Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets. Pediatric-onset CD affects the upper gastrointestinal tract more frequently than adult-onset disease and is more likely to follow a more severe disease course5. The progressive inflammation resulting from CD disrupts critical developmental periods in children, impeding physical and psychosocial growth6. Our current understanding of CD pathogenesis has expanded in recent years to include dietary and lifestyle factors, environmental influences, genetic predilection, and gut microbiome composition8. The initial clinical diagnostic evaluation of CD includes, but is not limited to, panels of blood and stool biomarkers, such as hemoglobin, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), albumin, and fecal calprotectin. These biomarkers have sensitivities ranging from 80 to 90% but relatively low pooled specificity to CD in pediatric patients10. For a definitive diagnosis, clinicians use invasive and often costly investigations such as endoscopy, biopsy, and small-bowel cross-sectional imaging2. Thus, there is a clear gap in highly specific biomarkers for CD diagnosis and subsequent prognosis.Crohn's disease (CD) is a chronic, relapsing inflammatory disease of the gastrointestinal tract. CD prevalence ranges between 96 and 318 per 100,000 person-years in North America alone, with pediatric patients being the fastest-growing CD incidence group13. Unfortunately, these medications have variable efficacy between patients and may cause severe side effects15. Therefore, clarifying pathways contributing to CD may reveal novel targets for directed therapy, reducing immunosuppression-related morbidity.The management protocols for CD hinge on achieving suppression of intestinal inflammation during an induction phase with subsequent maintenance of remission. Strategies for induction include a bottom-up or top-down approach using a combination of steroids, immunomodulators (azathioprine or 6-mercaptopurine), antibiotics, or biologics that inhibit tumor necrosis factor-alpha, such as infliximab and adalimumab16. Through the interrogation of transcriptomic variations in metabolism, we may be able to illuminate not only pathophysiological pathways for targeted therapeutics but also specific metabolic biomarkers of CD. Genome-scale metabolic Network Reconstructions (GENREs) provide a platform to study transcriptomic data in the context of metabolic shifts between various disease states17. Recon3D is the most comprehensive human metabolic network reconstruction to date; it includes gene, protein structure, and metabolite data from over 3,500 genes18. Genes related to metabolism are annotated and matched to the enzymes they code for and the reactions they catalyze to generate a catalog of gene-protein-reaction (GPR) associations. Flux Balance Analysis (FBA) is one method that can be employed to create a mathematical function of the flux of substrates along a given pathway in a metabolic network reconstruction with the assumption of a steady state19. FBA data obtained from constrained-based modeling can subsequently be used as inputs for machine learning models to perform analysis in a sample-specific manner to identify relevant targets for biotechnology and biomedical interventions. FBA-based methodologies effectively offer an approach to pruning unused pathways to identify relevant utilized pathways from the dense reaction-centric metabolic network. Using one pruning approach, parsimonious flux balance analysis (pFBA), metabolic reactions relevant to specific physiological or pathological conditions can be determined by identifying the most efficient reactions in generating biomass23. Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe) provides an additional method for pruning reactions by factoring in transcriptomic abundances to identify the most important energy-efficient metabolic pathways in a given biological state. In CD, this multi-tiered in silico approach potentially allows us to discern the metabolic underpinnings of CD and clarify possible biomarkers and druggable targets26.Constraint-based metabolic modeling has emerged as a useful in silico computational approach to investigate variations in the metabolism under certain biological conditions by analyzing large-scale relationships between genotypes and phenotypes27. We aimed to (1) identify a list of metabolic pathways that have altered flux or flow in CD versus controls using in silico metabolic modeling utilizing ileal transcriptomics (2) explore the overlap of metabolic pathways in CD ileal enteroids, and (3) pilot an approach to validate our findings of altered metabolism using untargeted mass-spectrometry-based metabolomics in ileal mucosal biopsies. We hypothesized that the predicted metabolic shifts in the CD ileum will mirror those in the enteroid model, as well as those metabolites identified using metabolomics, and thus this metabolic modeling approach potentially holds power to elucidate novel biomarkers and therapeutic targets, allowing for improved methods for the diagnosis and treatment of Crohn's disease.To this end, we have used an FBA-based machine learning (ML) framework for studying altered metabolic reactions between patients with CD and controls, leveraging 'omics data from a public repository from the Risk Stratification and Identification of Immunogenetic and Microbial Markers of Rapid Disease Progression in Children with Crohn's Disease (RISK) study, one of the largest pediatric inception cohorts of children with a new diagnosis of CD, to identify biomarkers found in the blood or stool to help predict which children are at risk of developing complications28. A descriptive summary of both cohorts is outlined in Table The RISK prospective pediatric inception study included n = 243 patients with a new diagnosis of Crohn's disease and n = 43 age-matched control patients. Our analysis included n = 163 patients  diagnosed with CD localized to the ileum with a mean (± SD) age of 11.9 (± 3.1) years and n = 42 controls  with a mean (± SD) age of 11.1 (± 3.1) years. In addition, publicly available RNA sequencing data from enteroids that were derived from a previously established cohort of n = 16 patients with CD with a mean age (± SD) of 15.8 (± 3.2) years, 88% of whom were male, and n = 12 non-IBD controls with a mean (± SD) age of 11.4 (± 3.5), 42% of whom were male was also used for this analysisTo identify peripheral biomarkers and metabolic pathways indicative of CD, we used a computational metabolic modeling approach to generate metabolic profiles of the disease Fig. . We inde1. We identified context-specific metabolic pathway activity in both CD and control groups . This metabolic profiling revealed that the activity of approximately 200 reactions varied between control and CD populations  to ensure the discriminability between CD and control patients to demonstrate the promise of enteroid models to serve as in vitro models of disease. The number of reactions in pruned in the enteroid dataset was 651  from recent studies has demonstrated that enteroids derived from patients with CD have a 90% similarity in protein-coding genes compared to intestinal crypts isolated from these patientsFollowing metabolic modeling of these enteroids derived from CD patients, we used the Recon3D database to categorize the reaction identification (IDs) of the most energy-efficient reactions in the small bowel. These reaction IDs were superoxide dismutase (SPODMm), catalase (CATm), RE3121R, a reaction involved in fatty acid oxidation, choline phosphatase (PCHOLP_hs), transport of phosphatidylserine (PSt3), passive diffusion of sphinganine into extracellular space (SPHGNte), palmitoyl coenzyme A hydrolase (RE0577C), exchange of coproporphyrinogen (EX_HC01610[e]), and HMR_0692, and a biomass reaction (biomass_reaction), two reactions involved in energy exchange  detoxification, fatty acid oxidation, glycerophospholipid metabolism, fatty acid synthesis, transport, and exchange/demandUsing the computational approaches detailed above, we generated metabolic models of CD in both RISK-derived tissue and enteroids. When these models were compared, we noted that fatty acid metabolism Fig. A was a cThese data indicated that the fatty acid metabolic profile in the tissue sufficiently maps to epithelial cell function. In addition, the gene count data shown in Fig. 31.As noted above, inflamed pediatric CD patient ileal tissue displays a distinct metabolic signature compared with non-inflamed tissue. Subsequently, we sought to experimentally confirm whether the CD ileum contained metabolic changes versus controls using a small external cohort. To this end, we used mass spectrometry-based lipidomics in an experiment complementary to our in silico metabolic modeling to characterize whether the changes in metabolic pathways correlate to specific changes in metabolite composition. A heat map depicting relative metabolite quantification of the most variable compounds between inflamed (red) and non-inflamed tissue (green) shows clusters of metabolites that demonstrate a distinct metabolic signature of CD compared to control samples Fig. A. DetailTogether, these findings demonstrate the utility of computational metabolic modeling to specify the characteristic, potentially functional profiles of CD compared to control tissue. Furthermore, the individual metabolic pathways identified by our in silico model displayed significant overlap with the findings from our in vitro enteroid model and our method using untargeted metabolomics, demonstrating the potential value of this metabolic modeling approach. Our work lays the groundwork for future studies to continue probing alterations in the mucosal transcriptome and tissue metabolome for further insights into CD.28. In enteroids, we found glycerophospholipid, linoleic acid, and sphingolipid metabolism to be altered  to identify metabolic pathway alterations using archival transcriptomic data and in vitro enteroid models. Cell-to-cell metabolic variability occurs within cell populations as these populations execute a diverse range of functions, which change depending upon environmental impacts on transcriptomic and epigenetic regulation. Metabolic modeling provides a functional snapshot of the ongoing biological processes in a given cell or organism based on complex genotype-phenotype-biochemical relationships. Identifying specific transcriptional signatures of disease through bulk RNA sequencing poses a significant challenge given the heterogeneity in gene expression between patients with the same disease. To distill large amounts of transcriptomic data into a contextualized disease model capable of elucidating alterations in metabolic pathways, which may serve as either disease biomarkers or targets for therapeutic intervention, we have employed metabolic modeling to distinguish alterations in metabolism in diseased versus control tissue across two different datasets. Alterations in metabolic flux can be validated and subsequently quantified externally through in vitro models such as enteroids, untargeted metabolomics, and lipidomics to clarify actionable biomarkers or therapeutic targets. To develop an in silico model, we applied our metabolic modeling approach to the archival RISK dataset, which showed that mevalonate metabolism, fatty acid oxidation, and uridine metabolism have variable flux when comparing patients with CD to controls Fig. , suggestred Fig. . Lastly,31. In addition, derivatives of mevalonate are known to be directly involved in limiting the cytotoxic effector response of T-lymphocytes in inflammation and the production of immunomodulatory precursors32. Alterations of this pathway, such as mevalonate kinase deficiency (MKD), lead to inflammatory bowel disease, or IBD-like intestinal inflammation, possibly due to decreased immunosuppressive isoprenoid intermediates formed through the mevalonate kinase pathway33. Therefore, the changes in mevalonate metabolism between CD and controls tissue identified here may contribute to establishing the inflammatory environment associated with CD.The metabolic modeling pipeline detailed in this study showed that reactions involved in mevalonate metabolism were among the top reactions that differentiated CD from controls in the RISK dataset Fig. B. Altera36. Recently, in-depth gene array analysis has