Escherichia coli communities with increasing complexity: uni-, bi-, and multi-directional cross-feeding of either substitutable metabolic byproducts or essential nutrients. The intermediate-scale model accurately fit empirical data and quantified metabolic exchange rates that are hard to measure experimentally, even for a complex community of 14 amino acid auxotrophies. By studying the conditions of species coexistence, the ecological outcomes of cross-feeding interactions, and each community’s robustness to perturbations, we extracted new quantitative insights from these three published experimental datasets. Our analysis provides a foundation to quantify cross-feeding interactions from experimental data, and highlights the importance of metabolic exchanges in the dynamics and stability of microbial communities.Social interaction between microbes can be described at many levels of details: from the biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing an appropriate level to model microbial communities without losing generality remains a challenge. Here we show that modeling cross-feeding interactions at an intermediate level between genome-scale metabolic models of individual species and consumer-resource models of ecosystems is suitable to experimental data. We applied our modeling framework to three published examples of multi-strain  The behavior of microbial communities such as the human microbiome is hard to predict by its species composition alone. Our efforts to engineer microbiomes—for example to improve human health—would benefit from mathematical models that accurately describe how microbes exchange metabolites with each other and how their environment shapes these exchanges. But what is an appropriate level of details for those models? We propose an intermediate level to model metabolic exchanges between microbes. We show that these models can accurately describe population dynamics in three laboratory communities and predicts their stability in response to perturbations such as changes in the nutrients available in the medium that they grow on. Our work suggests that a highly detailed metabolic network model is unnecessary for extracting ecological insights from experimental data and improves mathematical models so that one day we may be able to predict the behavior of real-world communities such as the human microbiome. Most microorganisms that affect the environments we live in  and thatMicroorganisms consume, transform and secrete many kinds of chemicals, including nutrients, metabolic wastes, extracellular enzymes, antibiotics and cell-cell signaling molecules such as quorum sensing autoinducers –8. The cIf a community is well-characterized and given sufficient data on population dynamics, it should be possible to parameterize the processes involved in microbe-microbe interactions by fitting mathematical models . Any modEscherichia coli (E. coli) strains with increasing levels of complexity: (1) unilateral acetate-mediated cross-feeding  is the antibiotic concentration and Ki is the inhibition constant. We assumed antibiotic concentration remains constant and chose Ki = 1 μM. The cheaters of each amino acid auxotroph were simulated by turning off all amino acid leakages of the auxotroph. They were mixed with the resident community in varying ratios at the beginning of simulations. For all three perturbation types, the feed medium glucose concentration is 0.2 wt.% in the unperturbed condition and serial dilution was run to steady state at 60 days.External perturbations were exerted upon the steady state of the 4-auxotroph community. Nutrient downshift was simulated by decreasing the feed medium concentration of glucose at the beginning of simulations. The effects of an antibiotic that inhibit growth of the amino acid auxotroph https://github.com/liaochen1988/Source_code_for_cross_feeding_paper.All computer codes used in this study are available at the following URL: S1 FigFor reference, a simplified version is shown in (TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(A) Violin plot of the parameter distributions. Gray circles indicate the median of these distributions and red crosses indicate the values obtained through manual fitting and used in simulations. (B) Pairwise scatter plot of these distributions except that the plots along the diagonal are replaced with histograms of parameter values. Parameters not listed here are either fixed to experimentally measured values or biological constants (see (TIF)Click here for additional data file.S4 Fig1,g and I3,a are the parameters that have the largest uncertainty Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 FigFor reference, a simplified version is shown in (TIF)Click here for additional data file.S7 FigΔl,k and VΔk,l) and those rates by their non-producing strains . Parameters not listed here are either fixed to experimentally measured values or biological constants (see Gray circles indicate the median of these distributions. The red box compares the maximum rates of amino acids uptake by their producing strains (V(TIF)Click here for additional data file.S8 Fig(A) Violin plot of these parameter distributions. Gray circles indicate the median of these distributions and red crosses indicate the values obtained through manual fitting and used in simulations. (B) Pairwise sca_er plot of these distributions except that the plots along the diagonal are replaced with histograms of parameters values. Parameters not listed here are either fixed to experimentally measured values or biological constants (see (TIF)Click here for additional data file.S9 FigAbbreviations: glucose (Glu), cysteine (C), phenylalanine (F), glycine (G), histidine (H), isoleucine (I), lysine (K), leucine (L), methionine (M), proline (P), arginine (R), serine (S), threonine (T), tryptophan (W), and tyrosine (Y).(TIF)Click here for additional data file.S10 FigA secretion is deemed as essential if its removal leads to strain loss. Each subplot turns off one secretion reaction: Δx→z indicates the secretion of amino acid z by the amino acid auxotroph Δx. Our simulation results suggest that ΔI→M, ΔM→T, and ΔT→K are essential secretion fluxes. Abbreviations: isoleucine auxotroph (ΔI), lysine auxotroph (ΔK), methionine auxotroph (ΔM), threonine auxotroph (ΔT).(TIF)Click here for additional data file.S11 Fig1 and E2 are the enzymes that control biosynthesis and consumption of the metabolite M respectively. Solid point arrows represent material flow. Dashed point arrows represent positive regulations and dashed blunt arrows represent negative regulations. The gray shading represents a cell. (B-E) Steady state values of various quantities by varying external substrate concentration (the antibiotic concentration is 0 μM). In particular, the model reproduces the observed Monod relationship  is small. (F-I) Steady state values of the same quantities by varying external antibiotic concentration (the substrate concentration is 100 μM).