Climate change continues to threaten global crop output by reducing annual productivity. As a result, global food security is now considered as one of the most important challenges facing humanity. To address this challenge, modern crop breeding approaches are required to create plants that can cope with increased abiotic/biotic stress. Metabolomics is rapidly gaining traction in plant breeding by predicting the metabolic marker for plant performance under a stressful environment and has emerged as a powerful tool for guiding crop improvement. The advent of more sensitive, automated, and high-throughput analytical tools combined with advanced bioinformatics and other omics techniques has laid the foundation to broadly characterize the genetic traits for crop improvement. Progress in metabolomics allows scientists to rapidly map specific metabolites to the genes that encode their metabolic pathways and offer plant scientists an excellent opportunity to fully explore and rationally harness the wealth of metabolites that plants biosynthesize. Here, we outline the current application of advanced metabolomics tools integrated with other OMICS techniques that can be used to: dissect the details of plant genotype–metabolite–phenotype interactions facilitating metabolomics-assisted plant breeding for probing the stress-responsive metabolic markers, explore the hidden metabolic networks associated with abiotic/biotic stress resistance, facilitate screening and selection of climate-smart crops at the metabolite level, and enable accurate risk-assessment and characterization of gene edited/transgenic plants to assist the regulatory process. The basic concept behind metabolic editing is to identify specific genes that govern the crucial metabolic pathways followed by the editing of one or more genes associated with those pathways. Thus, metabolomics provides a superb platform for not only rapid assessment and commercialization of future genome-edited crops, but also for accelerated metabolomics-assisted plant breeding. Furthermore, metabolomics can be a useful tool to expedite the crop research if integrated with speed breeding in future. In this era of global warming and unchecked population growth, food security is becoming a much more pressing concern. The dream of a world without hunger is only possible if agricultural productivity is significantly enhanced to fulfill the growing food requirements around the globe due to population growth and other factors ,2. It isMetabolomics is emerging as a powerful tool for crop improvement. Metabolomics involves the comprehensive characterization of the metabolome using advanced analytical chemistry technologies. The metabolome is formally defined as the complete set of low molecular weight (MW < 1500 Da) primary and secondary metabolites found in an organism . The metFrom the perspective of plants and plant breeding, metabolomics can be readily used as a diagnostic tool to assess the plant performance, to probe vital metabolic markers linked to biotic/abiotic stresses tolerance, perform mutant characterization, and conduct robust ecotype detection . MetabolIn this review, we will discuss the applications of advanced metabolomics for crop improvement  studies and genome wide association studies (GWAS) have been the traditional approaches to identify broad, macroscale phenotype-to-genotype relationships. However, the macroscale phenotypes used in these studies  are often “ambiguous” or not clearly defined, as they represent the downstream effects of hundreds of genes, proteins, and metabolites. These unclear effects are compounded by difficult-to-measure environmental and epigenetic effects. As a result, the apparent strength or influence of these genetic effects on macroscale plant phenotypes is greatly diminished or difficult to detect. By focusing on molecular-scale phenotypes, such as metabolite concentrations that can be quantitatively measured, one can identify more compelling and higher performing gene-phenotype relationships. As a result, metabolic qualitative trait loci (mQTLs) studies and metabolic genome-wide association studies (mGWAS) have emerged as powerful tools  to disseFor example, mGWAS analysis has proved to be crucial for dissecting the genetic and metabolic architecture of rice by identifying the genes associated with the natural variation seen in the rice metabolism. GWAS was used to identify 6.4 million SNPs from 529 different rice strains and identified 36 candidate genes that modulate metabolite levels of at least 34 known primary and secondary metabolites. The results showed that this technique can be crucial to perform molecular phenotypic trait mapping for the goal of rice improvement . Yadav eA comparative analysis using a combination of phenotypic genome-wide association studies (pGWAS) and mGWAS was carried out on a diverse collection of rice accessions. LC-MS/MS-based metabolomic analysis allowed the identification of more than 830 metabolic features having high heritabilities for flavonoids and amino acids. These results indicated a relationship among phenotype-metabolites that can be beneficial for complex phenotype trait mapping . Matros Metabolomics-based mGWAS analysis for individual metabolite content rations subjected to climatic stresses will give deep insight and knowledge about metabolic diversity and discover key metabolic regulators in response to different stresses. For instance, LC-MS/MS-based metabolomics was used to perform untargeted metabolic profiling of 282 inbred lines (ILs) of maize. This led to the identification of 3991 mass features that were then compared to >29 million SNP markers. Numerous hotspots for QTLs were identified that control a citrate synthase-like gene and benzoxazinoid biosynthesis genes that may play key roles in the synthesis of pathogen defensive metabolites. The genetic loci identified would be useful for marker-assisted breeding to develop resistant maize lines . LC-MS bLiu and coworkers analyzed 286 soybean varieties with a SNP chip that identified 54,294 SNPs. This SNP analysis was combined with a targeted metabolomic study that identified 52 metabolites for lipid metabolism and six oil-related traits. Additionally, multi-omics analysis identified 284 genes associated with oil-related traits. This work also allowed the authors to construct 133 genetic networks related to soybean metabolism, of which only 24 were previously known . Target Advances in genomics and metabolomics technologies have allowed mQTL mapping for diagnostic markers identification to study the plant performance and enabled to develop high density maps of candidate genes controlling the various metabolic pathways . MetabolShi et al.  examinedThe mGWAS and mQTL studies outlined earlier are now giving plant scientists a much clearer idea about the metabolic genes and metabolic gene networks involved in a variety of metabolic activities for many important crop plants. This set of improved functional genome annotations is opening the door to performing much more focused metabolic profiling. In particular, metabolomics is now being extensively used to study plant responses to biotic and abiotic stress. This is helping to elucidate the unique metabolic pathways and metabolic biomarkers linked to stress tolerance and disease resistance in globally important crop plants. In many cases, the pathways and genes associated with these metabolic biomarkers to stress are now well known.Rhizoctonia solani. This metabolomic analysis revealed an increased production of ADP, mucic acid, jasmonic acid, and glyceric acid in resistant cultivars (32R), while sensitive cultivars (29S) increased their production of inosine monophosphate. These data suggest that different metabolic strategies are adopted by different rice cultivars to fight R. solani infection and that metabolite-based selection and breeding for increased levels of these pathogen-fighting metabolites may improve rice resistance to this widespread fungus. The ascomycete fungal pathogen known as Fusarium graminearum, is a common crop pathogen that causes fusarium head blight (FBH). FBH is a devastating disease specific to wheat and barley and is responsible for billions of dollars in economic losses worldwide each year. With climate change and global warming, FBH in wheat is of increasing concern. FBH infection causes shifts in the amino acid composition of wheat, resulting in shriveled kernels and contaminating the remaining grain with mycotoxins, mainly deoxynivalenol, which inhibits protein biosynthesis, and zearalenone, an estrogenic mycotoxin. These toxins cause vomiting, liver damage, and reproductive defects in livestock. They are also harmful to humans when they appear in contaminated food [Suharti et al.  used twoted food .QTL-Fhb2 allele. Untargeted metabolomics showed increased accumulations of fatty acids, flavonoids, glycerophospholipids, lignins, phenylpropanoids, and terpenoids in FHB resistant RILs. In addition, transcriptome profiling detected numerous mycotoxin detoxification genes, transcription factors, receptor kinases, and disease resistance genes. By combining these datasets, several candidate resistance genes within the QTL-Fhb2 allele, including cinnamyl alcohol dehydrogenase (CAD), 4-coumarate—CoA ligase (4CL) and ABC transporter-4 (ABC4)—were identified.In a study described by Dhokane et al. , recombiFusarium graminearum. These metabolites can potentially be used as disease resistant biomarkers, or they may be used in metabolite-based breeding studies to select for increased levels of these pathogen-fighting metabolites. Indeed, the genes and metabolic pathways in wheat are already known for GABA, spermine, lactic acid, and putrescine production [In a related work, Cuperlovic-Culf et al.  conducteoduction .TaTIR1 gene. This result and the multi-omics data produced by this study provide a much more detailed mechanistic understanding of the wheat response to F. graminearum. Kim et al. [Rp1-D gene against P. sorghi The results showed that these defensive genes involved in secondary metabolism including terpenoid, flavonoids and phenylpropanoid pathways. Furthermore, many transcriptional factors such as MYB100, BZIP84, and WRKY53 were detected as targeted metabolites for specific stress response.While many resistance genes against wheat FHB have been identified, we still lack a good understanding about the stress-regulation mechanisms associated with these genes. Recently, Su and colleagues  employedm et al.  performeHvMPK3) and chitin elicitor receptor kinase (HvCERK1). Furthermore, knockouts of HvCERK1 gene in the tolerant barley genotype showed how important its role was in the FHB resistance mechanism and in the biosynthesis of disease-resistant metabolites.Karre et al.  performeTilletia indica). KB is a floret-infecting disease that damages wheat kernels and causes them to emit a pungent fishy odor arising from the fungal teliospores. This obviously reduces grain quality. In a multi-omics study, MALDI-TOF/TOF mass spectrometry was employed to perform a proteomic analysis of highly virulent and weakly virulent isolates of T. indica. This led to the detection of 21 proteins that were differentially expressed in the highly virulent isolates. Protein and DNA sequencing data from the identified T. indica proteins led to the determination that certain candidate pathogenicity proteins were expected to produce oxalic acid. This illustrates how integrated tools that combine proteomics with metabolomics offer an excellent platform to detect virulency-associated compounds in plant pathogens [Kernal bunt (KB) is another economically important disease of wheat caused by smut fungus  in the tolerant genotype. These findings highlight the benefits of integrating metabolomics and transcriptomics in next-generation breeding programs to create climate-resilient varieties [The hyper-production of primary metabolites including