also revealed that various genes involved in fatty acid uptake and synthesis are differentially expressed in the ileum of IBD patients37. One such pathway differently expressed in IBD patients involves uridine transport and exchange. Uridine is a critical regulator of lipid metabolism and a pathway identified in our current study . Remarkably, in studies utilizing uridine to treat mice with Dextran Sulfate Sodium (DSS)-Induced Colitis, levels of pro-inflammatory cytokines IL-6, IL-1β, and TNF in the serum, and mRNA expression in the colon were significantly decreased in the uridine-treated groups39, further demonstrating the critical role for fatty acid metabolism in regulating gastrointestinal inflammation.Fatty acid metabolism is a highly regulated process in which dysregulation can lead to an imbalance of pro- and anti-inflammatory mediators. Of note, the dysregulation of fatty acid metabolism contributes to both the type and degree of inflammatory responses in the intestine during IBDudy Fig. D38. As s40. This result suggests that most genes expressed in vivo in the epithelium are also expressed in organoid cultures, demonstrating the immense value of enteroids as an important tool for IBD research. As with the RISK dataset, we grouped altered metabolic reactions identified in organoids based on broad biochemical pathways. We noted that four of the top ten reactions were involved in the dysregulation of fatty acid and phospholipid metabolism , no morphological characteristics were observed that could be used to differentiate between CD and controls. Further, when gene expression profiles from enteroids were compared to profiles from freshly isolated intestinal crypts, a strong positive correlation was observed in the mean expression levels of many genesism Fig. B, C. The45. Ceramide decreases the release of tumor necrosis factor (TNF), most likely via post-translational regulatory mechanisms and the modulation of TNF-converting enzyme activity46. Furthermore, ceramides play an essential role in regulating autophagy, a process strongly linked to the pathogenesis of CD47. Sphinganine also blocks lysosomal cholesterol transport and has been linked to Niemann-Pick Type C disease, which predisposes to early-onset IBD with CD phenotype and granuloma formation48. However, the exact mechanism by which sphinganine contributes to CD remains unclear and will require further study.Another biological process that was found to be significantly altered in enteroids derived from CD patients was sphinganine transport Fig. D. Sphing49. Here, we identified two reactions in this pathway: superoxide dismutase and catalase  have been reported as novel risk factors for IBD53, further demonstrating that an imbalance in redox homeostasis contributes to the pathogenesis of IBD. Interestingly, In the RISK study, the authors highlight the role of oxidative stress in gut inflammation. Their works detail the enrichment of pro-inflammatory genes, including antimicrobial dual oxidase (DUOX2), and the decreased expression of anti-inflammatory and antioxidant genes, such as those resulting in the production of apoprotein A1 (APOA1) in the ileal tissue of patients with CD27.In addition, oxidative stress, caused by increased ROS production, is present locally and systemically in patients with CDase Fig. A. These 54. Thus, niacin supplementation can be a potential therapeutic target to be investigated in CD as well. In addition, untargeted metabolomics showed the dysregulation of pathways related to pyrimidine, glutamate, and nitrogen metabolism. Similarly, in the RISK dataset, we found uridine transport and metabolism to be altered between patients with CD and controls, further warranting assessment of the potential efficacy of uridine in CD management.Using mass spectrometry-based lipidomics, we aimed to pilot an additional level of validation complementary to our in silico metabolic modeling approach. The significant variations in metabolic alterations between diseased and not diseased individuals and within different diseased individuals provide a rationale for future metabolomic analyses of large cohorts examining phenotypic variations within patients with CD. Our metabolomics analysis showed overlaps with our in silico and in vitro modeling. For example, vitamin B3 was found to be significantly enriched, iterating the theme of the dysregulation of lipid metabolism present in CD. Interestingly, high-dose vitamin B3 treatment has been shown to ameliorate ulcerative colitis through increased prostaglandin D2 synthesis in mice17. Further, the inclusion of medium information specific to Crohn’s disease may improve FBA performance, as well as inclusion of detailed information on macromolecular synthesis rates in Crohn’s disease to support or negate assumptions of hypermetabolism . From a biological standpoint, variations in the mean age and ethnicity of patients also have varying effects on the metabolome and lipidome of the enteroids and, thus are confounders in our analysis. In addition, there is an incongruity between the sizes of the two cohorts, as enteroids were generated from a smaller population  transcriptomic data from the RISK study was accessed using the GEO accession series GSE57945. Our analysis included n = 163 patients with ileal CD and n = 42 controls who were not found to have CD on histology.RNA sequencing data was obtained from the RISK pediatric prospective inception cohort study28. Publicly available enteroid RNA-seq data was accessed using the accession key PRJNA643576. Data was analyzed using the analytical pipeline designed for the RISK dataset outlined below. RNA sequences from the enteroid study were normalized to RPKM and used for downstream in-silico metabolic modeling to validate our in-silico approach using a tissue model of CD.Enteroids were derived from a previously established CD cohort 18. As RNA sequencing was done on ileal biopsies of patients with Crohn’s disease and controls, by overlaying the gene count data onto Recon3D we obtain an ileum-specific model for this study. Genes present in both the Recon3D network and transcriptomic data were retained, while the rest were removed. Retained reactions were specifically linked with transcriptomically-abundant RISK and enteroid genes. Constraint-Based Reconstruction and Analysis (COBRA) Toolbox was used to remove reactions determined to be inactive and generate an ileal-specific metabolic network reconstruction to contextualize our downstream analyses60.The publicly available RISK transcriptomic dataset and enteroid transcriptomic data were overlaid onto Recon3D, a human metabolic reconstruction24.The RISK transcriptomic dataset or enteroid transcriptomic data with Recon3D was used to create a contextualized metabolic model for each patient based on the parsimonious usage of reactions defined by their associated transcriptomic analysis. Multiple possible flux values can be generated from the permissible range of flux bounds for all the active reactions in each patient. The RIPTiDe package randomly samples 50 to 500 flux values depending on the permissible flux ranges. The parameters for the implementation of the RIPTiDe package were kept at default, with the exception of the minimum percent of optimal objective value during the flux balance analysis which we increased from 0.8 to 1 to control the variability of flux ranges assuming a hypermetabolic state in CD63 . A random flux sampling method was used to generate multiple data points for training the machine-learning random forest model. The scikit learn implementation was used for random forest65. We kept the open_exchange parameter in the COBRA optimizer as “false” to allow the FBA module to automatically determine the maximum and minimum bound of reactions based on transcriptome contextualization and FBA optimization.RIPTiDe, a subset of parsimonious Flux Balance Analysis (pFBA), was used to prune the list of genes and reactions obtained from the diseased and control patient setsMany reactions identified by FBA are essential for normal tissue function and, thus, were found to have similar flow between patients with CD and controls. Therefore, we employed RIPTiDe to extract metabolic reactions that had altered flow between patients with CD and controls. A random forest classifier was used to classify CD versus control patients using reactions found to be altered in the diseased state. An 80–20% ratio was used for separating patient data points to create training data and test data for machine learning modeling. All flux points associated with patients in the training set were used to train the model, and the model was evaluated on flux data points associated with validation patients. Due to the small size of control patients in the RISK and enteroid datasets, a repeated validation approach was used to identify the top reactions in an unbiased approach. Multiple splits of train-validation patients were created to train and evaluate the model. This extensive cross-validation allowed us to demonstrate performance gains and takeaways across the dataset. Top reactions were extracted and aggregated from splits where the model had a high detection accuracy. Due to an imbalance between the control and CD patients, a random under-sampling method was used in each split to balance the number of controls and CD patients. A schematic overview of the methods used can be viewed in Fig. p value < 0.05 was considered significant. These top 20 reactions were further grouped based on their metabolic subsystems and description of their overall function based on the virtual metabolic human (VMH) reaction database of Recon3D30.The top 20 reactions identified by our metabolic modeling pipeline (detailed above) were extracted from the splits with an F1 score  greater than 70% and used for further analysis. This step ensured only reactions which were substantially altered between CD and control patients in the training and validation patients were included in the functional group analysis. Differences between patients with CD and controls were calculated using the Mann–Whitney U test, and a /z range. Data processing steps for LC–MS data were carried out using Compound Discoverer v3.0. Compound annotation was carried out based on accurate mass, retention time, isotopic pattern, and MS2 fragmentation pattern matching local and public libraries. Annotations were consistent with the current naming convention in the Metabolomics Workbench and reflected the extent of structural information contained in the collected data66.Ileal tissue samples from pediatric CD patients (n = 11) and control non-inflamed ileal samples (n = 14) were analyzed via non-targeted LC–MS lipidomics assays to semi-quantitatively measure thousands of non-polar compounds, including lipids from the eight main lipid classes  in these complex biosamples. Biopsies were cryopulverized prior to mixing with extraction solvent (IPA) to extract the non-polar lipids while minimizing highly polar compounds and proteins, which would suppress the signals of the desired analytes. The supernatant resulting from the extraction was stored at 4 °C until analysis. A pooled quality control sample was injected at regular intervals (every 5 samples) throughout the sample batch, bracketing the actual samples, which were randomized. Data collection was performed on a ThermoFisher Scientific Q-Exactive HF (QE HF) Hybrid Quadrupole-Orbitrap MS system coupled with a Vanquish Horizon LC system in positive and negative ion modes. Compounds were separated using a ThermoFisher Scientific Accurcore C30 . The chromatographic method for sample analysis involved elution with 80:20 water:MeCN with 10 mM ammonium formate and 0.1% formic acid (mobile phase A) and MeCN and 0.1% formic acid (mobile phase B) using the following gradient program: 0 min 5% A; 0.5 min 5% A; 8 min 60% A; 10.4 min 60% A; 10.5 min 5% A; 14 min 5% A. The flow rate was set at 0.4 mL/min. The column temperature was set to 40 °C, and the injection volume was 2 µL. Full MS data was acquired with 240,000 resolutions over the 150–2000 mAll experiments and methods were performed in accordance with the relevant guidelines and regulations at Emory University, including approval by Emory University under IRB MOD004-IRB00085516. All patient samples were collected with informed consent.Supplementary Information.


