Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput. Saccharomyces cerevisiae is a very well-studied organism, ∼20% of its proteins remain poorly characterized1. Many of these proteins are conserved between eukaryotes, including humans, providing a significant incentive to increase the pace of discovery.Even though 3. Previous work has implied that the most probable path forward is via fully autonomous systems in which active learning is applied to guide high-throughput experimentation4. The development of tools and methods for these types of systems is therefore a research priority, as different types of biological data will provide information that will not only elucidate new functions but also vastly accelerate our knowledge of functions, enabling fully autonomous functional discovery in multiple different modalities.In order to accelerate this process it will be necessary to deploy massively parallel experimental and computational methods5. With rapid advances in not only lab automation but also mass-spectrometry hardware and its connected software, this is likely to change6. Here we investigate the potential of automated cultivation procedures and untargeted intracellular metabolomics for high-throughput functional characterization, and as a source of information for active learning based experimental design during the diauxic shift, observed in the yeast S. cerevisiae. Thus, in S. cerevisiae growing on glucose in an aerated batch culture one can commonly observe a diauxic shift: during the first growth phase, yeast ferments glucose into ethanol; when the glucose has been consumed, yeast switches to an ethanol substrate using a respiratory mitochondrial metabolism. This transition requires substantial reconfiguration of the metabolic network and a similar phenomenon can be observed in cancer cells: the Warburg effect7. Despite extensive research, the regulation of the diauxic shift remains poorly understood9.Intracellular metabolomics has historically been an underutilized source of biological information, with previous studies indicating the difficulty of sample processing as a bottleneck in the way of increasing experimental throughput4. These were then individually and collectively investigated using their untargeted metabolic profiles with the goal of clarifying regulatory roles and biological consequences of gene deletion. This also served as an assessment of the suitability of untargeted metabolomics as a tool for guidance in high-throughput model improvements by evaluating model fidelity.Using dynamical flux balance analysis (dFBA) simulations constrained by semi-autonomously developed gene regulatory models produced by previous iterations of the robot scientist, a set of ten regulatory genes were selected due to their relevance to the shift and implications of previously unknown connections in literature4. The selected strains are shown in Table RTS3 and to some extent TDA1. Two of them have existing homologues in humans; DLD3 and FAA1.In order to identify genes with insufficient annotation in regards to the diauxic shift and to assess the validity of the model proposed by Coutant et al., the ten strains with the highest differences in post shift growth rates with and without proposed semi-automated revisions were selected for further study10. The metabolic profiles of the deletion mutants showed that Pearson correlation with the reference strain was phase-dependent for the majority of the strains, indicating context-specific regulation. The correlation analysis shows the relative insignificance of the GAL11 gene in the context of the diauxic shift and its small impact on overall metabolism in both phases. Conversely, the relative impacts of the rest of the available gene deletions are much more significant, especially in the post-diauxic growth phase.To identify phase-specific regulatory consequences of gene deletions, samples were taken during fermentation on glucose as well as during respiratory metabolism on an ethanol substrate for all strains in the study. An overview can be seen in Fig. There are also some significant differences in affected pathways between the phases, again indicating some phase-specific consequences, such as the impact on vitamin B6 metabolism post shift.TDA1 seem to have large effects on metabolism as shown by their low correlations with the reference strain. FELLA-based topological enrichment  and third generation metabolite enrichment making use of a linear model for significance testingp-value ≤ 0.1, linear model).The diauxic shift comprises of a series of large cellular transformations resulting in very distinct metabolic profiles before and after the shift using the 431 distinct metabolite features present after processing. Figure S. cerevisiae, see Supplementary Table The significant metabolites showcased in Fig. 14. A significant portion of the metabolites present in the intersection  were detected and significantly accumulated.The metabolic transition resulted in the accumulation of, among others, species involved in processes implicated in stress responses. Figure Overall enrichment in glycerophospholipid metabolism, as seen in Fig. The over-representation of tryptophan metabolism seems mainly centered around two key metabolites; kynurenic acid (4-hydroxy-2-quinolinecarboxylic acid) and indole-3-acetate, whilst tryptophan itself remains unperturbed, despite being an intermediate between the formerly mentioned metabolites.4. The high amount of significantly differing reactions between the two models brought forth by the changes caused by the semi-automated method proposed in Coutant et al. in combination with the pairwise Spearman correlation  and measured species intensity (linear model)) for available metabolites in the dataset corresponding to central metabolites belonging to either central carbon metabolism or signified as an amino acid, pyrimidine, or purine.Constrained simulations according to the framework provided in Coutant et al. were analyzed to validate the revisions produced by its semi-automated model-revision approachion Fig.  indicateM1Smart strictly outperformed M1 on 6 out of 10 selected metabolites, but most importantly, it predicted the activity of all the measured amino acids with higher accuracy. This seemingly showcased an improvement over the original iteration M1 using this type of evaluation. Keep in mind that neither of the models performed very well using the aforementioned metrics.1. And while some of this slowdown might be attributed to research bias, a major limitation of the methods used today is assessing functionality and generating hypotheses in a high-throughput manner while still maintaining sufficient resolution and precision. In this work, it is demonstrated that untargeted metabolomics has the potential to address this issue.Some evidence indicates that functional discovery is slowing downThe networks of kinases and transcription factors involved in regulation represent an enormously complex system, which plays a key role in almost all cellular processes. Determination of regulator function ideally requires linking gene manipulation with global profiling of intracellular molecules (with the help of omics-based techniques). The use of targeted panels of metabolites in combination with other omics-methods and technologies may lead to subtle metabolic events being poorly investigated. By leveraging a strength of untargeted metabolomics, namely the relatively unbiased feature-collection, one can identify even small, unexpected changes in metabolism.15. The latter, glutathione, has roles as not only an important ROS scavenger, but also as a contributor to overall cellular fitness in adverse conditions due to, among others, its implications in cell proliferation, DNA synthesis and protein glutathionylation16. Some of these biological consequences and stresses are most likely subsequently shown as perturbations in nucleotide metabolism further referenced in Fig. Untargeted metabolomics enables one to identify biochemical features of the phase-change, making it useful for classifying specific condition-based phenotypes as seen in Fig. 18.In the main intersection of proline and glutathione metabolism is spermidine and its reaction intermediary, 5-methylthioadenosine, both of which show significant accumulation during the respiratory phase. More specifically, spermidine is a polyamine that has previously implied relevance for membrane stabilization and enzymatic regulation, it is essential for normal cell growth, and has been implicated in human lifespan and inflammation20. Previous work has shown the importance of lipid regulation during and after the diauxic shift, especially showcasing the dynamics of glycerophospholipid metabolism19, something which is reinforced in this study. The change in activity is potentially caused due to stress on the cellular membrane, and an increased pressure to maintain membrane stability and relieving cell injury in an effort to keep the cells alive.The study also further supports existing literature on the more particular effects of the shift and its connection with glycerophospholipids and local metabolic changes in activity due to changes