specific amino acids such as branched-chain amino acids (BCAAs) also appear to provide a defense mechanism against severe environmental stresses. For example, in a study, integrated metabolomics and transcriptomics were used to study the BCAT genes and their corresponding metabolites in two durum wheat genotypes. Molecular characterization and expression analysis of the BCAT genes revealed the crucial role of the arieties .The malting quality of barley can be degraded by post-anthesis drought stress. This can significantly enhance the β-amylase and grain protein and decrease β-glucan and grain weight. To decipher the molecular mechanisms associated with malting and drought stress, Hong et al.  employedSalinity is another major abiotic stress that negatively influences crop growth and crop yield. Xu and co-workers  exploredIn another study conducted by Pan and colleagues , the salA number of other examples of metabolomics studies have also been published exploring metabolic and transcriptomic changes arising from heat tolerance , cold toAs highlighted here, there have been a large number of studies using integrated metabolomics to understand responses to biotic/abiotic stress and to help with metabolomics-assisted selection and breeding for crop improvement. The integration of metabolomics with other omics approaches will, no doubt, significantly enhance the efficiency and accuracy of future breeding programs, particularly for the screening of novel metabolic traits associated with biotic and abiotic stress resistance.Genetic engineering of plants for improved yields, stability, and herbicide resistance dates from the early 1990s. Some of the first genetically modified (GM) crops include the FLAVR SAVR tomatoes, introduced in 1992 , and RouIn contrast to transgenic plant engineering, plant metabolic engineering or metabolic editing is a promising strategy that does not require the introduction of foreign or non-plant genes into the host plant genome. Instead, metabolic engineering or metabolic editing is a method that accelerates the selective breeding process by modifying existing genes in the host plant’s specific metabolic pathways. These changes simply direct the biochemical reactions to produce more of the desired products . The purMetabolic editing has many significant applications in crop improvement such as increased production of secondary metabolites to confer abiotic stress tolerance in crop plants . Recent The advent of gene editing has led to the view that metabolically edited plants can and should be considered as non-GMO . This is4′OMT2, which regulates benzylisoquinoline alkaloid (BIA) metabolism, was efficiently disrupted by CRISPR/Cas9 cassette mutagenesis to reduce the production of thebaine and morphine [Generally, more than one gene is involved in a metabolic pathway, and therefore, editing only one gene at a time would not be sufficient for a useful metabolic editing result. Multi-gene or multiplexed gene editing via CRISPR/Cas9 appears to be the most promising metabolic editing system, as it allows multiple genes to be removed or inserted as shown in morphine .LCY-E, LCY-B1, LCY-B2, Blc, and SGR1 were selected for metabolic editing in the carotenoid biosynthesis pathway via multiplexed CRISPR/Cas9. The resulting metabolically edited tomato plant demonstrated a five-fold increase in the accumulation of lycopene. Similarly, in a study described by Li et al. [SSADH, CAT9, TP1, TP2, and TP3 which are involved in the GABA shunt metabolic pathway. This multiplexed metabolic editing led to a 19-fold enhancement in the biosynthesis of GABA in the engineered tomato plants. GABA is produced in large amounts during fruit development in tomatoes and is considered a health-promoting functional compound. It is also involved in various regulatory pathways, and GABA homeostasis directly influences the growth and development of many plants [Metabolic editing has not only been applied to alter the production of alkaloids, but it has also been used to change the levels of other secondary metabolites, such as lycopene. Lycopene is considered as a crucial product of the carotenoid biosynthesis pathway and is known to improve the quality and color of ripe tomato fruit. In a study described by Li et al. , five toi et al. , the pYLy plants .GmFNSII-1, GmF3H1, and GmF3H2 involved in isoflavonoid synthesis. Metabolic profiling of the metabolically edited plants revealed an enhanced production isoflavone content. Additionally, the higher levels of these isoflavones appeared to reduce the integrity of the protein coat of the soybean mosaic virus, which led to an increased resistance of the engineered soybean plant to the soybean mosaic virus.Leguminous plants, such as soybeans, have a diverse variety of isoflavonoid metabolites, which are known to have a positive effect on human health. They also play a significant role in regulating plant-environment or plant-pathogen interactions. A recent study by Zhang et al.  showed hMetabolic processes in plants can be regulated at several points including transcriptional and post-transcriptional (exon splicing) check points, as well as via translational, post-translational modifications, or protein-protein interactions. To date, most plant-based gene editing or metabolic editing efforts have focused on transcriptional modifications. However, metabolic editing may soon begin to focus on translational changes or protein engineering edits (such as amino acid changes to reduce or enhance protein stability or activity) and post-translational modifications to change protein–protein interactions . The useAs noted earlier, GM or transgenic crops are currently subject to complex regulatory rules and severe market controls due to the lack of social acceptance and widespread public health concerns . OutsideOne of the best routes to assess the safety, phenotype, and nutritional value of transgenic crops is through taking a snapshot of their metabolomes, because metabolomics and proteomics-based analysis are more closely related to endpoint phenotypes as compared to genomics and transcriptomics. Indeed, all the manipulations performed at the genomic level for plant GMOs should ultimately be “readable” or detectable at their metabolic and nutritional level . As a reAspergillus niger phyA2). The phyA2 gene encodes phytase and can catalyze the hydrolysis of phytic acid to release inorganic phosphorus. The increased phosphorus and decreased phytic acid in the transgenic maize could eliminate the need for phosphorus supplementation when maize is used as animal feed. This metabolomic study demonstrated that the concentration of nine metabolites were altered in transgenic maize compared to conventional (non-transgenic) maize lines. The altered metabolites included tyrosine, glucosaminate, myo-inositol hexakisphosphate (IP6), raffinose, citrate, phosphate, indole-3-acetyl-aspartate, 2-hydroxyglutarate, and gamma-glutamyltyrosine. Only the concentration of IP6 was downregulated while the concentration of all other eight metabolites were found upregulated in transgenic maize. All nine of these metabolites are commonly found in all plant crops and their concentrations in maize fell well within the ranges found for other crop plants or other food products commonly consumed around the world. In other words, this study confirmed that the PhyA transgenic modification introduced no safety risk to these GMOs [Metabolomics tools have also been used to distinguish between GMO and non-GMO maize and to probe the possible risks due to changed metabolic concentrations arising from the transgenic modifications. In a study conducted by Bernillon et al. , two traese GMOs .In 2013, Clarke and coworkers demonstrated a safety assessment analysis for transgenic soybeans (with engineered resistance to the herbicide Mesotrione) via a non-targeted metabolomics approach using UHPLC/MS/MS and GC/MS methods. This approach allowed the detection and quantification of allantoin, delta-tocopherol, myo-inositol-hexakisphosphate, ectoine, citrulline, asparagine, ribitol, phytate galactinol, gulano-1,4-lactone, genistin, and glycitin. Their results indicated that the metabolite composition of transgenic soybeans was not statistically different from that seen within the natural variation of non GMO soybean varieties . MetabolThe risk assessment of metabolically edited or gene-edited crops is likely to become a new area where metabolomics can play a role as elaborated in Speed breeding is an emerging technology in which the generation cycle of a crop is reduced to allow the more rapid introduction of new traits into plants. This innovation was inspired by NASA, which was attempting to grow wheat plants in space and by scientists from the University of Queensland who proposed the term “speed breeding” . Speed bTypically, a 22 h light period and 2 h dark period is applied under controlled temperatures to enhance the plants’ photosynthetic activity. Under normal conditions, only 1–2 generations of a given crop can be produced per year, but with speed breeding, up to six generations of a given crops can be produced in a single year . Speed bSpeed breeding can potentially be integrated with metabolomics approaches to fast-track crop improvement as illustrated in As yet, there is no published example of metabolomics-assisted speed breeding, however we believe it is only a matter of time before examples start appearing in the literature. We expect that metabolomics-assisted speed breeding will help in the rapid phenotyping of plants, in accelerating the detection of new metabolic pathways, in rapid gene identification, and the mapping of new genes controlling different stress-responsive mechanisms in crop plants.Metabolomics-assisted speed breeding will also provide an excellent platform to learn about the stress memory mechanism in plants and the effects of epigenetic modifications in crop plants at a metabolite level. We believe that metabolomics-assisted speed breeding will open many new avenues for crop improvement.Climate change, reduced land quality, shrinking arable land area, and population growth are all major threats to global food security. While many of these issues require social or political solutions, there are a number of scientific options that may help address some of these challenges. As we have attempted to show in this review, a convergence of innovations in genomics, bioinformatics, and metabolomics have created a powerful version of metabolomics. Metabolomics combines advances in metabolomics technologies, genomics technologies, and bioinformatics methods to allow a much more detailed exploration of gene–metabolite–phenotype relationships. As we have shown, metabolomics has emerged as an excellent tool for crop improvement and plant biology research. In particular, metabolomics is allowing the deciphering of novel metabolic pathways, the detection and functional annotation of new metabolic genes or gene networks, the deconvolution of different stress-responsive metabolic pathways, the detection of novel genes responsible for stress tolerance, and the elucidation of the relationship between genes, metabolites, and phenotypes in a wide variety of crop plants. With advances in gene editing and metabolite editing, along with advances in speed breeding and improved GMO risk assessment, the discoveries made by metabolomics could soon lead to healthier, safer, more nutritious, more resilient, faster growing crops. Metabolomics will also play a crucial role in the characterization of gene-edited and speed-bred crops. This integration of metabolomics into the regulatory process will also prove to be very beneficial for GMO risk-assessment and commercialization of gene edited-crops. The metabolite-based safety-assessment of gene-edited crops will improve the current methods of risk-assessment of biotech crops and address public concerns related to their social acceptance.


Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana. For the metabolic models of E. coli and S. cerevisiae we showed that: (i) the identified relative flux trade-offs depend on the carbon source used and that (ii) reactions that participated in relative trade-offs in both species were implicated in cofactor biosynthesis. In contrast to the two microorganisms, the relative flux trade-offs for the metabolic model of A. thaliana did not depend on the available nitrogen sources, reflecting the differences in the underlying metabolic network as well as the considered environments. Lastly, the established connection between relative flux trade-offs allowed us to identify overexpression targets that can be used to optimize fitness-related traits. Altogether, our computational approach and findings demonstrate how relative flux trade-offs can shape optimization of metabolic tasks, important in biotechnological applications.Trade-offs between traits are present across different levels of biological systems and ultimately reflect constraints imposed by physicochemical laws and the structure of underlying biochemical networks. Yet, mechanistic explanation of how trade-offs between molecular traits arise and how they relate to optimization of fitness-related traits remains elusive. Here, we introduce the concept of relative flux trade-offs and propose a constraint-based approach, termed FluTOr, to identify metabolic reactions whose fluxes are in relative trade-off with respect to an optimized fitness-related cellular task, like growth. We then employed FluTOr to identify relative flux trade-offs in the genome-scale metabolic networks of  However, due to the formulation of FluTO, these results: (i) do not hold in the scenario when the weighted sum of traits is environment-dependent, since the weighted sum equals an invariant flux, and (ii) do not provide insights on how the identified absolute flux trade-offs are related to the optimized fitness-related trait, like growth, since the trade-off was not determined at fixed flux through the biomass reaction.To overcome the challenge of determining traits in trade-off without relying on covariation, we have recently shown that systematic identification of absolute trade-offs can be achieved by determining a weighted sum of metabolic traits (i.e. fluxes), with non-negative coefficients, that is invariant with the environment. This can be seen as an extension of the Y-model to Here, we formulate a constraint-based approach, termed FluTOr, that allowed us to identify and enumerate relative flux trade-offs with respect to growth simulated by a given metabolic network. More specifically, we are looking for relations of the form Further, we note that if a relative flux trade-off expresses growth as a weighted sum of reaction fluxes, as stated above, then overexpression of the reactions in trade-off provides a direct way to further increase growth. In other words, if Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana to determine relative flux trade-offs with respect to growth and investigate the dependence of the findings on the simulated conditions. As a result, we show that FluTOr provides versatile means to enumerate trade-offs and use them in the identification of overexpression targets to optimize growth. Due to the constraint-based modelling formulation, FluTOr can be readily applied to study and compare trade-offs with respect to other cellular tasks, optimized by evolution, that can be modelled in the context of metabolic networks.By using FluTOr we then examine the extent to which the identified relative flux trade-offs differ from other seminal concepts in metabolic modeling, namely directional and partial coupling. In addition, we apply FluTOr to large-scale metabolic networks of 22.1E. coli, the unicellular eukaryote S. cerevisiae and the model plant A. thaliana. More specifically, we analysed the genome-scale metabolic model iJO1366 of E. coli str. K-12 substrain MG1655. This network consists of 1805 metabolites and 2583 reactions S. cerevisiae with 2691 metabolites and 3963 reactions A. thaliana, with 407 metabolites and 549 reactions A. thaliana we used one of the three biomass reactions, as detailed in the results.We apply FluTOr to constraint-based metabolic models of three model organisms, namely the bacterium In all models, we consider different environments, as described in the Results section, by activating a particular carbon and/or nitrogen nutrient. We also considered different lower boundary to modelled growth of 90%, 95%, and 99% of that obtained by flux balance analysis (FBA) 2.2Two unblocked reactions 2.3Fisher’s exact test is a statistical significant test used to determine if there are nonrandom association between categorical variables. Here, proteins are first divided into two categories based on whether they catalyze only one reaction or more than one reactions. Further, proteins are divided into three categories based on their involvement in trade-offs, namely: proteins for which all reactions are in trade-off, all reactions are not in trade-off, and some reactions are in trade-off. Then the p-value is calculated based on the resulting 2x3 contingency table. More specifically, 2.4https://github.com/seirana/FluTOr.FluTOr is implemented in MATLAB and is fully available at 33.1Here, we aim to identify relative trade-offs between reaction fluxes with respect to growth. More specifically, given a metabolic network with ee steps .Fig. 1IlFirst, we rely on flux variability analysis (FVA) eactions a. Upon pSecond, we rely on simplifying the metabolic network by identifying and merging fully coupled reactions based on flux coupling analysis (FCA) o rB see b.As a third step, provided a metabolic network, we formulate a constraint-based approach, FluTOr, to identify relative flux trade-offs with respect to the flux through a specified reaction s.t. Supplementary Note). In the rest of the manuscript, we present the findings from our analyses of relative flux trade-offs with Since we aim to enumerate all trade-offs, the formulation above can be readily modified to exclude all previously found solutions ; this is achieved by using integer cuts 3.2E. coli and S. cerevisiae. Since growth of these microorganisms depends on the carbon source provided, we wanted to examine the extent to which relative flux trade-offs may differ between these environments. In the case of E. coli we identified and enumerated the relative trade-offs with respect to growth under 21 carbon sources with ammonium as the sole nitrogen source. For each carbon source we considered three scenarios, corresponding to lower bounds to growth set to 90%, 95%, and 99% of the respective optimum, determined by flux balance analysis (FBA) Next, we applied FluTOr to investigate relative flux trade-offs with respect to growth simulated from curated and extensively used large-scale models of E. coli includes 2583 reactions, of which sixteen  appeared in all relative trade-offs. This is in line with the essential role of cofactors for cell growth. In addition, almost two thirds (i.e. 1659) of reactions did not appear in any relative trade-offs, while the rest (31%) were blocked  of the subsystems were never in relative trade-offs .Looking at the proteins associated with the reactions in trade-offs, we categorized them into those that catalyzed only reactions in trade-offs and those that, in addition, catalyze some reactions that did not participate in any trade-offs. The number of proteins that were associated with reactions that did not appear in relative trade-offs decreased from 73% to 65% by increasing the lower bound imposed on growth from 90% to 99%. There were few proteins that were associated simultaneously with reactions that take part in some or were not involved in any trade-offs, and these proteins were dependent on the available carbon source. For instance, with D-manose as a carbon source, nine proteins catalyze only reactions that appeared in trade-offs and two proteins that, in addition, catalyze reactions that did not participate in any trade-offs. On the other hand, with L-arabinose as a carbon source, 12 proteins catalyze only reactions that were in trade-offs, while 61 (84%) were associated to some reactions that did not participate in any trade-offs . To deteS. cerevisiae with 3963 reactions. We found that in S. cerevisiae, the identified trade-offs for the thirteen different carbon sources resulted in, on average, 47 (1.7% of non-blocked) reactions in relative trade-offs. Further, the minimum and maximum of reactions in relative trade-offs over the thirteen carbon sources were 11 and 89, respectively. Almost 30% of reactions were blocked for all carbon sources. Interestingly, we found that there was no reaction in trade-off for all simulated carbon sources, but 3% (117 of 3963) of reactions were in trade-off in at least one carbon source  of subsystems . Interestabolism . HoweverE. coli, we found that reactions in trade-offs tend to be catalyzed by promiscuous enzymes  malate dehydrogenase (E.C. 1.1.1.37) which, for lower bounds of 95% or 99% of the optimum growth, was found to be associated to some reactions in relative trade-off; however, it is not involved in any trade-offs at the lower bound of 90%, and (ii) L-lysine:tRNALys ligase (AMP-forming) (E.C. 6.1.1.6), that is associated to reactions in trade-off only at a lower bound of 90% of the optimal growth . These f enzymes .3.3A. thaliana can use different nitrogen sources (e.g. nitrate or ammonium) during autotrophic growth on the single carbon source CO2, and that the reference genotype Col-0 prefers nitrate over ammonium. To determine if the relative flux trade-offs depend on the different nitrogen sources, we employed the AraCore model, of A. thaliana metabolism, with three biomass reactions corresponding to optimal nitrogen (light-limiting), limiting nitrogen, and limiting carbon growth conditions A. thaliana model.Next, we asked if the change in relative flux trade-offs with the environment can also be identified in metabolic models of other eukaryotes, like plants. It is known that the model plant Intriguingly, by applying FluTOr, we found that the set of 42 reactions participating in relative flux trade-offs was invariant with the change in the lower bound on growth between 90% and 99% of the optimum, irrespective of the biomass reaction used and of the nitrogen source used . AltogetA. thaliana metabolic model were always and sometimes in trade-offs  of the 225 proteins appearing in gene-protein-reaction rules were associated with reactions that were in trade-offs. In addition, only two proteins, rade-off . Altoget3.4The proposed concept of relative flux trade-offs appears to be related to the concepts of directional and partial reaction couplings. Therefore, in the following we aimed to determine the extent to which relative trade-offs provide new insights in comparison to directionally and partially coupled reactions. From the formulation of FluTOr, it is clear that reactions in relative trade-off with respect to growth, must satisfy that To compare relative trade-offs with findings from QFCA, we relied on the notion of a directionally coupled equation (DCE). To illustrate a DCE, suppose there is a metabolite ectively a. If alldy-state b . By su+α6v6=v5 c, leadinE. coli only 17% and 10% of directionally or partially coupled reactions to biomass were in the identified relative trade-offs, as determined by QFCA and F2C2, respectively. Further, the reactions in relative trade-offs represented only 15% and 13% of partially or directionally coupled reactions. For the model of S. cerevisiae, on average only 0.18% and 1% of partially/directionally coupled reactions, determined by QFCA and F2C2, respectively, took part in relative trade-offs. As a result, FluTOr resulted in more partially or directionally coupled reactions in comparison to QFCA and F2C2 with the same imposed constraints. Further, among the reactions that were in relative trade-off, on average 1.6% and 77% were partially or directionally coupled detected per QFCA and F2C2 method  the means to specify overexpression targets aimed at optimization of the objective. As a result, the identified relative trade-offs provide insights in the relation between constraints that shape trade-offs and optimization of cellular tasks. For the case of By applying the proposed approach to three large-scale metabolic models in simulations of different environments, we demonstrated that relative trade-offs with respect to growth (modeled as the flux through the biomass reaction) are often condition-dependent, having important implications for optimization of fitness. Our findings corroborate that the coordinated activation of central metabolic processes underlie the relative flux trade-offs that we also found are species-specific. Future applications of FluTOr will examine the extent of interlinking between relative trade-offs due to optimization of multiple cellular tasks as well as connections to resource allocation in the context of protein-constrained metabolic models.Seirana Hashemi: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization. Zahra Razaghi-Moghadam: Data curation, Investigation, Methodology, Validation, Writing – review & editing. Roosa A.E. Laitinen: Funding acquisition, Supervision, Writing – review & editing. Zoran Nikoloski: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