The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets. Cancer involves a complex set of dysregulations in multiple biomolecular layers including metabolism. Metabolic changes in cancer result from and lead to profound changes in the behavior of cancer cells and their surrounding environment. Although extensively studied, these metabolic changes are difficult to accurately measure and model in an unbiased manner due to the need to consider a heterogeneous tumor environment encompassing different cell types, many difficult-to-measure metabolites, and lack of standardization of models . While rGenome-scale metabolic models (GEMs) can provide a compelling approach towards understanding cellular metabolism. GEMs are curated computational descriptions of entire cellular metabolic networks. Derived from genome annotations and experimental data, GEMs are composed of mass-balanced metabolic reactions and gene-protein associations that map the relationship of genes to proteins involved in each reaction Figure 1Compared to omics analysis, cancer metabolism may be more accurately modeled by combination of GEMs and a family of methods called Constraint-Based Reconstruction and Analysis (COBRA). COBRA methods perform systems-level analyses on metabolic networks to uncover how genetic and environmental factors affect phenotype on a biomolecular basis. COBRA framework utilizes a stoichiometric matrix that transcribes mass-balanced metabolic reactions of a cellular system, including the system’s uptake and secretion rates, into a matrix that represents the change in levels of reactants and products for each reaction Figure 1COBRA methods for metabolic network analysis are now incorporated into many software packages across several programming languages like MATLAB and Python . Of thesThis review will summarize the set of packages currently available in Python for various COBRA methods. We identify the advantages and shortcomings of the Python ecosystem to guide users’ decisions on their choice of a software platform and inspire future research ideas. We focus on the application of COBRA methods to cancer metabolism. Finally, we will explore the future directions of COBRA methods development and their importance in cancer modeling.in silico perturbation. Next, we summarize various methods for adding biological constraints like multi-omics and biophysics. In addition, we review methods for unbiased pathway analysis and sampling methods. We also summarize the development of COBRA methods for models at the single-cell and population level. Finally, we touch upon packages for visualization and interactive web applications.To make COBRA open-source and accessible, multiple Python packages have been developed by the scientific community to perform the different analyses within COBRA. Here we describe the major components of COBRA and list their associated packages Table 1,via GitHub and checks for correct annotation, model components, and stoichiometry (COBRA for Python (COBRApy) uses an object-oriented programming approach to represent models, metabolites, reactions, and genes as class objects with accessible attributes. Using this design, COBRApy recapitulates functions for standard metabolic flux analyses of its MATLAB counterpart while being extendible and accessible. First, it has the capabilities to read and write models in various formats such as MAT-file (for storing MATLAB variables), JSON, YAML, and Systems Biology Markup Language (SBML) , the curhiometry . To use hiometry Figure 2The most common COBRA method is flux balance analysis (FBA), which assumes the system is at steady state, follows mass-balance described in the stoichiometric matrix, and restricts reaction fluxes by bounds. Furthermore, FBA searches for sets of steady-state reaction fluxes that maximize or minimize an objective function representing a biological function, such as using biomass production objective to model cellular growth . The objvia SOA can be implemented using COBRApy and SciPy. Alternatively, a very different approach to dynamical metabolic modeling was proposed by DMPy, which translates a GEM into a dynamic reaction equation model using Michaelis-Menten approximations and infers missing kinetic constants using Bayesian parameter estimation with thermodynamics constraints  . There astraints . HoweverFlux distributions, even under an optimal objective, are usually not unique as many alternative fluxes can yield a maximum biomass production. The most representative solution can be found using geometric FBA in COBRApy, which looks for a unique flux distribution that is central to the entire solution space . To bettQuantitative flux predictions are useful to experimentalists because of their potential to explain or even predict the effect of environmental and genetic changes. For investigating the relationship between the external environment and the modeled system, COBRApy provides tools for specifying the growth medium and exchange rates of a model. Instead of extracellular conditions, intracellular changes such as genetic mutations and gene modulation can be interrogated as well. To identify essential genes and reactions for biological functions, FBA is performed with gene knockout simulations to assess the effects of the knockouts on objective functions . Similarin silico strain design, which determines optimal genetic changes that would maximize production of desired metabolites. Straight maximization of only the desired reaction is problematic, since it ignores the drainage of cellular resources needed for cellular growth. Therefore, strain design methods couple product yields with cellular objectives to optimize for fast-growing cells that have high productivity. Such metabolic engineering tools are available in a COBRApy-derived package called cameo . For example, we take the minimum expression of required subunits, but take the sum of isozyme expression. This calculation can be performed in Python packages like CORDA (Cost Optimization Reaction Dependency Assessment)  and MEWp3E , which 3E , INIT . However, due to stochasticity and existence of alternative optima, GEM reconstruction and gap-filling of the same network can give rise to multiple GEMs that could yield different flux predictions. To account for the uncertainty in network structure, ensemble modeling compresses such a set of alternative models into an ensemble object to reduce redundancy while capturing variation. Ensemble modeling can be performed through Medusa, a Python package for generating ensembles, performing ensemble simulations, and coupling ensembles with machine learning (ML) .via MEWpy, and a continuous TRN method called probabilistic regulation of metabolism (PROM) , post-translational modifications, epigenetics, protein–protein interactions and protein stability, allostery, and signaling networks . Severalm (PROM) , 114. Otm (PROM)  integratm (PROM)  integratExtraction of context-specific GEMs requires a reference GEM that is often manually curated. To automate the laborious process of GEM reconstruction, several tools were developed to reconstruct microbial GEMs from genome sequences . Severalvia removal of thermodynamically infeasible pathways or calculations of Gibbs free energy. The vastness of solution space can also be attributed to thermodynamically infeasible loops where metabolites are cycled infinitely. COBRApy includes two implementations for removing such loops: one method ll-FBA (add_loopless) utilizes mixed-integer linear programming  and molecular weight. These protein allocation constraints are applied by Python package MEWpy using a method called GECKO , which adds many pseudo-metabolites and pseudo-reactions to represent enzymes , which assumes that minimizing overall total flux approximately finds efficient pathways that minimizes the total enzyme mass . Availab enzymes . Another enzymes . AutoPAC enzymes . Studies enzymes , 70. Ins COBRAme  and was ynamicME . All pacvia lexicographic reverse search, an implementation that significantly improves performance and memory usage  analysis finds the minimum reaction sets  that can maintain steady state. Different variations of EFM have been implemented in Python. For example, EFMlrs is a Python package that performs EFM enumeration ry usage . In addiry usage , minimalry usage , and elery usage , 116. Exry usage .Another set of unbiased methods is Markov chain Monte Carlo (MCMC) sampling methods, which can characterize the solution space by estimating the probability distribution of feasible fluxes. This could be performed with or without constraining by an objective function. Currently, COBRApy integrated MCMC methods such as artificial centering hit-and-run (ACHR)  and opti+ biosynthesis using COBRApy . We here summarize these metabolic changes and provide the rationale for using COBRA methods to analyze cancer metabolism and TME. Indeed, COBRA methods have been utilized for various applications in cancer research in the past decades. We describe how the analyses begin with building cancer-specific metabolic models, from which one can infer metabolic dysregulation through pathway and network analyses. Next, we showed how these models