in membrane stability21. This is also somewhat mirrored for the deletion mutants themselves, but with additional implications for several processes heavily involved in signaling, including sphingolipid metabolism22.Overall, the metabolites that were found to be differentially expressed across the phases imply significant regulation of stress-responsive mechanisms, seemingly oriented mainly around ethanol exposure and accumulation of toxic metabolites produced by oxidative metabolism, such as glutathione metabolismRTS3 are currently classified as putative proteins, whilst Tda1 is a kinase of unknown function23. Untargeted metabolomics provides some initial insight into their roles, indicating their involvement in essential processes such as the citric acid cycle, vitamin B6 metabolism and the metabolism of several amino acids, hopefully facilitating further investigations into their biological effect. Additionally, the Tda1 protein has, outside of this work, previously been putatively implicated in phytosphingosine metabolism, which this study strengthens further24.Several of the genes targeted in this study, namely YGR067C and FAA1 and DLD3. This study shows some implications of their deletion, such as its effect on overall stress responses, but also in pathways affecting cell-membrane stability and other processes essential for overall cellular health.Other genes investigated are ones with known human homologues, such as FAA1 and DLD3, although being relatively well annotated genes, with main functionalities28. Some of which are putatively implicated here, such as the potential connection with sphingolipid metabolism, glycerophospholipid metabolism and tryptophan metabolism. There are also pathways that are affected differently when investigating strain specific differences during the different phases, such as differences in vitamin B6 metabolism only appearing post shift. This could potentially be due to its involvement in glucose and amino acid metabolism29.involved in fatty acid activation and 2-hydroxyglutarate dehydrogenase, the dynamic and complex nature of regulation likely makes them involved with several additional secondary roles30. By investigating pairwise correlation between strains, and also with the reference strain, one can infer overall similarity of the perturbation caused by the gene deletion. If we have sufficiently global enough coverage, the correlation coefficients could in this case serve as a proxy for metabolic impact, as in the case of GAL11 which correlated highly with the reference strain in both the fermentative and respiratory phase, indicating the relative non-essentiality of the gene in the context of the diauxic shift, as seen in Fig. Historically, untargeted metabolomics spectra are used as identifiable fingerprints for specific biochemical phenotypes4. In this particular case, the methodology supplies informative suggestions for deletant strains, with several of the strains having previously unknown implications for the diauxic shift, and some where metabolic profile perturbations seem minimal. This discrepancy in predictions indicates the potential need for model revisions.The use of dFBA and previously developed models and modeling frameworks in this study implies that they can be used for experiment selection and future applications in active learning and automated experimentation4. In order to assess the relevance of using untargeted metabolomics for this specific task, the metabolite accumulations were used as a proxy for metabolic activity. Comparing the model with the observations in a strictly effect-based manner shows that the revisions not only made the metabolic flux predictions more accurate, but also rerouted reaction fluxes. While other more sophisticated methods of integrating metabolomics-data into automated approaches will likely need to be developed for this purpose, this serves as a reasonable complement due to its holistic nature. The modeling paradigm of choice is essential to the form of metabolomics usage and using a FBA-centric modeling paradigm is limiting due to its steady-state assumptions regarding intracellular metabolite accumulations31.Model revisions made by a semi-autonomous system, as in the case of the work done in Coutant et al., where growth curves were used to infer changes in a gene regulatory network, need to be biologically validated using supplementary observationsS. cerevisiae, due to its potential use as a high-throughput form of analysis. It was also used as an indicator of model improvement for previous work done on active learning and scientific automation.In this study we investigated the potential of using untargeted metabolomics to form hypotheses regarding functionality of regulatory genes involved with the diauxic shift in the yeast The results demonstrate that it can be used to form initial hypotheses around gene function, contextual regulation, and the phenotypical consequences of gene deletion. It was also used to characterize the diauxic shift further, showcasing supplementary modes of action for stress responses induced by fully mitochondrial respiration and ethanol exposure in yeast and giving more specific insight into the role of involved regulators. It also served as an indication of metabolic activity for use as a model validation tool. In both of these use-cases, more data in the form of different types of biological measurements would be needed to fully validate these hypotheses, but its usefulness as a high-throughput alternative to other omics-methods in order to complement computational methods in functional discovery should not be understated.We conclude that untargeted intracellular metabolomics is well suited to generating data and hypotheses about gene function, and this could potentially be done automatically in high-throughput.An overview of the workflow and methodologies used in this study can be seen in Fig. BY4741, genotype: MATa his3∆1 leu2∆0 met15∆0 ura3∆0).All of the haploid yeast deletant strains present in the study were taken from the EUROSCARF deletant collection, with the strain background being 4. The difference in growth rate during the post diauxic shift phase was calculated and compared between the two models for all the deletant strains. Ten strains which were fully viable and available in the EUROSCARF collection with the largest absolute growth rate discrepancies between the revised models were then selected for further study. This to not only provide informative examples for model validation, but also to prime further investigations into their relevance to the diauxic shift.Fully constrained dynamic flux balance simulations according the method and framework described in Coutant et al. were performed for each possible single deletion of the 682 available regulatory genes and active-learning based model combinations using identical experimental settings as in the performed experimentsYeast strains from the EUROSCARF deletant collection were precultured in 250 μL YPD (yeast extract peptone dextrose) medium at 30 °C in a round bottom 96 well plate overnight. The plates were then centrifuged at 450 g for 2 min and subsequently washed in 250 μL YNB (yeast nitrogen base) before being centrifuged once more at 450 g for 2 min. Supernatant was removed and the cells were then resuspended in 250 μL of a YNB mixture, consisting of 10.5 g/L YNB without amino acids, 1.25 g/L glucose, 75 μg/L ampicillin and 0.625 g/L of L-methionine, L-leucine, L-histidine, and Uracil respectively.A Multidrop™Combi Reagent Dispenser was used to predispense YNB in 384 well plates . A Bravo Automated Liquid Handling Platform was then used to transfer suspension from the precultures into the plate. The volume of the transfer was calculated so that an initial OD  of approximately 0.07 could be reached, using a final cultivation volume of 80 μL. The outermost quadrants of the 384 well plate were not inoculated so as to not only serve as a media control but also to avoid edge effects due to evaporation.32. The plate was subsequently transferred from Eve’s Cytomat™ automated incubator (30 °C) to a Teleshaker Magnetic Shaking System, where it was shaked for 20 s at 1000 rpm, divided evenly between clockwise and counter-clockwise double-orbital shaking. After shaking the plate was transferred to a BMG Polarstar plate reader, where it underwent optical density measurements at 560 nM (the temperature in the plate reader was kept at a constant 30 °C). After measuring, the plate was returned to the incubator. The protocol was automatically repeated every 20 min for up to 48 h depending on the strain.After plate preparation, a growth profiling protocol was created using the automated laboratory cell “Eve”33. The polynomial was derived using an OD-ladder of serial dilutions spanning the dynamic range of the OD-reader (0 − 2 OD560) resulting in the polynomial in Equation 1 (x = OD).34.To adjust for non-linearity, OD measurements were then background corrected and adjusted using a third degree polynomial as in Jung et al.560 or as close to the middle of the initial fermentative phase as possible. For the post shift samples, they were collected at 1 OD560 or shortly after glucose