In order to elucidate the key metabolic features of the synthesis pathway of Thr in E. coli to provide clues for metabolic regulation or engineering of the strain, this study was carried out on an l-threonine over-producing strain, in terms of analyses of metabolic flux, enzyme control and metabonomics. Since environmental disturbance and genetic modification are considered to be two important methods of metabolic analysis, addition of phosphate in the media and comparison of strains with different genotypes were selected as the two candidates due to their significant influence in the biosynthesis of Thr. Some important targets including key nodes, enzymes and biomarkers were identified, which may provide target sites for rational design through engineering the Thrproducing strain. Finally, metabolic regulation aimed at one biomarker identified in this study was set as an example, which confirms that combined metabolic analyses may guide to improve the production of threonine in E. coli.Currently, industrial production of  This meIn this study, phosphate was observed as a significant factor affecting the biosynthesis of Thr , thus waE. coli TWF001 was used for the metabolic analyses for the biosynthesis pathway of Thr and W3110 was used as a control  is the rate of accumulation of intermediate metabolite i (mmol·(Lh)−1); rj(t) is the reaction rate of the reaction step j for the synthesis of i (mmol·(Lh)−1]; rk(t) is the reaction rate of the reaction step k for the synthesis of i (mmol·(Lh)−1); ∂ij is the stoichiometry coefficient of the reaction step j; ∂ik is the stoichiometry coefficient of the reaction step k. The rate equations at the metabolite nodes in the metabolic network were shown in l-tyrosine (Tyr), l-valine  and l-methionine (Met) were measured, which were used as known parameters and substituted into the above set of metabolic rate equations to obtain the metabolic flow distribution using lingo software.In this rate equation, rWhen the cell growth entered stationary phase (under pseudo-steady state), samples were collected and centrifuged at 10,000 rpm for 5 min in a refrigerated centrifuge at 4°C. 0.5 g wet weight of the cells was washed by 10 ml of sterile 0.01 M PBS phosphate buffer for twice. Then 10 ml enzyme extraction solution were added for sonication and fragmentation treatment was under the following conditions, sonication power (PW) of 80%, working time of 2 s, intervals of 1 s, repeated 30 times. The samples were then centrifuged at 4°C for 5 min at 8,000 rpm to remove the precipitates and the enzyme solution was kept at a low temperature before determination of enzyme activities. The activities of pyruvate kinase (PK), malate dehydrogenase (MDH), fructose 1,6-diphosphate aldolase (FBA), glucose-6-phosphate dehydrogenase (G6PD), phosphoenolpyruvate carboxylase (PEPC) and hexokinase (HK) were determined by kits purchased from Solarbio Biotechnology Ltd. Protein concentrations were measured using the Coomassie Brilliant Blue G250 staining method at 595 nm and protein content was detected according to the increase in peak absorption.λ > 1, the principal components cumulatively reflecting more than 85% of the original information. Using SPSS25.0 software, the equation Fj = a1jX1+a2jX2......+a10jX10  was obtained, where the coefficients of the expression of the principal component reflect the combined influence of each enzyme on the principal , and the mixes were shaken well and blown dry in a nitrogen blower. Then derivatization of metabolites was conducted by reactions of oximation and silylation. For the former, the samples were mixed with 350 µL of pyridinium methoxide hydrochloride solution (20 mg mL−1) for 2 h at 30°C. Afterwards, 350 µL of BSTFA-TMCS  solution were added into the solution and the reaction was carried out at 70°C for 1 h. Then samples were blown dry with a nitrogen blower, and 1 ml of hexane/dichloroacetic acid was added to re-dissolve them. The samples were treated with a 0.22 µm filter membrane and detected on a gas chromatograph-mass spectrometer (GC-MS) machine  with a column , using a helium carrier gas at a flow rate of 1.2 ml minThe metabolites were qualitatively analyzed by searching the spectrum in the GC-MS database or comparison with standards, and the relative contents of metabolites were determined according to the peak area of the internal standard ribitol in the solution. The data obtained were collated into csv format and imported into SIMCA-P software for principal component analysis and discriminant analysis. Cluster analysis made by Origin software was used to resolve intra- and inter-group relationship.Samples were centrifuged at 10,000 rpm for 2 min to remove the precipitates and then treated with 10% trichloroacetic acid at equal volume for 4–8 h to remove proteins from the samples.−1 disodium hydrogen phosphate and sodium tetraborate, and the mobile phase B consisted of methanol: acetonitrile: water = 45:45:10 (v/v/v). The gradient elution program was (volume ratio of mobile phase B) 5% for 0–0.35 min, 5%–57% for 0.35–13.40 min, 57%–100% for 13.40–13.50 min, 100% for 13.50–15.70 min, 100–5% for 15.70–15.80 min and 5% for 15.80–18 min at 40°C with a flow rate of 1 ml min−1 and a detection wavelength of 338 nm.The determination of Thr and other amino acids was carried out by HPLC with a C18 column  . The mobGlucose concentration was determined using a biosensor .From the results of the PB experiments in shake flask, phosphate is the most significant factor affecting Thr synthesis . In orde−1 phosphate was added, the maximum biomass (OD600) and Thr production were 56 and 42.3 g L−1, respectively. With the increment of phosphate amounts up to 24.8 g L−1, the biomass (OD600) was improved to 76, while the change of Thr did not keep increasing  under ideal condition. With the increase of phosphate concentration, the flow rate on the branch of Thr synthesis decreases, causing the increase of by-products.Cells were cultured under fed-batch mode with different initial phosphate concentration and samples were collected at the pseudo-steady state period. Contents of glucose, Thr, L-Val, L-Tyr and L-Met were measured and the rates of metabolite consumption and accumulation were calculated as shown in −1–24.8 g L−1, the r8 flow [C/mmol·(L h)−1] to the HMP pathway decreased from 24.6 to 15, while the r2 flow [C/mmol·(L h)−1] increased from 75.4 to 85  and 44% is used for the synthesis of l-glutamate (r22) under the ideal condition. Thus, l-glutamate is a key intermediate to provide sufficient substrate for the transamination required for the synthesis of the aspartate group of amino acids, the precursor of Thr.At Glc6P nodes, when phosphate concentration changed from 9.8 g L.4 to 85 . Conside.4 to 85 , 74.1% areported , therebyFirstly, correlation analysis was performed for the six enzyme in Thr biosynthesis network and correlation coefficients were calculated for them . The corTo avoid the overlap of information between these enzymes, their activities of six enzymes selected in Thr metabolic network were subjected to principal component analysis . The cumTo illustrate the range of the respective original variables represented by each principal component, an analysis was performed using the principal component matrix, as shown in Each score in the score coefficient matrix only represents the correlation coefficient between the principal component and the corresponding variable, which cannot represent the control effect of each enzyme on Thr flux. Under these circumstances, the coefficient matrix corresponding to each indicator in the principal component can be obtained by dividing the data in With the detection of GC-MS, the metabolites were determined by qualitative and quantitative analyses . OrganicR2(X) = 0.868 > 0.5 indicates a good model fit, and Q2 = 0.5651, which is less different from R2(X), indicating the stability of the fitted equations. PCA is an unsupervised analysis method that cannot ignore the errors of each group and eliminate random errors, so it is necessary to adopt a supervised approach to identify differences between groups. Orthogonal partial least squares discriminant analysis (OPLS-DA) is a supervised analysis method that combines partial least squares and discriminant analysis. OPLS-DA classifies the sample variable matrix X as both correlated and uncorrelated with Y, and removes the irrelevant variation variables, which enables a comprehensive analysis of between- and within-group variance. From R2(X) and R2(Y) describe the explanatory rate of the model, and Q2 = 0.899 represents the predictive power of the model. These three indicators are close to 1, indicating a good reliability of this model  and the control strain (sample W) and is model . The samR2(Y) (interpretability of the Y variable) and Q2 (model predictability) are important parameters for model evaluation, and their regression lines are crossed with the horizontal coordinates or less than 0, which indicates that the model is relatively accurate. The R2(Y) value (0.94) in the OPLS-DA model is approximately equal to 1, indicating that the model is relatively reliable and is able to reflect the real situation of the sample data. The intercept of Q2 on the Y-axis is negative, and the R2 = 0.218 and Q2 = -0.483 obtained from the replacement test are smaller than the initial values of R2 and Q2 in the OPLS-DA model, which suggests that the model is not over-fitted.An OPLS-DA model with good predictability and fit has been developed, while a 200-response permutation test model was also necessary to be constructed to prevent the model from over-fitting . Here RThe data set was scaled by the heatmap package in Origin 2018 and a cluster analysis was made based on the differential metabolites and their contents to further reflect the metabolic differences between the two strains. l-alanine, l-glutamic acid, l-aspartic acid and fatty acid compounds were significantly lower in TWF001 when compared with the control strain. TWF001 produced higher content of Thr with less carbon metabolic flow to other by-product amino acids. However, l-glutamate plays an important role in the pathway of l-aspartate, the precursor of Thr synthesis, thus up-regulation of the relative genes of glutamate synthesis has several sets of effects on Thr synthesis , and thereby reducing l-isoleucine and l-leucine concentrations to prevent Thr degradation. In addition, the higher C flow to by-product amino acids and fatty acid branched pathways in W3110 eventually led to lower Thr level in that strain.ynthesis . As a by, so Lee et al. wel-glutamic acid and inositol are the furthest from the origin, and the levels of these metabolites are proportional to the synthesis of Thr within a certain range. Therefore, these metabolites can be good biomarkers to be added in the medium to regulate the synthesis of Thr.When the intracellular environment is disturbed by the external factors, the metabolic change will occur to adapt to this disturbance and a new metabolic equilibrium is achieved. In PCA analysis, the load diagram represents the magnitude of the contribution made to differentiate between experimental and control groups, with the further away from the origin simply indicating that the metabolite is making a more significant contribution and can be regarded as a biomarker. As shown in l-glutamic acid as an example to verify the reliability of the biosynthesis pathway analyses. l-glutamic acid was added into medium and fed-batch fermentation was conducted to evaluate the performance. As can be seen from 600 value of was not improved, but the Thr production was promoted up to 77 g L−1, 10% higher than that without addition of l-glutamic acid. This fact indicates that l-glutamic acid may function by migrating the metabolic flow to Thr rather than enhancing biomass.From above analyses, some key nods, enzymes and biomarkers have been identified as important targets for the production of Thr. Here, we selected addition of Some metabolic regulators, such as glycine, sodium nitrate and sulphate, are activators or inhibitors of key enzymes in the pathway. However, addition of them is often not sufficient to achieve the desired effect. In addition to environmental disturbance, some key enzymes need to be engineered to change the flow of metabolic flux in order to achieve the most reasonable flux distribution for Thr synthesis.


Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present lipid network explorer (LINEX Lipid network explorer (LINEX Lipids play a fundamental role in cells across all domains of life. They are not only crucial for the long-term storage of energy but can also influence the activity and occurrence of membrane proteins , as wellNowadays, an increasing part of the lipidome can be identified and quantified using mass spectrometry (MS). Lipidomics, is becoming more relevant for clinical applications , potentiSuch interactions can be studied through biological networks. On the metabolic level, these networks describe reactions between metabolites that are catalyzed by enzymes. When considering lipid metabolic networks an additional constraint is the inherent complexity of the lipidome and its chemical reactions. One lipid enzyme usually catalyzes a reaction for a group of lipids that, e.g. belong to one lipid class but differ in their fatty acyl composition. This is reffered to as multispecifity . The comMetabolic networks are commonly studied with dynamic modeling or constraint based modeling. These techniques allow predictions of the system dynamics, for example the distribution of energy resources. Parameterization of such models requires large amounts of data covering the entire molecular state . Especiade-novo network enrichment, pathway enrichment identifies significantly altered categorized pathways. For metabolites, this can be performed with the KEGG [Another way to analyze biological networks is through network enrichment. By comparing two experimental conditions, the goal is to find highly connected molecular subnetworks that are enriched with significant features. The rationale behind this approach is to propose a mechanistic hypothesis for observed dysregulations. Many algorithms have been developed over the years , mainly the KEGG  or Reactthe KEGG . A recenthe KEGG . So far,Here we present LINEXlication , 35 and Homo sapiens) were parsed from Reactome.We obtained lipid-related reactions from the Rhea  and Reacet al. [After parsing, all lipids and reactions were manually curated. Lipids were annotated and assigned to classes according to an updated version of lipid nomenclature from Pauling et al.  with 107Curated class reactions from databases are used to infer lipid species networks. To properly evaluate the reactions, molecular lipid species are required. This means that for each lipid the attached fatty acid must be available. Therefore, all lipid species, which are only available as sum species, are converted into a set of possible molecular species. As an example, a PC(40:2) has to be converted into possible molecular species such as PC(20:0_20:2) or PC(22:2_18:0). For this, possible common (class-specific) fatty acids can be added by the user. Only if at least one molecular species can be generated that has the same sum formula as the original sum species, it is considered for the network extension.Extension of lipid class metabolic networks to lipid species networks can be divided into two steps: extension of the class metabolism and fatty acid metabolism. A detailed explanation can be found in the Supplementary Methods.P-value estimation. All steps are described below.We developed a novel network enrichment algorithm for lipid networks. The methodology involves 1) building a reaction network from a LINEXTo convert the lipid network to a reaction network, we generate a unique reaction identifier for each reaction (edge) in the network extension. This is especially important for reactions with more than one substrate and product, with multiple edges corresponding to one lipid species reaction. In the next step, all lipid species reactions are converted to a new network representation with reactions as nodes. Edges between two reaction nodes are drawn, if the reaction belongs to the same lipid class reaction or at least one lipid species can be found in both reactions.Substrate-product change calculation Substrate-product changes are calculated using the lipidomics data matrix Similarly, the relative substrate–product difference Within the calculation, the mean or root is used to correct for bias towards reactions with multiple products or substrates. The final score for each reaction node in the network is then calculated as follows: As previously explained, reactions of the fatty acid metabolism or ether lipid conversions are heuristic, to improve network connectivity. They do not occur directly on the lipid level. For that reason, they are also considered in the network enrichment but penalized (default = –1) to favor the selection of the non-heuristic reactions.Local search optimization investigates the search space by applying local changes to candidate solutions, such that the objective function value is increasing. The changes are applied until no more local improvements can be made. To avoid stagnation in a local maximum, the simulated annealing procedure  allows nWe employ local search on the reaction network P-value. For that, we sample reactions in the range of the minimum and maximum subnetwork size. These reactions are not connected, as in the subnetwork of the enrichment. This creates a distribution of scores. The distribution is then used to estimate a P-value for the solution found by the enrichment. The number of samples can be decided by the user, with more samples giving a better estimate of the distribution at increased runtime. The rationale behind sampling unconnected solutions is to estimate how much the connected (mechanistic) subnetwork scores compared to unconnected (non-mechanistic) solutions.The network enrichment algorithm results in a subnetwork with a score for each run. To indicate if this subnetwork/score provides a significant insight compared to an equally sized random set of reactions, we compute an empirical Visualizations of reaction ratios were performed for each lipid class reaction individually. Reaction ratios per sample are computed in the same as for the substrate–product change calculation, without averaging over all individuals: All ratios per experimental condition are compiled into a list and the density for each considered experimental condition is plotted.et al. [The (combined) abundance of lipid features was implemented inspired by the glycan substructure method by Bao et al. . We usedet al. [et al. [et al. [The lipidomics data for MOBAT7 WT and knockout mice were taken from Thangapandi et al. . The dat [et al. . It cont [et al. . Lipid shttps://doi.org/10.6084/m9.figshare.20508870.For all data sets analyzed with LINEXThe workflow of a lipidomics experiment can be divided into five steps: sampling, sample preparation, data acquisition, data processing and data interpretation . LINEX\dics data . The LINly shown , and facH. sapiens (HSA), R. norvegicus (RNO) and M. musculus (MMU)  . After dus (MMU) , followehttps://exbio.wzw.tum.de/linex2). This way LINEXTo keep the freely available LINEXFor interpreting quantitative changes in molecular networks, network enrichment can be a powerful approach. In the context of metabolic or lipid networks, such methods can reveal underlying changes in enzymatic activity. In PPI networks, changes in protein abundances correspond directly to functional changes of the nodes, representing proteins, in the network. However, when analyzing (lipid-)metabolic networks enzymatic changes can only be approximated from changes in metabolite abundances between experimental conditions. In lipid-metabolic networks, an additional challenge comes from the multispecificity of involved enzymes. In LINEXOur method is designed to explicitly take multispecificity into account. Therefore, a hypernetwork, establishing connections not only between lipids but also reactions, is required. Based on this representation, the enrichment algorithm can connect solutions from the same class reaction, promoting solutions explainable by a few metabolic reactions. eactions . The reaet al. [et al. [As a proof of principle for the enrichment, we selected data from Thangapandi et al. . In this-6), AA) . The dat [et al.  are wellhttps://doi.org/10.6084/m9.figshare.20508870). It consists only of PI, LPI species and one class reaction. This class reaction represents the transformation between LPI and PI. LINEXy for AA . This pry for AA  elucidatP-value estimation procedure . The MBOAT7 enrichment result . The details of the implementation can be found in the Supplementary Materials.The LINEXanalysis , and moiWe present a method to generate and analyze lipid-metabolic networks. Using curated lipid class reactions from common metabolic databases our method computes data-specific lipid networks. Furthermore, we developed a network enrichment algorithm, to propose hypotheses for enzymatic dysregulation from lipidomics data. As a proof of principle, we applied the approach to liver lipidomics data, where the deficient MBOAT7 enzyme was successfully identified from the data.The challenge in generating mechanistic hypothesis from metabolomics or lipidomics data lies in the fact that dysregulation on the enzymatic level is not measured directly. Instead it can only be inferred based on changes in the metabolome, unless full-scale proteomics experiments are run in addition. For lipid networks, only one tool, BioPAN, is available so far . In conto BioPAN , we compo BioPAN  on the so BioPAN  performeet al. [A limitation of our enrichment algorithm is that it computes substrate–product ratios independent from each other. In reality, however, reactions are linked through shared substrates or products and metabolic changes are propagated through the network. These effects can be due to, e.g. metabolic self-regulation  and struet al. , better With the ability to connect enzymatic activity to lipidomics data, LINEXisorders , 16, 49.With our LINEXData-specific lipid networks are computed based on reactions from the Rhea and Reactome databases.A novel enrichment method identifies enzymatic dysregulation in custom lipidomics datasets.Moiety analysis elucidates relevant lipid structural features contributing to dysregulation.We apply the approach on clinically relevant lipidomics data to generate mechanistic hypotheses adipocyte expansion.https://exbio.wzw.tum.de/linex2.LINEXhttps://gitlab.lrz.de/lipitum-projects/linexLINEX is free software. Source code: GitLab (aGPLv3 License): https://gitlab.lrz.de/lipitum-projects/LINEX2-paper-codeFigure reproducibility: https://gitlab.lrz.de/lipitum-projects/LINEX2_package/-/tree/master/LINEX2/dataALEX123 lipid classes and curated database reactions: J.K.P. supervised the project and secured the funding. N.K., T.D.R. and J.K.P. planned and conceptualized the work. N.K. and T.D.R. developed the web service. N.K., O.E.L. and T.D.R. designed and implemented the network enrichment procedure. L.F., L.K. and T.D.R. parsed and curated the reaction databases, and implemented the network extension. N.K. and T.D.R. applied, validated and interpreted the approach on lipidomics data. N.K., O.E.L., T.D.R. and J.K.P. wrote the manuscript. All authors read, reviewed and accepted the manuscript in its final form.LINEX2_BiB_revision_Supplementary_bbac572Click here for additional data file.SupplementaryTableS1_bbac572Click here for additional data file.