were used for quantitative prediction of cancer metabolic activity and drug targets. Finally, we highlight the frontiers of modeling the TME using multicellular or single-cell COBRA methods.The dramatic functional and environmental changes that occur during cancer formation and progression are accompanied by accordingly dramatic metabolic reprogramming in cancer cells Figure 3via FBA which requires an objective function. While designing an objective function for tissue-specific eukaryotic cells is usually challenging, cancer cells can be reasonably modeled by biomass objective function, because cancer is mainly characterized by cellular growth  , 184, thvia knockouts and analyzed potential gain of function mutations by adding promiscuous reactions predicted by chemoinformatics , but also new drug targets explained by protection against oxidative stress and methylglyoxal toxicity . AnotherWhile the presence of pathways is indicative of activity, analyzing the pattern of how these pathways connect could provide additional insights. For this purpose, topological analysis is a network-based analysis that characterizes metabolic models based on network properties that describes the degree and patterns of connection between metabolites, genes, and reactions. The same models from Agren et al.  were conin silico knockouts  that could reduce cancer growth  and requires more expertise than the typical omics measurements for constraining COBRA methods. Python packages for modeling label tracing data are available via FluxPyt and mfapy  and the Parker Institute for Cancer Immunotherapy (JH). YS is a Damon Runyon Quantitative Biology Fellow supported by the Damon Runyon Cancer Research Foundation (DRQ-13-22). YS was additionally supported by the Mahan Fellowship at the Herbold Computational Biology Program of Fred Hutch Cancer Research Center and the Translational Data Science Integrated Research Center New Collaboration Award at Fred Hutch Cancer Research Center and in part through a pilot fund from the NIH/NCI Cancer Center Support Grant P30 CA015704. PB acknowledges the support of 5U01AG061359-02 and 5U01AG061359-03 from NIA and 5R01HD091527-06 from NICHD. JL was funded by the Brotman Baty Institute Catalytic Collaborations Trainee Grant program.JH is a board member of PACT Pharma and Isoplexis and receives support from Gilead, Regeneron and Merck.The remaining 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.


Yarrowia lipolytica (Y. lipolytica) and Saccharomyces cerevisiae (S. cerevisiae). We aim to increase the production capacity of beta‐carotene (β‐carotene) and succinic acid, which are among the highest market demands due to their versatile use in numerous consumer products. We performed simulations to identify in silico ranking of strains based on multiple objectives: the growth rate of yeast microorganisms, the number of used chromosomes, and the production capability of β‐carotene (for Y. lipolytica) and succinate (for S. cerevisiae). Our multiobjective optimization methodology identified notable gene deletions by searching a vast solution space to highlight near‐optimal strains on Pareto Fronts, balancing the above‐cited three objectives. Moreover, preserving the metabolic constraints and the essential genes, this study produced robust results: seven significant strains of Y. lipolytica and seven strains of S. cerevisiae. We examined gene knockout to study the function of genes and pathways. In fact, by studying the frequently silenced genes, we found that when the GPH1 gene is knocked out in S. cerevisiae, the isocitrate lyase enzyme is activated, which converts the isocitrate into succinate. Our goals are to simplify and facilitate the in vitro processes. Hence, we present strains with the least possible number of knockout genes and solutions in which the genes are turned off on the same chromosome. Therefore, we present results where the constraints mentioned above are met, like the strains where only two genes are switched off and other strains where half of the knockout genes are on the same chromosome. This study offers solutions for developing an efficient in vitro mutagenesis for microorganisms and demonstrates the efficiency of multiobjective optimization in automatizing metabolic engineering processes.Our research aims to help industrial biotechnology develop a sustainable economy using green technology based on microorganisms and synthetic biology through two case studies that improve metabolic capacity in yeast models  m” strains (genetic vector on the far left) and simultaneously optimizes yield and growth rate that are computed using flux balance analysis. The framework produces a Pareto Front (on the far right), from which this study selects seven Pareto optimal strains of Yarrowia lipolytica and seven strains of Saccharomyces cerevisiae. These strains are Pareto Optimal strains with the least knockout genes, maximal yield and growth rate and genes silenced on the same chromosome(s). Such strains simplify and facilitate the production of sustainable chemicals.Pareto Optimal Metabolic Engineering Framework. A multiobjective evolutionary algorithm takes a population of “ This is Section , 2019.The β‐carotene is the main source of provitamin A; this is its main nutritional function. Vitamin A is also known as retinol, which is used as a visual pigment chromophore in the eyes. It is also implicated in the growth and reproductive efficiency of the epithelial tissue. Thus, retinoids have been used in dermatological treatments, such as for acne. The antioxidant property of carotenoids is linked to their capacity to bind with singlet oxygen by a conjugated double bond system. Moreover, β‐carotene is used as a food colorant and as a nutritional supplement of vitamin A. In fact, 100% of β‐carotene can be converted into vitamin A. The international trade of β‐carotene is dominated by private companies such as Roche and BASF.These companies produced β‐carotene through a synthetic approach. Both companies started the synthesis using the same molecule, namely, β‐ionone, but used different methods. Roche used a synthesis for polyenic aldehydes in the form of enol–ether condensation. BASF used the Witting condensation for the production of β‐carotene. The aspect that enhances the interest in the natural β‐carotene is that it contains several other carotenoids in low concentrations, which provides further health benefits. Only a low percentage of the total β‐carotene produced worldwide is natural, and it is really interesting to increase the natural production of this chemical and make the process more environmentally friendly  andn is the number of simulated reactions (each column is a reaction). The matrix S permits the definition of a system ofm mathematical constraints, given by a mass balance equation: Sv=0, wherev is a vector of variables representing flux through all reactions in a steady‐state. Solving Sv=0 is known as flux balance analysis (FBA) . However, such a system of constraints defines an unconstrained solution space. Therefore, a feasible solution space is usually defined by applying a lower bound and upper boundlbi andubi on each fluxvi. These bounds are applied to internal reactions and external exchange reactions simulating the uptake and secretions of chemicals to and from the extracellular regions. These bounds mainly represent the environmental conditions in which a cell is located and its metabolic footprinting.The constraints  v that is optimal for a specific objective function vbio, which is defined as linear combination vectorsv andc, wherec is a vector of weights indicating the quantity of each reaction (flux intensity) that contributes to the objective function. The objective function usually maximizes flux through a single artificial reaction simulating biomass production or the growth rate. The definition of this objective function is crucial for the precision of fluxes. This stoichiometric definition of objective is often complicated in both its composition and values of coefficients, even though it only refers to the exponential growth phase of a cell's cycle. The optimal point v can be found by optimizing systems by solving mmol gDW−1 h−1  and by using linear programming asOnce a feasible solution space is defined, it is possible to select a specific pointv of the growth rate is established, a subsequent optimization called flux variability analysis (FVA)  is critical for evaluating the robustness of single strain prediction. For example, if we consider the production of a compound, a small range could mean that, despite the variance that the predictions might have along the edge‐link in the reaction graph, there is still a minimum production always predicted regardless of other fluxes, and this prediction is comparable to the theoretical maximum under stress growth conditions. The parsimonious FBA (pFBA) is another approach that could lead to more reliable prediction, specifically for the fluxes of internal metabolic reactions occurring in the internal compartments, different from the extracellular region Palsson, .yield andproductivity of specific chemicals. These quantities are, respectively, defined as:Similar to