depletion when additional growth could be verified. Samples that did not adhere to this were subsequently removed after processing.Strains were grown in the same conditions as in the section above. For the pre shift samples, they were collected at 0.5 ODFor metabolomic samples, cells were pooled and as quickly as possible transferred to 2 mL microcentrifuge tubes containing absolute methanol (99% purity) prechilled on dry ice. The ratio between sample and methanol was kept at 1:1 v/v. The tubes were kept in dry ice during the process and were as quickly as possible transferred to a centrifuge in which they were centrifuged at 2040 g for 5 min. The supernatant was then discarded, and the pellets snap frozen in −80 °C ethanol and subsequently transferred and stored at −80 °C. Samples were further freeze dried overnight and later kept at −80 °C pending further analysis.Preparation of sQCs  was conducted by pooling equal amounts of each sample for the respective batches. The sQCs were subject to the same sample preparation procedure as the actual samples. sQCs were injected at the beginning, at the end, and systematically between sets of samples throughout the batch sequence.Metabolite extraction was done by pouring 75% ethanol  over the freeze-dried yeast biomass samples in a microcentrifuge tube. Samples were then immediately vortexed for 1 minute and the tube was then placed on a heater for 3 min, and allowed to cool down for 10 min at 4 °C. Further, the tubes were centrifuged for 15 min  and the supernatant was transferred to another microcentrifuge tube and stored at −80 °C until analysis.The analysis of samples was performed on an Agilent UHPLC-qTOF-MS system which consisted of a 1290 II Infinity series UHPLC system with a 6550 UHD iFunnel accurate mass qTOF spectrometer. During the analysis, the samples were kept at 4 °C. Analytes were separated by using reverse phase ) chromatography. The Agilent MassHunter workstation was used to operate and monitor the instrument and acquire data. Reversed phase mobile phase included (A) water and (B) methanol, both containing 0.04% formic acid. The linear gradient elution was: 0–6 min, 5–100% B, 6–10.5 min, 100% B. Mobile phase flow was set at 0.4 mL/min. Metabolites were ionized by a Jetstream electrospray ionization (ESI) source. The mass spectrometer was operated in positive mode. The spectrometer parameters were set as follows: drying gas (nitrogen) temperature at 175 °C and flow at 12 mL/min, sheath gas temperature at 350 °C and flow at 11 L/min, nebulizer pressure at 45 psi, capillary voltage at 3500 V, nozzle voltage at 300 V, fragmentor voltage at 175 V. Data were acquired within a 50–1600 m/z range in centroid mode with the acquisition rate set at 1.67 spectra/s. The MS abundance threshold was set at 200. Iterative MS/MS data acquisition was performed in positive mode with 10 eV, 20 eV and 40 eV collision energies and with the same chromatographic conditions as for the MS analysis. It was performed on sQC samples ensuring that spectra were collected from ions present in a significant majority of the samples. Positive mode MS2 fragmentation spectra were collection using Agilent Auto MS/MS acquisition mode with a precursor ion exclusion list of commonly occurring positive mode high-intensity precursor ions. The exclusion list is included in 35. Identified peaks with an average value below included blanks were removed along with peaks that had a fill beneath 0.1, signal to noise ratio below 10 and if they were not detected in two thirds in any of the experimental batches. Measured ion intensities of distinct samples taken in both phases were corrected for potential signal drifts using quality control sample based robust locally estimated scatterplot smoothing (QC-RLSC) using the NormalizeMets library (v.0.24) in R (v.4.2.1)36. Batches were then concatenated based on identifications, adduct type, m/z similarity (±0.01) and retention time (±0.75). Feature intensities were then normalized with quantile normalization using the preprocessCore library (v.1.52.1) using the algorithm outlined in Bolstad et al.37.Initial peak processing was done using MS-Dial (v4.7), peak detection made use of a weighted moving average method and allowed for a minimum peak-width of 5 s and minimum peak height of 2000 counts. Identification was done by manual comparison with the Riken library on both MS and MS/MS profiles when available (m/z only)38. For significance testing of the identified peaks, a linear model was set up treating experimental batches as covariates, with the exception of across-shift analysis, where strain and batch was set up as covariates. The sample size for the cross-phase analysis was 102, with 52 and 50 samples in the post and pre shift phases respectively. Each strain/condition combination had between 3 and 6 replicates. See Supplementary Tables 40. Identified metabolites were matched to the KEGG-database using InChIKeys supplied by MS-dial (v4.7)35. Correlation analysis was done using hierarchical clustering based on Pearson correlation in R (v.4.2.1), using an average across replicates.Discriminatory analysis via oPLS-DA and LIMMA linear modeling was done via the MetaboAnalyst-suite (v.5.0.0)41.i.To infer metabolic activity of specific metabolites, the basal flux-sum, Φ, was calculated for all of the metabolites present in iMM904 using Equation 2, similar to the work presented in Chung et al.p-value < 0.1) simulated reactions fluxes and metabolite flux-sums were identified using a two-sided Wilcoxon signed rank test using thirteen simulations from both models as observations. To ensure that the model revisions were not only based on uniform increases or decreases of reaction fluxes, a pairwise Spearman correlation was calculated using simulations from both models.Significantly differing (unadjusted 42. The metric used was balanced accuracy, to be able to manage the imbalance of activity present in the different deletion mutants and phase-changes.Difference in metabolite flux-sums were then compared with measured accumulations to estimate accuracy of the flux reroutes suggested by model revisions, treating the metabolite flux-sums as predictors of species accumulation, a modification of the approach suggested in Mo et al.Further information on research design is available in the Supplementary InformationSupplementary Tables 3-22Reporting Summary


A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Our work is of great value for the structural improvement of constraints-based models.Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells. The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years. However, if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of  For theiML1515 stoichiometric framework [During ETGEM development, we realized that due to the separation of some chemical reactions in the ramework , some prramework . Thereforamework . ConversSaccharomyces cerevisiae and Yarrowia lipolytica, here are more than ten independent cell divisions such as mitochondria and other organelles [The concept of compartmentalization is frequently mentioned both in model construction and practical research. Prokaryotic GEMs are generally divided into two or three regions including cytoplasmic, extracellular and periplasmic compartments, whereby the latter is only present in Gram-negative bacteria, while conditional connectivity is achieved through transport and exchange reactions. In GEMs of eukaryotes such as of ganelles . The comganelles . Dating ganelles . UnfortuiML1515 by integration of thermodynamic and enzymatic constraints. By analyzing the synthetic pathways of l-serine and l-tryptophan, we revealed the necessity of considering the evolutionary strategy of multifunctional enzymes and enzyme complexes as metabolic compartments to effectively avoid unreasonable conflicts between different constraint levels in the extended GEMs. Finally, we demonstrated the application method and value of the multiple-constraint GEMs in the analysis of product synthesis pathways and improving the basic framework quality of GEMs.In this work, we used the model EcoETM , an exte22.1All simulations were conducted using Jupyter Notebook with ETGEMs, a Python-based tool for constructing multi-constrained metabolic network models, which adopts the Cobrapy toolbox  and Pyomhttps://github.com/tibbdc/ETGEMs/tree/master/EnzCompart.The code files, input files, and result files employed in this study are available for access at 2.22 (0.5–200 μM) [2 (0.1–100 μM) [The concentration boundaries for metabolites (0.5 μM–20 mM), including O–200 μM) ,32 and C–100 μM) , were th–100 μM) . In addi–100 μM) ,35, the –100 μM) .2.31)The lower limit of the flux for purpose product synthesis is set, and then the achievable MDF for the pathway is calculated.2)Subsequently, the obtained MDF value is utilized as the constraint's lower bound to calculate the maximum product synthetic flux.3)Taking both the maximum flux and MDF values mentioned above as constraints, solving the pathway flux distribution through the pFBA algorithm.4)Solving the maximum MDF of each reaction in the pathway.5)Finally, the reaction where the maximum MDF is equal to MDF in step 