Gardnerella vaginalis, and metabolites enriched in term pregnancies, such as tyramine. We investigate these associations using metabolic models. We use machine learning models to predict sPTB risk from metabolite levels, weeks to months before birth, with good accuracy (area under receiver operating characteristic curve of 0.78). These models, which we validate using two external cohorts, are more accurate than microbiome-based and maternal covariates-based models (area under receiver operating characteristic curve of 0.55–0.59). Our results demonstrate the potential of vaginal metabolites as early biomarkers of sPTB and highlight exogenous exposures as potential risk factors for prematurity.Spontaneous preterm birth (sPTB) is a leading cause of maternal and neonatal morbidity and mortality, yet its prevention and early risk stratification are limited. Previous investigations have suggested that vaginal microbes and metabolites may be implicated in sPTB. Here we performed untargeted metabolomics on 232 second-trimester vaginal samples, 80 from pregnancies ending preterm. We find multiple associations between vaginal metabolites and subsequent preterm birth, and propose that several of these metabolites, including diethanolamine and ethyl glucoside, are exogenous. We observe associations between the metabolome and microbiome profiles previously obtained using 16S ribosomal RNA amplicon sequencing, including correlations between bacteria considered suboptimal, such as  Characterization of the vaginal microbiome and metabolome reveals that vaginal metabolites, including several exogenous xenobiotics, are predictive of spontaneous preterm birth. PTB also reflects a notable racial disparity, manifesting in a substantially higher PTB rate in Black women3. This disparity is driven by various factors, such as the persistent stress of systemic and environmental racism and a lack of access to maternal care4. Spontaneous preterm birth (sPTB), PTB not medically induced, accounts for two-thirds of all PTBs1. Despite extensive efforts, methods for early prediction, prevention or treatment of PTB are lacking6, and its prevalence remains high1.Preterm birth (PTB), childbirth before 37 weeks of gestation, is the leading cause of neonatal death, and may lead to a variety of lifelong morbidities11. The vaginal microbiome, specifically, has been repeatedly associated with sPTB and other adverse pregnancy outcomes17. However, a clear consensus on the relationship between the vaginal microbiome and sPTB has yet to emerge18, and our knowledge of specific mechanisms underlying potential host–microbiome interactions in sPTB is lacking.The human microbiome is a strong biomarker of many complex diseases22. Their study has been facilitated by metabolomics, which enables the measurement of thousands of small molecules present in an ecosystem, and paired microbiome–metabolome studies have yielded potential mechanistic insights into host–microbiome interactions in various pathologies24. A few studies of the vaginal metabolome described associations with the microbiome, inflammation and PTB26. However, studies of demographic groups at high risk for sPTB, with measurements of a broad set of metabolites and which generate robust prediction models for sPTB, are still needed to advance our understanding of the role of the vaginal ecosystem in prematurity and other pregnancy outcomes.Metabolites produced or modified by the microbiome have emerged as a prominent factor with potential local and systemic effects on the host14. We show that the vaginal metabolome partially corresponds to community state types (CSTs), reveal associations between metabolites measured in the middle of pregnancy and subsequent sPTB, and propose that some of these metabolites are of an exogenous source. Finally, we devise machine learning algorithms that use the vaginal metabolome to predict subsequent sPTB an average of 3 months before delivery, which we validate on two external cohorts. Our results demonstrate a promising approach for studying potential causes of prematurity as well as for early risk stratification, and highlight the need to study environmental exposures as a risk factor for sPTB.Here, we measured the second-trimester vaginal metabolome of 232 pregnant women, for whom the microbiota was previously characterized using 16S ribosomal RNA gene amplicon sequencing14 (Supplementary Table N = 80), as well as similar term birth controls  were included (Table P = 3 × 10−4).We used mass spectrometry to profile 232 vaginal samples collected between 20 and 24 weeks of gestation from women with singleton pregnancies, for which the microbiota was previously characterized from the same timepoint27.We quantified 635 identified metabolites, as well as 110 unnamed spectral features . Metabol28. We demonstrated the same for this cohort14  P < 0.001; Fig. P < 0.001; Fig. P < 0.001), as previously described29. However, specific CSTs were not as well separated. While the metabolomes of women with CST-I (dominated by Lactobacillus crispatus) and CST-IV (enriched with diverse anaerobes) microbiomes were well separated from the rest of the cohort (PERMANOVA P < 0.001 for both), neither the metabolomes of women with CSTs IV-A and IV-B, nor with CST-II (dominated by Lactobacillus gasseri) and CST-III (dominated by Lactobacillus iners), were well separated from one another . Overall, these results demonstrate a strong but imperfect correspondence between the vaginal microbiome and metabolome.The vaginal microbiome clusters to well-defined CSTsk-medoids clustering of the metabolome, revealing six ‘metabolite clusters’ . Amino-acid-related metabolites were similarly enriched in MCs A,B and D , and xenobiotics in MC-C . While MCs A–D are mostly paired with Lactobacillus-dominated CSTs (54–93%), MC-F is composed entirely of CST-IV, and MC-E is evenly split , we found that several MCs are significantly associated with sPTB in Black women ; tartrate ; and diethanolamine , all higher in sPTB, appear to be of exogenous source36. We confirmed this using AMON37  and MC-D , the MCs we found to be enriched with sPTB , previously reported to be altered in pre-eclampsia45; spermine , previously shown to be increased in the blood of preterm infants46; hydroxybutyl carnitine , a ketocarnitine shown to be depleted in the blood of low-birth-weight full-term neonates47; and glutamate γ-methyl ester . Tyramine, a biogenic amine, was significantly lower in samples from White women who delivered preterm  were treated with intravaginal progesterone before or close to sample collection (at weeks 18–23 of gestation), we performed the same analysis only in women not treated with vaginal progesterone. One association, between glutamate γ-methyl ester and TB in Black women , which accounts for about a quarter of the amino acid residues of collagen49, and is integral to the extracellular matrix; spermine, a polyamine important for placental angiogenesis50, which was lower in Black women with subsequent sPTB  and betaine . N-acetylarginine , which is produced from proline and is necessary for the synthesis of polyamines such as spermine, was also lower in Black women with subsequent sPTB. Both disordered placental angiogenesis and extracellular matrix remodelling have been associated with sPTB51.As several participants in this cohort . We limited this analysis to Black women, due to their high proportion among such deliveries . We identified 13 metabolites that were associated only with these earlier sPTBs . Citraconate was likewise negatively associated with extremely PTB , and was previously found to have lower concentrations in placental mitochondria of women with severe pre-eclampsia52. We also found several sugar or sugar alcohol metabolites to be higher in early PTB, including mannose , previously associated with uropathogens such as Escherichia coli53; arabinose , previously associated with bacterial vaginosis (BV)54 and mannitol/sorbitol , previously associated with PTB55. Ethylenediaminetetraacetic acid (EDTA), an additional xenobiotic whose likely exogenous source58 was also confirmed by AMON . EDTA was shown to be cytotoxic in vaginal epithelial cells59, and is teratogenic in rats at non-maternotoxic doses60. EDTA was detected in 100% of women in this cohort  pathwaysDialister species or Enterococcus faecalis and tyramine64 . Additionally, higher tyramine concentrations were previously found in BV67, supporting the associations we found with BV-associated microbes . DEA, for example, shows only weak correlations with all vaginal microbes . This observation provides further support for an exogenous source for these metabolites.We note that xenobiotics positively associated with sPTB have significantly weaker correlations with vaginal microbes than those observed for the rest of the metabolites  and many others, were previously reported to be associated with negative outcomes, such as BV68, PTB17 and other adverse pregnancy69 and neonatal70 outcomes. We found a similarly paradoxical negative correlation between Staphylococcus epidermidis, previously associated with BV71 and late-onset sepsis in preterm neonates72, and both tartrate and ethyl glucoside ; 74, such as putrescine and histamine , we focused on tyramine, which previous studies suggest is produced by vaginal microbescuration , the preP = 0.05, Extended Data Fig. 75. Furthermore, tyramine prediction accuracy was not sensitive to constraints on metabolite uptakes or to the representation of low-abundance taxa , to predict subsequent sPTB. We used boosted decision trees, which were superior to alternative models , race, PTB history and nulliparity) and microbial abundance data, obtained limited accuracy (area under the receiver operating characteristic curve (auROC) of 0.59, area under the precision-recall curve (auPR) of 0.46 for clinical data; auROC = 0.55, auPR = 0.41 for microbiome data; N = 24) (ref. 76), maternal serum metabolome and clinical data  (ref. 77), maternal urine and plasma metabolome  (ref. 78), blood cell-free RNA measurements  (ref. 79) or vaginal protein biomarkers  (ref. 80), many of which have small sample sizes, lack demographic