5% of the unique reactions and 80% of the unique metabolites in iTMU798 are shared with at least one of the previously published models , 49 C. elegans orthologs of the iTMU798 essential genes are also essential in either in iCEL1314 or Worm1 and 16 of them overlap with the OGEE database. Only 3 of iDC625 essential genes overlap with iTMU798 . As the annotation of the T. muris genome is incomplete, we add a note of caution in that it may be that some of the genes we identified as essential may not be. However, this does not detract from the validity of the iTMU798 model as exemplified by the prediction and validation of the importance of genes involved in Tm-TrxR activity and an absolute requirement for the amino acid tryptophan.High-quality GSMMs capture the metabolic capabilities of an organism, enhancing our understanding of host-pathogen interactions and enabling the identification of therapeutic targets against pathogensT. muris, 233 chokepoint enzymes were identified and 50 of these were prioritised based on their given scores. Phenotypic assays using 66 potential drug-like compounds targeting the top 10 most prioritised enzymes were carried out. Of these, 6 compounds that target malate dehydrogenase, phosphatidyl-inositol kinase and phosphor-diesterase showed significant effects on adult T. muris in vitro38. However, these 3 enzymes were not identified as chokepoint enzymes in iTMU798 because the metabolites participating in the reactions that are catalysed by them can be also produced or consumed in alternative reactions. Moreover, whether the predicted compounds directly inhibit these 3 enzymes in T. muris has not been tested. The reduced motility of adult worms in the phenotypic assays might be due to the identified compounds acting on other targets and pathways.In another study focusing on the metabolic network of T. muris  in S. mansoni42 support the glutathione reduction. The GR activity of T. muris and its inhibition by auranofin  but Tm-TrxR does not have Grx domain in its protein sequence. Therefore, Tm-TrxR might act similar to TrxR1 in D. melanogaster, in which reduced thioredoxin by TrxR1 is used as a substrate for the reduction of oxidised glutathione. The potential glutathione reduction mechanism in T. muris is supported by the inhibition of GR activity by auranofin in T. muris  products of adult whipworms26, was predicted as an essential amino acid by iTMU798 22: PRJEB12620. The reference models WormJam18, ElegCyc14, and iCEL131419 were utilised as a starting point to reconstruct a GSMM for T. muris. COBRApy (version-0.25.0)46, which is a Python version of the well-known constraint-based reconstruction and analysis (COBRA) toolbox47, was used to import the reference model, reconstruct the T. muris metabolic model and perform analyses such as Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA) using Gurobi as a solver . The homology search between T. muris and C. elegans was performed using BioMart (version-0.9)21. Names, charges, and formulas for metabolites were collected from ChEBI (release-213)23, PubChem24, and KEGG48. The reaction identifiers in the model were labelled ‘WBPTMRXXXX,’ where ‘X’ represents a number, and their database identifiers from RHEA (release-123)49 or KEGG were utilised as reaction names. To find additional reactions in RHEA, Enzyme Commission (EC) numbers of T. muris were found in UniProt (release-2022_3)50. We used BioMart to uncover more reactions in KEGG to find ortholog genes between T. muris and Trichinella spiralis, in addition to C. elegans - T. muris orthologs, because T. spiralis is a phylogenetically closer species to T. muris than C. elegans. Principles of the previously published protocol51 were followed for the reconstruction of a GSMM for T. muris. As the literature lacks information about T. muris, the biomass reaction of iCEL1314 was adjusted and set as an objective function for iTMU798.The annotated genome of 19, and ElegCyc14 and databases such as KEGG48, Rhea49 and metabolic atlas52 to the draft model.We took a two-step automated approach to fill the gaps in the draft model before its manual curation. Firstly, we temporarily provided metabolites of the blocked reactions in exchange reactions and identified how many blocked reactions gained flux. Then, we temporarily added demand reactions for the metabolites of the blocked reactions in the model and identified the number of reactions that were blocked and turned to non-zero flux reactions. Comparison of the maximum number of the unblocked reactions following the addition of exchange or demand reactions enabled us to find missing transport reactions between compartments and to add more reactions to connect sets of blocked reactions with the manual search in different GSMMs such as iCEL131453.MEMOTE (version-0.13.0) was used to test the quality of the completed model based on its structural format, basic tests, annotation, stoichiometry and biomass reactionTo find essential amino acids, boundaries of every amino acid exchange reaction of iTMU798 were set to zero individually and flux balance analysis was carried out based on the biomass reaction as the model objective function. Amino acids that caused zero flux for the biomass reaction were considered as essential, whereas the ones that gave higher flux than zero for the biomass reaction were considered as non-essential. Non-essential amino acids were classified based on the presence of