(A) The schematic diagram. Ecircles: ) in (B) (TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file. 3 Apr 2020Dear Dr. Xavier,Thank you very much for submitting your manuscript "Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence" for consideration at PLOS Computational Biology.As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.Both the reviewer expressed interest in the finding of the paper, but also raised a few concerns regarding the model, the presentation of the results, and the parameter fitting.Also, please note that, as written in the instructions, stating "data available on request from the author" is not sufficient.We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.When you are ready to resubmit, please upload the following:[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Jacopo GrilliAssociate EditorPLOS Computational BiologyStefano AllesinaDeputy EditorPLOS Computational Biology***********************Both the reviewer expressed interest in the finding of the paper, but also raised a few concerns regarding the model, the presentation of the results, and the parameter fitting.Also, please note that, as written in the instructions, stating "data available on request from the author" is not sufficient.Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: In this manuscript, the authors used resource competition models coupled with abstract cellular metabolism to investigate microbial ecology, focusing on the problem of cross-feeding. The model is of three layers: growth substrates, metabolic intermediates, and biomass. Variants of the model with different assumptions are applied to three experimental E. coli communities: acetate-mediated cross-feeding, bilateral amino-acid-mediated cross-feeding, and multilateral amino-acid-mediated cross-feeding. the experimental data are used to determine the parameters of each model, manually or by combining automatic estimation and manual calibration. Predictions about how external conditions such as resource supply, leakage fraction, antibiotics, and cheaters influence the community behavior are provided by model prediction. Overall, this manuscript is inspiring in successfully connecting a simple theory with experiments. However, I have a number of concerns regarding both the model and the presentation of results.Major concerns:(1). Leakage. My largest concern is about the flux of the metabolic intermediates (M in their model). For models of cross-feeding, conversion of intracellular substrate (S^) into intracellular intermediates (M^), then release of some of the intracellular intermediates back into the environment is crucial. However, for simplicity, in the current model no intracellular reactions are explicitly modeled (Equation S7-S9). The extracellular S is directly transformed by cells into the extracellular M, without intracellular metabolic details. Thus in the model there are two divergent pathways, one converting S into M directly in the environment, the other converting S into intracellular M^ that contributes to biomass accumulation. Why does the model assume leakage is proportional to conversion rate from substrate to metabolite, rather than proportional to intracellular metabolite concentration?This biologically unrealistic feature of the model could cause unwanted outcomes. For example, accumulation of M^ inside a cell should presumably increase the leakage of M^ into the environment, but that cannot happen here because there is no such reaction from M^ to M. The current model could lead to strange behavior, e.g. high leakage even at very low internal metabolite concentration, or vice versa. In particular, I would expect that leakage could then depend on growth rate, i.e. a cell growing slowly because of lack of one metabolite could build up another metabolite, and therefore leak the latter at a higher rate. The current model assumes the opposite - slow growth means slow substrate uptake, which means slow leakage. The study would be much more convincing if the model treated leakage more realistically, i.e. as emerging from an internal metabolite pool rather than as a fixed fraction of intake flux. Short of that, the authors need to do much more to justify their approximation, e.g. showing for simple single-strain model that their approximation does a reasonable job compared to a more realistic model over a range of external conditions.(2) Uptake. Another seemingly unrealistic assumption concerns external amino acid uptake: "Here we assume that each auxotroph only uptakes the amino acid it is unable to produce and does not uptake other amino acids that it can synthesize. (SI p.12)" Certainly, E. coli cells in rich media that includes amino acids grow faster than in minimal media. The authors either need to justify this assumption, which seems contrary to evidence, or do a more careful job explaining how this assumption influences their results, and when it might be important to treat such uptake of external amino acids by non-auxotrophs more realistically.(3) Stoichiometry. In the model, the flux of intermediates follows both flux balance and stoichiometry, but flux balance and stoichiometry generally cannot be satisfied simultaneously without metabolic overflow. In the model, the flux from the environmental substrate S splits into three fluxes: F1: this flux is converted into environmental metabolites M; F2: this flux is converted into cellular metabolites M^ (then into biomass), and F3: whatever flux is left goes directly into biomass. However, if the growth rate takes the min form of the S and M fluxes, which implies that biomass requires a fixed stoichiometry of S and M, there is flux either from S or M (whichever is not limiting) that cannot enter biomass, and this excess flux need to overflow back into the medium.(4) Coexistence criteria. The conditions for coexistence are rather underdeveloped. First, in the SI section on coexistence criteria the authors only considered the fixed point solution , but did not consider the stability of these fixed points. It is unclear whether these analytically derived (approximate) conditions are tested by simulation . Second, more generally the work would benefit from clearer statements about the conditions for coexistence and possible types of coexisting metabolisms. The current model is very similar to that of Taillefumier et al. eLife 2017, which came to definite conclusions about what types of coexistence are possible and when. The current work could benefit from more definite statements along these lines, and a discussion of the relation to this previous study. Here are specific examples that would benefit from more clear statements: on ll. 205-7 "...the glucose specialist releases more acetate than the amount needed to help the acetate specialist overcome its basal growth disadvantage, causing a declining self-balancing capacity of population dynamics and reduced likelihood of coexistence." It's not clear what "likelihood" implies here. Similarly, on ll. 216-21 "Interestingly, growth of the dominant and rare auxotrophs are always limited by its auxotrophic amino acid and glucose respectively, which suggests an implicit negative feedback loop that maintains their relative abundance ratio before and after the switch: increasing population size of the dominant auxotroph impairs the growth of the rare auxotroph by consuming more glucose but eventually, its own growth is inhibited because a smaller amount of amino acid it needs to grow can be produced by its partner." This phrasing is rather complex, and to my mind ambiguous about the origins of the observed behavior. One suggestion is for the authors to produce a graphical representation of the conditions for coexistence, e.g. showing contours of growth rate = dilution rate for each species in the space of nutrient concentrations, and considering possible points where these could overlap to yield coexistence. Such a graphical approach is standard in the field and can provide clear intuition for the classes of outcomes possible.