diversity or focus on high-risk cohorts. Overall, our results demonstrate the promising utility of vaginal metabolites as early and accurate biomarkers of sPTB.Our metabolome-based model is superior or similar in accuracy to several previously published models, such as those using amniotic fluid metabolomics . Interestingly, while the metabolome-based model showed a slight decrease in accuracy ; and a case–control study of 50 women (20 PTB), mostly (88%) Black, presenting with symptoms of preterm labour and no PTB history, with samples collected at 22–34 weeks of gestation  (ref. 55).To test the generalizability of our metabolome-based model, we validated its accuracy in two independent cohorts : a case–82 and finally, as data were generated 4–6 years earlier, only a small fraction of metabolites used by our predictor were measured (34% and 39%). To emphasize this, only one and two  of the ten associations we detected between vaginal metabolites and sPTB . These sPTB-associated metabolites are probably important features for prediction, making generalization across these cohorts difficult. Despite this challenging setting, our metabolome-based predictor, trained only on the 232 samples profiled here, without any retraining or adaptation, provided relatively accurate predictions in both external cohorts Mobiluncus mulieris14 and Finegoldia magna85, and of Lactobacillus14 and Dialister species15  Finally, there is additional unexplored potential in using even earlier samples for prediction. A larger sample size, and combination with other sources of data, such as maternal urine or serum metabolomics, vaginal metagenomics or cell-free RNA measurements, could further improve prediction accuracy.Our predictive modelling approach has several noteworthy limitations: (1) our use of a case–control cohort enriched for sPTB limits our ability to assess population-level predictive value, and further validation is required in prospective studies. (2) As this cohort was focused on sPTB, we are unable to assess if our models are specific to sPTB or are detecting a general risk for adverse pregnancy outcomes. (3) The use of race in our models, while common throughout medicineOur results demonstrate the utility of vaginal metabolites as early biomarkers of PTB, and identify xenobiotic metabolites as potentially modifiable sPTB risk factors, which may also disproportionately affect Black women. The strong associations we observe motivate the investigation of the vaginal microbiome and metabolome in the context of other adverse pregnancy outcomes such as pre-eclampsia, indicated PTB and BV.NCT02030106) (ref. 14). This cohort was approved by the institutional review board at the University of Pennsylvania (IRB 818914) and the University of Maryland School of Medicine (HP-00045398), and all participants provided written informed consent. The Motherhood and Microbiome cohort recruited 2,000 women with a singleton pregnancy before 20 weeks of gestation. Women were followed to delivery, and sPTB was defined as delivery before 37 weeks of gestation with a presentation of cervical dilation and/or premature rupture of membranes. Of these, the vaginal microbiota of 503 women was previously characterized via 16S rRNA gene amplicon sequencing (V3–V4 region) of vaginal swabs collected between 20 and 24 weeks of gestation, and total bacterial load was assessed using the TaqMan BactQuant assay14. For this study, out of women with available microbiome data, all available samples were selected from women who delivered preterm (N = 80), in addition to samples from 152 controls who delivered at term. The selected cervicovaginal samples were replicates of those used for 16S rRNA gene sequencing, collected using a double shaft dacron swab. Cervicovaginal swabs were either self-collected or collected by a research coordinator during a study visit14.We analysed banked samples from the previously collected and described Motherhood and Microbiome cohort (N = 80), and no statistical methods were used to pre-determine sample sizes; our sample size is similar to those reported in previous publications26. Samples were randomly distributed across metabolomics batches and metabolomics analysis was performed by Metabolon, who were blinded to the outcome assessment of each sample. Two-sided Mann–Whitney U tests (SciPy 1.5.2) and logistic regression (Statsmodels 0.12.1) were used to identify associations between metabolite levels and sPTB. Two-sided Fisher’s exact tests (R stats 3.6.1) were used to identify associations among MCs, CSTs, race and sPTB. PERMANOVA tests (scikit-bio 0.5.6) were used to identify associations among the microbiome, metabolome, CST, race and metabolomics batches. Metabolite set enrichment analysis  platformm/z peaks detected during the first round of mass spectrometry. m/z peaks were identified and annotated by Metabolon using proprietary software and comparisons to their database of retention indices and fragment ion spectra. The areas under annotated m/z peaks were taken as metabolite measurements. A comprehensive overview of all chromatographic and mass spectrometry parameters is available in Supplementary Table Following a methanol-based small-molecule extraction, samples were divided into 5 µl aliquots and each was resuspended in an appropriate extraction solvent and separated via one of four chromatography techniques. Each chromatographic method was optimized for the extraction of hydrophobic, basic or polar compounds. The chromatographic method used for the quantification of each metabolite is provided in Supplementary Table N = 556) were tier 1 identified by Metabolon via fragmentation spectra matches to experimentally measured library standards, only tier 2 assignments are available for independent identification due to the proprietary nature of the Metabolon platform. Metabolite measurements were volume normalized to the volume of buffer used, which may not necessarily account for differences in the original tissue. This was followed by robust standardization27 of the log (base 10) transformed values . The Shapiro–Wilk test was used to determine that log (base 10) transformed values deviated from normality for the majority of metabolites (389 of 635 named metabolites). For this reason, non-parametric tests were used in subsequent metabolomic analyses.While the majority of named metabolites (100) and SpeciateIT14, available from Supplementary Data 14. A single exception to this are predictive models, which were trained on 97% clustered operational taxonomic units (OTUs) using the USEARCH pipeline101. We obtained raw sequences from the database of Genotypes and Phenotypes (dbGaP) under study accession: phs001739.v1.p1. Primers were aligned to reads and then trimmed, followed by end merging and quality filtering (-fastq_maxee 1.0). The filtered reads were then pooled together, dereplicated, clustered with a 97% threshold and chimera filtered with the UPARSE algorithm to produce the OTU count matrix.All microbiome-based analyses were done using data previously processed with DADA2 , also with the Canberra distance. We determined the optimal number of clusters by comparing the within cluster sum of square error and the gap statistic for clustering solutions with k between 1 and 15 .PERMANOVA analysis was performed using Bray–Curtis distance for microbiome data and the Canberra distance for metabolites data, which is robust to outliers and sensitive to differences in common features. De novo clustering of metabolite vectors was done using the U test for metabolites that were present in at least half of the cases. All associations with early PTB were calculated using only samples from Black women, due to their high proportion among these deliveries (21 of 26 for childbirths <32 weeks of gestation and 14 of 15 for childbirth <28 weeks). To identify functional sets of metabolites that were perturbed between sPTB and TB, we compared, for each set, the Mann–Whitney P values for differential abundance between PTB and sPTB for metabolites within the set to the same P values for metabolites outside the sets, using an additional Mann–Whitney U test. We calculated significance by comparing the P value of the latter test to 10,000 similar P values calculated on random permutations of sPTB and TB labels. For functional sets, we used definitions of super and subpathways provided by Metabolon, as well as KEGG61 pathways. False discovery rate (FDR) correction was performed separately for each metabolite set type.Differential abundance tests between metabolite levels were done using the two-sided Mann–Whitney 37 is a method that uses functional annotations according to the KEGG database61 to predict metabolite origins for all metabolites that could be matched to a KEGG entry (N = 334 of 635 named metabolites). We used PICRUSt2 (ref. 103) to generate functional profiles for each sample, and then applied AMON37 to predict whether metabolites that had matching entries in the KEGG Database are products of human or microbial metabolism. When both were false, we interpreted the metabolite to be a xenobiotic.AMON105, and calculated Spearman correlations with the levels of metabolites we found to be associated with sPTB. Across all correlation network analyses -based panelses Fig.  we incluses Fig.  or by thses Fig. , we used106), using models from AGORA2 (ref. 107). All computations were performed in MATLAB version 2019a (Mathworks), using the IBM CPLEX (IBM) 12.10.0 solver.Microbiome metabolic modelling was done using Microbiome Modeling Toolbox (COBRA toolbox commit: 71c117305231f77a0292856e292b95ab32040711) , we used Prodigal111 to predict open reading frames in up to 200 randomly selected Refseq112 assemblies, and searched them for evidence of TDC using the hmmsearch function of Hmmer3.3.2 (ref. 113) and a profile hmm for TDC114 (NCBI HMM accession TIGR03811.1). We then curated our metabolic models, making sure that the corresponding reaction exists in models for which at least one assembly contained the corresponding gene.To support and improve the accuracy of our tyramine predictions, we validated the presence of the 107) (Supplementary Table