their biosynthesis pathways in iTMU798.T. muris (Edinburgh strain) were maintained in male and female C.B.17 SCID mice and adult worms harvested at day 42 p.i. Adult worms were incubated for 4 h or overnight and eggs collected. Eggs were allowed to embryonate for at least 6 weeks in distilled H20 before mice were infected with 150–300 embryonated eggs and worm burdens established at day 14 or day 21 p.i.C.B.17 SCID mice  were housed in the facility at 22° ± 1 °C and 65% humidity with a 12 – hour light – dark cycle and had free access to food and water. Stock infections of 2. The number of eggs per well was also enumerated to assess worm fecundity.For adult worm assays, worms were harvested at day 35 p.i. and washed in RPMI 1640 supplemented with 10% (v/v) FCS, 2 mm l-glutamine, 100 U/mL penicillin and 100 µg/mL streptomycin . Four females and 4 males per well were placed in a 24 well plate in 1 ml of the same media and incubated with 0.1uM, 1uM or 10uM Auranofin dissolved in DMSO, or equivalent concentration of DMSO as controls. Movement of adult worms was used to assess survival – no movement was defined as dead. To further confirm viability, adults that were assessed at 46 h incubation were washed in media (as above) and incubated for a further 24 h at 37 °C, 5% (v/v) C0T. muris eggs were incubated for 2 h at 37 °C in a 33% (w/v) sodium hypochlorite solution . Eggs were then washed in the same media as the adults (as above) centrifuged at 720 g for 10 minutes to pellet them, resuspended in fresh media, and left to ‘hatch’ for 5 days at 37 °C, 5% (v/v) C02. ~50 L1 larvae were put into a 96 well plate with 200ul media incubated with 0.1uM, 1uM or 10uM Auranofin dissolved in DMSO, or equivalent concentration of DMSO as controls. Movement of L1 larvae was used to assess survival – no movement and a straight morphology was defined as dead.For L1 assays, embryonated T. muris worms were harvested from SCID mice at day 42 p.i. and intact worms were placed in complete RPMI 1640 medium or RPMI 1640 medium without amino acids  or RPMI 1640 media lacking amino acids supplemented with 0.2%(w/v) casamino acids , which specifically lack just the amino acid tryptophan. An equal number of worms was placed in each well at an equal ratio of male:female worms. Worms were incubated for 48 h at 37 C in 5% C02. Worms were removed and the number of eggs per well was counted and expressed as number of eggs produced per female worm.For amino acid studies, adult T. muris adults (50 mg) were homogenised in the assay buffer of thioredoxin reductase and glutathione reductase activity kits from Abcam  and additional protease inhibitor (1:1000) in Lysing Matrix D tubes  with a homogeniser  with shaking 2 ×40 seconds. The homogenate was centrifuged at 10000 g for 15 min at 4 °C and the supernatant was collected for the protein concentration measurement at 512 nm. Thioredoxin reductase activity was measured with 3 biological replicates and glutathione reductase activity was measured with 3 biological replicates based on their absorbance at 412 nm and 405 nm, respectively. Auranofin, bovine serum albumin (BSA), and DMSO were purchased from Sigma-Aldrich. Auranofin was dissolved in DMSO for the in vitro treatment and enzyme activity assays.Pooled www.graphpad.com) using a two tailed, one-way ANOVA with Bonferroni’s multiple comparison or Welch’s ANOVA with Dunnett’s T3 multiple comparison tests. Normality of the data was tested using the Shapiro-Wilk test. P < 0.05 was considered significant and all P values are shown in the corresponding figures. Statistical details are provided with the Source Data file.Statistical analysis was carried out in GraphPad Prism (version 10.0.1 for MacOS X, GraphPad Software, San Diego, California USA, Further information on research design is available in the Peer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Source Data


Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy with a rapidly increasing incidence. The pathogenesis of PTC is unclear, but metabolic and lipidomic reprogramming may play a role in tumor growth. We applied ultra-performance liquid chromatography-tandem mass spectrometry to perform widely targeted metabolomics and lipidomics on plasma samples from 94 patients with PTC and 100 healthy controls. We identified 113 differential metabolites and 236 differential lipids, mainly involved in branched-chain amino acid metabolism, glutamate and glutamine metabolism, tricarboxylic acid cycle, and lipid metabolism. We also screened three potential metabolite biomarkers: sebacic acid, L-glutamine, and indole-3-carboxaldehyde. These biomarkers showed excellent diagnostic performance for PTC in both discovery and validation cohorts, with areas under the receiver operating characteristic curves of 0.994 and 0.925, respectively. Our findings reveal distinct metabolic and lipidomic features of PTC and provide novel targets for diagnosis and treatment. Early diagnosis and treatment of PTC are efficient strategies for improving the prognosis of patients with PTC2. Although molecular markers are valuable in diagnosing PTC, they lack specificity or have a limited positive predictive value3. Therefore, exploration of the pathogenesis and diagnostic biomarkers of PTC is urgently needed.Recently, the incidence of papillary thyroid carcinoma (PTC), the most common endocrine malignancy, has been rapidly increasing4. Metabolic reprogramming is an important