(5) Comparison to Lotka-Volterra (LV). Does the model actually perform better than an LV model? One of the central claims of the paper is that the level of modeling including explicit nutrients is superior to higher-level models. This sounds reasonable, but isn't satisfactorily demonstrated. The Authors should formulate an LV model with comparable complexity and demonstrate that the current model is actually superior. The authors do provide a comparison to the model in Mee et al., but that model is not sufficiently well explained or justified to judge whether this is a fair comparison.(6) Parameter sensitivity analysis. Since the analytical results require approximations (S34-35), and the parameters are manually tuned, I think a parameter sensitivity analysis for coexistence is quite important.Minor points:(7) Why does the coexistence regime in Figure 3 so narrow? In Lenski's expeirment, the coexistence is quite stable.(8) What is the definition of a generation? Presumably ~ 1/D.(9) Fig. 3B - why is the leakage rate set so high, at 0.5?Reviewer #2: In this manuscript, the Authors provide a detailed study of a consumer-resource model augmented by explicit cross-feeding dynamics of metabolites and resource dynamics. The Authors compare the model output to three experiments between cross-feeding E. coli strains at increasing levels of complexity, and discuss the results in the light of ecological interactions and community robustness. The Authors propose that by fitting the community dynamics to their modeling framework one can quantify exchange rates that are difficult to measure experimentally. I have enjoyed reading the manuscript, which I find interesting and well written. I agree with the Authors conclusion, but I have one major concern that I think should be addressed.In the introduction and in the discussion, the Authors mention the fact that manual fitting of a model has the downside of being subjective, time consuming and requiring an expert operator. I would add that perhaps the main downside of manual fitting is that one cannot infer correlations among parameters. Especially for chemical reaction models, where chemical species are produced and consumed at unknown rates, fitting parameters can be highly correlated because one can often rescale production and consumption rates to achieve the same average species concentrations. Especially given that the Authors propose this as a framework that can be used to quantify exchange rates that are hard to measure experimentally, I think that the paper requires a rigorous parameter identification approach. At least for the two-species communities, it should be feasible to adopt Markov-Chain-Monte-Carlo methods to estimate the posterior distribution of the parameters, and highlight possible correlations among them. This would also allow the Authors to report confidence intervals for various quantities that are inferred from the data.Minor points:- In line 128, the Authors mention that they used Monod kinetics for the cell death. My interpretation of this statement is that cell death is a saturating function of some chemical species or of cell density, but this doesn't seem to be the case looking at the equations, where cell death occurs at a constant rate.- Line 165-167: it would help to state explicitly here that the strains that are auxotroph for lysine and leucine can export them to the extracellular environment- Supplementary Tables: I would suggest adding a column with literature values for those best-fit parameters that can be compared to experimentally-measured values**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. 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A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Jacopo GrilliAssociate EditorPLOS Computational BiologyStefano AllesinaDeputy EditorPLOS Computational Biology***********************************************************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have addressed all of my concerns in the revised manuscript. I am now happy to recommend publication in PLOS Computational Biology.Reviewer #2: I think that the Authors did a good job in revising the manuscript. I have only one minor comment left: looking at Fig. 3, it seems that some of the parameters' posterior distributions  appear to have a lower bound imposed on them (both appear to cluster at the lower end of the domain). Was a lower bound imposed on the MCMC search? I haven't seen this stated in the methods.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: None**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  1223-442824 | ploscompbiol.org | @PLOSCompBiolPLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom


Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria  Studies of the gut microbiome have been shown to have a major impact on health and the risk for disease. Predicting metabolic output in the gut microbiome would be particularly beneficial in understanding how different nutrient sources influence metabolite production. Microbial metabolic products have been shown to be involved in intercellular communication and immune response mechanisms in the gastrointestinal tract ,2. It wom/z) of significantly changing features detected from XCMS Online [Citrobacter sedlakii (C. sedlakii), having well-established metabolic models [Mass spectrometry-based metabolomic data are already being used to assist in characterizing metabolic models by comparing the metabolites produced in cell cultures to expected biomass output via flux balance analysis (FBA) ,17,18,19S Online  results.c models ,23, was C. sedlakii in minimal media supplemented with 0.2% glucose. The resulting metabolite list was imported into MS_FBA and stored as a dataframe for integration with high-resolution liquid chromatography mass spectrometry (LCMS) data. MS_FBA was implemented on both the reverse phase (RP) and hydrophilic interaction liquid chromatography (HILIC) datasets. After comparison with the metabolic model, a graphical output was generated to illustrate matches between XCMS Online [PyFBA  was usedS Online  significC. sedlakii metabolic model, using glucose as a carbon source. Isotope matches and adducts were readily validated in the raw data and in the XCMS feature results, as demonstrated with glutamic acid from the RP data in The metabolite features identified as significantly changing between time points on the growth curve were matched to a PyFBA predicted metabolite list based on a In addition to the predicted metabolite list, the ModelSEED database was also input as a full list of metabolites to assess compounds in the metabolomic data that were not matched to the PyFBA model. The RP data were run in positive mode and the HILIC data in negative mode to achieve better separation of both polar and nonpolar metabolites and to improve detection of analytes that are not readily ionizable in both MS polarities. The results of the MS_FBA analyses are reported in P-value threshold of 0.05. Of those features, 135 were matched to expected compounds list from PyFBA . The identification of feature adducts assists in positively identifying features, particularly in the cases where there is an isotope match. In the case of glutamic acid , we took this as a challenge to see how low our metabolome detection could get, as well as to generate an original yet simple set of data to use with the MS_FBA script. This had an obvious impact, with only 41% of compounds matched to the PyFBA expected list in both the combined RP and HILIC datasets. Considering the cell density was this low, the amount of coverage the MS_FBA script was able to obtain was surprising. However, optimizing the growth portion of the experimental