Candidatus Lachnocurva vaginae (BVAB1), for which no suitable AGORA model was available. To generate species-level models, we combined metabolic models from available strains using the function createPanModels.m of the Microbiome Modeling Toolbox73. Altogether, our microbiome metabolic models included 95 different species, with an average of 20 species in each sample. As the vaginal microbiome has a very skewed distribution28, this resulted in a median (interquartile range (IQR)) of 96.7% (88.4–98.8%) of the total abundance across samples represented by our models , it was imputed to the mean total load using only training samples.We constructed predictive models separately using the clinical , microbiome and metabolomics data, as well as a combination model consisting of all of these data types combined. As race had very strong interactions with microbiome and metabolomics data, we trained a composite predictor for microbiome, metabolomics and combination models, whereas a separate model was trained for Black women. Despite the smaller sample size for each model, this empirically improved prediction performance , optional principal component analysis (PCA) transformation, and feature selection using sparsity, SHAP83 feature importance, information gain and/or Spearman correlation, followed by prediction using LightGBM116, with all steps performed strictly using training data. The selected models were then evaluated, without retraining, on classification of extremely (GAB <28 weeks) or very (GAB <32 weeks) PTB on the outer fold. Benchmark analyses , block-stratified for deciles of gestational age at birth (GAB), and for microbiome, metabolomics and combined models, also stratified for race. To account for stochasticity in the division to ten folds, we repeated this process five times. Train–test sterility was strictly maintained. To tune the optimal set of hyperparameters (including parameters for feature engineering and selection), and to obtain a robust estimate of the generalization error, we used nested cross-validation. In this extension of the training–test–validation framework, the training set was further split to five folds (‘inner folds’), on which we used 1,000 iterations of a random set of hyperparameters (Supplementary Table N = 232), using the hyperparameters selected for each of the outer folds (50 models), and picked the model with the best auROC on the same cohort (training fit). The final parameter set for each model is listed in Supplementary Table 81) cohort, as this cohort contained mostly White women; and similarly applied the metabolomics model used for Black women to metabolomics data from the Ghartey 2017 (ref. 55) cohort. For validation of associations of metabolites with sPTB . Sample IDs refer to Supplementary Data 2 of ref. 14. Supplementary Table 3Metabolite origin predictions by AMON. Supplementary Table 4Metabolite annotations and extraction platforms. Supplementary Table 5Tyramine prediction accuracy with metabolic models using different media definitions. Values are Spearman ρ between NMPC values and tyramine measurements. Supplementary Table 6Shapley values of prediction models. Supplementary Table 7Chromatography and mass spectrometry parameters. Listed are all technical parameters for each of Metabolon’s LC–MS/MS platforms. Supplementary Table 8Tyramine, putrescine and histamine predicted NMPCs. Sample IDs refer to Supplementary Data 2 of ref. 14. Supplementary Table 9Assignments of SpeciateIT species to AGORA models. SpeciateIT species are the columns of Supplementary Data 2 of ref. 14. Supplementary Table 10Metabolites included in the vaginal media used in metabolic models. Listed are the metabolites included, along with their AGORA identifiers. Supplementary Table 11Hyperparameter sets used to optimize prediction models. Supplementary Table 12Parameters of final prediction models. Supplementary Table 13Measurement characteristics for highlighted xenobiotics.


Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed. Genome-scale metabolic models (GEMs) transform cell growth and metabolism processes into a mathematical model based on a stoichiometric matrix and solve the optimal solution of the target equation at a steady-state . GEMs haHaemophilus influenzae RD was reported in 1999 [Escherichia coli [Saccharomyces cerevisiae [Bacillus subtilis [E. coli, one of the most important model organisms, was reported in 2000 [E. coli, the metabolism-expression (ME) model was reported, which reconstructs the complete pathway of transcription and translation during cellular metabolism [S. cerevisiae was the first eukaryotic microorganism whose genome was sequenced [S. cerevisiae have been reported, and the latest Yeast8 can dissect the metabolic mechanism of cells at multiscale levels [B. subtilis, seven GEMs have been reported. The latest eciYO844 integrates the enzymatic data of central carbon metabolism to guide the design of high-producing poly-γ-glutamic acid strains [Since the first GEM of  in 1999 , various in 1999 . In parthia coli , Saccharrevisiae , and Bacsubtilis . The fir in 2000 . In the tabolism . FoldME tabolism , Oxidizetabolism , and Acitabolism  were devequenced . Thus fae levels . In B. s strains .To explore the relationship between the genotype and phenotype in cells, flux balance analysis (FBA) is widely used to characterize cellular metabolism . Then, dHowever, the single gene–protein-reaction relationship in GEM often leads to mispredictions due to the multifactorial regulation of microbial metabolism. Therefore, multiscale GEMs that add constraints, such as thermodynamic, enzymatic, or kinetic constraints, or integrate omics data, such as proteomic, transcriptomic, or other omics data, have been developed based on traditional GEMs, and have been widely used in silico biodesign. This review summarized the construction workflow and toolkits of multiscale models. It also discussed how to use artificial intelligence, such as machine learning, to improve the qualities of multiscale GEMs. Finally, this review analyzed the challenges and perspectives of multiscale GEM development in the future. This review may aid biological engineers in the in silico design of versatile cell factories for sustainable bioproduction.GEMs have been widely used to simulate metabolic phenotypes at the systems level, often relying only on constraints on metabolite uptake rates. However, cellular metabolism, a fundamental biological process used by all organisms to generate and consume energy to promote growth, not only depends on the regulation of interconnected mechanisms within cells, but is also affected by the external environment. Multiple factors regulate cellular metabolism for an organism to respond to various conditions. Therefore, the basic form of GEMs cannot explain the complex regulatory mechanisms within the cell. This limitation of GEMs has prompted the development of multiple constraints to integrate regulatory mechanisms, thereby improving the predictive power and broadening the scope of GEMs. Several constraint-based models have been developed, including thermodynamic, enzymatic, and kinetic constraint models .Although classic GEMs can achieve flux analysis of metabolic networks, single stoichiometric and metabolite concentration limitations hinder their scope of application . TherefoThe development of thermodynamic constraints relies on three main algorithms: energy balance analysis (EBA), network embedded thermodynamic analysis , and thermodynamically based metabolic flux analysis TMFA; . EBA proHowever, the standard Gibbs free energy of metabolites is mostly unknown, and the temperature, pH, and ionic strength of different cells can have huge effects on the detection of Gibbs free energy. To overcome this challenge, Mavrovouniotis proposed a method for estimating Gibbs free energy and the equilibrium constants of biochemical reactions by multiple linear regression from group contributions . In the E. coli, iHJ873, evaluated the thermodynamic feasibility of the reactions in the model through Gibbs free energy, focused on the thermodynamic study of a single reaction, and explored the flux direction of the reaction [B. subtilis, identifying 653 (45%) irreversible reactions in the model, bringing its prediction accuracy from 89.7% to 93.1% [Based on the above algorithms and frameworks, many efforts have been made to explore the construction and analysis of thermodynamically constrained models. The first thermodynamically constrained model in reaction . Gibbs fto 93.1% . In addito 93.1% . The quato 93.1% . The effying 653 % irreverModels based on stoichiometric relationships and thermodynamic constraints have been widely used to predict cell growth rates, explore the interactions of metabolic pathways, and identify potential targets for metabolic engineering. However, the limitations of substrate uptake rates and the thermodynamic feasibility of metabolic reactions are insufficient to describe complex metabolic networks in which enzyme kinetics are a nonnegligible factor in regulating cellular metabolism.kcat, substrate concentration, and cytoplasmic density) [Four frameworks or toolkits can be used for constructing enzymatic constraint GEMs. (1) FBA with molecular crowding (FBAwMC) limits the concentration of enzymes that catalyze various metabolic reactions in the crowded cytoplasm, and each enzyme can solve the crowding factor based on six parameters  . (2) Metdensity) . (3) A cdensity) . In GECKdensity) . This aldensity) . It simpAside from the four toolkits, several algorithms have been developed to introduce enzymatic constraints in GEMs. Integrative omics–metabolic analysis quantitatively integrates proteomic and metabolomic data with GEMs, taking into account the concentration levels of enzyme substrates and products to predict metabolic flux distributions more accurately . Enzyme E. coli, a model of metabolic flux balance was constructed based on FBAwMC, which can activate cellular metabolism by systematically recognizing environmental changes [S. cerevisiae was defined at the protein level, and the expected constraints of each flux were specifically considered by