Saccharomyces cerevisiae response to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, we used published repeated substrate perturbation regime experimental data, extended it with proteomic measurements and used both for modelling approaches. Multiple types of data were combined; including quantitative metabolome, 13C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How  Saccharomyces cerevisiae, also commonly known as baker’s yeast, has been used by mankind for thousands of years for the production of relevant beverages, foods and chemicals. However, despite its extensive use in industry [S. cerevisiae production processes to industrial scale poses several interesting and fundamental challenges. The source of most challenges is spatial inhomogeneities due to mixing limitations in large-scale bioreactors leading to gradients throughout the reactor. A cell dispersed in the reactor is therefore exposed to rapid changes in its extracellular environment, which in turn will impact intracellular metabolic regulation [industry ,2,3, scagulation ,5. SimilAlthough dynamic conditions are encountered for industrial applications as well as environmental habitats, many physiological studies on yeast are performed under (pseudo-) steady-state conditions. Clearly, with the vast available reference data, reliable measurements and reproducibility, steady-state (SS) experiments are very useful in the quantification of intracellular fluxes. However, for the identification of in vivo kinetic parameters, dynamic metabolic experiments are required . To bridWhile this SP approach is very useful for the identification of kinetic parameters of networks adapted to the pre-perturbation limited steady-state, it cannot describe adaptations that may occur upon the subsequent perturbations observed under industrial conditions . To emulS. cerevisiae cultures show different metabolic phenotypes compared to SP or SS cultures [Under such dynamic substrate conditions, cultures . After acultures ,17,18. Ecultures . In factcultures , both focultures ,22, and cultures . HoweverFor the identification of kinetic parameters and putative regulation mechanisms, quantitative data from different approaches will be required. Especially, to generate comprehensive models, in addition to intracellular concentrations, carbon tracing is required to identify bidirectional, cyclic or parallel reactions ,25. WorkS. cerevisiae using either in vitro and in vivo parameters [Understanding metabolism, especially under dynamic conditions, requires integration of stoichiometry and enzyme kinetics. Such kinetic metabolic models have been developed for rameters ,27, addirameters  or subporameters . Nonetherameters , leadingUnder dynamic conditions, further pathways have been described to play a regulatory role—glycogen and trehalose metabolism. For both, the reaction stoichiometry is known, and in vitro parameters have been broadly studied ,31,32,33Especially for cyclic pathways, such as the trehalose cycle, quantifying in vivo parameters can be challenging as both in- and outfluxes influence the concentration change and no in- or outflux is directly observable. This correlation, plus the fact that the networks are getting larger, leads to a danger posed by local minima and ill-conditioning and consequently sloppy parameter estimates . To over13C enrichment data, to evaluate which proteome changes are most relevant to explain the experimentally observed change in metabolic response.Here, we specifically studied the impact of proteome adaptation to substrate perturbations on the changed metabolic response. To this end, we expanded upon existing state-of-the-art kinetic models, combining both metabolome and fluxome, as well as Saccharomyces cerevisiae CEN PK 113-7D strain was grown at 30 °C, pH5, first in a batch phase and then in chemostat at dilution rate 0.1 h−1 [The haploid yeast The experimental datasets used in this work consisted of metabolite concentration measurements  and the Extracellularly, concentrations were measured for carbohydrates glucose and trehalose. Intracellularly, concentrations were measured for carbohydrates involved in glycolysis, the trehalose cycle, PPP, the glycerol branch and the TCA cycle and for adenosine nucleotides. Dynamic fluxes were estimated as piece-wise linear functions  using a 1.New reactions were added to represent a complete trehalose cycle and glycogen synthesis and degradation:The A-glucoside transporter (AGT1) mobilizes trehalose between the extracellular space and cytosol . Its reaA vacuolar transport of trehalose was added to mobilize trehalose between cytosol and vacuole-like compartments. Even though trehalose can be compartmentalized in vesicles in the cytosol, the kinetics of the process are not known. Here it was assumed that reversible MM kinetics determine this process, as with AGT1.Acid trehalase  degrades trehalose to glucose. It acts in more acid environments that the cytosol, such as the vacuole or the intracellular space , even thUDP-Glucose phosphorylase  carries out the reaction from G1P to UDP-glucose, which is later used as substrate for glycogen synthesis. This reaction was adapted from  and modeGlycogen synthesis was not modelled by enzymatic kinetics but interpolated from the experimental data in this study, with an added UDP-glucose saturation factor.Glycogen degradation was also interpolated from the experimental data in this study, with an added UDP-glucose saturation factor.2.−1, the fluxes observed were higher than the ones seen under the repeated substrate perturbation regime. As a result, the flux simulated in repeated substrate perturbation towards the TCA cycle via the sink of pyruvate was overestimated, resulting in a lesser flux towards the fermentative direction and more CO2 being produced than measured. A factor was added to the reaction accounting for the pyruvate sink to reduce its flux and fit the CO2 produced in the experiment.The sink reactions were optimized for chemostat growth  in the pA kinetic model of yeast central carbon metabolism was adapted in this work . The oriIn the following section, further details on model implementations are discussed.The model consists of a series of ordinary differential equation representing the mass balances for each metabolite in the model. The model contained three compartments: cytosol, vacuole and extracellular space. The metabolites that are part of glycolysis, trehalose and glycogen cycles, glycerol branch and cofactors metabolism were located in the cytosol. Trehalose could be compartmentalized in the vacuole or secreted to the extracellular space, and glucose was modelled extracellularly as well. Meanwhile, the amount of carbon structure inside the cytosol depended on the inflow of glucose and outflow of the system. Moiety conservations were used for cofactors as in . The sum−1). This unit refers to the intracellular volume. It could be correlated with the biomass dry weight via the biomass volume fraction (defined as 0.002 L per gram biomass dry weight). The kinetic rate expressions can be seen in detail in The reaction rate equations used in this model followed Michaelis–Menten kinetics in most cases, but there were exceptions: PFK kinetics are affected by multiple regulators and the alternation between tense and relaxed state , PYK and13C labelled. All the simulations were carried out with the abovementioned mechanistic model. Matlab version 9.3.0.713579, R2017b and the ode15s solver were used. A graphical view of how simulations were constructed can be found in The simulations were performed in three steps aimed at resembling the experimental process that cells underwent in the experiments in . The firAs described by , reactio13C-labelled fraction. In this secondary mass balance, the input and output reaction rates were the same as for the total metabolite concentration mass balance, but it was multiplied by the respective fraction of labelled metabolite. Whereas the metabolite total concentrations depend solely on the enzymatic rates, the metabolite labelled concentrations also depend on the fractions of other labelled metabolites  that the reactions use as substrate. Moreover, now the enzymatic fluxes need to be adjusted for reversible enzymatic reactions.For each carbon-based metabolite in the model, a mass balance was added to account for its respective 13C enrichment over the cycle time, obtained from [For mass balances equations of total metabolite concentrations, reversible enzymatic fluxes are defined with a positive value in one direction and negative in the reverse direction. However, to implement the mass balances equations of labelled metabolite concentrations, these are multiplied by metabolite’s labelled fractions, so they need to be always positive. Thus, enzymatic fluxes that change directions during the simulation are implemented as a forward and backwards flux. These are only used in the equations for labelled metabolite concentrations and are defined in the model with conditional statements. An example of the mass balance of labelled and unlabelled acetate can be seen below. Reaction reversibility is already accounted for inside the calculation of the reaction rate:ned from .The initial parameter values were obtained from the original model . These pSome reactions in the model underwent changes during the substrate perturbation cycles. For instance, the proportions of the isoenzymes HXK/GLK changed. To account for its effect on kinetic parameters, such as ues from  were useues from , was useSelection of enzymes: Multiple enzyme combinations were tested. These combinations contained the trehalose cycle and added different enzymes from glycolysis each time. The combination selected was the one that described experimental data properly while making physiological sense (such as including the changes in HXK/GLK) and having the smallest number of enzymes possible. Simultaneously, random combinations of enzymes were also tested to confirm results and give robustness to the method.Combination of weighting factors: It was not clear at first if it would be possible to describe all the experimental data simultaneously. For this purpose, each of the abovementioned enzyme combinations was run repeated times, each of them with a different set of weighting factors. The errors for every metabolite were normalized so that they would contribute with the same weight to the cost function. Additional weighting factors changed these weights in three orders of magnitude at most.Experimental quantification of isoenzymes pointed at the couple HXK/GLK experiencing the biggest deviation prior to and after the substrate perturbation cycles, but minor changes were also confirmed for the other enzymes in glycolysis. Still, estimation of all the kinetic constants in glycolysis simultaneously was undesirable due to the appearance of parameter dependencies  which coParameter dependencies could still appear for a selection of enzymes or within a single enzyme, even though less generalized than if all enzymes had been optimized together. To avoid this problem, parameters were estimated again after the previous round of data fit which was only based on selecting enzymes and cost function weights. This time, L1-type regularization was implemented to force the parameter estimates closer to the initial