FVA, the pFBA approach calculates the optimal growth rate from FBA optimization as a constraint and then minimizes the sum of absolute values of fluxes through all reactions in a network. Thus, this new optimization returns a parsimonious distribution of fluxes  throughout the network, avoiding numerical predictions that do not have any biological justification, a common issue in FBA Palsson, . For an yield ≤ 1 using the corresponding molar masses vcs, the yield becomes a scalar quantity, expressing the fraction of chemicals produced over the quantity of glucose vglc  that the cell has used. The theoretical upper bound for yield equals 1, that is, when all the glucose is converted to the specific chemical without waste. The productivity, instead, is measured as (h−1), which gives the rate of speed at which a product can be obtained, where vobj is the growth rate. Moreover, Using the extreme ranges obtained from the FVA for each reaction, the ranges for yield and productivity can be obtained, and this will give us the energy and carbon source quantity that each reaction uses.After normalizing simulated conditions come from evaluating the metabolic network. The next question is then how the linear programming problem must be changed to reproduce the metabolic engineering techniques, namely, in our case, to simulate the gene deletions. We use an MOEA to address this issue.The predictions on the distribution of reaction fluxes obtained in wild‐type (WT) and 2.2We created our metabolic engineering framework Figure  by applyUsing a series of logical rules defined by atomic propositions referring to single genes of the simulated genome and logical connectors AND OR, the genes are simulated in the model as a prerequisite for each reaction to be active. The logical connectors help represent complex relationships, such as the isoenzymes and subunits. The rules for each reaction can be evaluated from the atomic true and false values, and the reactions with satisfied propositions (that refer to genes) are considered in the metabolic network. A reaction that must be excluded from the model following one or more gene deletions is simulated, posing the lower and upper bounds equal to 0 and forcing the corresponding flux variable to assume a null value.2g for g genes. Even when considering a fixed maximum number of deletions, it would be equal to a sum of ∑d(g) for d gene deletions (knockout). Hence, a multiobjective optimization framework is useful for efficiently exploring the large solution space of gene deletions. We propose using a modified version of the multiobjective metabolic engineering algorithm . The fitness functions, in our case, are growth rate and production capacity. Such an iterative process evolves a population in the next generation fitter than the previous generation and helps obtain the near‐optimal solutions in the final populations.Multiobjective optimization algorithms are used for this class of problems since an exhaustive evaluation of all the possible gene deletion combinations is practically infeasible, even using a simple approach such as FBA. The number of different configurations to be considered for such a study is equal to multiobjective optimization problem, in which it is possible to define a set of optimal points, called Pareto Front , phytoene synthase/lycopene cyclase, and phytoene dehydrogenase (carPR and carB from Mucor circinelloides) , six sites belonged to nonfunctional pseudogenes due to frameshift , and the last site LIP1 is engaged in lipid metabolism.There are several other examples of the strategy to optimize g et al.  enhancedg et al.  successfBlakeslea trispora, where these two genes were codon adapted for a better expression. Here, metabolic balance is an important factor. In the biosynthesis of β‐carotene, there are four enzymes that limit the rate of the process: tHMGR, GGS1, carRA, and carB. The metabolites (intermediates) that are converted by these enzymes are HMG‐CoA, FPP, GGPP, lycopene, and phytoene. Therefore, an inadequate expression of these enzymes will lead to a high accumulation of these intermediates, which results in a small production of β‐carotene. Hence, they overexpressed the genes tHmgR, Ggs1, carRA, and CarB with Snf, Lip1, Pox3, and Pox4 as the target sites, which caused the deletion of these genes that led to increases in lipid body formation that allowed more storage space for β‐carotene.Liu et al.  suggesteDID2 genes. This gene is a subunit of the ESCRT . This complex is made up of cytosolic protein complexes known as ESCRT‐0, ESCRT‐I, ESCRT‐II, and ESCRT‐III that, together with other accessory proteins, enable a remarkable way of membrane remodeling. The DID2 gene in Y. lipolytica was amplified and inserted into pJN44, leading to pJN44‐Did2. As a subunit of the ESCRT protein complex, the DID2 improves β‐carotene production (increased by 260%) and does not cause metabolic stress for the host cell. To understand why this gene improved β‐carotene production in Y. lipolytica, they studied the messenger RNA (mRNA), protein, and precursor that are part of the β‐carotene pathway. From the study of mRNA of Thmg, Ggs1, carRA, and carB genes, they realized that due to the introduction of the DID2 gene, the mRNA levels of β‐carotene pathway genes were high. In their study, DID2 elevated the mRNA level of the β‐carotene synthesis pathway genes in Y. lipolytica. They found that the DID2 also increased glucose consumption during the exponential growth phase and stationary phase, which is an important feature in metabolic engineering.F. Yang et al.  proposedIn our study, for an in silico simulation, we set the growth conditions of yeast and configured two main objectives: maximization of β‐carotene production and maintenance of biomass as close as possible to that of the WT. We employ MOEA for retrieving gene deletions leading to suboptimal strains. MOEA starts from a population of WT strains with the highest growth rate prediction, but a null β‐carotene production and is located at the bottom of the Pareto Front .cheap carbon source with two different media, rich yeast extract peptone dextrose (YPD)medium and synthetic yeast nitrogen base (YNB) medium, and with different carbon source concentrations , keeping the amount of nitrogen constant. They selected culture media YPD10, YPD60, YNB20, YNB30, YNB60, and YNBGLY60 for β‐carotene production and found that the production varied significantly based on culture media usage. Additionally, Braunwald et al. . We further study the frequently silenced genes YALI0F17996g and YALI0A05379g in Table YALI0F17996g has a length of 4527 bp, GC% = 53.15%, and is a part of chromosome F. This gene translates to a protein that catalyzes the reaction of ergosterol transport. The gene YALI0A05379g has a length of 2361 bp, GC% = 51.16%, and is part of chromosome A. This gene translates an enzyme, chorismate:l‐glutamine aminotransferase, for para‐aminobenzoate (PabA) synthase ABZ1. This enzyme is composed of two parts, PabA and PabB. In the absence of PabA and glutamine, PabB converts ammonia and chorismate into 4‐amino‐4‐deoxychorismate (in the presence of Mg2+). On the other hand, the PabA converts glutamine into glutamate only in the presence of stoichiometric amounts of PabB. Additionally, this enzyme is coupled with EC 4.1.3.38, aminodeoxychorismate lyase, to form 4‐aminobenzoate. Thus, the reaction catalyzed by this enzyme is chorismate + l‐glutamine → 4‐amino‐4‐deoxychorismate + l‐glutamate, and the standard Gibbs free energy (ΔrG′°) for this reaction is −2.0558853 kcal mol−1, which is an exergonic reaction. Therefore, this indicates a spontaneous reaction.We examined the strains from Table YALI0F17996g and YALI0A05379g, we set up an additional simulation, where we knocked in the heterologous genes  and knocked out YALI0F17996g and YALI0A05379g. We obtained an increase in β‐carotene productivity and a lower d‐glucose exchange than a WT strain , which is essential in central nitrogen metabolism (CNM). CNM contains two pathways ‐encoding genes are unknown, these glutaminases may exist because mutants grow well on glutamine even without the GOGAT enzyme. Some authors  and exhibits 60% and 30% activity of wild‐type activated ACDH  and glucose. Since the deletion of a mutant is feasible on glucose, the enzyme encoded by ALD6 is not solely responsible for producing cytosolic acetyl‐CoA  with the lowest biomass losses. By examining knockout genes, we characterize pathways influenced by the knockout. We found seven strains of Y. lipolytica and seven strains of S. cerevisiae capable of producing a high amount of β‐carotene and succinate. Such in silico processes of strains creation save costs, leading to a high biosustainability.We developed an automated tool for in silico implementation of genetic deletions and a precision modulation of the phenotype of the host yeasts to select optimized strains. We implemented an MOEA and its refinements to optimize strains to obtain results that can realize a sustainable synthesis of metabolic precursors used in large‐scale manufacturing processes. Our approach optimizes two yeasts Supplementary information.Click here for additional data file.