GECKO [In  changes . Vazquez changes . Further changes . Howeverby GECKO . MoreoveE. coli), and the expression of 20 proteins was optimized by modular engineering, resulting in a lysine titer of 193.6 ± 1.8 g/L, which increased by 55.8% [Bacillus coagulans) estimated the protein efficiency of major ATP-producing pathways in cells, paving the way for a comprehensive understanding of B. coagulans metabolism [iJB1325 (enzyme-constrained model for Aspergillus niger) predicts the differential expression of enzymes under different growth conditions and significantly reduces the solution space of the model by 40.10%, explaining the changes in metabolic phenotypes at the proteomic level [Based on GECKO, the protein requirements for lysine synthesis were predicted by ec_iML1515  The in 2002 . (3) Opt in 2002 . ORACLE  in 2002 . (4) Ens in 2002 . (5) App in 2002 . However in 2002 .Based on the five toolkits, various algorithms have been developed and applied to the construction and analysis of kinetic models. EM for robustness analysis (EMRA) was developed based on numerical continuation and EM to investigate the robustness of unnatural metabolic pathways. The bifurcation robustness of the two synthetically central metabolic pathways (nonoxidative glycolysis and the reverse glyoxylate cycle) that achieve carbon conservation was analyzed by EMRA, weighing robustness and performance in the regulation of metabolic flux . An in sE. coli, k-ecoli457, was constructed by combining a genetic algorithm (GA) and EM, and the model was parameterized by minimizing the difference between the model predictions and the steady-state flux distributions of the 25 mutant strains. The prediction results showed that the average relative error of k-ecoli457 for the prediction of 129 product yields in 320 designed strains was within 20% of the measured value, showing the accuracy of k-ecoli457 in predicting the phenotype of E. coli under different growth conditions of genetic perturbation [B. subtilis, a kinetic model was developed to describe growth and sporulation as the process of differentiation from vegetative cells to spores. The growth kinetics of spores was described by two specific parameters: time and probability of spore formation. In addition, the biological significance of sporulation parameters was assessed experimentally, qualitatively, and quantitatively at the physiological level of the sporulation process in B. subtilis [Clostridium thermocellum, the core kinetic energy model of C. thermocellum was constructed based on EM, named k-ctherm118, and the model was parameterized by the fermentation data of 19 metabolites of lactic acid, malic acid, and the hydrogen production pathway so that k-ctherm118 could capture the upregulation of amino acid production and predict the direction and extent of changes in cytosolic concentration under ethanol stress [Based on the above frameworks and toolkits, a kinetic model for urbation . In B. ssubtilis . For Clol stress .Although multiple kinetic modeling frameworks have been developed and kinetic models of multiple strains have been constructed to reveal the regulatory mechanisms of metabolic networks, datasets for model parameterization and computational power hinder the development of kinetic constraint models. Therefore, comprehensive GEMs integrating more constraints were developed.E. coli [Yang et al. proposed a Pyomo-based model framework integrating enzymatic and thermodynamic constraints and constructed a multiconstrained model for E. coli . MoreoveE. coli .E. coli, which extended the transcription and translation processes in cell growth metabolism based on the traditional GEM (M-model) [E. coli [In addition, the most classic example of multiconstraint GEMs is the ME model. The ME model was reported for M-model) . Unlike [E. coli . In addi[E. coli . Thus, c[E. coli . Importa[E. coli .Thermotoga maritima, the ME model was constructed to accurately simulate changes in cell composition and gene expression, in which experimental values of the transcriptome and proteome containing 651 genes were positively correlated with the simulated values, and the Pearson correlation coefficients were 0.54 and 0.57, respectively [Clostridium ljungdahlii, the first ME model of Gram-positive bacteria was reported, covering the synthetic pathways of biomass composition, revealing the influence of protein partition and medium composition on the metabolic pathways and energy conservation of the strain and significantly broadening the model prediction range [E. coli has undergone several updates, such as iOL1650-ME  [With the development of the software COBRAme , the conectively . For Cloon range . In addiroducts) , iJL1678roducts) , and iJLroducts) . To addrroducts) , Oxidizeroducts) , and Aciroducts) .E. coli than iJO1366 [S. cerevisiae, which extends the eukaryotic system  based on ETFL and constrains proteins assigned to metabolism and cellular expression. Therefore, yETFL can capture the Crabtree effect only by integrating experimental data [Based on the ME model, Salvy et al. developed a framework for expression and thermodynamics flux models (ETFL), which formulated a mixed-integer linear program (MILP) to integrate metabolites, proteins, and mRNA, enabling the simultaneous consideration of expression, thermodynamics, and growth-related variables . This fr iJO1366 . Furthertal data ,106.Although the multiconstraint approach in GEMs allows researchers to explore cellular metabolic networks, there are still certain difficulties in analyzing complex regulatory mechanisms in cells . TherefoTranscriptional regulation is one of the important mechanisms by which microorganisms transform their metabolic flux in response to changing environments. TRNs have been widely reported in bacteria after the standard procedure for reconstituting TRNs was proposed . TRNs usTwo tool platforms for the integration of TRN into GEMs based on logical Boolean rules were developed: the toolbox for integrating genome-scale metabolism (TIGER)  and FlexFurthermore, the probabilistic regulation of metabolism (PROM) , gene exE. coli, TRN-integrated GEMs were constructed from quantitative cell growth data [Mycobacterium tuberculosis, an expanded knowledge base of metabolic networks and regulatory mechanisms was constructed with 104 TF regulatory networks based on ChIP-seq interactions linked to 810 GEMs [S. cerevisiae, a TRN-integrated GEM was constructed involving 25,000 regulatory interactions and controlling 1597 metabolic reactions [For wth data . Six strwth data . For Myc810 GEMs . The kno810 GEMs . For S. eactions . The modeactions . Furthereactions . The proeactions .GEM construction relies on the mining of multiomics and the analysis of cellular metabolic processes, in which protein–protein interactions control a wide range of cellular processes, such as signal transduction ,111 and E. coli [E. coli and T. maritima were reported, revealing growth limitation by protein instability through features such as temperature conditions, protein folding, and substrate specificity [Brunk et al. proposed the GEM with a protein structure (GEM-PRO) framework, which directly maps genes to transcripts, PROs, biochemical responses, network states, and, ultimately, phenotypes . The masE. coli . GEM-PROcificity . The estcificity .E. coli and yeast, PRO-integrated GEMs have not been widely used, and the acquisition of accurate PRO data may be the main limiting factor for its development.Recently, an integrated GEM based on protein synthesis and degradation was reported in yeast, which systematically alters the growth rate and determines its protein expression level . ImportaCellular metabolism is regulated at multiple levels, so a single integrated model cannot accurately predict cellular phenotypes under various environmental conditions. Therefore, the development of comprehensive models facilitates the exploration of cellular metabolism at multiscale levels.E. coli, a comprehensive modeling framework (EcoMAC), which unifies various biological processes and multilayer interactions, was developed to combine gene expression data from genetic and environmental perturbations, transcriptional regulation, signal transduction and metabolic pathways, and growth measurements [E. coli, iML1515, was reported, which contained not only transcriptome, proteome, and metabolome data, but also unique metabolite response information and complete PRO data [In urements . In thisurements . Notablyurements . A knowlPRO data . The knoPRO data . These aS. cerevisiae, a genome-wide tool for multiscale modeling data extraction and representation (GEMMER) was developed. This tool aids the visualization of the physical, regulatory, and genetic interactions between proteins and genes and integrates existing database information to support multiscale modeling efforts [S. cerevisiae) [For  efforts . Lu et aevisiae) . This moevisiae) .Although various multiscale integrated models have been established to simulate cell growth and metabolism, many subcellular processes have not yet been incorporated, such as chromosome initiation and replication, protein activation and folding, and RNA decay and modification . TherefoWhole-cell models are computational models that explain the integrated function of every gene and molecule in a cell, aiming to predict the cellular phenotype from the genotype by representing the entire genome, the structure and concentration of each molecular species, each molecular interaction, and the extracellular environment .The construction of whole-cell models can be divided into five stages: (1) Training data. The biological system of cells is divided into functional modules, and the data for the cellular process of each module are collected . These dMycoplasma genitalium [E. coli [S. cerevisiae [M. genitalium, which describes the life cycle of a single cell at the level of individual molecules and their interactions [Currently, whole-cell models have been constructed for nitalium  and expl[E. coli  and S. crevisiae , providiractions . A totalractions . Resultsractions . In addiractions . TherefoE. coli, a large-scale mechanistic model that evaluates large