feature of tumors5. Even when sufficient oxygen is present, tumor tissue needs to consume large amounts of glucose through glycolysis, this is called the Warburg effect. Glucose-related metabolites are substantially altered in PTC6 and are associated with its stemness and aggressiveness7. Amino acids are a resource for protein synthesis and are involved in the biosynthesis of other macromolecules. Metabolic reprogramming of amino acids promotes tumor proliferation and metastasis8. Disturbances in the metabolism of some amino acids in PTC have also been observed, but the metabolic pattern of amino acids in PTC remains unclear2. The relative concentration of branched-chain amino acids in saliva was remarkably decreased in PTC2. Lipids and their metabolites are used in cell membrane formation, signaling, and energy storage in normal cells, and associated with carcinogenic pathways9. Lipid metabolism is reprogrammed in tumors, and the perturbation of blood lipids has been identified as a risk factor for tumorigenesis11. Therefore, we speculated that metabolic reprogramming is an important feature of PTC and provides diagnostic biomarkers for PTC. Although the metabolomics and lipidomics for PTC have been studied3, the metabolic reprogramming characteristics of PTC have not been fully elucidated and are rarely used for PTC diagnosis.Circulating metabolic biomarkers support the understanding of tumor biology, and early diagnosis with minimum invasion. Tumor cells are highly metabolically active and undergo many metabolic reprogramming to sustain faster proliferationIn this study, the widely targeted metabolomics and lipidomics methods were performed to explore the metabolic reprogramming and potential metabolite biomarkers of PTC and provide potential targets for its comprehensive treatment.The study was approved by the Ethics Committee of Scientific Research and Clinical Trial of the First Affiliated Hospital of Zhengzhou University. The ethics review approval ID was “2021-KY-1011-002”. All methods were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all participants, and fasting whole preoperative blood samples were collected. A total of 106 preoperative blood samples from patients with PTC were collected the day before surgery from the Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, between September 2021 and May 2022. The inclusion criteria were as follows: (1) patients who underwent thyroid surgery for the first time, (2) patients pathologically diagnosed with PTC, and (3) patients willing to sign an informed consent. Patients with other malignancies, thyroid dysfunction (hyperthyroidism or hypothyroidism), or two or more thyroidectomies were excluded from this study. After exclusion, 94 patients with PTC were included. In addition, 100 healthy controls (HC) were recruited from a population receiving routine physical examinations at the First Affiliated Hospital of Zhengzhou University. The inclusion criteria for HC included: (1) age- and sex-matched to the PTC group, (2) no other diseases, including liver or renal failure, and multiple organ failure combined; and (3) subjects willing to sign an informed consent. The enrollment flowchart of the participants is shown in Fig. The reagents and sample preparation were provided in the supporting information.12.Widely targeted metabolomics and lipidomics were performed using a QTrap 6500 triple quadrupole linear ion-trap mass spectrometer. Multiple reaction monitoring was performed in pneumatic-assisted electrospray ionization mode while monitoring positive and negative ions. Data collection was performed using Analyst 1.7.1 software p < 0.05, fold change (FC) ≥ 1.2 or ≤ 0.83, and VIP > 1.The raw data files were pre-processed using MultiQuant 3.0.2  software for retention time (RT) correction, peak identification, and peak integration. An appropriate internal standard (IS) for each compound was selected for peak area calibration according to the principles of similar chemical structures and RT. SIMCA14.1 software was used for principal component analysis (PCA), and the orthogonal partial least squares discriminant analysis (OPLS-DA) method was used to construct the model. Two hundred permutation tests were performed to assess whether the model overfitted. The variable importance in the projection (VIP) in the OPLS-DA model was used to identify and differentiate two groups of compounds with the highest contribution. Differential compounds were defined as http://www.metaboanalyst.ca/) and compared with those in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database for enrichment and pathway analyses.Heatmaps were drawn based on R 4.1.3. The pheatmap package was used for heatmaps. Multivariate logistic regression was constructed using stepwise regression based on the differential compounds, and receiver operating characteristic (ROC) curves were established using R 4.1.3. The pROC, rms, and readr packages were used for the multivariate logistic regression and construction of the ROC curve. In the discovery cohort, differential compounds were screened, and the model was built and validated in the validation cohort. The differential compounds were imported into MetaboAnalyst , FA, and acylcarnitine decreased in the PTC group.Figure PTC Fig. C. The loBased on our findings, changes in metabolic pathways and metabolic characteristics of PTC are summarized in Fig. 14. Differential metabolites were put