Saccharomyces boulardii is a probiotic yeast that exhibits antimicrobial and anti-toxin activities. Although S. boulardii has been clinically used for decades to treat gastrointestinal disorders, several studies have reported weak or no beneficial effects of S. boulardii administration in some cases. These conflicting results of S. boulardii efficacity may be due to nutrient deficiencies in the intestine that make it difficult for S. boulardii to maintain its metabolic activity.S. boulardii to overcome any nutritional deficiencies in the intestine, we constructed a S. boulardii strain that could metabolize l-fucose, a major component of mucin in the gut epithelium. The fucU, fucI, fucK, and fucA from Escherichia coli and HXT4 from S. cerevisiae were overexpressed in S. boulardii. The engineered S. boulardii metabolized l-fucose and produced 1,2-propanediol under aerobic and anaerobic conditions. It also produced large amounts of 1,2-propanediol under strict anaerobic conditions. An in silico genome-scale metabolic model analysis was performed to simulate the growth of S. boulardii on l-fucose, and elementary flux modes were calculated to identify critical metabolic reactions for assimilating l-fucose. As a result, we found that the engineered S. boulardii consumes l-fucose via (S)-lactaldehyde-(S)-lactate-pyruvate pathway, which is highly oxygen dependent.To enable S. cerevisiae and S. boulardii strains capable of metabolizing l-fucose have been constructed. This strategy could be used to enhance the metabolic activity of S. boulardii and other probiotic microorganisms in the gut.To the best of our knowledge, this is the first study in which The online version contains supplementary material available at 10.1186/s12934-022-01926-x. Saccharomyces boulardii is a probiotic yeast known to interact with its host and exhibits antimicrobial activity  or the (S)-lactaldehyde-pyruvaldehyde®lactate-pyruvate pathway -lactate pathway had 39 models, and the LAD to pyruvaldehyde pathway had 51 models -lactate pathway, 1 M l-fucose was metabolized to synthesize up to 110.35 g/L biomass or up to 1 M 1,2-PDO and 47.46 g/L biomass -lactate pathway model, all fucose was converted into biomass, which was consistent with the production of 1,2-PDO and biomass in the fermentation experiment -lactate pathway nor the LAD to the pyruvaldehyde pathway could be maintained under anaerobic conditions because NAD+ could not be regenerated quickly enough -lactaldehyde-(S)-lactate-pyruvate pathway under aerobic conditions; however, under anaerobic conditions, it synthesizes large amounts of 1,2-PDO to generate energy but does not produce much biomass. During fermentation under aerobic conditions, S. boulardii FCT used most of the l-fucose to synthesize biomass. This result was consistent with the predictions of multiple EFMA models for the LAD to (S)-lactate pathway when only l-fucose was available for biomass synthesis -lactate pathway used l-fucose for the simultaneous synthesis of relatively small amounts of biomass and large amounts of 1,2-PDO -lactate pathway. Under microaerobic and strict anaerobic conditions, S. boulardii FCT metabolized l-fucose, and synthesized more 1,2-PDO, along with little or no biomass -lactate pathway and towards the production of 1,2-PDO and energy. Although pyruvate has not been quantified in anaerobic fermentation profiles, this could be because pyruvate is used in many different metabolic fluxes, as the molecule is a major metabolite in various metabolic processes, including glycolysis, gluconeogenesis, and pyruvic acid metabolism.sis Fig. C. In addPDO Fig. C. On theata Fig. C. These ass Fig. C, D. TheS. boulardii FCT can consume l-fucose and synthesize energy and biological building blocks, it may have better metabolic activity than wild-type S. boulardii in the intestine. Although S. boulardii FCT showed a decrease in biomass synthesis under low-oxygen conditions, it still grew under microaerobic conditions  medium containing 100 µg/mL ampicillin at 37 °C and 200 rpm for amplification of plasmids. The S. cerevisiae or S. boulardii strains harboring the plasmids were cultured in yeast synthetic complete (YSC) medium containing 6.7 g/L yeast nitrogen base without amino acids, 20 g/L l-fucose , and 0.70 g/L CSM-HIS-TRP-URA . For tests under aerobic conditions, S. cerevisiae or S. boulardii were cultured in 100 mL fucose media in 250 mL flasks with shaking at 200 rpm. For tests under microaerobic conditions, S. boulardii was cultured in 100 mL fucose media in 160 mL serum bottles in a jar containing Anaero Pack-MicroAero  without shaking. After each sampling, the Anaero Pack was replaced. For tests under strict anaerobic conditions, S. boulardii was cultured in 100 mL fucose media in 160 mL serum bottles in an anaerobic chamber  without shaking. The atmosphere in the anaerobic chamber consisted of 90% nitrogen (v/v), 5% carbon dioxide (v/v), and 5% hydrogen (v/v).The RS426GPD . E. coli. PK2-1D  and S. b MYA-796  were usel method . E. colid-glucose (Sigma-Aldrich), l-fucose (Sigma-Aldrich), and 1,2-PDO (Sigma-Aldrich), a high-performance liquid chromatography (HPLC) system equipped with a refractive index detector, Agilent 1100 (Agilent Technologies), and an Aminex HPX-87 H organic acid column (Bio-Rad) was used. We used a constant flow rate of 0.5 mL/min at 65 °C and 0.01 NH2SO4 as the mobile phase. The retention times of each metabolite were 17.373 min for d-glucose, 12.828 min for l-fucose, and 20.132 min for 1,2-PDO, respectively.For the identification and quantification of extracellular metabolites, including l-fucose (2 g/L) medium at 30 °C and 250 rpm for 4 h, and then 1 mL of cells was collected. The harvested cells were reconstituted in 500 µL of DW containing glass beads and vortexed for 1 h. The samples were centrifuged at 13,000 rpm for 30 min and 400 µL of the supernatant was collected and dried. The dried samples were reconstituted in 120 µL of DW, and the fucose concentration of each sample was analyzed by HPLC.To investigate the fucose transport efficiency of each hexose transporter, the HXT-Null strain and the strains overexpressing HXT 1–7 were cultured in 5 mL of yeast extract and peptone (YP) media. The HXT-Null strain was cultured in YP containing ethanol (20 g/L) and the rest of the strains were cultured in YP containing glucose (20 g/L), and cells entering the intermediate exponential phase were harvested (OD 13/mL). The harvested cells were washed three times with distilled water (DW) and then cultured in 1 mL of YP with S. cerevisiae  [S. cerevisiae with the ability to metabolize l-fucose. To enable the simulation of fucose metabolism, β-l-fucose exchange, β-l-fucose transport, α-l-fucose exchange, α-l-fucose transport, fucose isomerase, fuculose kinase, fuculose-1-phosphate aldolase, 1,2-PDO oxidoreductase, and 1,2-PDO transport reactions were included in the model , Gurobi (version 8.0.1), and Cobra Toolbox (version 2.26) [The GEM for n 8.5.0)  was usedon 2.26) , 47 wereS. cerevisiae [(S)-lactaldehyde dehydrogenase, (S)-lactic acid dehydrogenase, methylglyoxal reductase, and (D)-lactate dehydrogenase into the model. In addition, to obtain the absolute amount of the synthesized dried cell weight, we summed up the number moles of the building blocks  required for cell growth and regarded it as the cell dry weight (15.74 g/L). The OD was converted to yeast dry cell weight based on a previously reported value [To elucidate the pathways of fucose metabolism in more detail, we modified the previously reported EFMA of revisiae . Becauseed value .Additional file 1. Genome-scale metabolic model of Saccharomyces cerevisiae that metabolizes fucose. Table S1. Sequences of the plasmids constructed in this study.


Vibrio natriegens, consisting of essential metabolic engineering and optimization of process conditions. The final producer strain V. natriegens Δlldh Δdldh Δpfl Δald Δdns::pycCg (Succ1) yielded 1.46 mol of succinate per mol of glucose under anaerobic conditions  and revealed a particularly high biomass‐specific succinate production rate of 1.33 gSucc gCDW−1 h−1 compared with well‐established production systems. By applying carbon and redox balancing, we determined the intracellular flux distribution and show that under the tested conditions the reductive TCA as well as the oxidative TCA/glyoxylate pathway contributed to succinate formation. In a zero‐growth bioprocess using minimal medium devoid of complex additives and expensive supplements, we obtained a final titre of 60.4 gSucc l−1 with a maximum productivity of 20.8 gSucc l−1 h−1 and an overall volumetric productivity of 8.6 gSucc l−1 h−1 during the 7 h fermentation. The key performance indicators  of this first engineering approach in V. natriegens are encouraging and compete with costly tailored microbial production systems.The biotechnological production of succinate bears serious potential to fully replace existing petrochemical approaches in the future. In order to establish an economically viable bioprocess, obtaining high titre, yield and productivity is of central importance. In this study, we present a straightforward engineering approach for anaerobic succinate production with  Vibrio natriegens for the anaerobic succinate production from glucose. By the optimization of production conditions and essential metabolic engineering we increased the succinate yield 10‐fold. Finally, we achieved a maximum productivity of 20.8 g l−1 h−1 in an anaerobic bioprocess with resting cells, highlighting the potential of V. natriegens for succinate production. Here we describe the first approach to tailor  Saccharomyces cerevisiae and Yarrowia lipolytica), as well as bacteria that naturally secrete large quantities of succinate, such as Actinobacillus succinogenes, Anaerobiospirillum succiniciproducens, Mannheimia succiniciproducens and Basfia succiniciproducens. Excellent reviews summarize the wealth of knowledge about metabolic engineering and fermentation strategies of aerobic and anaerobic succinate production , ≥ 0.90 gSucc gGlc−1), and reached final titres of up to 1.24 M (146 g l−1) in anaerobic batch and fed‐batch processes , which was considered the minimum volumetric productivity to meet economic viability  and anaerobic conditions (µ = 0.92 h−1), and even anaerobically resting cells show a high biomass‐specific glucose consumption rate of 1 g g−1 h−1 , oxidative branch of the TCA cycle (oxTCA) and glyoxylate pathway  of 2 molSucc molGlc−1 can be achieved on the carbon basis . In this scenario, 1 mol pyruvate is decarboxylated by the PDHC to acetyl‐CoA which is subsequently converted to 1 mol acetate yielding 1 mol ATP and NADH respectively. Based on the assumption that all pathways are fully operative under anaerobic conditions, the maximum yield of 1.71 molSucc molGlc−1 .Succinate secretion has been observed as a minor product of fermentative glucose metabolism by anaerobically growing and resting cells of  WT Fig. . By inoc WT Fig. . To prov50% Fig. . Under tV. natriegens