heterogeneous datasets by deeply managing the process of mapping multiple layers was constructed [S. cerevisiae, Ye et al. explored the framework of a whole-cell model in which the functions of 1140 essential genes were characterized and associated with five levels of phenotypes, enabling the real-time tracking of the dynamic allocation of intracellular molecules to simulate cell activity [For structed . Inconsistructed . The disactivity . HoweverAlthough multiscale GEMs have made important progress in exploring the regulation of metabolic networks, there is still a lack of multilevel resolution schemes to systematically reveal cellular growth and metabolic processes. Machine learning has become an indispensable tool for revealing the regulatory mechanisms of metabolic networks due to its multidimensional data processing capabilities and intelligent analysis strategies . In addiAlthough various automated tools have been developed to build GEMs, the critical step of filling gaps in models still needs to be managed manually. Recently, the automated metabolic model ensemble-driven elimination of uncertainty with statistical learning (AMMEDEUS) was developed to identify metabolic responses that significantly affect simulation performance . First, Identifying enzyme committee (EC) numbers is critical for an accurate understanding of enzyme function. However, the reported EC number prediction tools contain computational time and coverage limitations. Therefore, a deep learning-based computational framework was developed for predicting EC numbers from protein sequences (DeepEC) . DeepEC E. coli K-12 MG1665, the GEM framework was restricted by the proteomics and fluxomics of 21 strains. The model significantly improved the prediction accuracy of quantitative proteomic data by integrating a comprehensive machine learning strategy of RF, elastic network, and neural network, and its prediction error was reduced by >40% [Improving prediction accuracy is a huge challenge for models, and combining machine learning with GEMs can aid in solving this problem. Zhang et al. developed a method for engineering targets identified by GEMs and machine learning-trained screening of high-throughput biosensors to explore optimal synthetic pathways for tryptophan . First,  by >40% .S. cerevisiae, Culley et al. proposed a multimodal learning framework based on fluxomics and transcriptomics, which utilizes transcriptome data and GEMs to predict the phenotypic characteristics of cells [S. cerevisiae mutants were constructed using this framework. The introduction of mechanoflux signatures significantly increases the range of discernible mechanobiological insights, providing analytical tools for uncovering unknown interactions between biological domains [For of cells . In partof cells . Three mof cells . Importaof cells . Therefo domains .John et al. used Bayesian inference and linlog kinetics to develop a scalable metabolic ensemble modeling simulation capable of inferring kinetic parameters of large metabolic models with multiomics-scale datasets. This provides a solution for a complete kinetic description in the kinetic model . In addiS. cerevisiae and explains their metabolic regulatory mechanisms [GEMs are often used to explore synthetic pathways of target products, and clarifying their metabolic network is conducive to specifying a comprehensive metabolic regulation strategy. The introduction of machine learning is conducive to analyzing datasets from multiple perspectives and comprehensively exploring and analyzing the distribution of metabolic fluxes of cells. A regularization optimization framework combining PCA and a stoichiometric flux analysis method, primary metabolic flux pattern analysis (PMFA), was proposed, identifying the flux patterns that explain most flux variations . In addichanisms .http://mflux.org) was developed based on three machine learning algorithms, support vector machines (SVM), k-nearest neighbors (k-NN), and decision trees, to predict bacterial central metabolism. It utilizes 10,013 bacterial metabolic data and integrates machine learning with GEMs to explore the complex relationship between influencing factors and metabolic fluxes [An automated procedure based on a two-stage GA was developed to automatically generate hypotheses to explain negative interactions between genes . The proc fluxes . MFlux cc fluxes .After two decades of development, GEMs have become an indispensable tool for systematically exploring cell growth and metabolism. With the development of biochemical research and omics technology, GEMs are not only limited to the exploration of the metabolic network but also extended to the gene level , proteinM. genitalium, the cellular processes of most industrial microorganisms are extremely complex, and there are many unknown areas. Unclear data and mechanisms make it difficult to build the model. Second, the perfect framework is a key factor in building a model. The cell morphology and life cycle of different strains are different, and a single model framework is not suitable for other strains. Finally, efficient and novel toolkits and powerful computing power are indispensable conditions for constructing and analyzing whole-cell models. Overall, advances in assays and algorithms will facilitate the whole-cell modeling of multiple strains, advancing microbial biological discovery and the comprehensive design of cell factories.In the future, whole-cell models will eventually become the goal for building models of different strains. Although multiple whole-cell models have emerged, the development of truly fully functional whole-cell models remains a challenge. First, it is difficult to clarify all cell mechanisms and obtain accurate and massive experimental data because, compared to


Genetics play an important role in the development of metabolic diseases. However, the relative influence of genetic variation on metabolism is not well defined, particularly in tissues, where metabolic dysfunction that leads to disease occurs. We used inbred strains of laboratory mice to evaluate the impact of genetic variation on the metabolomes of tissues that play central roles in metabolic diseases. We chose a set of four common inbred strains that have different levels of susceptibility to obesity, insulin resistance, and other common metabolic disorders. At the ages used, and under standard husbandry conditions, these lines are not overtly diseased. Using global metabolomics profiling, we evaluated water-soluble metabolites in liver, skeletal muscle, and adipose from A/J, C57BL/6J, FVB/NJ, and NOD/ShiLtJ mice fed a standard mouse chow diet. We included both males and females to assess the relative influence of strain, sex, and strain-by-sex interactions on metabolomes. The mice were also phenotyped for systems level traits related to metabolism and energy expenditure. Strain explained more variation in the metabolite profile than did sex or its interaction with strain across each of the tissues, especially in liver. Purine and pyrimidine metabolism and pathways related to amino acid metabolism were identified as pathways that discriminated strains across all three tissues. Based on the results from ANOVA, sex and sex-by-strain interaction had modest influence on metabolomes relative to strain, suggesting that the tissue metabolome remains largely stable across sexes consuming the same diet. Our data indicate that genetic variation exerts a fundamental influence on tissue metabolism. Metabolic phenotypes such as body composition and energy expenditure vary widely among individuals within a population. This variation contributes to the ability to maintain a healthy body weight, and to the long-term risk of metabolic diseases such as obesity and type 2 diabetes (T2D). Genetic background is a major contributor to inter-individual differences in metabolic traits. For example, large-scale studies of twins estimate the genetic contribution to variation in body mass index, which is widely used to assess obesity, to be as high as 85% .Fundamental differences in metabolism at the cellular level contribute to variation in metabolic phenotypes between individuals. Metabolomics has emerged as a valuable platform for querying cellular metabolism and efficiently quantifying large sets of metabolites in experimental and clinical samples. Some classes of metabolites are thought to be highly heritable ,3. AccorInbred strains of laboratory mice are a valuable set of models with which to understand the influence of genetic background on metabolism. Inbreeding allows genetic replicability, and laboratory husbandry provides the ability to control the diet and environment. Although mice and humans differ, many aspects of metabolism are shared between the two species. In addition, mice allow metabolomes to be profiled at the tissue level, where the underlying inputs to organismal level metabolic differences arise. Here, we utilize this control to evaluate the impact of both genetic background and sex on tissue metabolomes. We used untargeted metabolomics to quantify metabolites in three key metabolic tissues  from four common inbred strains of mice  maintained under the same diet and husbandry conditions, and of the same age. Like individuals in a population, these strains represent varying levels of adiposity, metabolic health, and response to common human-relevant diets, and the variation in these traits differs between males and females. For example, A/J mice are relatively resistant to diabetes and obesity, while C57BL/6J mice are susceptible to these same disorders ,9,10,11.p = 0.052) were observed for weight  and heat output (p < 0.001)  . VO2, anth sexes C. Heat oth sexes E. C57Bl/th sexes . Femalesth sexes F. The reth sexes D. Collecm/z and retention time have been matched to standards in our library) were detected in one or more tissues. In addition, several thousand spectral features corresponding to unidentified metabolites  were detected in liver, skeletal muscle, and adipose, respectively.Liver, skeletal muscle, and adipose tissue are fundamental to systemic energy balance and metabolic health. Global metabolomic profiling of