1) is identified as the bottleneck reaction(s).The bottleneck reaction  represen2.4Limiting metabolites  are the 2.5Reactions catalyzed by key enzymes often have a larger flux control coefficient  and high33.1l-serine synthesis pathway, and the other four reactions are also central to the glycolysis pathway. This subset is suitable as an example to illustrate the concept of distributed bottleneck reactions, but the conclusion that the classical EMP pathway cannot coexist with the conventional l-serine synthesis process is inconsistent with observations. Therefore, we analyzed the serine synthesis pathway in detail. The maximal thermodynamic driving force (MDF) of the l-serine synthesis pathways is variable, with 13 turning points, as shown in In our previous publication , we idenThe process of serine synthesis is divided into 13 stages according to the MDF levels. The MDF level in the first seven stages can reach more than 4 kJ/mol, and the thermodynamic feasibility is ideal. The MDF levels of the middle four stages are close to zero, and the feasibility is quite low. Finally, the MDF of the last two stages is negative, so it can be considered that they are thermodynamically infeasible.Further analysis revealed that there are 2 localized and 11 distributed bottleneck reactions in total, representing the combination of reactions located in the central metabolic pathways  . For theFrom the eighth stage, the MDF fell to 1.57 kJ/mol, which was caused by the localized bottleneck reaction of PGCD alone. Although the participation of this reaction reduced the MDF level of the pathway(s), it contributed significantly to the increase of yield, so that the synthetic flux of the pathway(s) reached 17.175 mmol/gDW/h, which was equivalent to 81.7% of the maximal yield.From the ninth to the twelfth stage, the thermodynamic feasibility of the pathway(s) decreased gradually, which was caused by several distributed bottleneck reactions. The common point of these combinations of distributed bottleneck reactions is that the PGCD reaction is always sharing limiting metabolites with other bottleneck reactions, the overall thermodynamic feasibility of the pathway(s) is lower than in the eighth stage . According to the second law of thermodynamics (MDF ≥0), the flux limit of the last thermodynamically feasible pathway(s) at the end of the eleventh stage is 20.66 mmol/gDW/h, which is equivalent to 98.2% of the maximal yield.Desulfovibrio desulfuricans [Clostridium drakei [2 fixation [iML1515 model. As a reaction that consumes reduced flavodoxin, POR5_r inevitably shares fluxes with FLDR2. In addition, FLDR2 also shares the b0684 and b2895 genes (fldA and fldB), encoding flavodoxin 1 and 2 respectively, with the POR5_r reaction. The coupling relationship between FLDR2 and POR5_r has been described by Maurice et al. [2, consuming a reducing equivalent provided by NADPH  can be increased from 42.7% to 98.2% of the theoretical yield predicted by the initial GEM, iML1515. According to the MetaCyc database, the PGCD reaction should indeed appear in the l-serine synthesis pathway of E. coli, which was proved by experimental results , so ARHGDx is redundant and cannot participate in flux balance analysis. The S-configuration substrate of the AHGDx reaction  can only be used as the substrate of another irreversible reaction, SHGO, namely there are no other means to consume it. Therefore, it can be considered that the positive reaction direction of AHGDx in the model is also redundant, and only its reverse reaction is effective. The AHGDx_reverse reaction consumes reducing force, so when it is coupled with the SHGO reaction, the generation and consumption of carbonaceous metabolites can also be offset. This means that when the combined reaction continues to be coupled with PGCD, the thermodynamic feasibility of the overall reaction can be promoted by balancing the reducing force, and the metabolite 2-oxoglutarate  can also be supplemented, as shown in In the d ARHGDx A. The abl-serine revealed by Zhang et al. [It is worth noting that the coupling of reactions is often used in enzyme activity detection , the cong et al.  is fundag et al. .ΔrGi′⁰ of 2.1 kJ/mol was added to the model, and the equation was set as “3 pg_c + akg_c--> 3php_c + S2hglut_c”, which was adopted to replace the original two partial reactions PGCD and AHGDx_ reverse. As the catalytic rate of an enzyme is constrained by the step with the weakest kinetic parameter, the lower kcat value of the AHGDx_reverse reaction was attributed to the combined reaction SerA, as illustrated in Then, a combined reaction catalyzed by SerA with a E. coli, the SerA forms a homotetramer, with each subunit possessing a complete set of substrate binding regions and catalytic functions. Studies have also shown that when the tetrameric structure is disrupted into dimers, catalytic activity decreases by 600-fold. These facts provide evidence for the compartmentalization of this enzyme [+ replenishment but also helps prevent the reverse reaction, offering multiple forms of assistance in the transformation from 3 PG to PHP. As shown in 60 and K141, for the substrates of the two partial reactions are closely located in spatial proximity to each other.In s enzyme . Indeed,l-serine synthesis process was recalculated . As shown in Because the POR5_r, FLDR2 and PFL reactions only work under anaerobic conditions, we closed the oxygen exchange reaction and combined the three reactions into one reaction named POFLx , and the2  and formate (product) are all retained in the combined reaction. On the one hand, the thermodynamic feasibility of the partial reaction is overestimated due to the lack of a concentration ratio constraint of reduced and semi-oxidized flavodoxin in the model. Notably, it is especially easy to obtain this unfair advantage when the concentration constraint is relaxed in a multi-substrate reaction such as POR5_r. On the other hand, the enzyme conformation change of PFL involves the participation of high-energy S-adenosylmethionine (SAM) [Pyruvate carboxylase, Oxaloacetate acetylhydrolase, Acetate-CoA ligase, and Pyruvate synthase) has provided a direct basis for the feasibility of this process. By increasing the CO2 concentration  of the original three partial reactions. Then, we listed the optimal concentration levels of all metabolites involved in the three partial reactions  and the ne (SAM) , so that = 2.579 ) and the = 2.579 , this pr3.3l-Tryptophan is a synthetic precursor of many bioactive components with medicinal value [l-serine production should affect the performance of the l-Trp synthesis pathway. In previously published work, based on the unmodified EcoETM model of the l-serine synthesis process, the MDF levels of the l-tryptophan synthesis pathways were variable and always had acceptable thermodynamic feasibility  in the TCA cycle. Based on the understanding of the partial reaction process catalyzed within the same whole enzyme structure, although cis-aconitate is the product of the ACONTa reaction and the substrate of ACONTb, it is essentially different from the phenomenon of limited metabolite sharing between bottleneck reactions. Its generation and consumption are catalyzed by the same enzyme structure, which saves the time and energy costs for diffusion and capture, improving the affinity of the enzyme for the substrate molecule. By investigating the enzyme catalytic mechanism of aconitase, it can be found that the intermediate metabolite cis-aconitate normally does not dissociate from the active site, so that almost none is released into the cytoplasmic environment [In ironment ,65. Therl-tryptophan, TRPAS2_reverse often appears in pairs in the distributed bottleneck reactions from the third stage by sharing indole with the TRPS3 reaction. Contrary to expectations, l-serine is not required for TRPAS2_reverse to synthesize l-tryptophan, which directly leads to the absence of l-serine from the l-tryptophan synthesis pathway. Subsequently, we listed all the reactions in the iML1515 model that may generate l-tryptophan directly, as shown in Most of the bottleneck reactions at stages 2–8 are found in central carbon metabolism, but there are also two additional high-frequency reactions, termed TRPS3 and TRPAS2_reverse. In the second stage, TRPS3 becomes one of the bottleneck reactions because it shares the metabolite G3P with the central metabolic reactions. As a direct synthesis reaction of ΔrGi′⁰ values. Among them, both TRPS1 and TRPS2 use l-serine as substrate and share the identical gene ID. Based on the literature [α and β subunits [All four reactions in terature , it can subunits , should l-tryptophan synthesis [l-tryptophan decomposition [After this adjustment, TRPAS2_reverse will be silenced by the shutdown of indole generation reaction TRPS3. According to the literature, the indole produced by the TRPS3 reaction will be transmitted to TRPS2 through a channel inside the enzyme , so thatynthesis , but itsposition . Furtherposition .l-tryptophan in the model, and its substrate N-methyltryptophan (nmtrp_c) only participates in this one reaction, that is, it can only be consumed and not regenerated, so that the reaction