procedure will help when applying MS/MS validation methods in our algorithms.In total, 35 out of 253 features were identified as being the same in both RP and HILIC datasets. Reverse phase chromatography typically provides coverage for non-polar metabolites, whereas HILIC provides polar metabolite selectivity [Although the overall signal intensity and metabolite production were lacking due to the low glucose concentration, there were hundreds more features that had potential matches with the ModelSEED database compared to the PyFBA generated metabolic model. This indicates that the gap filling methods are not sufficient to predict all the metabolic pathways and transport mechanisms that are actually present in the bacterium. Gap filling  in PyFBAC. sedlakii as a model system, on which the metabolomics workflow was performed. Five biological replicates of C. sedlakii cultures were prepared by upscaling the same workflow used to generate the genomic model [4Cl, 0.25 mM NaSO4, 1.0 mM MgSO4, 1.32 mM K2HPO4, 10mM KCl, 0.5 μM CaCl2, 5 mM NaCl, and 6 μM FeCl3 [To compare raw metabolomics data to metabolic models generated via flux balance analysis, we grew ic model . From a μM FeCl3 . All culEach of the five biological replicates had three replicate samples for three different extraction time points. The negative growth control was inoculated with 2 μL of the sterile wash buffer to ensure sterility during the wash step and was also prepared in triplicate, giving a total of 18 samples. The bacteria were grown to mid, late, and stationary phases before each set of samples  were quenched and extracted. Extraction of the internal metabolites  was perfC. sedlakii extracts on an Elute UHPLC and Bruker Impact II quadrupole time of flight (QTOF) LCMS system. The first analysis was performed on a Scherzo SM column  in reverse phase. A gradient elution was used from 98% channel A (0.1% formic acid in water) to 98% channel B (0.1% formic acid in acetonitrile) over 22 min, using a flow rate of 200 µL/min and a 15 µL injection volume. The mass spectrometer was operated in positive mode using a mass range of 50–1000 m/z at a scan rate of 3 Hz. The capillary voltage was set to 5500 V, with nebulizer gas pressure set at 1.8 bar, dry gas flow set at 4.0 L/min, and dry gas temperature set at 200 °C. Ion funnels 1 and 2 were set to 100 and 200 V, respectively. The collision RF was set to 750 V, with a transfer time of 60 µs and a pre-pulse storage time of 5 µs.Two separate untargeted LCMS analyses were performed on the m/z at a scan rate of 3 Hz. The capillary voltage was set to 3500 V with nebulizer pressure of 2.0 bar, a dry gas flow of 6.0 L/min, and dry gas temperature of 200 °C. Ion funnels 1 and 2 were set to 100 and 200 V, respectively. The collision RF was set to 350 V, with a transfer time of 75 µs and a pre-pulse storage time of 5.0 µs. Data were acquired using Bruker HyStar v4.1 SR2 and converted to mzxml format using Bruker CompassXport v3.0.7.The second analysis was performed on a Luna NH2 column  using HILIC. A gradient elution was used from 98% channel B (acetonitrile) to 98% channel A  over 55 min using a flow rate of 100 µL/min and a 15 µL injection volume. The mass spectrometer was operated in negative mode using a mass range from 50 to 1000 https://www.metabolomicsworkbench.org.The data obtained in this study will be accessible at the NIH Common Fund’s National Metabolomics Data Repository  website, the Metabolomics Workbench, XCMS Online was used to perform feature detection, retention time correction, and to identify statistically significant features that changed between mid to late exponential phases and between late exponential and stationary phases. XCMS Online is a freely available metabolomics data processing and analysis software (xcmsonline.scripps.edu) ,24,28,29http://linsalrob.github.io/PyFBA/. Details on how to generate a PyFBA metabolite list from an annotated genome can be found as previously described. Any flux balance analysis software can be used, provided that the metabolite list is in the proper format described at https://github.com/mmarney/MS_FBA.PyFBA  is the fhttps://github.com/mmarney/MS_FBA and can be run on any standard computer with R installed. For details on installation and implementation of MS_FBA, see https://github.com/mmarney/MS_FBA/blob/master/INSTALLATION.md. Details on script parameters are available in the The metabolite prediction against the flux balance model was performed using a script developed in-house named MS_FBA. MS_FBA is an R script available for download from


To the Editor — Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.4 limit GEM reuse. Moreover, numerical errors5 and omission of essential cofactors6 in a single biomass objective function can have substantial impact on the predictive performance of a GEM. Failure to make checks for flux cycles and imbalances can render model predictions untrustworthy7.Incompatible description formats and missing annotations8. We believe that it is essential to optimize GEM reproducibility and reuse. Researchers need models that are software-agnostic, with components that have standardized, database-independent identifiers. Default conditions and mathematically specified modeling formulations must be precisely defined to allow reproduction of the original model predictions. Models must produce feasible phenotypes under various conditions. Finally, data used to build any model must be made available in a reusable format.Every year, increasing numbers of manually curated and automatically generated GEMs are published, including those for human and cancer tissue models9 as the primary description and exchange format. The SBML3FBC package adds structured, semantic descriptions for domain-specific model components such as flux bounds, multiple linear objective functions, GPR rules, metabolite chemical formulas, charge and annotations. The SBML and constraint-based modeling communities collaboratively develop this package, updating it based on user input. It has been adopted by a wide range of constraint-based modeling software and public model repositories (http://cbmpy.sourceforge.net/ and refs. 15), and should therefore be considered the standard for encoding GEMs.A dual approach could be used to improve GEM reuse and reproducibility. First, we advocate adoption of the latest version of the Systems Biology Markup Language (SBML) level 3 flux balance constraints (SBML3FBC) package17. MEMOTE accepts stoichiometric models encoded in SBML3FBC and previous versions as input. In addition to structural validation analogous to the SBML validator18, MEMOTE benchmarks metabolic models using consensus tests from four general areas: annotation, basic tests, biomass reaction and stoichiometry.Second, we present MEMOTE , an open-source Python software that represents a unified approach to ensure the formally correct definition of SBML3FBC and provides quality control and continuous quality assurance of metabolic models with tools and best practices already used in software development19, that all primary identifiers belong to the same namespace rather than being fractured across several namespaces, and that components are described using Systems Biology Ontology (SBO) terms20. A lack of explicit, standardized annotations complicates the use, comparison and extension of GEMs, and thus strongly hampers collaboration4.Annotation tests check that a model is annotated according to community standards with minimum information required in annotation of models (MIRIAM)-compliant cross-references21, are