parameter set, as long as experimental data could be properly fit, helping to identify the important parameter changes for a specific dataset ,37. In tAs mentioned before, cells exposed to a block-wise feeding (repeated substrate perturbation regime) showed a remarkably different response compared to glucose-limited cells from chemostat conditions. The proteome during both conditions was measured and subsequently analysed on their composition . Major cRef.  observedA kinetic model of yeast glycolysis was previously developed to fit various SS and SP datasets . For the13C enrichment data, were in close agreement , but with a few exceptions A. For ingreement B, exceptIn the substrate perturbation simulations, the increase in residual glucose during feeding resulted in transient changes in glycolytic metabolites, which returned to the initial value by the end of the cycle C. Upper Trehalose metabolic dynamics were different between repeated substrate perturbations and SP. During the repeated substrate perturbations, the maximum flux towards production of trehalose was less than 10% of the HXK reaction rate, in comparison to the 30% observed in SP , and parSmall changes in protein expression occur between cells in a population. One way to examine the possible resulting phenotypes of the network upon perturbation is by means of ensemble modelling ,59. To tGlycolytic enzyme expression changed from chemostat to dynamic substrate conditions, most notably for HXT and HXK . As eacho differ . To ideno differ . To bypaIn Step 1 A, good fset from  . This might be related to the fact that the initial model simulated the response to a GP of 20 g L glucose , which i glucose . The finGlucose uptake has been widely modelled as an equilibrium-driven passive transport reaction ,27, wherrameters ,62,63. Hrameters  needs to−1 at 20 s when the uptake rate reaches its maximum . This tSuarez-Mendez et al., (2014) already noticed a similar phenomenon when modelling glucose uptake dynamics. Here, we assumed that the threshold was an effect of glucose sensing under certain conditions . GlucoseMetabolic and flux profiles agreed between simulations and experimental data . Even thConversely, the accuracy of model simulations was also limited. Enrichment of T6P decreased slower and glycolytic metabolites faster than expected during the late cycle, which could indicate that there is a surplus of glycogen recirculation which was mostly unlabelled. This might be explained by the current glycogen metabolism kinetics, which were simplistic here. Small deviations from the experimental value can have a great impact on the late cycle stage, given that fluxes in the network are generally low. We initially aimed at representing glycogen synthesis and degradation as mass action or Michaelis–Menten kinetics, but unfortunately, this did not resemble the experimental reaction rates due to the high and relatively constant glycogen concentrations. Therefore, simplified phenomenological expressions were used  ,70 and wIn this work, we combine metabolome, fluxome and proteome data to develop a metabolic model. Based on detailed analyses, we suggest enzymatic reactions whose reaction kinetics adapt to the dynamic substrate conditions and locations where our knowledge is limited. Testing different subsets of parameters for recalibration highlighted transporters and phosphorylation reactions as crucial for the adaptation. This in silico approach is comparable to the experimental approach of metabolic reverse engineering  but muchFurthermore, disagreements between experimental and simulated data suggest that the assumed mechanistic kinetics cannot sufficiently describe the intracellular flux and metabolome. Here, glucose uptake could not be explained by facilitated diffusion only (Equation ) but reqSaccharomyces cerevisiae, which was domesticated by mankind thousands of years ago, and is used to produce a wide range of products, such as bread, beverages and biofuels. When cultured in industrial-scale bioreactors, this cell factory is impacted by environmental perturbations which can challenge the bioprocess performance. The repeated substrate perturbation regime has been proposed as an experimental setup to downscale these industrial perturbations. Intracellularly, these perturbations impact central carbon metabolism, including carbon storage. Even though kinetic metabolic models have been developed to study the effect of single extracellular perturbations, they have not explored repeated substrate perturbations and their implications on carbon metabolism. We developed a model construction and parameter identification pipeline and used it to expand the existing models to represent carbon metabolism under dynamic substrate conditions. We used computer simulations to point at adaptations in yeast metabolism and locations in the model where our understanding is not entirely accurate. We found that combining multiple types of data, despite being challenging, can be very beneficial by providing a comprehensive and realistic representation of the cell.Kinetic metabolic models are used to understand how biological systems deal with dynamic perturbations in their environment. A well-known case of their application is the microorganism


Cordyceps militaris is an industrially important fungus, which is often used in Asia as traditional medicine. There has been a published genome-scale metabolic model (GSMM) of C. militaris useful for predicting its growth behaviors; however, lipid metabolism, which plays a vital role in cellular functions, remains incomplete in the GSMM of C. militaris. A comprehensive study on C. militaris was thus performed by enhancing GSMM through integrative analysis of metabolic footprint and transcriptome data. Through the enhanced GSMM of C. militaris , it contained 1469 genes, 1904 metabolic reactions and 1229 metabolites. After model evaluation, in silico growth simulation results agreed well with the experimental data of the fungal growths on different carbon sources. Beyond the model-driven integrative data analysis, interestingly, we found key metabolic responses in alteration of lipid metabolism in C. militaris upon different carbon sources. The sphingoid bases  and ceramide were statistically significant accumulated in the xylose culture when compared with other cultures; this study suggests that the sphingolipid biosynthetic capability in C. militaris was dependent on the carbon source assimilated for cell growth; this finding provides a comprehensive basis for the sphingolipid biosynthesis in C. militaris that can help to further redesign its metabolic control for medicinal and functional food applications. Cordyceps militaris is a medicinal fungus, which has been widely used for the production of commercially important metabolites, such as cordycepin, adenosine, D-mannitol, polysaccharide and ergosterol [C. militaris is commonly found on the decomposing bodies of dead insects under particular conditions [C. militaris is rather expensive because its traditional production cannot meet the increasing market demand. Regarding the safety concern of consumers, the product derived from the naturally occurring fruiting body of the parasitized insects may be contaminated with other microorganisms, which leads to the limitation of market expansion of this alternative medicine [gosterol ; these mgosterol ,3. Additgosterol ,5. In nanditions . Therefomedicine .C. militaris. Many studies have been devoted to the process improvement of cordycepin production of C. militaris [C. militaris has been published [Cordyceps species revealed that the Cordyceps growth and cordycepin production were dependent on carbon sources, in which glucose was the optimal carbon source for biomass production, and sucrose was favorable for the cordycepin production. Xylose was also an alternative carbon source for cordycepin production, although it was less favorable for fungal growth than glucose and sucrose [C. militaris showed that the genes coding for the catalytic enzymes in the glycolysis pathway were upregulated when glucose was used as a carbon source [Cordyceps spp. were significantly different, and these differential lipid profiles  potentially served as effective indicators for discriminating the geographical origin of nature Cordyceps spp. or distinguishing Cordyceps spp. In addition to the structural component of bilayer membranes, sphingolipids and unsaturated fatty acids have been reported in association with pharmacological functions [Cordyceps species under different cultivations is useful for exploring its metabolic regulation underlying the lipid metabolism and other cellular metabolisms under particular conditions.Lately, artificial cultivation has been developed for mycelial and fruiting body production of ilitaris . The corilitaris ,10. Withublished  and its ublished ,13,14. Tn source . In addin source . A recenn source  have repunctions ,18,19,20C. militaris to enhance growth and cordycepin production. The earlier iNR1329 model of C. militaris has been published [Genome-scale metabolic model (GSMM) is a computational tool useful for optimizing cultivation conditions of C. militaris in this work. Therefore, we aimed to enhance the genome-scale metabolic model of C. militaris through metabolic footprint with the integration of transcriptome data of the cultures grown under varied carbon sources. Initially, the metabolic footprinting was implemented using high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (LC/ESI-QTOF-MS). A comparative annotation of metabolic footprint data of C. militaris across different carbon sources was done through XCMS and METLIN. Incorporating the identified footprint metabolites into the earlier model [C. militaris cultivation process from carbon assimilation towards the production of fungal mass enriched in targeted metabolites of industrial relevance.Exometabolomics, known as metabolic footprinting, is a powerful approach for differentiating physiological traits of organisms of interest. Accordingly, the non-targeted footprint metabolites were thus considered as inputs together with the significantly expressed genes for reconstructing the genome-scale metabolic model of er model  was perfC. militaris strain TBRC6039 was used in this study. The stock culture was maintained as a mycelial suspension in 10% (v/v) glycerol at −80 °C. For inoculum preparation, 0.5 mL of stock solution was added in a 250-mL Erlenmeyer flask containing 75 mL of yeast extract-peptone-dextrose (YPD) medium, and the culture was grown at 22 °C with shaking at 250 rpm for 7 days under dark conditions. For the mycelial cultivation, 5% (v/v) C. militaris inoculum was transferred into individual sterile 250 mL jars containing 75 mL of defined medium enclosed with a sterile filter cap. One liter of the defined medium consisted of 40 mM (NH4)2SO4 and 20 g of sugar, which was supplemented with trace elements, including 0.5 g MgSO4·7H2O, 0.5 g K2HPO4·3H2O, 0.5 g KH2PO4, 0.1 g CaCl2 and 0.1 g FeSO4·7H2O. The static cultivation was performed at 22 °C for 60 days under dark conditions. The fermentable sugars used for individual cultivations were xylose (C5), sucrose (C12), and glucose (C6). Three biological replicates of each culture condition were used for the analysis.v/v) using vortex QL-866  at 3000 rpm for 3 min. After