does not participate in the FBA calculation process in most cases (except when the substrate N-methyltryptophan is directly supplied). Accordingly, it is redundant in the model and is also not a reasonable candidate for l-tryptophan synthesis.In addition, the MTRPOX reaction does not have the ability to synthesize l-tryptophan synthesis process is abnormal due to improper representation of stoichiometric equations, the thermodynamic MDF levels of l-tryptophan pathway(s) should be redrawn after adjusting the stoichiometric framework based on the above analysis. The adjusted reactions are listed in Returning to the analysis of MDF levels, since all the bottleneck reactions at stages 9–10 are central metabolic reactions, and the l-tryptophan synthesis process is shown in The combination of the two groups of reactions is shown in l-Trp synthesis process. To test the rationale of reaction adjustment, we visualized the l-tryptophan synthesis pathway as shown in E. colil-tryptophan synthesis pathway in the MetaCyc database. The synthesis pathway of l-tryptophan has three main confluence points, respectively corresponding to the ANS, ANPRT and TRPS1 reactions. For easy comparison, the pathways predicted by the iML1515 model and the original EcoETM are shown in l-serine. Therefore, although the introduction of enzymatic and thermodynamic constraints can reduce the yield solution space and reproduce the acetate overflow metabolism, the model is still not sufficiently realistic to avoid the prediction error of pathway structure and targets.Kishore et al. published a quantitative and systematic study of the apparent thermodynamic difference between the overall biochemical reaction and the separate reactions  in the l4l-serine and l-tryptophan illustrate the trade-off between pathway yield and thermodynamic driving force. In previous studies on thermodynamics, due to the lack of Gibbs free energy parameters of some reactions, the exposure of intermediate metabolites with yet unquantifiable energy level was reduced by combining reactions [l-serine synthesis is currently only less than 50% [Incomplete reactions in GEMs lead to unrealistic opportunities to participate in the pathway alone and increase the number of reaction combinations, which will expand the solution space of synthetic pathways. However, the separation of linked reactions will cause the exposure of intermediate metabolites and strengthen the constraints, which is unfavorable to the solution space of thermodynamic feasibility . Converseactions ,72. Simieactions ,73. In teactions . Thus, cthan 50% ,76, thisfadAB genes in the fatty acid β-oxidation pathway [β-ketoacyl-CoA can be released and then integrated into a variety of bio-polyesters [N-acetylglucosamine 1-phosphate uridyltransferase and glucosamine 1-phosphate acetyltransferase (GlmU)” [Multifunctional enzymes or enzyme complexes can also be considered as cell compartmentalization strategies, which is of great significance for the feasibility of metabolic processes. This concept should be fully referenced in both the construction and application of constraints-based GEMs. At present, the enzyme-constrained models only consider the effects of multifunctional enzymes and enzyme complexes in terms of the molecular weight of proteins ,14, but  pathway  can be dlyesters . In addilyesters ,81. Therlyesters  and “fus (GlmU)”  expressecis-aconitate is difficult to leak from the enzyme structure in E. coli, we consider the two partial reactions catalyzed by aconitase as an overall reaction. As evidence, the yield of itaconate synthesized from cis-aconitate in E. coli is much lower than that in its natural production strain, Aspergillus terreus [cis-aconitate produced by the TCA cycle from mitochondria to the cytoplasm in Aspergillus terreus, which leads to high production of itaconate. In contrast, E. coli lacks the compartmentalization necessary to maintain membrane structure. Last year, Ye et al. [cis-aconitate, which is orthogonalization with the aconitase. Their work fully recognizes that the structure of enzymes constitutes compartmentalization, which is consistent with the core idea of this manuscript. Therefore, it can provide vivid support for the rationality of the overall reaction and processing methods in our work, and also demonstrate that compartmentalization and decompartmentalization are very valuable strategies. It can be said that trade-offs are ubiquitous. The compartmentalization of aconitase in E. coli improves the efficiency of TCA cycle, and also compresses the production capacity of cis-aconitate and its derivatives. Accordingly, improved in-depth analysis of protein structure and function is crucial for the simulation and prediction of cell behaviors [Thus, it is important to set the parameters in the process of model correction and analysis based on the real intracellular conditions to obtain effective prediction information. At the same time, it is meaningful to investigate the structure and catalytic mechanism of enzymes ,85, as t terreus . This ise et al.  developeehaviors .In addition, the bottleneck reactions predicted in this study often include reactions in the central metabolic pathways, which may help us understand why there are oscillations in central metabolic processes such as glycolysis . All bioThe intelligence of cells is beyond our imagination, and how to reproduce cell space-time in digital cells will also be an interesting challenge, whereby whole-cell modeling  arguablyXue Yang: conceived the project, developed the ETGEMs and constructed the EcoETM model, performed the calculation, Visualization, wrote the manuscript. Zhitao Mao: developed the ETGEMs and constructed the EcoETM model, maintains the calculation tool. Jianfeng Huang: contributed to the analysis and discussion. Ruoyu Wang: maintains the calculation tool, All authors read and approved the manuscript. Huaming Dong: contributed to the analysis and discussion. Yanfei Zhang: contributed to the analysis and discussion, wrote the manuscript. Hongwu Ma: conceived the project, wrote the manuscript.The authors declare no competing financial interests.


The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2  accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host–microbiome metabolic interactions. A resource of microbial metabolic models reveals how an individual’s gut microbiome influences drug metabolism. The gut microbiota synthesizes bioactive metabolites, such as short-chain fatty acids, hormones and neurotransmitters2, and participates in the metabolism of commonly prescribed drugs3, resulting in drug inactivation, activation, detoxification or re-toxification4. Human gut microorganisms have been shown to metabolize 176 of 271 tested drugs5, with activity varying between individuals6. Consequently, precision medicine interventions that take diet, genetics and the microbiome into account have been proposed7. Predicting such personalized treatments would require detailed knowledge of the distribution of drug transformation reactions across human microbial taxa as well as the stoichiometry of these transformations.Trillions of microorganisms inhabit the human gastrointestinal tract, with a high interindividual variation depending on factors, such as sex, age, ethnicity, lifestyle and health status8. COBRA relies on genome-scale reconstructions of the target organisms which are often manually curated based on the available literature8. These reconstructions can be converted into predictive computational models through the application of condition-specific constraints9, including (meta-) omics and nutritional data, and linked together to interrogate strain-resolved, personalized microbiome models11. Hence, the COBRA approach is well suited for the exploration of metabolic human microbiome cometabolism13. To facilitate the genome-scale reconstruction of the thousands of known species inhabiting humans14, semiautomated reconstruction tools, such as CarveMe15, MetaGEM16, MIGRENE17 and gapseq18, have been published. Despite their many advantages, these tools provide limited support for curation against manually refined genome annotations and experimental data from peer-reviewed literature. Both are crucial for the inclusion of not yet routinely annotated species-specific pathways 9. To overcome these limitations, we have developed a semiautomated curation pipeline guided by manually assembled comparative genomic analyses and experimental data19, which previously enabled the generation of AGORA, a resource of 773 genome-scale reconstructions of human gut microorganism strains, representing 605 species and 14 phyla20.A mechanistic systems biology approach that includes a detailed stoichiometric representation of metabolism is constraint-based reconstruction and analysis (COBRA)21 and the organ-resolved, sex-specific, whole-body human metabolic reconstructions22. We demonstrate the use of AGORA2 for the prediction of personalized gut microbial drug metabolism for a cohort of 616 individuals. Taken together, the AGORA2 reconstructions can be used independently or together for investigating microbial metabolism and host–microbiota cometabolism in silico.Here, we