also checked.Basic tests check the formal correctness of a model and verify the presence of components such as metabolites, compartments, reactions and genes. These tests also check for metabolite formula and charge information, and GPR rules. General quality metrics, such as the degree of metabolic coverage representing the ratio of reactions and genes6.A model is tested for production of biomass precursors in different conditions, for biomass consistency, for nonzero growth rate and for direct precursors. The biomass reaction is based on the biomass composition of the modeled organism and expresses its ability to produce the necessary precursors for in silico cell growth and maintenance. Thus, an extensive, well-formed biomass reaction is crucial for accurate predictions with a GEM7 and permanently blocked reactions are identified by MEMOTE. Errors in stoichiometries may result in the production of ATP or redox cofactors from nothing2 and are detrimental to the performance of the model when using flux-based analysis4.Stoichiometric inconsistency, erroneously produced energy metabolitesMEMOTE enables a quick comparison of any two given models, in which individual test results are quantified and condensed to calculate an overall score (Supplementary Note https://github.com), GitLab (https://gitlab.com/) or BioModels12 (http://wwwdev.ebi.ac.uk/biomodels/). MEMOTE is tightly integrated with GitHub. Models generated and versioned in MEMOTE can easily be uploaded to GitLab and BioModels. Collaborative model reconstruction with MEMOTE as benchmark can occur using all three software platforms  of GEMs.We validated MEMOTE using models from seven GEM collections Fig. , that coent Fig.  and massent Fig. . Of the 24 and known reactions with undiscovered genes, or if GPR rules were annotated in nonstandard ways.During GEM reconstruction, metabolic reactions are defined based on functional gene annotations, and this information is output as GPR rules. We found that ~15% of reactions in models we tested are not annotated with GPR rules Fig. . For pub25 and Path2Models22 have a very low fraction of universally blocked reactions, whereas models from AGORA26 and KBase14 contain ~30% blocked reactions, and BiGG13 models and OptFlux15 models contain ~20% blocked reactions . To encourage integration as opposed to duplication, MEMOTE provides a Python application programing interface (API) as well as being available as a web service. MEMOTE has already been integrated in several services and tools (Supplementary Note We propose that an independent, central library of tests and a tool to run them offers an unbiased approach to quality control because the tests are continuously reviewed by the community. This resource will be maintained under stewardship of Nikolaus Sonnenschein by the openCOBRA consortium (We recommend that MEMOTE users reach out to GEM authors to report any errors and thereby enable community improvement of models as resources. Using inconsistent GEMs for hypothesis generation could lead researchers down blind alleys, so we weighed the influence of ‘consistency’ and ‘stoichiometric consistency’ and SBO terms higher than tests for metabolite, reaction and gene annotations.We are committed to keeping MEMOTE open to support community principles. Robust benchmarking will only work if it is actively supported by the whole community, and we call on any interested experts to join this endeavor and enable its continual improvement.Further information on research design is available in the Supplementary MaterialsSupplementary Figs. 1–162, Notes 1–5, Methods, and Tables 1 and 2Reporting Summary


These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date.Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NHA. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium  •Azotobacter vinelandii DJ achives over 90% accuracy.Genome-scale metabolic model of •iDT1278 is the most comprehensive model to simulate diazotrophy.•A. vinelandii.Determining the most suitable culture conditions to produce polymers •Constraint-based modeling unravels links among nitrogen fixation and production of organic nitrogen. PHB is te (PHB) . Both bite (PHB) .A. vinelandii has been shown to grow under a broad range of heterotrophic conditions and is able to metabolize, different sugars, alcohols, and organic acids as well as nitrogen-containing compounds   , enablinAzotobacter vinelandii DJ we used a systems biology approach, which offers tools to predict the organism behavior based on mathematical representations of biological data. M-models can be reconstructed using semi-automated tools that generate a draft model. This draft model is further curated manually to increase its quality. To date, only two core M-models of Azotobacter vinelandii are available that contain a reduced number of metabolic reactions. These core reactions are in general related to nitrogen fixation or PHB and alginate production, disregarding most of the central metabolism of the microorganism   . Here we22.1A. vinelandii DJ was generated using The COBRA  number that could be used to obtain the GPR (gene-protein reaction) associations. The final list of reactions with EC number and gene association not previously present in the model were balanced and added to the model before analyzing GPR associations.We used PATRIC  to identA. vinelandii DJ proteins and the exogenous proteins in the GPRs to identify A. vinelandii (AVIN) genes closely related to the exogenous proteins. We identified proteins based on BLASTp criteria of ≥ 40% identity, e-value ≤ 1e−4, and query coverage ≥ 85%. A second step of manual curation was performed based on protein function, type of metabolic reaction, and GPR associations. Then all the GPR associations were manually curated to catalyze biological reactions that they were associated with. PATRIC essential genes previously identified were added in this step of manual curation. Remaining reactions with mixed AVIN and exogenous genes in the GPR association were manually curated in order to remove the genes that did not belong to A. vinelandii. Reactions with exclusively exogenous GPR associations were identified through previous manual curation steps. Afterwards, Flux Balance Analysis (FBA) was performed to identify which of these reactions carry any flux under experimental conditions . From these results, gap-filling was used to connect pathways through the data retrieved. A second step of gap-filling was accomplished to connect the metabolites from the medium conditions retrieved using literature information  through 2.2.3in-silico GPR simulations to verify if the GPR associations are correctly assigned using the COBRA Toolbox algorithms. Next, we performed Mass Balance simulations on the model to check for unbalanced reactions added during the model refinement. Ultimately, the final model was tested looking for ATP, NADH, and NADPH free energy production, removing exchange reactions, and calculating their accumulation.Final quality check was performed by a person who did not perform the manual curation to assess the quality of the data. We performed 2.32 consumption conditions from the literature  and 95 nitrogen sources (Biolog plates PM3) to validate model predictions. Biolog microplates experiments to test carbon and nitrogen assimilation were performed in the present work, measuring the growth rate values in the plate reader for 96 h. For carbon sources evaluation, ammonium assimilation was not fixed to a specific value (non-diazotroph conditions). The experimental results from Biolog plates