by inactivating competing pathways. V. natriegens Δlldh Δdldh Δpfl has been constructed recently to abolish/mitigate lactate, acetate, formate and ethanol formation ; however, the YP/S was only slightly improved to 1.01 ± 0.08 molSucc molGlc−1  yielded 1.46 ± 0.08 molSucc molGlc−1 , accounting for 12% of the glucose‐derived carbon , this fraction contains four glucose‐derived carbon atoms .Last, we attempted to increase the succinate yield through the mitigation of acetate formation, which might be catalysed by the phosphate acetyltransferase and acetate kinase. Both enzymes are annotated by two independent genes, respectively. The deletion of the V. natriegens Succ1 to a lab‐scale bioreactor, which we inoculated to a starting biomass concentration of 18 g l−1. We used VN minimal medium lacking any complex additives and expensive nutrients. The anaerobic process was highly reproducible and reached within 7 h a final succinate titre of 520.7 ± 7.7 mM (i.e. 60.4 g l−1) from glucose and KHCO3 which corresponds to a YP/S of 1.14 ± 0.02 molSucc molGlc−1. YP/S remained constant during the entire fermentation. Acetate was the major by‐product with a yield of 0.18 ± 0.02 molAc molGlc−1, followed by alanine with a yield of 0.02 molAla molGlc−1 , and decreased with the overall process performance. The anaerobic process achieved an overall volumetric productivity of 74.4 ± 1.1 mmol succinate l−1 h−1 (i.e. 8.6 ± 0.1 g l−1 h−1). During the first hour of the process with V. natriegens Succ1, the maximum volumetric productivity of 179.3 ± 1.9 mmol succinate l−1 h−1 (i.e. 20.8 ± 0.2 g l−1 h−1) was reached.Finally, we scaled up the anaerobic resting cell approach with V. natriegens 10‐fold through optimization of the anaerobic cultivation conditions and applying a minimal metabolic engineering approach. Under optimized conditions, the YP/S of V. natriegens WT reached about 55% of the designated succinate producer M. succiniciproducens WT . Accordingly, succinate was produced by both pathways – redTCA and oxTCA (or glyoxylate pathway).In this study, we increased the succinate yield of V. natriegens Succ1 could finally be translated into an exceptionally high overall volumetric productivity (8.6 gSucc l−1 h−1 during the production phase), when we performed the production under pH‐controlled bioreactor conditions. Within only 7 h, we achieved final succinate titres that compete with commonly reported literature values  is at the same level as a recently developed process using engineered M. succiniciproducens of 20.9 gSucc l−1 h−1 (= 21.3 g succinic acid l−1 h−1) in an identical set‐up  did not consume glucose as sole carbon and energy source. Apparently, neither the gene duplicates on chromosome 2 nor other annotated CoA‐transferases or acetaldehyde/alcohol dehydrogenases, that could theoretically catalyse the same reaction, compensated the deletion of the pta‐ackA operon in this strain background. In this regard, V. natriegens differs from other organisms, in which the disruption of the pta‐ackA pathway was a successful engineering approach to abolish or reduce acetate formation under anaerobic conditions  and might play an indispensable physiological role in V. natriegens. The acetate generation from acetyl phosphate might be catalysed by the acylphosphatase, if both acetate kinase genes are deleted. The corresponding gene is annotated in the genome of V. natriegens  glycerol. E. coli S17‐1 λpir cells were cultivated in 2× YT medium containing per l: 16 g Bacto tryptone, 10 g yeast extract and 5 g NaCl  of NaCl and 50 mM MgCl2. Main cultures (growth experiments and succinate production) were set up in VN minimal medium containing per l: 5 g (NH4)2SO4, 21 g 3‐(N‐morpholino)propanesulfonic acid (MOPS), 1 g KH2PO4, 1 g K2HPO4, 15 g NaCl, 10 mg CaCl2 ∙ H2O, 250 mg MgSO4 ∙ 7 H2O, 16.4 mg FeSO4 ∙ 7 H2O, 10 mg MnSO4 ∙ H2O, 1 mg ZnSO4 ∙ 7 H2O, 0.3 mg CuSO4 ∙ 5 H2O, 0.02 mg NiCl2 ∙ 6 H2O  were PCR amplified with the primer pairs ald1/ald2 and ald3/ald4, respectively (Table pta‐ackA operon (PN96_03360‐65), the ackA1 gene (PN96_03365) and the ackA2 gene (PN96_21510) were PCR amplified with the primers as indicated in Table pyc gene was targeted to the dns gene locus (PN96_00865) by amplifying 500 bp sequences with the primer pairs dns1/dns2 and dns3/dns4, and the expression module Ptac‐pyc‐TrrnB was placed in between the two flanks. The pyc gene was amplified from the chromosomal DNA of C. glutamicum ATCC 13032 using the primer pair pyc1/pyc2. The native GTG start codon was replaced with the stronger ATG during the amplification step with a primer encoded point mutation. Promoter and terminator elements were amplified from pJC4‐Ptac‐egfp  were applied as described in the literature . For aerobic shaking flask experiments, the cell pellet was re‐suspended in 0.9% (w v−1) NaCl and the optical density at 600 nm (OD600) of the suspension was adjusted, in order to inoculate the main culture  to a starting biomass concentration of 26 mgCDW l−1 (OD600 = 0.1). Experiments were performed in 500 ml baffled shaking flasks containing 50 ml VN medium and 55 mM glucose as sole carbon and energy source . The inoculum from stationary phase cells was taken from 5 ml O/N cultures straight away and processed likewise.Routinely, anaerobic succinate production was performed in 50 ml test tubes with screw caps (to prevent aeration during the experiment) containing 50 ml VN minimal medium with 27.5 mM glucose as sole organic carbon and energy source and 100 mM KHCO600 of about 5 was reached (after 2.5 h cultivation). The bioreactors were aerated at 0.5 vvm with pressurized air and stirred at 600 rpm (with three Rushton turbines) to achieve microaerobic conditions during the cultivation. The dissolved oxygen concentration was detected with a polarographic probe . Precultures were harvested by centrifugation , and the cell pellet was used to inoculate 400 ml VN medium to a starting biomass concentration of about 18 gCDW l−1. VN medium initially contained 555 mM glucose and 150 mM KHCO3. For succinate production, bioreactors were not aerated and the stirrer speed was set to 180 rpm. The culture pH was monitored with a standard pH probe  and maintained at 7.5 by a two‐side regulation using 13.3 M NH4OH and 1.1 M H3PO4. A linear KHCO3 feed was applied at 100 mmol l−1 h−1.The final anaerobic succinate production processes were performed in 2 l glass vessels of a DASGIP bioreactor system . To obtain enough biomass, precultures were set up in 3× 1.5 l 2× YTN medium and incubated at 37 °C until an ODCDW) was monitored by measuring the optical density at 600 nm (OD600). The correlation factor to convert cCDW to gCDW l−1 had been determined from exponentially growing cells for the spectrophotometer : cCDW = OD600 × 0.26. The growth rate (µ) was determined in the exponential phase through a regression curve, fitting the slope of the biomass concentration in a semilogarithmic plot over time.The biomass concentration  and detected with a refractive index detector (RID). Analyte concentrations were calculated by comparing the peak area of the sample with the peak area of a 7‐point external standard ranging from 1 to 200 mM  using an Agilent 1260 infinity II series device  equipped with a Hi‐Plex H column  and Hi‐Plex H guard cartridge  for protection. Analytes were eluted at 50 °C isocratically using 5 mM Ho‐phthaldialdehyde (OPA) prior to separation through a gradient of polar  versus non‐polar phase  by increasing the linear gradient stepwise from 2–43% non‐polar fraction within 10 min and 43–100% within 2:30 min, at a total flow rate of 1 ml min−1, 40 °C. The analytes were detected with a fluorescence detector . Peak areas were normalized to the internal standard , and concentrations were finally calculated by comparing the peak area of the sample with the peak area of a 7‐point external standard ranging from 1 to 400 µM  column  and AdvanceBio AAA guard column  for protection. Primary amino acids were online derivatized with All experiments were performed at least in independent biological triplicate on different days. Seed trains of all replicates were started by streaking cell material of a cryogenic culture on a fresh agar plate. Data represent mean values of at least triplicate, and error bars indicate standard deviations. If appropriate, standard deviations were calculated by error propagation.et al. , isocitrate dehydrogenase and α‐ketoglutarate dehydrogenase complex).The carbon balance for the anaerobic resting cell approach was calculated on the C‐molar basis in accordance with Buchholz et al. . C‐molar2/HCO3‐ evolution. In accordance with Villadsen et al. (i) per C‐mole were calculated  and KEGG database , reductive branch of the TCA cycle (redTCA), oxidative branch of the TCA cycle (oxTCA) and glyoxylate pathway  production from glucose in way Fig. . Based oThe theoretical environment was an anaerobic production scenario with resting cells, premising no biomass generation from glucose and full conservation of reduction equivalents. Reduction equivalents were considered as the aggregate of NAD(P)H and quinones. We assumed that all pathways were fully operative under the given conditions, and reduction equivalents were interconvertible. ATP energetics were not considered in the calculations, as we could not experimentally verify the carbon flux through either of the energetically different reactions (i.e. PEP carboxylase/pyruvate carboxylase vs. PEP carboxykinase). The optimum flux distribution for a maximum succinate yield from glucose was calculated by balancing the consumption/production of reduction equivalents (1) and (2). In addition, the maximum yield was calculated in a second scenario considering a potentially inactive oxTCA under anaerobic conditions (contribution of (2) = 0).The metabolic flux distribution during the small‐scale experiment Succ1) was estimated by feeding experimental data of glucose, succinate and acetate quantification into equations . Alternatively, only three carbon atoms of succinate originate from glucose and the fourth carbon atom is provided by CO2/HCO3 ‐ during the carboxylation of PEP and/or pyruvate . With regards to their origin, glucose‐derived carbon atoms are shown as black circles, whereas CO2/HCO3 ‐ derived carbon atoms are highlighted as yellow circles.Fig. S2. Degree of reduction balance of anaerobic succinate production in test tubes containing 50 mL VN minimal medium with 27.5 mM glucose. Reduction degree times C‐molar concentration (κi * ci) of each product (i) is described as fraction of glucose (κGlc * cGlc)−1.Fig. S3. Aerobic shaking flask cultivations of V. natriegens WT and V. natriegens Succ1 in VN minimal medium containing 10 g glucose l−1. Data points represent the mean and error bars indicate the standard deviation of three independent biologicals replicates.Table S1. Oligonucleotides used in this study.Table S2. Comparison of the carbon fraction recovered by balancing carbon atoms and degree of reduction.Click here for additional data file.