present an expansion in scope and coverage of AGORA, called AGORA2, consisting of microbial reconstructions for 7,302 strains, 1,738 species and 25 phyla. AGORA2 summarizes the knowledge and experimental data obtained through manual comparative genomics analyses and literature and textbook review, and demonstrates high accuracy against three independently collected experimental datasets. AGORA2 has been expanded by manually formulated molecule- and strain-resolved drug biotransformation and degradation reactions covering over 5,000 strains, 98 drugs and 15 enzymes, some of which were validated against independent experimental data. The AGORA2 reconstructions are fully compatible with the generic20 data-driven reconstruction refinement pipeline, deemed DEMETER (Data-drivEn METabolic nEtwork Refinement)19. Overall, the DEMETER workflow consists of data collection, data integration, draft reconstruction generation, and translation of reactions and metabolites into the Virtual Metabolic Human (VMH)23 name space, and simultaneous iterative refinement, gap-filling and debugging19. Reconstruction refinement follows standard operating procedures for generating high-quality reconstructions9 and is continuously verified through a test suite19  genomes using PubSEED26 . Cross-phylum metabolic differences also translated to differences in reconstruction sizes and predicted growth rates automated reconstruction tools bridge the gap between automated draft and fully manually curated reconstructions including CarveMe15, gapseq18 and MIGRENE17. To further access the quality of AGORA2 and the DEMETER pipeline, we compared AGORA2’s predictive potential and model properties with other resources of microbial genome-scale reconstructions. For this purpose, we retrieved 8,075 reconstructions built through gapseq18, 1,333 reconstructions built through MIGRENE, deemed MAGMA17, as well as 72 manually curated genome-scale reconstructions deposited in the BiGG database28. Additionally, we built CarveMe15 reconstructions for 7,279 AGORA2 strains and gapseq18 reconstructions for a subset of 1,767 AGORA2 strains . Note that our reconstructions represent knowledge bases; thus, if genetic or biochemical evidence exists for a gene or reaction, it will be included in the reconstruction. In contrast, CarveMe by design removes all flux inconsistent reactions from a metabolic reconstruction15. Compared with the KBase draft reconstructions, AGORA2 had a significantly higher percentage of flux consistent reactions despite being larger in metabolic content, as well as a significantly higher flux consistency than gapseq and MAGMA . It was also observed that all resources except AGORA2 and gapseq produced very high amounts of ATP  on the complex medium for at least a subset of models , containing only positive data, had been used to refine AGORA2. Next, we mapped species-level positive metabolite uptake data, retrieved from Madin et al.32, for 185 species (328 strains) in AGORA2 (‘Madin’ data). Finally, we retrieved strain-resolved positive and negative metabolite uptake and secretion data for 676 AGORA2 strains as well as positive and negative enzyme activity data for 881 AGORA2 strains from the BacDive database33. Neither the Madin dataset nor BacDive had been used during the refinement of AGORA2. For metabolite uptake and secretion, the AGORA2 reconstructions captured the known capabilities of the target organisms very well , the drug-metabolizing predictions against independent published experimental data for 253 drug–microbe pairs , and biotransformation 35 was only found in the Bacteroidetes phylum  and 251 healthy controls38 that had previously allowed us to interrogate the metabolic capabilities of each gut microbiome and validate the fluxes against metabolomic data37. A total of 97% of the named species could be mapped onto AGORA2 (compared with 72% for AGORA). For each individual’s gut microbiome, we built and interrogated a community model  prodrug balsalazide through the azoreductase activity, the highest secretion flux of the active form of balsalazide  achieved by any microbiome was 339.81 mmol d−1 per person, while the average was 25.47 ± 40.84 mmol d−1 per person , nitrosochloramphenicol was increased in cancer cases . Women had significantly higher taurocholate metabolizing capability, and slightly, but significantly, lower conversion potential of the chemotherapy drug gemcitabine  to the drug-metabolizing capacities of the microbiome Fig. . Five pr41. We calculated the fecal net secretion rate for 52 AGORA metabolites  < 0.05. Particularly well covered were amino acids and known fermentation products , as well as amines . Moreover, we included 26 genomes of Eggerthella lenta strains54 available at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA412637. Finally, we retrieved 761 human microbial genomes from the Human Gastrointestinal Bacteria Culture Collection55 in FASTQ format from https://www.ebi.ac.uk/ena/data/view/PRJEB23845 and https://www.ebi.ac.uk/ena/data/view/PRJEB10915. Together with AGORA1.03, which was obtained from the VMH23, these combined efforts resulted in 7,302 strains and 1,738 species included in AGORA2.First, we retrieved 4,185 genomes of human gut-associated strains that were available on PubSEED56  prescribed functional role for the protein, (2) sequence similarities of the protein to proteins with previously confirmed functional roles and (3) genomic context  for one or more enzymes in a pathway may result in blocked reactions in a metabolic reconstruction. To avoid this, we estimated the completeness of metabolic pathways during the genome annotation. For each potentially synthesized metabolite, all the biosynthetic pathways were collected in agreement with the KEGG PATHWAY resource62 was used: (1) The gene corresponding to the gap and present in the genome for the related organisms  was used as a query for a BLAST search in the genome with the gap. (2) Possible BBHs were defined as homologs for that alignment with the query protein having an e-value < −50 and protein identity ≥50%. (3) For each possible BBH, the reverse search was done for the genome that was a source of the query protein. (4) If the query protein and its best homolog in the analyzed genome formed a BBH pair, the gap was filled. (5) A similar genomic context for the query protein and its ortholog was considered as an additional confirmation for orthology of the identified BBH pair.For the gapped pathways, the bidirectional best-hit (BBH) methodl-tyrosine decarboxylase  4.1.1.25) and cytidine deaminase , we found that genomic context is conserved between species and we also analyzed the genomic context. If the genomic context of a candidate gene was similar to that of a known drug-metabolizing gene, the candidate was considered as an ortholog of the known protein. Otherwise, it was considered to as a false positive prediction and excluded from further analysis  Identify genes known to encode for drug-metabolizing enzymes in a range of microbial organisms, from the scientific literature 64 using the following parameters: an e-value ≤ 0.01 and a maximum number of hits equal to 500. For the prediction of protein subcellular localization, the CELLO65 web tool was used. Alignments were performed using MUSCLE v.3.8.31 (ref. 66). For every multiple alignment, position quality scores were evaluated using Clustal X68. Thereafter, all positions with a score of zero were removed from the alignment and the modified alignment was used for construction of the phylogenetic trees. Phylogenetic trees were constructed using the maximum-likelihood method with the default parameters implemented in PhyML-3.0 (ref. 69). The obtained trees were midpoint-rooted and visualized using the interactive viewer Dendroscope, v.3.2.10, build 19 (ref.70).The PubSEED platformhttps://www.ncbi.nlm.nih.gov/pubmed/). Information on 132 carbon sources, 30 fermentation pathways, 64 growth factors, consumption of 73 metabolites and secretion of 51 metabolites was subsequently manually extracted on the species and/or genus level from 732 peer-reviewed papers and >8,000 pages of microbial reference textbooks71. Moreover, the traits of each reconstructed strain including taxonomy, morphology, metabolism and genome size were retrieved through database searches. The taxonomic classification of the strains was retrieved from NCBI Taxonomy (https://www.ncbi.nlm.nih.gov/taxonomy/). Information on morphology, habitat, body site, gram status, oxygen status, metabolism, motility and genome size was manually retrieved from the Integrated Microbial Genomes and Microbiomes72 database (https://img.jgi.doe.gov/)  app. Genomes in FASTQ format were directly imported into the narrative through the ‘Import Paired-End Reads From Web’  app after retrieving the links to the corresponding files from https://www.ebi.ac.uk/ena/data/view/PRJEB23845 and https://www.ebi.ac.uk/ena/data/view/PRJEB10915. The imported assemblies were annotated using RAST subsystems73 through the ‘Annotate Multiple Assemblies’  app. Draft metabolic reconstructions were generated through the ‘Create Multiple Metabolic Models’  app and exported in SBML format through the ‘Bulk