were matched with data retrieved from the literature to determine and evaluate model precision during the simulations. During the nitrogen condition simulations, pyruvate was used as the unique carbon source. Statistical parameters were calculated according to the comparison between the metabolic predictions and the experimental values. The model accuracy from the Biolog plates results was compared with the in-silico predictions of the A. vinelandii model from CarveMe to identify the quality of the model simulations of the present work. The alginate production capability of the model was tested using four different carbon sources (carbohydrates) from the literature  for MATLAB (MathWorks). Percent error between experimental values and in silico results were calculated to obtain model accuracy.Experimental data from the literature were retrieved to calculate the initial medium constraints. For each growth condition, the carbon, nitrogen, and hydrogen fluxes were initially determined depending on every value obtained from the literature. The constraints related to mineral compounds and exchange reactions are summarized in terature  were useterature . The car2.4in-silico for all the carbon sources (PM1 and PM2) experiments. The reactions were grouped in general subsystems that represented the complete metabolism of A. vinelandii DJ. Subsequently, an average flux per subsystem was calculated using the flux values of all the reactions belonging to the subsystem. This procedure was performed to calculate the carbon and nitrogen distribution in each general subsystem under diazotrophic and non-diazotrophic conditions. Ultimately, the carbon and nitrogen distributions (the grouped average fluxes per subsystem) were compared through a linear correlation analysis in order to determine how the fluxes change through the experimental conditions for carbon sources set in the Biolog plates. High linear correlation coefficients (r) indicate a similar distribution of a specific subsystem (above 0.9), while low coefficients suggest different carbon or nitrogen distributions when comparing diazotroph and non-diazotroph conditions.Experimentally determined growth phenotypes using Biolog plates were used to validate predicted carbon and nitrogen flux distributions. The internal fluxes for all the reactions of the model were calculated 33.1A. vinelandii DJ (A. vinelandii DJ was reconstructed using the genome annotation from PATRIC (Genome ID: 322710.5). Five manually curated and validated M-Models were used as protein homology templates: Escherichia coli str. K-12 substr. MG1655 , were assigned functionality based on information in the bioinformatics databases. The assigned genes in the GPR rules were replaced with the A. vinelandii genes (AVIN).Model refinement was performed using two principal steps: manual curation and gap filling. Every gene-protein-reaction (GPR) association was verified using multiple databases  and available information from the literature. Manual curation was based on protein sequence similarity. The genes annotated in the GPR associations were aligned to protein sequences of The original draft model consisted of 1,532 genes A corresp3.1.2After the manual curation, the total number of reactions and metabolites in the model was 2,416 and 1,976, respectively. We used literature information and experimental data from the Biolog plates results (PM1 and PM2 for carbon sources and PM3 for nitrogen sources) to add or remove reactions in the model. Each reaction added to the model in this step was manually reviewed to maintain concordance in the GPR associations. Overall, a total of 51 reactions and 29 metabolites (mostly reactions related to carbohydrate and amino acids catabolism) were added to the model during the gap filling process , Fig. 2C3.1.3The Azotobacter vinelandii DJ metabolic model (iDT1278) consists of 2,003 metabolites, 2,469 reactions and 1,278 genes . Specific details about the reactions and metabolites from the model are summarized in iDT1278 was validated using experimental data under nitrogen fixation (diazotroph) and ammonium assimilation (non-diazotroph) conditions. iDT1278 contains all the reactions and genes involved in nitrogen fixation, PHB, and alginate biosynthesis (ynthesis B.iDT1278 are shown in A. vinelandii DJ such as nitrogen fixation (nitrogen metabolism), PHB and alginate production (glycan and secondary metabolites biosynthesis) represent around 3% of the metabolic reactions. Template models used during the reconstruction share 208 reactions with iDT1278. Most of the reactions  were taken from the first template  (2 consumption pathways were obtained from the templates iHN637(Clostridium ljungdahlii DSM 13528) and iAF987 . iDT1278 shares 208 reactions among all the template models  A. Nitroge models C which a CarveMe  contains3.1.4iDT1278 includes two biomass reactions: 1) An initial BOF was obtained from the first template  based on their physiological similarity (Gram-negative bacteria); the stoichiometric coefficients of the amino acids present in the BOF were calculated based on the theoretical amino acid abundance in the genome, using 55% of the biomass composition from amino acids. 2) A second BOF was determined from the first reaction to predict the alginate production since A. vinelandii DJ produces alginate only under specific metabolic conditions (A. vinelandii.The biomass objective function (BOF) contains the principal constituents and the abundance of each metabolite involved in biomass production. The proportion of each metabolite participating in the BOF composition is determined per gram of biomass. nditions . The sec3.23.2.1iDT1278 was tested under six different experimental conditions, specifically, carbohydrates under diazotrophic and non-diazotrophic conditions  when predicting growth rates using ammonium as nitrogen source compared to N2. Subsequently, flux balance analysis , using high-throughput phenotypic data as well as literature information. Initially, nditions . The M-msis FBA,  was perfnditions . Statistnditions C an accunditions C. The faA. vinelandii DJ. Out of 190 carbon sources from the Biolog plates, 123 compounds were identified in the model; the simulations were performed under two specific conditions: diazotrophic and non-diazotrophic simulation conditions. However, experimental results were obtained only under growth with ammonium as the unique nitrogen source. The same procedure used in PM1 and PM2 experiments was followed to estimate the growth rates with 75 different nitrogen sources. For this case, simulations were performed using pyruvate as the carbon source. iDT1278 and the CarveMe metabolic model accuracy. Details of CarveMe predictions and statistical parameters using Biolog plates data are presented in Additional experimental validation was performed using Biolog plates for a set of carbon (PM1 and PM2) and nitrogen (PM3) sources to determine the growth rate values of iDT1278 compared to CarveMe simulations . These statistical results support the clear differences between the quality predictions of iDT1278 and other metabolic models available for A. vinelandii. Significant changes in the growth rate values were not observed when using amino acids as carbon sources since these organic molecules contain nitrogen, which provides an indirect supply of this element to the microbe. For the nitrogen source validation, an overall accuracy of 91% was accomplished, nonetheless, the number of non-growing conditions increased considerably in comparison with the carbon condition experiments , indicating a reduced capability to grow in a wide range of nitrogen sources.For carbon sources validation, an accuracy of 89% was achieved with 58 true positive predictions (95% positive predicted) and 50 true negative estimations (84% negative predicted). For this subset of compounds, accuracy decreased significantly in negative predictions . These negative prediction disagreements involve carbohydrates and some amino acids as carbon sources. Some false negatives appear to be related to the lack of evidence about required transporters. Simulated growth rate values in ammonium assimilation conditions were significantly higher than in the nitrogen fixation conditions (>26%). Higher accuracy was observed in both positive and negative predictions in 3.32 and NH4) conditions (disregarding transport fluxes). Similar tendencies were observed for the internal nitrogen flux distributions across the subsystems for diazotrophic and non-diazotrophic growth. However, the average flux distributions of carbon and nitrogen decline under diazotrophic conditions. Regarding global metabolic fluxes, the global carbon flux drops 4.9% and the nitrogen global flux value decreases 5.5%. The pathways with higher variation between carbon and nitrogen fluxes in diazotrophic and non-diazotrophic conditions were riboflavin metabolism (45%), glycine, serine and threonine metabolism (22%), alanine, aspartate and glutamate metabolism (26%), and nucleotide synthesis (10%). These subsystems are well-known for containing metabolites with high nitrogen content. The decline in the carbon and nitrogen global flux values of these pathways can be related to the low available nitrogen under diazotroph conditions due to the high energy cost of nitrogen fixation.The average metabolic fluxes of carbon and nitrogen elements were determined for all carbon sources with growth rate values greater than 0.001 from Biolog plates data . The complete dataset with the experimental data is summarized in 2 and NH4) conditions. −30) were observed in all the amino acids pathways, biomass and maintenance functions, energy metabolism, carbohydrate metabolism, and some subsystems related to lipid metabolism (lipopolysaccharide biosynthesis and glycerophospholipid metabolism), demonstrating similar metabolic flux distributions through these specific subsystems when comparing diazotrophic and non-diazotrophic conditions. However, weak correlation values were obtained in riboflavin metabolism (r = 0.66), oxidative phosphorylation (r = 0.32 in carbon fluxes and r = 0.37 in nitrogen distributions) and transport to the inner membrane , showing an average flux distribution decrease of 45% for these specific subsystems when comparing carbon and nitrogen activity in diazotrophic against non-diazotrophic conditions. Additionally, strong correlation values were observed in specific subsystem clusters for both nitrogen conditions.The grouped average fluxes of carbon and nitrogen elements per subsystem calculated for diazotrophic and non-diazotrophic conditions were compared through a linear correlation analysis to determine how the subsystem flux values behave under both nitrogen , which encompass a complex protein system to synthetize the alginate polymer . The disagreement appears to be associated to the lack of information in the literature and bioinformatic databases about the essential enzymes in the metabolism of this disaccharide in A. vinelandii DJ.We evaluated the model accuracy to growth and alginate production using four carbon sources under diazotrophic and non-diazotrophic conditions. Experimental data retrieved from the literature  was onlyA. vinelandii consumes ammonium as a nitrogen source instead of molecular nitrogen. Additionally, a second comparison of alginate production rates between ammonium and molecular nitrogen exhibited the same increasing tendency when ammonium is used as a unique nitrogen source instead of molecular nitrogen (around 27%).We compared the growth rates determined under diazotrophic and non-diazotrophic conditions for alginate production. The simulation analysis showed a significant increase in the growth rates (close to 28%) when the A. vinelandii we incorporated a sink reaction to the model iDT1278. Simulated flux distributions about PHB production were validated using fluxomic data retrieved from 2 and NH4) conditions. A total of 16 out of 19 reaction flux estimations presented a global accuracy above 90% for diazotrophic and non-diazotrophic conditions. Disagreements were detected in three specific reactions: the transketolase (TKT2), aconitase (ACONTa/ACONTb) and isocitrate dehydrogenase (ICDHyr) in which the percent errors were above 20%. According to the predicted and experimental results, a higher PHB production is obtained under non-diazotroph conditions due to higher energy cost for nitrogen fixation. Additionally, most of the carbon coming from the glucose is metabolized through the Entner-Doudoroff (ED) pathway, generating less energy than glycolysis, but producing pyruvate (PHB precursor) in fewer steps. The available pyruvate generated though the ED pathway is mostly used in the TCA cycle and the PHB synthesis  that focuses on nitrogen assimilation, nitrogen fixation, as well as on alginate and PHB production. The model consists of 1,278 genes involved in 2,469 reactions. Compared to the first microorganism template used in the present work , the percentage of metabolic genes per genome decreases from 31% (iML1515) to 26% (iDT1278). However, a higher percentage of metabolic genes (from 26% of A. vinelandii to 12% of the average genes in the photosynthetic models) was observed when iDT1278 was compared to three photosynthetic organisms in the BiGG database: C. vulgaris UTEX 395, iCZ843  in the predictions compared to the CarveMe model simulations. Additionally, iDT1278 predicted accurately the growth ratio and production values of alginate and PHB production under diazotrophic and non-diazotrophic conditions. To our knowledge, this is the first M-model at genome-scale capable to simulate several carbon and nitrogen conditions (close to 250 conditions) with a high precision even when comparing internal metabolic fluxes.Here we have created the first comprehensive genome-scale metabolic model for , iCZ843 , Synecho, iJN678  and C. r iCR1080 . The M-m4.24.2.1iDT1278 accurately predicts the growth of A. vinelandii using different carbon sources under diazotrophic and non-diazotrophic conditions. The model contains all required reactions and constraints to successfully simulate the BOF representing the growth of the organism. Model predictions have been confirmed by experimental validation using Biolog plates . With this information we elucidated the preferred mechanism used by the A. vinelandii DJ to fix nitrogen while growing with different carbon sources. The N2 uptake depends on the ammonium concentration and metal cofactor concentrations required for the nitrogenases  behave very similar in both nitrogen conditions. These flux distribution correlations suggest that the major discrepancies in the metabolism of A. vinelandii fixing nitrogen are related to the cofactor and vitamin pathways (riboflavin metabolism) and oxidative phosphorylation (energy generation). According to the model predictions (iDT1278), the vitamin, cofactor and oxidative phosphorylation pathways in A. vinelandii are involved in the generation of the precursors for the BOF; when ammonium is used as a nitrogen source, the carbon and nitrogen fluxes in these pathways are significantly higher (30%) than the