into the stepwise logistic regression, and a model with a minimal Akaike information criterion value was established to identify potential biomarkers that could be used to distinguish PTC from HC. Finally, a panel of three metabolites, including sebacic acid, L-glutamine, and indole-3-carboxaldehyde, was selected to build the diagnostic model. ROC curves were used to assess the performance of a diagnostic test based on multiple classification rules, with 1-specificity on the x axis and sensitivity on the y axis. In summary, the area under the ROC curve (AUC) of individual compounds ranged from 0.781 to 0.946 in the discovery cohort, indicating that all three compounds have predictive potential for PTC. The AUC of the combined diagnosis of the three compounds was 0.994, with a sensitivity and specificity of 93.8% and 97.1%, respectively  by branched-chain amino acid transaminases. Eventually, BCKAs are catabolized to acetyl-coenzyme A (acetyl-CoA), which enters the TCA cycle to supply energy for tumor cells. BCAAs may be beneficial to tumors by providing nitrogen for Deoxyribonucleic Acid (DNA) synthesis18. Elevated levels of BCAAs in the plasma and tumor tissue are often accompanied by a decrease in the breakdown of BCAAs18, and tumor growth may be promoted when BCAAs catabolism is inhibited19. The accumulation of BCAAs in tissues results in chronic activation of the mammalian target of rapamycin complex 1 (mTORC1), which is associated with cell growth and tumorigenesis20. A possible mechanism is that leucine binds to its sensor, Sestrin2, to activate mTORC120. The activation of the mTORC1 is associated with cell growth and tumorigenesis in many human and animal models17. mTORC1 triggers a series of signaling pathways that regulate autophagy and lipid, nucleotide, and protein synthesis by phosphorylating its downstream effectors, including eukaryotic translation initiation factor 4E-binding protein 1, p70S6 kinase, and SREBP21. Therefore, elevated BCAAs levels may serve as potential targets for PTC therapy.Metabolomic and lipidomic data showed that patients with PTC have markedly different metabolic patterns compared to those of healthy subjects. Patients with PTC had higher concentrations of metabolites related to BCAAs biosynthesis, including L-threonine, L-valine, L-leucine, and L-isoleucine Fig. . Metabol22. Glutamate is metabolized in the mitochondria to α-KG, which participates in the TCA cycle and provides energy for cell growth. Glutamine is a critical substrate for cytoplasmic nucleotide biosynthesis23. Although glutamine is considered a non-essential amino acid, most tumor cells cannot proliferate or survive in media lacking it24. The inhibition of GLS expression can delay tumor growth8. Glutathione (GSH), an antioxidant, requires glutamine to provide nitrogen and carbon sources for its synthesis. The GSH-mediated increase of antioxidant capacity may promote malignant cellular progression25. Ammonia, produced by glutamine catabolism, may reduce cellular sensitivity to tumor necrosis factor-α (TNF-α) through an autophagy-dependent mechanism, promoting the occurrence of tumors26. Because of the critical role of glutamine, the inhibition of GLS activity is a potential target for tumor therapy8.Pathway analysis showed that glutamate and glutamine metabolism and the TCA cycle were altered in PTC Fig. . Althoug27. In our study, the relative concentrations of PA, PE, CE, and SM were increased in PTC, whereas the acylcarnitine and FA contents decreased , a key enzyme in SM synthesis, is related to tumor progression. SMS2 knockout or SMS2 inhibitors can reduce tumor progression32. In addition, SM is associated with tumor angiogenesis33, potentially providing new targets for tumor treatment.In addition, the proliferation of tumor cells requires the supply of lipids, such as PE, CE, and SM, which serve as substrates for energy metabolism, are involved in the construction of the cell membrane bilayer structure and cell signal transduction34. For example, the trauma caused by FNAB may result in histological changes that affect correct diagnosis after thyroidectomy35. Moreover, incomplete sampling often produces inconclusive results36. The introduced biomarkers for PTC such as BRAF, RAS, have poor specificity and positive predictive value3. Therefore, metabolite biomarkers identified in this study may provide a minimally invasive complementary approach for PTC diagnosis with high sensitivity and specificity. Furthermore, the altered metabolic pathways or related critical proteins may serve as the potential therapeutic targets, and the metabolic interventions may be exploited as new treatment strategies for PTC37.As reprogramming of metabolites may provide circulating biomarkers for cancer diagnosis, potential metabolite biomarkers for PTC were screened in this study. Finally, a panel of three metabolites, including sebacic acid, L-glutamine, and indole-3-carboxaldehyde, was selected to build the diagnostic model, achieving a very good discriminative effect in both the discovery and validation cohorts Fig. . Current39. Jiang et al. revealed the biomarkers of PTC based on lipidomics34. However, without a combination of metabolomic analysis, it cannot more comprehensively explain the metabolic characteristics of PTC. Chen et al. analyzed the plasma metabolite characteristics of PTC, but their study had a small sample size38. Although Huang et al. had a larger sample