Download Modeling Objects’  app.Draft reconstructions were generated through the KBase19, which had been previously used to build AGORA20. Briefly, DEMETER was developed by testing gap-filling steps in few reconstructions and propagating identified solutions to many reconstructions. Curation against experimental data is performed in DEMETER by gap-filling the appropriate reconstructions with a complete pathway for each experimentally demonstrated function. Biomass production under aerobic and anaerobic conditions and on defined media as well as biosynthesis of cell wall components are also enabled through gap-filling solutions that had been previously determined in few reconstructions. Similarly, futile cycles are solved by identifying and correcting the affected reactions in few reconstructions and propagating these changes during the development of DEMETER. More details on DEMETER are provided in ref. 19. A detailed tutorial is available as part of the COBRA Toolbox47.We developed a semiautomated refinement pipeline, DEMETER23 nomenclature; (2) introduced additional gap-filling reactions, where needed, to enable biomass production under anoxic conditions on a complex medium with thermodynamically consistent reaction directionalities; (3) removed futile cycles resulting in thermodynamically implausible ATP production by making the responsible reactions irreversible; (4) ensured through gap-filling and/or deletion of appropriate reactions that all reconstructions captured the collected experimental data; and (5) adjusted biomass objective functions to account for class-specific cell membrane and cell wall structures as well as introducing a periplasm compartment  translated ~1,000 additional reactions and ~800 metabolites from KBase to VMH19. Specifically, it systematically tested whether each reconstruction (1) grew anaerobically on complex medium; (2) had correct reconstruction structure, that is, mass and charge balance, and correct syntax for gene–protein–reaction associations; (3) was thermodynamically feasible, for example, produced realistic amounts of ATP; and (4) captured known metabolic traits of the organism according to the collected experimental and comparative genomic data. Supplementary Table Curation efforts were verified via a test suite medium;  had corrhttps://www.vmh.life/files/reconstructions/AGORA/1.03/AGORA-1.03.zip) also underwent refinement through DEMETER. The AGORA1.03 reconstruction of Staphylococcus intermedius ATCC 27335 was removed since it was a duplicate of the newly reconstructed strain Streptococcus intermedius ATCC 27335. The names of eight AGORA1.03 reconstructions were changed to correct strain determination and/or spelling  v.R2020b.DEMETER has been implemented in the COBRA Toolbox47. The quality checks included are consistent with the Memote42 checks, as were the calculations of the scores. All 7,302 reports can be accessed via https://metaboreport.live.The quality control reports and associated scores were determined for each AGORA2 reconstruction using the MetaboReport tool in the COBRA Toolbox76 using PubSEED subsystems53. Additional information on the presence of the analyzed genes was retrieved from refs. 78.A literature search for microbial enzymes known to transform, degrade, activate, inactive or indirectly influence commonly prescribed drugs was performed, yielding 15 enzymes in total Fig. . To enab79, DrugBank79 and the Transformer database80. Reactions were formulated based on the collected experimentally determined drug structures, drug downstream product metabolite structures and reaction mechanisms. Both cytosolic and extracellular enzymatic reactions were formulated depending on the identified subcellular protein locations. Since at least six drugs undergoing glucuronidation in the human body have been shown to be substrates for the microbial ß-glucuronidase82 (Supplementary Table 83 and three glucuronidated hormones from Recon3D (ref. 21). New metabolites and reactions were assigned VMH IDs following standards in nomenclature used for COBRA reconstructions9, and formulated while ensuring mass and charge balance through the reconstruction tool rBioNet75. In total, for 98 drugs  1,894 out of 3,533 metabolic structures from the AGORA2 reconstructions were collected from the SMILES and InChIs associated with their metabolites and different chemical databases, such as VMH23, KEGG74, HMDB79, PubChem84 and ChEBI85 databases; the metabolic structures were standardized based on the InChI algorithm86 and can be found in the VMH database23; (2) the standardized metabolites and the reaction stoichiometry in the AGORA2 reconstructions were used to generate 5,583 out of 7,300 MDL RXN files; (3) 5,583 out of 7,300 AGORA2 reactions were atom mapped using the Reaction Decoder Tool algorithm87 for active transport reactions and a custom algorithm47 for passive transport reactions and coupled transport reactions. Atom–atom mappings can be found in the VMH database23 and are freely available at https://github.com/opencobra/ctf.The COBRA Toolbox47. Flux balance analysis (FBA)34 was used to simulate metabolic fluxes. All additional scripts for data generation, data analysis and data visualization are available at https://github.com/ThieleLab/CodeBase.All simulations were performed in MATLAB (MathWorks) v.R2020b with IBM CPLEX (IBM) as the linear and quadratic programming solver. Computations were carried out on a tower with a 2.80-GHz processor and 64-GB RAM with 12 cores dedicated to parallelization. The simulations were carried out using functions implemented in the COBRA Toolbox28 (http://bigg.ucsd.edu/). Reconstructions generated through gapseq18  were downloaded from ftp://ftp.rz.uni-kiel.de/pub/medsystbio/models/EnzymaticDataTestModels.zip and exported in SBML format through the sybilSBML package in R using a custom script. MAGMA17 reconstructions  were downloaded from https://www.microbiomeatlas.org/data/MSP_GEM_models.zip. To enable comparability with AGORA2, exchange reactions in all retrieved reconstructions were translated to VMH23 nomenclature through custom MATLAB scripts. Moreover, an ATP demand reaction (VMH reaction ID: DM_atp_c_) was added if not already present and otherwise translated to VMH nomenclature.Manually and semiautomatically curated reconstructions compared with AGORA2 were retrieved as follows: 72 fully manually curated reconstructions were downloaded from the BiGG databasehttps://www.ncbi.nlm.nih.gov/assembly) or ENA (https://www.ebi.ac.uk/ena) and subsequently used to run CarveMe. The remaining 23 AGORA2 strains were excluded as a corresponding protein FASTA file was not available. Reconstructions for 7,279 strains were generated with CarveMe15 v.1.5.1 on Python 3.7.13 (retrieved from https://www.python.org/downloads/release/python-3713) and relying on DIAMOND88 v.0.9.14.Protein fasta files corresponding to 7,279 AGORA2 strains were downloaded from either NCBI  was used as the linear programming solver.Genome FASTA files retrieved as described above were used as the input for gapseq29, was retrieved through the ‘findFluxConsistentSubset’ and ‘findStoichConsistentSubset’ functions implemented in the COBRA Toolbox47. The fraction of stoichiometrically and flux consistent reactions, excluding exchange and demand reactions, was subsequently determined for each AGORA2 reconstruction and corresponding KBase draft reconstruction as well as for 5,587 reconstructions generated through CarveMe15, 8,075 reconstructions generated through gapseq18, 1,333 MAGMA17 reconstructions and 73 curated reconstructions from the BiGG database28. Briefly, the subset of stoichiometrically consistent reactions in a reconstruction includes all reactions that are mass and charge conserved, excluding exchange, demand and sink reactions, which are by definition mass and charge imbalanced29. The subset of flux consistent reactions consists of all reactions can carry flux under the defined set of constraints29.The subset of flux and stoichiometrically consistent reactions, as defined in ref. 33 and mapped onto the VMH23 nomenclature through custom MATLAB scripts. The experimental data included species-level positive and negative metabolite uptake and secretion data for 457 species  and 269 metabolites in AGORA2 from the NJC19 resource30, and species-level positive metabolite uptake data from ref. 32 for 184 species (328 strains) and 85 metabolites in AGORA2. Moreover, strain-resolved positive and negative metabolite uptake and secretion data for 676 AGORA2 strains and 220 metabolites, and positive and negative enzyme activity data for 881 AGORA2 strains and 31 enzymes, were retrieved from the BacDive database33. The enzyme data were mapped to the respective reactions in each of the compared reconstruction resources’ namespaces. Positive data indicated that the metabolite uptake, secretion capability or enzyme activity had been demonstrated in a microorganism, while negative data indicated that the microorganism has been shown not to possess the capability. For each retrieved positive or negative data point, the capability of the respective model to take up or produce the corresponding metabolite was calculated using FBA on unlimited