In addition to volatile compounds, metabolites also have a great effect on the flavour of food. Fresh finger citron cannot be eaten directly because of its spicy and bitter taste, so it is made into a preserved fruit product known as Laoxianghuang (LXH). To investigate the metabolites that have an effect on the flavour of LXH, untargeted metabolomics was performed using an ultrahigh-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS), and the metabolites of the Laoxianghuang samples from different locations in the Chaoshan area were compared and analysed. A total of 756 metabolites were identified and distinct differences were revealed among the different Laoxianghuang samples. A total of 33 differential metabolites with the most significant changes were screened through further multivariate analytical steps, and each group of samples had unique metabolites. For instance, pomolic acid had the highest content in the JG sample, while L-glycyl-L-isoleucine was rich in the QS sample. Moreover, flavonoid metabolites made the greatest contribution to the unique flavour of Laoxianghuang. The metabolic pathways involved are the biosynthetic pathways of flavonoids, isoflavonoids, flavones, and flavonols. This study can provide some creative information for distinguishing the quality differences of Laoxianghuang from the perspective of metabolites and offer preliminary theoretical support to characterise the formation of flavour substances in Laoxianghuang. In recent years, people’s dietary demand has increased as their standards of living have developed. Consumers pay attention not only to their foods’ nutritional value but also to their palatability and their unique flavour, with the result that food aroma has become increasingly popular by virtue of its multifunctional nutrient value and its distinctive flavour generated from microbial metabolism ,2. DurinCitrus medica L. var. sarcodactylis Swingle), widely known as “Foshou” in China, is the fruit of evergreen shrubs or small trees of the Rutaceae. It is used as a traditional medical food with the functions of expectorating phlegm; relieving cough and asthma; having anti-inflammatory, anti-bacterial, anticancer, and anti-depressive properties; and lowering blood pressure ) and the correlation (p (corr)) of the principal components and metabolites, in which the metabolite markers closest to the bottom left and top right are considered to have the most significant variance. The metabolites with VIP values greater than or equal to one are marked by red dots, whereas those with VIP values smaller than one are marked by green dots in The variable importance in projection (VIP) values, following the supervised OPLS-DA model, reflect the degree of influence of the difference between groups of corresponding metabolites in the classification and discrimination of samples in the model; the VIP values were applied to further identify and screen for differential metabolites. Metabolites with VIP values ≥1 are usually regarded as significantly different metabolites.To explicitly clarify the influence of different regions on the metabolites of all samples and to further screen for differential metabolites, volcano plots of comparisons between pairs of samples were performed, as shown in As shown in the Venn diagrams of Based on the VIP values >1 and fold change ≥2 or ≤0.5 mentioned above, Log2 (fold change) values of differential metabolites were sorted, and the top 10 metabolites with the highest different multiples (upregulated and downregulated) in each pairwise sample are listed in Chrysobalanus icaco), loquats (Eriobotrya japonica), and rosemary, are widespread, their pomolic acid contents are usually very low [A heatmap with hierarchical cluster analysis was applied to characterise the distribution of different metabolites among the LXH samples as shown in very low . Mixturevery low . Previouvery low  and is avery low . 6-Hydrop < 0.01). Among the 275 correlation pairs, 206 positive correlations  and 69 negative correlations  are observed. The correlation pairs were further screened for extremely strong correlations and significance . For instance, licoflavonol, 6-O-caffeoylarbutin, licoagrochalcone D, licoagroside B, and 4′,5-dihydroxy-3′,5′-dimethoxy flavone are strongly positively correlated with licoarylcoumarin, while chrysin (r = −0.970) is strongly negatively correlated with luteolin-6-C-arabinoside-7-O-glucoside, and orientin-7-O-arabinoside and luteolin-7-O-glucoside-5-O-arabinoside are highly positively correlated with isolicoflavone B and apigenin-7-O-neohesperidoside. It was reported that apigenin could significantly inhibit UV-induced mouse skin tumorigenesis, and luteolin exhibited a high 2,2-diphenyl-1-picrylhydrazyl scavenging activity, with naringenin having a similar effect [In this study, biological duplication between the samples within a group can be observed; the higher the correlation coefficient of the samples within a group compared to the samples between groups, the more reliable the differential metabolites obtained. As shown in r effect . In addir effect .Some correlation pairs also show extremely positive correlations (r = 0.999), including isoglyasperin D with licoflavanol and 4′,5-dihydroxy-3′,5′-dimethoxyflavone and licoarylcoumarin with isoglyasperin D and glabrene, respectively. N-Glycyl-L-leucine is strongly positively correlated with only L-glycyl-L-isoleucine, whereas it shows highly negative correlations with rubrofusarin-6-O-glucoside, naringenin-4′-O-glucoside, isochlorogenic acid A, cutin-7-O-glucoside, 6-O-caffeoylarbutin, persicoside, and syringesinol-4′-O-glucoside.p < 0.05) are nine, five, five, three, eight, and three, respectively as shown in Differential metabolites interact in organisms to form different metabolic pathways. Pathway enrichment analysis of differential metabolites is helpful to understand the mechanisms of metabolic pathway changes. The numbers of metabolic pathway diagrams with significant differences  or modifying the odour and flavour of other compounds . Among tPlant polyphenols, a class of nonvolatile secondary metabolites of plant origin, apart from their sensory properties of bitterness, also exhibit antioxidant, lipid-lowering, anti-inflammatory, anti-tumour, and cardiovascular prevention activities and are important ingredients for the preparation of functional foods ,44. In aThis study used untargeted metabolomics analysis based on UPLC-MS/MS to identify and screen for differential metabolites from four kinds of Laoxianghuang samples collected from different regions in the Chaoshan area. A total of 756 metabolites, including 280 flavonoids, 94 lipids, 67 phenolic acids, 62 amino acids and their derivatives, 54 organic acids, 40 lignans and coumarins, 32 alkaloids, 32 terpenoids, 20 nucleotides and their derivatives, 8 tannins, 1 steroid, and 74 other categories, were identified. Distinct differences were revealed among the different LXH samples, with 33 differential metabolites; the most significant changes were screened through further multivariate analytical steps, and each group of samples was found to have unique metabolites. For instance, pomolic acid had the highest content in JG, while L-glycyl-L-isoleucine was rich in QS. It is possible that different specific production formulations and manufacturing processes are responsible for the differences in metabolite composition among the samples. The unique flavour of Laoxianghuang is the result of the interaction of various metabolites, especially flavonoids, phenolic acids, and organic acids. Moreover, flavonoid metabolites make a great contribution to the unique flavour of Laoxianghuang. This study can provide some creative information for distinguishing the quality differences of Laoxianghuang from the perspective of metabolites, which could be utilised in quality control and offer novel insights into the characterization of the formation of distinctive flavour substances of Laoxianghuang. This study investigated the changes in non-volatile metabolites of different brands of LXH and related metabolic pathways, but there are still some limitations. Correlation analysis with microorganisms, volatile components, and other indicators can be conducted in subsequent studies. Further analysis of the functions of the individual microorganisms in these pathways will be beneficial to elucidate the metabolic mechanism.