size than ours40, the untargeted metabolomics method was utilized to identify the differential metabolites based on databases, with some of them being not confirmed by the standards. Moreover, mass spectrometry detector in the full scan mode in untargeted metabolomics is easily saturated, leading to the limited linear ranges, repeatability, and quantitative accuracy41. In our study, the standard solutions of metabolites were used to develop the targeted metabolomics and lipidomics methods using the multiple reaction monitoring mode, which has the advantages of high sensitivity, high specificity, and excellent quantification ability42. Nevertheless, our study has some limitations. Our research is a single-center study, which led to a lack of external verification. Therefore, a multicenter prospective study is required to validate our findings.Compared to most other studies, we enrolled a larger cohort and constructed a diagnostic model with good discriminationIn conclusion, we performed an integrated approach of widely targeted metabolomics and lipidomics to explore metabolic reprogramming and potential metabolite biomarkers of PTC. Distinct metabolic reprogramming was observed in PTC, especially for metabolites related to energy metabolism, such as BCAAs, L-glutamate, L-glutamine, FA, and lipids. Furthermore, we identified a panel of three circulating metabolites that may serve as diagnostic biomarkers for PTC. This panel showed excellent discrimination efficiency between PTC and HC in both the discovery and validation cohorts. Thus, it provides new targets for the comprehensive treatment of PTC.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Information.


Escherichia coli as the representative organisms, we summarized the metabolic engineering strategies on the optimization of CCM in eukaryotic and prokaryotic microbial chassis, such as the introduction of heterologous CCM metabolic pathways and the optimization of key enzymes or regulatory factors, to lay the groundwork for the future use of CCM optimization in metabolic engineering. Furthermore, the bottlenecks in the application of CCM optimization in metabolic engineering and future application prospects are summarized.Central carbon metabolism (CCM), including glycolysis, tricarboxylic acid cycle and the pentose phosphate pathway, is the most fundamental metabolic process in the activities of living organisms that maintains normal cellular growth. CCM has been widely used in microbial metabolic engineering in recent years due to its unique regulatory role in cellular metabolism. Using yeast and  Since its inception, metabolic engineering has rapidly promoted the biosynthesis of natural products , 2. In tMetabolic engineering employs genetic engineering techniques to increase the yield of target products by modifying metabolic pathways within cells. The most common methods in metabolic engineering include manipulation of promoter and copy number of target enzymes , 12, traSaccharomyces cerevisiae, thereby increasing the production of the target product farnesene by 25% [Deinococcus radiodurans response regulator DR1558 into E. coli improves the expression efficiency of the genes related to CCM, and induces the excess generation of NADPH from PPP and supplies the cofactor requirements during PHB biosynthesis [CCM is a major source of energy for cell growth and development and provides precursors for other metabolic activities. Modification of the CCM, which is upstream of biological metabolic activities, often results in the rearrangement of the global metabolic flux of the cell and has a high potential for metabolic engineering applications. On the one hand, optimization of CCM can increase the precursors supply for the targeted compounds. For example, the introduction of the heterologous phosphoketolase phosphotransacetylase (PHK) metabolic pathway can increase the rate of acetyl coenzyme A (acetyl-CoA) synthesis and trigger CCM (glycolysis and the PPP) rearrangement in e by 25% . On the ynthesis .E. coli were selected to summarize the application and potential of CCM in metabolic engineering.In recent years, metabolic engineering strategies on the optimization of CCM has produced remarkable results in the biosynthesis of many natural products. However, the current status of the application of this optimization strategy has not yet been systematically discussed. In this work, representative chassis strains of yeast and Saccharomyces species could improve the carbon flux between different pathways of CCM, and promote the biosynthesis of target compounds  from F6P and X5P to acetyl-CoA via a transacetylation reaction [In the CCM of yeast, glucose-6-phosphate (G6P) can generate either fructose-6-phosphate (F6P) in glycolysis or ribulose-5-phosphate (Ru5P) and xylulose 5-phophate (X5P) into the PPP pathway. The introduction of PHK in reaction . The onlYarrowia lipolytica blocked the metabolic flux of G6P in glycolysis and caused the redox imbalance with excess NADPH production. Then the introduction of the PHK pathway resulted in the accumulation of acetyl-CoA and correction of the redox imbalance by providing a route towards the NADPH-oxidizing lipid synthesis pathway, and a 19% increase in total lipid production was derived in the host strain [Pichia pastoris. Coupled with the subsequent overexpression of NADPH-generating enzymes in TCA cycle and PPP, the engineered strain produced 23.4 g/L of