centrifugation at 12,000 rpm for 2 min, the supernatant was used for further analysis.For dry cell weight determination, the mycelial cells were harvested through a filter paper, washed at least three times with distilled water and then dried using a freeze dryer at −110 °C until a constant weight was obtained. The samples of cell-free broths obtained from the cultures grown at the logarithmic phase were subjected to the metabolic footprint analysis. One milliliter of each cultured broth was freeze-dried, and the lyophilized samples were then extracted with 100% methanol  for MS analysis. Samples (3 µL) were separated on an Agilent ZORBAX RRHD Eclipse Plus C18  using a gradient elution program with aqueous acetonitrile  at a flow rate of 400 µL/min. Positive ion electrospray ionization mass spectra were acquired over a mass range of 50–1000 Da. The data acquisition and processing were performed using Agilent Mass Hunter Qualitative B.07.00 software , and the following parameters were used to extract the peaks from the raw data; retention times between 0.00 and 33.00 min, and the mass spectra with the m/z ions between 50 and 1000.To analyze the footprint metabolites in the cell-free broths of https://xcmsonline.scripps.edu/, accessed on 24 August 2021) software version 3.7.1  [https://metlin.scripps.edu/, accessed on 24 August 2021) [m/z tolerance was set at 10 ppm. For the measurement of accuracy and resolution mass spectrometry, the LC-MS/MS-Q-TOF 6545 was calibrated using the Agilent G1969-85000 with tuning mix solution (10 times dilution). The diluted tuning mix solution (3 µL) was then injected, and the mass range was 3200 m/z in the positive and negative modes were detected afterward. The information on biological functions and pathways of the relevant compounds was acquired by mapping the detected metabolites into the relevant biological pathways retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database .Agilent Mass Hunter Qualitative B.07.00 software was used for metabolite data acquisition of total ion chromatograms (TIC). All spectra were exported for further analysis by using interactive XCMS Online, which is publicly available via XCMS (st 2021) , where mwww.smartdraw.com/venn-diagram/venn-diagram-software.htm, accessed on 24 January 2022). Volcano plots of differentially accumulated metabolites (DAMs) were generated using R package version 3.6.1. For statistical data analysis, one-way analysis of variance (ANOVA) followed by Tukey’s test for the post-hoc analysis was used for multiple comparisons via Minitab version 17.0. The significant DAMs were then identified using |fold change| ≥ 1.5 (or |log2 fold change| ≥ 0.5) under p-value of ≤ 0.05.Relative quantification of metabolites was based on chromatographic peak areas of the LC-MS analysis. The data derived from the metabolic footprinting were normalized for the principal component analysis (PCA) via the XCMS Online interface . A heat C. militaris cultures to the carbon sources tested, the earlier transcriptome data of this fungal strain, which was grown in the same conditions, were retrieved from the BioProject accession numbers of PRJNA416937 (BioSample: SAMN07969453 and BioSample: SAMN07969454) [C. militaris. The iNR1329 [C. militaris cultures using varied carbon sources. Initially, the data of newly identified footprint metabolites, including metabolite names, chemical formula, molecular weight and IUPAC International Chemical Identifier (InChI) using the RAVEN toolbox 2.0 [iNR1329 model. Then, the gap-filling of metabolic reactions was performed by MeGaFiller [C. militaris [To identify the metabolic responses of 7969454) , and sublbox 2.0 , PubChemlbox 2.0 , ChEBI [lbox 2.0 , and Metlbox 2.0  as databilitaris ,31, whic2O, and H+ were unconstrained to provide basic nutrients for cell growth. Lastly, the biosynthetic capabilities of the enhanced model for sphingolipid and its derivatives were tested by FBA in this study. To explore this, the secretion reaction of corresponding products was set as the objective function to perform FBA. During the model simulation for sphingolipid biosynthetic capability, glucose was set as the sole carbon source with an uptake rate of 25 mmol/g DW/h and a growth rate of 1 h−1. The predicted production rates under 1 × 10−10 or infeasible results were considered as no production; otherwise, the model gained the corresponding biosynthetic capacity. The simulations were carried out in MATLAB (R2020b)  with the RAVEN 2.0  [C. militaris are available at a public GitHub repository .The relevant transport reactions were then added throughout the network connectivity. For model evaluation, the prediction results were validated with the experimental data taken from the previous publication . Biomass Sweden) . The scrC. militaris strain TBRC6039 was initially cultured under different carbon sources. The metabolic footprint in association with the fungal growths on xylose (C5 culture), sucrose (C12 culture) or glucose (C6 culture) was further investigated by LC/ESI-QTOF-MS analysis. The PCA by XCMS was performed to determine the metabolic footprint profiles among all samples. The results were calculated using the feature intensities across all samples. The LC-MS data matrix of each sample was subjected to multivariate analysis of PCA  ,34, whic2 fold change| ≥ 0.5 and p-value ≤ 0.05 between pairwise comparisons of C5 vs. C6 cultures, C12 vs. C6 cultures, and C5 vs. C12 cultures. Detailed information on the DAMs is listed in To distinguish the footprint metabolites among the cultures using different carbon sources, the DAMs were identified using pairwise comparisons of the metabolic footprint data . The sigThere were 225 out of 470 significant DAMs in all pairwise comparison sets of the cultures using different carbon sources, which were categorized into 22 known classes . The remCordyceps [C. militaris . Further, we examined the identified 39 metabolites that were commonly accumulated between the C5 vs. C6 cultures versus the C12 vs. C6 cultures by hierarchical clustering analysis to assess the patterns of significant DAMs between the xylose or sucrose culture when compared to the glucose culture. The heat map result is shown in To investigate the metabolites commonly and specifically accumulating in response to the type of carbon sources, significant DAMs were identified. As seen in ordyceps ,39. In aC. militaris according to their structural and functional roles; these complex lipids constitute a sphingoid base as a backbone in which a fatty acyl and specific head groups are attached, such as dihydrosphingosine (sphinganine), phytosphingosine, or sphingosine; they are found on both the inner and outer membranes of eukaryotic cells [Cordycepssinclairii suppressed the proliferation of lymphocytes in mouse allogeneic mixed lymphocyte reaction [Promisingly, the metabolites in the sphingolipids class were more greatly accumulated in responses to the C5 vs. C6 cultures, and the C12 vs. C6 cultures, including C16 sphinganine METLIN: 41556), -2-aminoheptadecane-1,3-diol_1 and -2-aminoheptadecane-1,3-diol_2 (METLIN: 479551), D-erythro-sphingosine (sphingosine) (METLIN: 41557), and phytosphingosine (METLIN: 297165). Most of the common significant DAMs were up-accumulated between two pairwise comparison sets, in which 26 and 14 metabolites were specifically accumulated in the C5 vs. C6 cultures and C12 vs. C6 cultures, respectively . Among tic cells , and thu556, . The formation of ceramide leads to the biosynthesis of a group of metabolites, such as ceramide-1-phosphate (C1P), sphingosine, and sphingosine-1-phosphate (S1P), which are key regulators of inflammation mechanism . Previoua rileyi ; this fiC. militaris cultures using different carbon sources  was performed based on experimental data from the previous study [iPC1469 was employed to investigate the biosynthetic capacities of C. militaris for sphingolipid derivatives, i.e., sphinganine, phytosphingosine, and sphingosine as shown iPC1469 model versatility for sphingolipid and its derivatives production. We suggested that this enhanced GSMM of C. militaris could be used as a template model for further cellular metabolism studies of other related fungal species.To enhance the GSMM of vely see ; moreoveC. militaris (iPC1469) together with integrative transcriptomic data, we identified potential metabolites with corresponding differentially expressed genes, which attributed the fungal growths on carbon sources at molecular and biochemical levels. The published transcriptomic data of the C. militaris, which was grown in the same culture media and temperature at the dark condition [2 fold change of 4.35, which was higher than that of the sucrose and glucose cultures (log2 fold change of 2.93); this particular metabolite was associated with glycerolipid metabolism. In addition, the differentially accumulated metabolites in the sphingolipid biosynthesis were largely detected when comparing the profiles of the xylose vs. glucose cultures, e.g., sphinganine, phytosphingosine, sphingosine as well as the sucrose vs. glucose cultures, e.g., ceramide. Of them, several key genes involved in the sphingolipid biosynthesis were also specifically activated, corresponding to the altered metabolic footprint profiles. The detailed results are as follows.Using the enhanced GSMM of ondition  (see SupC. militaris (iPC1469), including glycerol kinase , glycerol-3-phosphate acyltransferase , 1-acylglycerol-3-phosphate O-acyltransferase , phosphatidate phosphate , and phospholipid:diacylglycerol acyltransferase , which were highly expressed in the xylose vs. glucose cultures as compared with the sucrose vs. glucose cultures  (Triacylglycerol) with up-accumulated content, as expected, a set of upregulated genes was identified in the subnetwork of glycerolipid metabolism through the enhanced GSMM of cultures . In addiC. militaris, iPC1469, was further used for the integrative analysis of transcriptome and metabolic footprint data. The sphingolipid biosynthetic pathway in C. militaris was elaborated by introducing the genes and their associated metabolites as well as biochemical reactions from the previous study reported by Raethong et al. [C. militaris focusing on the sphingolipid biosynthesis under the growths on different carbon sources, we mapped significantly detected metabolites on the relevant pathway, as shown in C. militaris upon different carbon sources. Particularly, we found that sphingolipid biosynthetic capability as indicated by sphingoid bases, e.g., sphinganine, sphingosine and phytosphingosine, and ceramide, which were significantly accumulated in the xylose culture (C5 culture) when compared with other cultures throughout enhanced GSMM together with metabolic footprint and transcriptome data of C. militaris  model of C. militaris was reconstructed in this study and applied with the integration of exometabolome and transcriptome data. In addition to the biomass production, it unveiled sugar utilization towards the lipid biosynthetic capability upon carbon sources, particularly in the sphingolipid and glycerolipid biosynthesis, which might contribute to cellular functions of the fungal cells; this finding serves to further redesign its metabolic control of C. militaris for medicinal and functional food applications.The enhanced GSMM (