these tissues was used to characterize the effects of sex and strain on tissue metabolism. A total of 167 identified metabolites  were significantly affected by one or more factors, in one or more tissues A, with aPartial least squares discriminant analysis (PLS-DA) was performed using both identified and unidentified metabolites in each tissue to visualize the relative extent to which the metabolites discriminated the strains in each tissue . Both A/n = 9), although not in adipose or muscle. Metabolites that differed between strains in adipose tissue were also involved in amino acid (arginine and proline) metabolism (n = 5) and aminoacyl-tRNA-biosynthesis (n = 6), while the pathway for pantothenate and CoA biosynthesis (n = 5) was affected by strain in liver. In muscle, the sets of metabolites that differed significantly between strains and between sexes were enriched for components of protein synthesis (aminoacyl-tRNA-biosynthesis (n = 6)). These results highlight purine and pyrimidine metabolism, particularly in liver and adipose tissue, as pathways that may contribute to the effects of genetic background on overlying systems level metabolic phenotypes.Functional annotation based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was used to characterize the effects of strain, as well as sex and sex-by-strain interaction, on tissue metabolomes . The comAssessing intermediary genes, using the mouse phenome database, between the metabolites identified in purine metabolism revealed potential single nucleotide polymorphisms (SNPs) that may contribute to metabolite abundance between strains . These S2. This module was inversely related to adiposity, further indicating that metabolite abundance in tissues reflects the interrelationships between higher order metabolic traits.Metabolic pathways are intertwined, and the levels of individual metabolites within a given pathway are highly interdependent. To incorporate this behavior into the analyses, weighted gene co-expression analysis (WGCNA) was implemented to identify clusters of highly intercorrelated metabolites (modules) within each tissue. WGCNA was developed to extract co-expressed sets of genes from transcriptomic datasets, but more recently has been adopted for use with metabolomics data. Using this approach, a signed and weighted network was constructed from metabolite abundance based on correlations, and sets of highly interconnected metabolites were represented as modules. Modules were then associated with phenotypes based on the correlation of the module’s representative eigenmetabolite with each physiological trait. There were nine, nine, and eleven modules identified within adipose, muscle, and liver, respectively . Five ofGenetic background influences metabolic health, but the relationship between genetics and metabolism at the tissue level is poorly understood. Metabolites are a product of cellular processes in the body and are therefore thought to be most closely related to phenotypes. We used untargeted metabolomics to investigate the effects of strain and sex on the metabolomes of adipose tissue, liver, and skeletal muscle because of their central roles in metabolism and energy expenditure. We also associated metabolites to body composition and energy expenditure. Similar to individuals within a population, the mouse strains chosen vary in terms of genetic backgrounds and potential for metabolic disease, and in ways that differ between males and females.We found that genetic background had a significant effect on the tissue metabolite profiles, regardless of the tissue that was profiled. The majority of known metabolites detected on our platform were significantly affected by strain in at least one tissue, based on the ANOVA results. Likewise, PLS-DA discriminated strains based on metabolite profiles, with A/J and C57BL/6J each distinctly separated from FVB/N and NOD mice. Our findings support the concept of individual “metabotypes” (metabolic phenotypes), a term used to describe the variation in metabolite profiles that exists between individuals . More spThe integration of metabolomics in genome-wide association studies (GWAS) supports the concept that genetic background influences metabolite profiles. Loci linked to metabolite profiles (mQTLS) have been identified in numerous studies . FurtherNearly a third (30 of 103) of metabolites in liver that were influenced by strain are involved in purine and pyrimidine metabolism. Purines and pyrimidines are central to the function of all cells as subunits of nucleic acids. In addition, these nucleotides and their derivatives carry energy, allosterically control enzymatic reactions, mediate intra- and intercellular signaling pathways, and participate in the control of cellular metabolism. Purine metabolism, in particular, is linked to pathways that govern cellular energy allocation through its relationship to AMPK (5′ AMP-activated protein kinase), a kinase that is allosterically activated by a rise in AMP:ATP. AMPK mediates the response to an energy deficit by activating the pathways that generate ATP and inhibiting those that consume ATP. AMPK can also be activated by the ZMP (5-aminoimidazole-4-carboxamide ribonucleotide), an intermediate in de novo purine synthesis that acts as an AMP mimetic. Recent studies have shown that altering purine metabolism in ways that increase ZMP activates AMPK and elicits the same types of metabolic changes, such as the increased oxidation of fatty acids, that result from an increase in AMP. Our assessment of SNPs identified several potential variants that may contribute to genetic differences in nucleotide metabolism between the inbred strains used here. In general, our data are consistent with a recent study of the tissue metabolomes of three inbred strains, in which purine metabolism was also identified as a pathway in the differentiated mice of different genetic backgrounds ,24.In conclusion, we have used the replicability of inbred strains and the control provided by laboratory husbandry to compare the effects of genetic background, sex, and their interactions on the metabolomes of key metabolic tissues. We provide evidence that genetic background, more so than sex, significantly influences tissue metabolomes, particularly in liver. Future studies are needed to determine whether and how these underlying differences in metabolism contribute to the risks of various metabolic diseases that are represented by this collection of inbred strains. Our data further support the concept of individual metabotypes, which has important implications for the emerging field of precision health.2), and heat output via heat dissipation, and activity level by laser detection through the ActiMot2 module. Activity levels measured voluntary movement of the mouse in the x and y plane by quantifying horizontal and vertical beam breaks. The mice were euthanized at 28 weeks of age by CO2 asphyxiation. Perigonadal adipose tissue was dissected and weighed as a measure of adiposity. Samples of adipose tissue, the left lobe of the liver, and the vastus medialis, vastus lateralis, and rectus femoris muscle were snap-frozen in liquid nitrogen and stored at −80 °C for metabolomics analysis.All husbandry and experimental procedures were approved by the Institutional Animal Care and Use Committee of the University of North Carolina. Four-week-old C57BL/6J mice were purchased from The Jackson Laboratory . The mice were allowed to acclimate for 14 days and consumed a standard mouse chow  during this period. At 42 days of age, five male and five female mice from each strain were switched to a marginally lower protein standard chow diet (19% protein) and fed ad libitum. The mice were maintained on a 12 h light/dark cycle throughout the study. At age 18 weeks, after 12 weeks on the diets, the mice were housed in Phenomaster metabolic chambers  for 48 h for the measurement of metabolic rate and activity. The chambers measured respiratory exchange rate (RER), volume of oxygen (VO13C-labeled E. coli metabolite pool) was added to each sample.The frozen tissue samples were pulverized under liquid nitrogen and approximately 0.025 g of tissue was added to a 2 mL Eppendorf tube containing pre-chilled methanol (1.3 mL). Samples were extracted using the method described in [6. The samples were analyzed with a resolution of 140,000. A scan window of 85 to 800 m/z (mass-to-charge) was used from 0 to 9 min, and a window of 110 to 1000 m/z from 9 to 25 min. Solvent A consisted of 97:3 water:methanol, 10 mM tributylamine, and 15 mM acetic acid. Solvent B was methanol. The gradient from 0 to 5 min was 0% Solvent B, from 5 to 13 min was 20% Solvent B, from 13 to 15.5 min was 55% Solvent B, from 15.5 to 19 min was 95% Solvent B, and from 19 to 25 min was 0% Solvent B, with a flow rate of 200 µL/min.The samples, kept at 4 °C, were placed in an autosampler tray. A total of 10 μL from each sample was injected through a Synergi 2.5 micron Hydro-RP 100, 100 × 2.00 mm LC column  kept at 25 °C. The mass spectrometer (MS) was run in full scan mode, with negative ionization mode, using a method adapted from Lu et al. . The eluE. coli metabolites were labeled with 13C and extracted using a method adapted from Bennett et al. [t et al. ,28 and dt et al. .m/z) (within ±5 ppm) and retention time (r.t.) (within ±30 s) to values measured from purified standard compounds that were used to construct a database for validated metabolites. The peak quality for all identified metabolites was checked manually, and metabolite abundance was quantitated as ion counts that were integrated from the area under each peak. Unidentified metabolites were chosen based on a peak-picking algorithm provided in MAVEN. These compounds were named as m/z:r.t. pairs, and the peak area was then integrated as described for the identified compounds using an algorithm described in [Raw spectra files generated by Xcalibur were converted to mzML, an open-source format, using msConvert . An openribed in . Three s13C-labeled E. coli as internal standards in the samples, triplicate aliquots of the labeled extracts were analyzed using the LC–MS metabolomics method [13C-labeled