free fatty acids and 2.0 g/L of fatty alcohols [SEL641P following the introduction of the PHK pathway, which provides acetyl-CoA and NADPH, in S. cerevisiae inhibited the synthesis of ethanol and yielded 5100 ± 509 g/CDW (cell dry weight) of fatty acid ethyl esters [The PHK pathway facilitates the direct synthesis of acetyl-CoA and the biosynthesis of lipid compounds using acetyl-CoA as a precursor. The knockout of phosphofructokinase (PFK) in t strain . In the t strain . The moualcohols . Overexpl esters .S. cerevisiae. The PHK pathway catalyzes the conversion of F6P to acetyl-CoA, which decreases the consumption of metabolic flux in glycolysis while indirectly increasing the metabolic flux in the PPP and promoting E4P accumulation, which provides a large number of precursors for the synthesis of aromatic compounds. The introduction of the PHK pathway in S. cerevisiae shifts the glycolytic flux to E4P synthesis, avoiding the loss of metabolic flux at multiple steps upstream of the glycolysis and PPP. The subsequent promoter optimization and dynamic regulation resulted in a yield of 12.5 g/L for p-hydroxycinnamic acid and a maximum yield on glucose of 154.9 mg/g [The introduction of the PHK pathway also addresses the issue of insufficient erythrose-4-phosphate (E4P) synthesis in 4.9 mg/g . The het4.9 mg/g . Therefo4.9 mg/g . Hence, S. cerevisiae increased the PPD yield to 152.37 mg/L [S. cerevisiae increased 3-hydroxypropionic acid (3-HP) production by 41.9% and decreased glycerol production by 48.1%. In addition, reducing the expression of phosphoglucose isomerase and overexpressing acetyl-CoA carboxylase (Acc1) and malonyl-CoA reductase (MCR) promoted the metabolic flux to the PPP, yielding 864.5 mg/L of 3-HP  [S. cerevisiae strain containing the ethanol degradation pathway, the Aspergillus nidulans-derived PHK pathway could be used for the synthesis of polyhydroxybutyrate (PHB) with a yield of 56.4 mg/g in ethanol medium [The regulatory strategy of the PHK pathway in chassis strain has also been applied to the biosynthesis of other classes of compounds. Protopanaxadiol (PPD), an active triterpene compound, serves as a precursor of high-value ginsenosides. The introduction of the PHK pathway and multicopy integration of endogenous transaldolase 1  and transketolase 1 (Tkl1) in .37 mg/L . In addi strain) . Furtherl medium .A. nidulans increased the mevalonate yield to 2-fold by directly converting citric acid to acetyl-CoA in the TCA cycle of S. cerevisiae [E. coli can directly convert pyruvate from the glycolytic pathway to acetyl-CoA. The absence of ATP consumption in this process conserves more energy for other CCM reactions. After NADP+-dependent modifications in S. cerevisiae, the introduction of PDH pathway resulted in a 2-fold increase in acetyl-CoA [E. coli can not only catalyze the conversion of acetaldehyde to acetyl-CoA, but also promotes the accumulation of large amounts of redox cofactor NADH in the cytoplasm, which would balance the relationship between NADPH consumption and NADH generation in yeast cells [S. cerevisiae resulted in the accumulation of large amounts of acetyl-CoA. Optimization on this basis yielded 279.0 ± 13.0 mg/L of β-amyrin [Other pathways, in addition to the PHK pathway, can modulate the CCM of the yeast chassis. ACL from revisiae . But thietyl-CoA . Despitest cells , 38. TheS. cerevisiae. The knockdown of the PDC gene effectively shifts the metabolic flux from the ethanol synthesis pathway to the pyruvate-related pathway in the CCM, reducing consumption of the CCM flux by the ethanol synthesis pathway. Pyruvate accumulation was also confirmed by deletion mutants of pdc1 and pdc5 in S. cerevisiae [S. cerevisiae, knocking out hexokinase 2 (HXK2) and glucokinase 1 (GLK1) in glycolysis and using tetracycline transactivator protein (tTA) to control hexokinase 1 (HXK1) transcription can shift the metabolic flux from glycolysis to the gluconate synthesis pathway, promoting efficient gluconate biosynthesis, with the final strain showing a 50-fold increase in gluconate production compared to the control strain [P. pastoris, which effectively inhibits the carbon flux of glycolysis, can promote inositol biosynthesis, with inositol production reaching 30.71 g/L [The modification of key enzymes in each CCM pathway can rearrange the metabolic flux and facilitate the synthesis of target products. Pyruvate decarboxylase (PDC) initiates the catalysis of pyruvate (a product of glycolysis) to ethanol in revisiae . A yieldrevisiae . In S. cl strain . Overexp0.71 g/L .S. cerevisiae and the introduction of 2-pyrone synthase (2-PS) in Gerbera hybrida promote the decarboxylation/condensation reaction of acetyl-CoA and malonyl coenzyme A  to produce triacetic acid lactone (TAL). This process ultimately leads to a 37-fold increase in TAL yield to 2.2 g/L and a 50-fold increase on glucose yield to 0.13 g/g [The above reports were optimized for only a few (1–3) key enzymes in CCM. In the metabolic engineering of some target compounds, large-scale optimization of enzymes in CCM has also been performed. The knockdown of 15 relevant CCM enzyme genes in 0.13 g/g .Komagataella phaffii, an ethanol-inducible and constitutive transcriptional regulatory signaling amplifier designed with the transcription activation region of the transcription factor MIT1 can increase ethanol-inducible expression capacity by nearly 20-fold. In addition, this process enables