medium by either minimizing or maximizing the corresponding exchange reaction, respectively. For enzyme data, it was tested whether at least one reaction mapped to the respective enzyme was present in the model and could carry a nonzero flux. If the data point was positive and the corresponding model could also take up or secrete the metabolite or produce flux through the corresponding enzymatic reactions(s), this resulted in a true positive prediction, while a false negative prediction occurred when the microorganism was known to have this capability, but the corresponding model did not capture the trait. If the data point was negative and the corresponding model also could not take up or secrete the metabolite or did not produce flux through any reaction(s) mapped to the enzyme, this resulted in a true negative prediction, and otherwise the prediction was a false positive.For an independent assessment of predictive potential of genome-scale reconstructions, independent  experimental data on metabolite uptake and secretion were retrieved from three sourceshttps://github.com/ThieleLab/CodeBase.Prediction accuracies were calculated for the three experimental datasets. For an assessment of the predictive potential of AGORA2 compared with other reconstruction resources, the analysis was repeated for the strains in KBase draft reconstructions; CarveMe reconstructions; and BiGG, gapseq and MAGMA reconstructions that overlapped with the AGORA2 organisms with available data. To this end, the predictive value of all resources was tested via mixed effect logistic regressions with the in silico prediction as predictor and the in vivo behavior (binary) as response variable, while introducing the model as random effect variable accounting for the stochastic dependencies of predictions for different metabolites stemming from the same model. Moreover, the accuracy per model was calculated for all resources, and then compared with the AGORA2 accuracies via nonparametric sign rank tests. The list of all strains in the compared reconstruction resources that were tested against the three datasets is shown in Supplementary Table P values were calculated by Fisher’s exact test and, for sensitivity analysis, by mixed effect logistic regression including the model as random effect variable, accounting for the stochastic dependency of predictions stemming from the same model.A literature search was performed for in vitro experiments demonstrating the capabilities of human microbial strains to metabolize reconstructed drugs through the 15 annotated enzymes, resulting in 253 drug–microbe pairs . Demand reactions for ammonia as well as CO2 and pyruvate (as proxies for carbon sources)  were added, while a demand reaction for ATP (VMH ID: atp) already existed in each reconstruction. Next, the uptake of each drug metabolite  was allowed one by one at an uptake rate of 1 mmol per gdry weight per h. For each drug metabolite, the yields of ATP, ammonia, CO2 and pyruvate from each drug metabolite were computed using FBA by maximizing the flux through the respective demand reactions. As control, yields were also computed for 1 mmol per gdry weight per h of glucose and without any metabolites added.To determine each strain’s capability to metabolize drugs, all AGORA2 strains were constrained with a simulated Western diet38. Species-level abundances for this cohort, which have been determined with MetaPhIAn2 (ref. 90), were retrieved from https://www.nature.com/articles/s41591-019-0458-7#MOESM3. Unclassified taxa on the species level, eukaryotes and viruses were excluded. Of the remaining 517 species, 501 (97%) could be mapped onto the 1,738 AGORA2 species. Pan-species models for AGORA2 were created through the ‘createPanModels’ function. From the pan-species models, personalized microbiome models for each of the 616 samples were built through a computationally efficient pipeline43 with the species-level abundances as input data and parameterized as described elsewhere60. For each individual, we integrated all microbial models having a nonzero abundance in the sample into one personalized microbiome model. To contextualize the models with appropriate diet constraints, a simulated Average Japanese Diet described previously41  that reflect the contribution of each metabolite in the model to the flux through the objective function8. A nonzero shadow price for a metabolite indicates that this metabolite has importance for the total flux capacity through the optimized objective function, that is, in our case, the secretion of a drug metabolic product. A shadow price of zero indicates that increasing the availability of this metabolite would not change the flux through the objective function. To determine the species that were bottlenecks for the conversion potential of the 13 drugs in each microbiome model, nonzero shadow prices for species biomass metabolites (‘species_biomass[c]’), which reflect the contribution of the species to the community biomass reaction, were retrieved.To determine species in microbiome models that were of importance for the microbiome’s combined potential to metabolize a drug, a shadow price analysis was performed as described previously91 using four knots  resulting in three spline variables, each to test on potential nonlinear relationships. Significance was then determined by testing the three spline variables belonging to age  simultaneously on zero via the Wald test91. While for age substantial nonlinearities were found, no indication for nonlinear BMI effects could be identified. The final models included, therefore, only the linear BMI term. Second, we tested for potential associations of net production capacities with case control status. This test was done via generalized linear regressions (logistic or linear) with the net production capacity being the response variable (dichotomized or metric), while adjusting for age (restricted cubic splines), sex  and BMI (linear). We corrected for multiple testing using the FDR, adjusting significance values for 13 tests per analyses stream. A test was considered nominal significant with P < 0.05 and FDR-corrected significant if FDR < 0.05. For sensitivity analysis, we recomputed the drug-metabolizing capabilities using an average European diet instead of a Japanese diet. Then, we calculated Pearson correlations for each drug metabolite between the secretion potentials under Japanese and an average European diet. All statistical analyses were performed with STATA 17/MP. All scripts are available at https://github.com/ThieleLab/CodeBase.We analyzed statistically the net production capacity of 13 drug metabolites Fig.  among 25n = 347) from ref. 38. To test whether AGORA2-based community modeling is capable of predicting the sign of statistical associations between species presence and fecal metabolite concentrations in the CRC sample, we calculated maximal net secretion for 52 metabolites with fecal metabolome data with more than 50% of the samples having concentrations above the limit of detection. Metabolite net secretion was computed using the mgPipe module in the Microbiome Modeling Toolbox43 while relying on computationally efficient flux variability analysis92. Then, we calculated for each species present in at least 10% of the microbiomes and at max 90% of the microbiomes the effect of species (binary predictor: species present versus species not present) on each fecal metabolite concentration in multivariable regressions, adjusting for age, sex, BMI and study group. We then filtered for all species metabolite associations with P < 0.05. Next, we calculated the effect of the species presence on the community net secretion of the corresponding metabolite in analogous regressions. Finally, we calculated for each metabolite the agreement in signs between the in vivo association statistics and the in silico association statistics. Significance was determined by Fisher’s exact test and FDR correction was applied, accounting for 52 tests. Note that the P values should be treated with care since the signs of the various association statistics may cluster due to the multivariate nature of both the metabolome and the microbiome data.We utilized the publicly available metabolome dataset (https://phylot.biobyte.de/) and visualized in iTOL (https://itol.embl.de/)93. Violin plots were generated in BoxPlotR (http://shiny.chemgrid.org/boxplotr/). Clustering of taxa by reaction presence through t-distributed stochastic neighbor embedding (t-SNE)52 was performed using the t-SNE implementation in MATLAB with Euclidean distance, barneshut set as the algorithm and perplexity set to 30. Taxa with fewer representatives than 0.5% of all clustered strains were excluded from the t-SNE plots. Significance of differences in coordinates across taxonomic units was determined by Kruskal–Wallis tests. Circle plots were generated using the online implementation of Circos94. Figure 89.The phylogenetic tree of AGORA2 organisms was constructed in PhyloT (Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41587-022-01628-0.Supplementary InformationSupplementary Notes 1–11 and Figs. 1–10.Reporting SummarySupplementary Data 1Supplementary Tables 1–12.