E. coli internal standard was used as a normalization technique, verification that non-significant amounts of 12C-label remained within the samples was performed for the identified metabolites. Unlabeled metabolites were matched to their corresponding 13C-labeled internal standard if detected, or to a 13C-labeled standard of the same compound class if no stable isotope-labeled standard was detected . The same 13C-labeled metabolite for a particular class type was used for the normalization of all metabolites of the same class type missing an exact 13C-label metabolite match . Class types were identified using the Human Metabolome Database (HMDB) [13C-labeled standard in order to correct for instrumental variability among the sample analyses.Prior to the use of diluted s method . The rese (HMDB) ,33,34 usp-values (p < 0.05).All statistical analyses were performed in the language R (3.1.0 and 3.2.2) . An ANOVThe same ANOVA model was used to identify the significant effects of strain, sex, and sex-by-strain interaction on metabolites, controlling for false discovery rate (FDR) to account for multiple testing (described in further detail below). Metabolite peak area data files were read into R using the package XLConnect . MetabolThis linear model was performed prior to formal statistical analysis to determine the coefficients for tissue weight and internal standard, which were utilized in Equation (2) to obtain metabolite abundances adjusted for tissue weight and internal standard.Coefficients for the terms for tissue weight and internal standard were used to adjust metabolite abundance for technical variation using Equation (2):p-values were estimated by calculating the q-value. Venn diagrams were created using the package VennDiagram [Adjusted metabolites were pareto scaled across all mice for each metabolite using the package MetabolAnalyze , normalinDiagram .2 ellipses were created using the packages plyr and Car [Partial least squares discriminant analysis (PLS-DA) was used to characterize the strain, sex, and sex-by-strain effects on the tissue metabolome using identified and unidentified metabolites. The PLS-DA algorithm was implemented using the package DiscriMiner . Scores  and Car ,43. Extep-values were FDR-adjusted using the Benjamini–Hochberg procedure. Correlations were visualized using the Cytoscape app MetScape [Associations between the physiological measurements and the metabolite measurements across tissues were assessed using Pearson correlation from the package Hmisc  and the MetScape .p-values were adjusted using the Benjamini–Hochberg procedure. Pathway topology was performed using relative-between centrality.Overrepresentation pathway analysis for the identified metabolites was performed using MetaboAnalyst . KEGG IDTo identify potential metabolite networks, weighted gene co-expression network analysis (WGCNA) was performed using the ‘WGCNA’ R package . The sofANOVA was performed for each module using the linear model:


This was achieved by model scope extension and refinement, implemented in a multi‐atom transition model, within the statistical framework of Bayesian model averaging (BMA). Using BMA‐based 13C15N‐metabolic flux analysis, we jointly resolve carbon and nitrogen fluxes quantitatively. We provide the first nitrogen flux distributions for amino acid and nucleotide biosynthesis in mycobacteria and establish glutamate as the central node for nitrogen metabolism. We improved resolution of the notoriously elusive anaplerotic node in central carbon metabolism and revealed possible operation modes. Our study provides a powerful and statistically rigorous platform to simultaneously infer carbon and nitrogen metabolism in any biological system.Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the tuberculosis pathogen, this is particularly important in informing the development of effective drugs targeting the pathogen's metabolism. Here we performed  13C15N‐MFA is an approach for simultaneous quantification of intracellular carbon (C) and nitrogen (N) metabolic fluxes in any living system. It is applied to Mycobacterium bovis BCG to measure the first CN flux map, highlighting glutamate as the central node for nitrogen metabolism.Bayesian  The method infers in vivo metabolic reaction rates (fluxes) by using a system‐wide biochemical reaction model that tracks C atom rearrangements throughout the metabolic pathways and by fitting these fluxes to the emerging labeling patterns  or nuclear magnetic resonance (NMR); Wiechert, et al, et al, 13C‐MFA resolves the activity of biochemical reactions through computational modeling which can differentiate between parallel pathways and determine bidirectional fluxes  to deploy the isotopically non‐stationary (INST) MFA framework  metabolism plays a key role, not only in amino acid and nucleotide metabolism, but also in the synthesis of many cofactors  that causes tuberculosis (TB).C and N metabolism are interdependent in all organisms  and 15N‐flux spectral ratio analysis (FSRA) for identifying the probable spectrum of C and N substrates in Mtb ex vivo  as these reactions must be modeled bidirectional to adequately describe the co‐labeling enrichments  MIDs  and two N (#N=2) atoms, #C+#N+1= 9 univariate MIDs exist. More advanced analytical platforms such as high‐resolution LC–MS , weighted by the model probability in view of the data pMiD:Mi are structural variants that differ in their bidirectionality setting and, hence, number of flux parameters (vMi). Equation (Equation 1) is solved computationally by using a recently developed tailored Markov chain Monte Carlo (MCMC) approach  and 20% [15N1]‐ammonium chloride (NH4Cl) until an isotopic steady state was reached, as confirmed by GC–MS of amino acids  of 15 proteinogenic amino acids.We applied the te et al . M. bovi1 Tables A–C. Cultkor) and succinyl‐CoA synthetase (scs) reactions are reduced. Rv2454c‐Rv2455c encoding kor and Rv0951‐Rv0952 encoding scs in Mtb participates in the decarboxylating steps of the TCA cycle, which are bypassed using glyoxylate shunt, instantiating the GAS pathway  node, involving the fluxes 13C‐MFA analysis (22), net fluxes of the anaplerotic reactions, pyruvate carboxylase (pca), PEP carboxykinase (pck), and malic enzyme (mez), could not be resolved as seen from their large CrIs in Fig et al, pca and mez were aggregated by lumping oxaloacetate (OAA) and malate (MAL). In this study, the three anaplerotic reactions were modeled in detail without imposing any assumption on their reaction directionalities or bidirectionalities.As in our former −1 h−1 (approx. 10% of glycerol uptake). However, the two‐dimensional (2D) marginal posterior probability distributions shown in Fig 13C15N data set effectively narrows down the joint space of possible values considerably further to concise, ring‐like regions within the flux space. With these inferences, many flux constellations are ruled out. For instance, given the data set at hand, it is very unlikely that the reaction pairs pca and pck, pca and mez, or pck and mez, carry zero fluxes. This insight, which was not implemented in the flux model a priori, is indeed experimentally supported: the mentioned anaplerotic reactions catalyze gluconeogenic utilization of carbons to replenish the TCA cycle and operation of these fluxes has been demonstrated to be important for the survival of Mtb  inferred here by BMA‐based 13C15N‐MFA represents an independent replication of the previous 13C‐MFA‐derived C fluxes. The C flux maps derived from the two labeling approaches and the two MFA platforms are comparable, while our current Bayesian approach imposes fewer modeling assumptions, provides more reliable flux uncertainties, and delivers an increased flux resolution, in particular for the previously non‐inferable anaplerotic node in BCG. The results from our Bayesian analysis provide explanations of the analyzed data set, which narrow down the range of likely fluxes , with an expected value of 0.072 mmol g biomass−1 h−1 (95% CrI: 0.071–0.074 mmol g biomass−1 h−1), followed by bsGLN (glutamine synthetase), bsASP (aspartate transaminase) and bsSER (serine deaminase). The fluxes of the remaining amino acid and nucleotide synthesis including adenosine monophosphate (AMP), guanosine monophosphate (GMP), cytidine monophosphate (CMP), inosine monophosphate (IMP) and uridine monophosphate (UMP), are one order of magnitude lower, with values rendering the proportion to which they contribute to biomass formation  is the largest biosynthetic net carbon flux, being one order of magnitude larger than glutamate dehydrogenase (bsGLU). In this study, we found that bsALA (95% CrI: 0.006–0.007 mmol g biomass−1 h−1) is one order of magnitude lower than bsGLU (95% CrI: 0.071–0.074 mmol g biomass−1 h−1). This apparent discrepancy is explained by the nitrogen flux that is not within the scope of 13C‐MFA. More precisely, conventional 13C‐MFA can only give a lower bound on the nitrogen requirement of an amino acid by summing up the (C) fluxes of reactions that incorporate nitrogen from the donor. For example, adding the fluxes to alanine (ALA), asparagine (ASN), glutamate, glutamine (GLN), leucine (LEU), lysine (LYS), phenylalanine (PHE), serine (SER), tyrosine (TYR), tryptophan (TRP), valine (VAL), ornithine (ORN), arginine (ARG), and the contributions to nucleotide synthesis, gives a value of 0.061 mmol g biomass−1 h−1, which underestimates the bsGLU net CN flux expected value of 0.071 mmol g biomass−1 h−1 derived by 13C15N‐MFA in this study.When interpreting fluxes determined using ‐MFA Fig  or compa13C15N‐MFA is that it provides quantitative flux measurements for N metabolism, along with the C fluxes. The N flux map shown in Fig et al, M. bovis BCG with a transposon mutation in gltBD, a gene encoding glutamine oxoglutarate aminotransferase (GOGAT) that catalyzes the synthesis of GLU from OXG and GLN , the gltBD mutant was able to grow only on glutamate as the N source  that catalyzes glycine (GLY) biosynthesis was determined to be bidirectional with 100 ± 0% probability. In contrast, serine deaminase, isoleucine, and leucine transaminase  were found to be unidirectional