host cells to produce acetyl-CoA independently of CCM using ethanol as a fermentation substrate as well as an acetyl-CoA precursor and inducer. Direct production of acetyl-CoA from ethanol in the cytoplasm via a three-step catalytic process and construction of a biosynthetic pathway increased the yield of the cholesterol-lowering drug simvastatin intermediate, monacolin J, to 3.2 g/L [In yeast, acetyl-CoA is primarily derived from CCM. The partitioned distribution and insufficient amount of acetyl-CoA limit the ability to synthesize the target product. In Crabtree-negative strain  3.2 g/L .S. cerevisiae [S. cerevisiae and overexpressing ribose-5-phosphate ketol-isomerase (Rki1) in the PPP resulted in a 7-fold increase in the yield of shikimic acid (SA) to 2.5 g/L. The total yield of muconic acid and the intermediate product protocatechuic acid in the strain developed on this basis was 2.7 g/L [Ric1 is a transcriptional repressor of multiple genes in the aromatic amino acid biosynthetic pathway in revisiae . Decreas 2.7 g/L . A summaSynechocystis sp., the lower carbon flux of the TCA cycle limits the biosynthesis of target products. Increased copy number of the phosphoenolpyruvate carboxylase (PEPC) gene and increased carbon flux into the TCA cycle significantly increased the production of ethylene to 10.5 µg/mL/OD/day), a 1.64-fold increase compared to the original strain [Synechocystis sp. is also an excellent chassis strain for succinate production. Overexpression of PEPC increased succinate yields to 162.3 mg/L after increasing temperature to decrease glycolytic carbon flux and increase TCA cycle carbon flux [In cyanobacterium l strain . Synechobon flux .E. coli uses glucose as a carbon source, the sugar phosphotransferase system (PTS) transfers the phosphate group from phosphoenolpyruvate (PEP) to glucose to produce G6P and pyruvate. PTS is the primary PEP consumption pathway for E. coli growth [E. coli metabolic engineering.When i growth . PEP is i growth . TherefoE. coli decreased its carbon consumption, and inhibiting alanine: H+ symporter activity attenuated cell growth inhibition. These optimizations increased β-alanine production to 4.36 g/L [E. coli with the galactose transport system resulted in a substantial accumulation of PEP. Further use of PEPC to reroute the metabolic flux of glycolysis to the TCA cycle resulted in a fumaric acid yield of 1.53 g/g dry cell weight [E. coli and overexpression of PK increased the precursors of aromatic amino acid synthesis pathway, PEP and E4P. The resulting engineered strain produced 41.7 g/L of tryptophan after fermentation in a 5 L bioreactor [The deletion of the PTS system in 4.36 g/L . The del4.36 g/L , 53. Fur4.36 g/L . The kno4.36 g/L . The repl weight . Displacoreactor .E. coli, PGI and ZWF regulate the metabolic flux into the glycolytic pathway and PPP, respectively. Therefore, CCM optimization by regulation of PGI and ZWF is common in E. coli metabolic engineering. Simultaneous knockdown of PGI and ZWF can increase metabolic flux to the methylerythritol 4-phosphate (MEP) pathway, resulting in the accumulation of isoprenoids and their derivatives and facilitating terpenoid biosynthesis. In high-lycopene production strains constructed using this strategy, lycopene yields of 6.85–7.55 mg/g DCW were achieved [In achieved , 59. PGIachieved . ZWF knoachieved . Triosepachieved .CRISPR/Cas9 technology for large-scale gene editing of CCM pathways was used for the optimization of CCM. The use of CRISPR/Cas9 technology to silence a dozen enzymes in the glycolytic and TCA cycle pathways could redirect metabolic flux to the malonyl-CoA synthesis pathway that subsequently increases (2 S)-naringenin production to 421.6 mg/L, a 7.4-fold increase compared to the control strain . Based oE. coli [E. coli [E. coli, and by knocking down lactate dehydrogenase (LDHA) and pyruvate oxidase (POXB), it was able to accumulate 17.1 g/L of pyruvate in fermentation [E. coli was provided in Fig. CCM can also be optimized by regulating the synthesis of key CCM intermediates such as acetyl-CoA and pyruvate. Acetyl-CoA enters the TCA cycle via citrate generation, a process that depletes acetyl-CoA. Overexpression of TPIA and fructose-bisphosphate aldolase (FBAA) decreases acetyl-CoA flux to the TCA cycle, resulting in a 3-fold increase in PHB concentration in E. coli . In addi[E. coli . Similarentation . A summaD. radiodurans response regulator DR1558 in E. coli increased its tolerance to oxidative stress. Moreover, the upregulated expression level of genes involved in CCM and increased accumulation of NADPH form PPP induced by this regulator resulted in PHB production of 5.31 g/L [The overexpression of the 5.31 g/L .E. coli fermentation process into growth and production phases. The system regulates the expression of pyruvate carboxylase and oxaloacetic decarboxylase at different temperatures, rebalancing the carbon flux between pyruvate and oxaloacetate. The introduction of this switch system resulted in a record L-threonine yield of 124.03% [CCM can also be regulated by the switch system. A temperature-sensitive switch system can divide the  124.03% . Another 124.03% .C. glutamicum is a chassis-engineered strain used for the biosynthesis of polyphenols, where an insufficient supply of acetyl-CoA is an important limiting factor for increasing polyphenol product yield. A moderate reduction of carbon flux in the TCA cycle can