with high probability .Due to the lack of evidence about the reversibility of mycobacterial transaminases, we set the transaminase reactions in the model as potentially bidirectional to capture the flexibility of the amination network and to avoid the risk of any bias that would arise from setting a transamination reaction erroneously unidirectional. All transamination reactions were modeled as bidirectional reaction steps, resulting in a flux estimation problem with 86 degrees of freedom  to be recovered from 144 measurements (109 univariate MIDs and 34 rate and biomass efflux measurements). Here, BMA was used to minimize overfitting (see methods and protocols). This technique explores the space of all possible combinations of uni‐ and bidirectional reactions  and weights each model variant by its ability to explain the labeling data  and credibility regions (2D) are derived.The multi‐model approach captures flux parameter and model formulation uncertainty, thereby providing consolidated flux uncertainty quantification.13C15N‐MFA reliably evaluates low‐demand co‐labeling data, without the need of settling for potentially biasing model reductions.BMA‐based Here we generalized the well‐established methodology of 13C15N‐MFA, together with the extension of scope and refinement in terms of the C and N metabolic network, enable us to estimate C and N fluxes in one consolidated approach and provide reliable estimates for the uncertainties in the parameters. This enabled the rigorous statistical assessment of all reactions, even those with unknown bidirectionality and cyclic network structures that were previously unresolvable.These key advantages of BMA‐based 13C‐MFA demonstrated conserved carbon flux distributions between BCG and Mtb during growth on glycerol demonstrating metabolic consistency between the two mycobacterial systems , fructose bisphosphatase (fbp), aldolase (fba), glucose‐6‐phosphate dehydrogenase/glucolactonase (gnd), transketolase  and transaldolase  were not quantified accurately with our previous 13C‐MFA and recent 13C15N‐MFA study because PPP reactions lack of sufficient labeling information from proteinogenic amino acids. Incorporating labeling information from glycogen, ribose moiety of DNA, glucosamine moiety from peptidoglycan and lipopolysaccharides, such as suggested by Kohlstedt and Wittmann (pca), PEP carboxy kinase (pck), and malic enzyme (mez), which play an important role in metabolism connecting catabolism and anabolism with energy generation, are notoriously difficult to determine in general  was found to be bidirectional, whereas serine deaminase (bsSER) and isoleucine and leucine transaminase (bsILE and bsLEU) were determined to be unidirectional. C and N metabolic profiling has been attempted using isotopic labeling by other studies. Blank et al  biosynthesis, providing novel information, which cannot be deduced from our former nk et al  investig13C15N‐MFA, a powerful tool for simultaneous quantification of intracellular C and N metabolic fluxes in a living system. We applied it to M. bovis BCG. BMA‐based 13C15N‐MFA identified glutamate as the central node of nitrogen metabolism, revealed the most likely operational modes of the anaplerotic fluxes, and resolved the uni/bidirectionalities of glycine, serine, isoleucine, and leucine biosynthesis. Our 13C15N‐MFA workflow described here is applicable to any CN isotopic co‐labeling experiment, and the computational platform developed in this work allows analyses of low demand, low‐ and medium‐resolution MS data to provide rigorous, consolidated quantification of C and N metabolic fluxes in any biological system.In summary, we have developed Bayesian M. bovis BCG in the bioreactor, roisins minimal medium with composition‐ KH2PO4, 1 g l−1; Na2HPO4, 2.5 g l−1; NH4Cl, 5.9 g l−1; K2SO4, 2 g l−1; ZnCl2, 0.08 mg l−1; FeCl3, 0.4 mg l−1; CuCl2, 0.02 mg l−1; MnCl2, 0.02 mg l−1; Na2B4O7, 0.02 mg l−1; NH4MoO4, 0.02 mg l−1; MgCl2, 0.0476 g l−1; CaCl2, 0.055 g l−1; Tyloxapol, 01% (v/v); Glycerol, 0.5% (v/v).Middlebrook 7H11 agar and Middlebrook 7H9 broth containing 5% (vol/vol) oleic acid‐albumin‐dextrose‐catalase enrichment medium supplement (OADC) and 0.5% (vol/vol) glycerol were used to grow cultures from frozen stocks and for counting the numbers of culturable bacteria in chemostat samples. Brain heart infusion agar was used to assess culture purity. For cultivation of M. bovis BCG batch cultures with (a) [13C3] glycerol (12.5%) + unlabeled NH4Cl, (b) unlabeled glycerol + [15N1] NH4Cl (20%), and (c) [13C3] glycerol (12.5%) + [15N1] NH4Cl (20%) substrates. Samples for mass isotopomer analysis were harvested at approximately same time points in the late exponential growth phase. Mass isotopomers were corrected for natural abundance of unlabeled atoms. Univariate C and N, as well as multivariate CN mass isotopomer data of selected proteinogenic amino acids are shown in Appendix Fig We performed single‐ and co‐labeling (with two isotopic substrates) of Mycobacterium bovis BCG strain was cultured in a 2 liter bioreactor under growth conditions  and [15N1] NH4Cl (20%) were the carbon and nitrogen isotopically labeled substrates in the media. Isotopic stationary state was assessed by measuring % label in the proteinogenic amino acids of cultures drawn at different times during label feed  extraction. Briefly, cultures were filtered using membrane Filter, 0.22 μm pore size, filter apparatus and the filter was immersed into methanol:chloroform, mixed, incubated on ice for 30 min and water was added, followed by centrifuging at room temperature for triphasic metabolite separation. The upper phase was collected separately and dried and used for mass spectrometry analysis. The lower and intermediate phase were mixed into one phase by addition of one more volume of methanol and chloroform and centrifuged for 30 min at room temperature. Supernatant was discarded, the pellet was hydrolyzed in 6 M hydrochloric acid for 24 h at 100°C and the hydrolysate was dried for mass spectrometry analysis.et al, 13C15N fractional abundances were calculated from two independent chemostat cultivations  and quantitation of metabolite pool sizes was done using calibration curves trifluoroacetamide, MSTFA and dried hydrolysate were derivatized using tert‐Butyldimethylsilyl chloride (TBDMSCl) were analyzed using GC–MS . All biosynthesis pathway fluxes relevant for growth of M. bovis are modeled as effluxes, whose values represent their share in the biomass composition  and 184 metabolic reactions . The most comprehensive model in the model set has 71 independent flux parameters (36 net and 35 exchange fluxes). The corresponding CN atom transition network model is formulated in the standardized document format for isotope‐based MFA, FluxML v3 . Parallel tempering with dynamic temperature selection was applied to sample from potentially multi‐modal distributions. In total, 10 parallel chains were run from independent starting points, where per chain 15 × 106 forward simulations were performed. For each chain, the first 5 × 106 samples were discarded (burn‐in). Proper convergence of the MCMC runs was diagnosed by measuring the Potential Scale Reduction Factor . Computations were run on a workstation with dual Intel(R) Xeon(R) Gold CPU (61300 @ 2.8 GHz). PSRF, mixing plots in parameter as well as model spaces are provided in Table Flux inference with Bayesian Model Averaging: Instead of conventional optimization‐based single‐model flux inference, in this work metabolic fluxes were estimated using a Bayesian multi‐model approach. More precisely, net fluxes and reaction bidirectionalities were inferred simultaneously by employing BMA, implemented using a tailored MCMC approach . Formally, the posterior probability of a model Mi out of the model set Mii=1…N is given bypMi, the prior knowledge about model Mi, is considered equal for all models. pDMi represents the high dimensional marginalization integral over all possible fluxes the model Mi can take. 95% CrIs were determined for each net flux , discarding the upper and lower 2.5% of the values. In addition, as point estimator, the expected value for the net flux is reported. The marginal distributions for the net fluxes are provided in Appendix Fig plot_kde method of arviz , and the co‐labeling (12% [13C3] glycerol +12% [15N1] ammonium chloride) experiments were simulated. Results are shown in Appendix Fig 15N1 ammonium chloride labeling strategy is uninformative about the fluxes, as it is solely determined by the applied N labeling percentage. For the co‐labeling experiment with dual species 13C and 15N labeling, simulations and experimental data are comparable, implying that inferences are plausible with respect to the fluxes.Prediction of labeling data: A calibrated t‐test, analysis of variance (ANOVA), and linear regression analyses were performed in GraphPad Prism 8.0.Students Khushboo Borah Slater: Formal analysis; funding acquisition; investigation; visualization; methodology; writing – original draft; writing – review and editing. Martin Beyß: Software; formal analysis; investigation; visualization; methodology; writing – original draft; writing – review and editing. Ye Xu: Writing – review and editing. Jim Barber: Writing – review and editing. Catia Costa: Writing – review and editing. Jane Newcombe: Writing – review and editing. Axel Theorell: Software; writing – review and editing. Melanie J Bailey: Resources; funding acquisition; writing – review and editing. Dany J V Beste: Writing – review and editing. Johnjoe McFadden: Conceptualization; resources; supervision; funding acquisition; investigation; writing – original draft; writing – review and editing. Katharina Nöh: Conceptualization; resources; software; supervision; investigation; methodology; writing – original draft; writing – review and editing.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Table EV4Click here for additional data file.Dataset EV1Click here for additional data file.Source Data for Figure 4Click here for additional data file.Source Data for Figure 7Click here for additional data file.Source Data for Figure 8Click here for additional data file.