effectively decrease the consumption of acetyl-CoA. A 90% reduction in the catalytic activity of CS in the TCA cycle could lead to a 10-fold increase the yield of the target compound naringenin to 19 mg/L [ 19 mg/L .C. glutamicum resulted in a 9.3% increase in the (3R)-acetoin yield to 11.96 g/L [PEP is a key precursor in various biosynthetic pathways. Therefore, reducing PEP depletion can significantly increase the yield of related target products. The knockdown of PEPC in 1.96 g/L . The sam1.96 g/L . Another1.96 g/L . The int1.96 g/L .B. licheniformis is widely used as a chassis strain for the synthesis of various biochemicals. Optimization of B. licheniformis CCM can effectively increase the yield of poly-γ-glutamic acid (γ-PGA). Overexpression of PDH and CS directed more carbon fluxes to the TCA cycle, resulting in γ-PGA yields of 34.93% and 11.14%, respectively. Furthermore, knockdown of pyruvate formate-lyase reduced bypass depletion and increased γ-PGA yield by 30.70% [Because of its robustness and rapid growth, y 30.70% . In 2-PEy 30.70% .Pseudomonas putida has a substantial advantage in the production of some toxic products due to its high tolerance to organic solvents. The introduction of a promoter-optimized rhamnolipid synthesis pathway in P. putida could direct the metabolic flux of glycolysis and acetyl-CoA to rhamnolipid synthesis, leading to a one-fold increase in rhamnolipid production to 3 g/L [P. putida using CRISPR/Cas9 technology resulted in an 8-fold increase in acetyl-CoA production [B. subtilis enables autonomous dynamic control of CCM, resulting in 527 mg/L glucaric acid production, a 154% increase compared to the control strain [Actinosynnema pretiosum reduced the carbon flux in PPP and resulted in a 3-fold increase in the production of ansamitocins [Zymomonas mobilis resulted in the efficient synthesis of lactate and isobutanol, with lactate and isobutanol production reaching 70% and 65% of the theoretical maximum, respectively [Mannheimia succiniciproducens attenuates the inhibition of carbon catabolism and increases the availability of pyruvic acid. Ultimately, succinate production could be increased to 69.2 g/L by reconstituting the succinate biosynthetic pathway [to 3 g/L . Regulatoduction . The devl strain . The knomitocins . The useectively . The kno pathway .Gtg in Geobacillus thermoglucosidasius, the CCM metabolic flux was redirected to the riboflavin biosynthetic pathway, resulting in a 1.51-fold increase in riboflavin production from 171.6 mg/L to 260.3 mg/L [Saccharopolyspora pogona, increased the metabolic flux in glycolysis, and decreased the metabolic flux in the TCA cycle and PPP, and resulted in the accumulation of pyruvate and acetyl-CoA, and subsequently increased butenyl-spinosyn production [Methylobacterium extorquens increased acetyl-CoA production by approximately 7%, with mevalonate production reaching 2.67 g/L [After knocking out the transcriptional regulator of glyceraldehyde-3-phosphate dehydrogenase and phosphoenolpyruvate carboxykinase CcpN0.3 mg/L . Overexpoduction . The dev2.67 g/L . The appThe CCM-based optimization strategy can rearrange the metabolic flux in various microbial host strains to increase the supply of precursors in the biosynthetic pathway of target compounds, thereby improving the substrate conversion rate. Therefore, it is critical to maximize the production potential of microbial cell factories and improve compound production efficiency. This strategy is currently being employed to increase the yield of target compounds through metabolic engineering optimization in various chassis strains. Notably, in eukaryotic hosts, optimization of all the three CCM pathways could be used for the biosynthesis of acetyl-CoA derived compounds . In the meantime, the optimization of glycolysis and TCA cycles can also be available for the production of organic acids , and PPP optimization is used for the metabolic engineering of shikimic acid and aromatic amino acid derivatives . While in prokaryotic hosts, manipulation of glycolysis and PPP is used for the production of a wide range of compounds, including different amino acids  and acetyl-CoA derived chemicals . And the optimization TCA cycle is available for the biosynthesis of organic acids and flavonoids .However, the regulatory mechanism of CCM is not yet clear due to a large number of genes included, resulting in insufficient CCM optimization approaches. CCM optimization strategies at this stage typically focus on a few functionally defined and repeatedly validated methods, limiting the wide application of CCM optimization in metabolic engineering. These issues can be addressed in the future in the following ways:To deepen our understanding of CCM regulation network based on species and genetic diversity. As the regulation of CCM is sophisticated, the exploration of regulation mechanisms of CCM from the microbial hosts and other species will be helpful in developing more manipulation methods for CCM optimization.To develop more transcriptional regulatory factors and gene expression regulatory switches. These regulatory elements can be used to optimize multiple genes and promote the bottleneck reaction(s) of rate-limiting enzyme(s) involved in CCM.To reduce or eliminate precursor/product consuming pathways related to CCM by balancing metabolic flux or regulating metabolic pathway genes dynamically using key enzymes at metabolic pathway intersections.