fluxes estimated using molecular nitrogen as nitrogen source.Growth rate values decrease considerably when 4.3A. vinelandii and is synthetized to reduce oxygen availability and thus increase nitrogenase activity for enhanced nitrogen fixation  involved in PHB synthesis and pathways related to the generation of PHB precursors and energy metabolism  for diazotrophic and non-diazotrophic conditions. The model is capable of accurately simulating all the variations presented in the flux distribution values for every reaction involved in the PHB synthesis under different conditions (ammonium or molecular nitrogen as unique nitrogen sources) using only metabolic data (stoichiometry and biochemistry data).PHB, like alginate, is synthesized by polymers . iDT1278A. vinelandii has been well studied using exclusively genetic and regulation perspectives since PHB accumulation mainly occurs during oxygen limitations  and False Positive  predictions from the model related to carbohydrates and amino acids substrates could not be confirmed to belong to a pathway or to an enzyme that converts these carbon sources to internal metabolites present in the model either from literature information or available genomic and metabolomic databases. Moreover, when using amino acids as growth substrates, the model predicts very similar growth rate values for diazotrophic and non-diazotrophic conditions since some amino acids can release ammonium further supporting growth. These discrepancies between experimental evidence and model predictions could be related to the limited information available concerning specific metabolic mechanisms for metabolizing carbon sources as carbohydrates, lipids and amino acids in nelandii , transcrnelandii . Furthernelandii , potenti4.6iDT1278 could benefit low-cost media optimization for A. vinelandii and to further improve production processes as PHB, alginate and other biopolymers with high industrial value. Although this environmentally important model organism has been isolated and studied for more than 100 years, its physiology and some metabolic pathways have yet to be fully understood. We believe the current model provides a valuable step along the path towards better characterization of this important microbe in isolation as part of a microbial community , 10.13039/100006132Office of Science, Office of Biological & Environmental Research under Awards DE-SC0012658 and DE-SC0019388. D.T. was in part supported by Mexican 10.13039/100013101National Research Council, 10.13039/501100003141CONACYT, fellowship No. 932962.This material is based upon work supported by the Diego Tec Campos: Investigation, Methodology, Writing - original draft. Cristal Zuñiga: Conceptualization, Supervision, Project administration. Anurag Passi: Data curation, Writing - review & editing. John Del Toro: Investigation. Juan D. Tibocha-Bonilla: Validation. Alejandro Zepeda: Validation, Funding acquisition. Michael J. Betenbaugh: Investigation, Funding acquisition. Karsten Zengler: Conceptualization, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Microbes commonly use metabolites produced by other organisms to compete effectively with others in their environment. As a result, microbial communities are composed of networks of metabolically interdependent organisms. How these networks evolve and shape population diversity, stability, and community function is a subject of active research. But how did these metabolic interactions develop initially? In particular, how and why are metabolites such as amino acids, cofactors, and nucleobases released for the benefit of others when there apparently is no incentive to do so? Here, we discuss the hypothesis that metabolite provisioning is not itself adaptive but rather can be a natural consequence of other evolved biological functions. Microbes commonly use metabolites produced by other organisms to compete effectively with others in their environment. As a result, microbial communities are composed of networks of metabolically interdependent organisms. How these networks evolve and shape population diversity, stability, and community function is a subject of active research. But how did these metabolic interactions develop initially? In particular, how and why are metabolites such as amino acids, cofactors, and nucleobases released for the benefit of others when there apparently is no incentive to do so? Here, we discuss the hypothesis that metabolite provisioning is not itself adaptive but rather can be a natural consequence of other evolved biological functions. We outline two examples of metabolite provisioning as a by-product of other functions by considering cell lysis and regulated metabolite efflux outside their canonical roles and explore their potential to facilitate the emergence of interdependent metabolite sharing. Existence in the microbial world is contingent on the ability to compete in environments where even the tiniest advantage can mean the difference between survival and extinction. One way to gain an advantage over competitors is to use metabolites produced by others rather than performing all necessary metabolic functions independently. Indeed, most microbes depend on others for some metabolites and, at the same time, release metabolites into the environment , 2. ThesDependence on others to fulfill core metabolic requirements starts with the loss of metabolic capabilities via loss-of-function mutations  and C. T4–Unlike the examples above, metabolic end products such as amino acids, cofactors, and nucleobases are not considered inherently leaky because they are produced and used inside the cell. However, genomic analyses indicate that the majority of microbes are unable to produce one or more amino acids, cofactors, or nucleobases  –11, sugg–Escherichia coli is supported by a partner in coculture, suggesting that actively proliferating microbial populations can be leaky for some intracellular metabolites . However, the highly interconnected nature of metabolic networks fundamentally limits the extent to which metabolite production levels can be optimized   .Mycoplasma pneumoniae is estimated to be 167 molecules per cell (E. coli are present at fewer than 10 copies per cell (peak), inherent fluctuations in protein levels (under). Overproduction can also decrease fitness when resources such as precursors and energy are wasted or when metabolites accumulate to toxic levels (over)  . The lats (over) .E. coli is supported by other E. coli mutants in coculture, reinforcing the idea that some amino acids may be actively exported (Though seemingly wasteful, a strategy in which cells constantly overproduce to fulfill minimal production requirements, and export excess metabolites to avoid accumulation and toxicity, can be adaptive if overproduction is less harmful than underproduction . Therefoexported . Furtherexported .The pervasiveness of auxotrophy throughout microbial genomes suggests that the release of intracellular metabolites is prevalent. Metabolite provisioning for the benefit of others can arise through various adaptive and non-adaptive mechanisms. For example, metabolites can be provided in the context of coevolved partnerships, where evolved reciprocity or genome reduction leads to metabolic interdependence  and D. H


Eriocheir sinensis Y-organ GSMN based on the transcriptome sequencing of Y-organ, which includes 1,645 reactions, 1,885 unigenes, and 1,524 metabolites distributed in 100 pathways and 11 subsystems. Functional module and centrality analysis of the GSMN show the main metabolic functions of Y-organ. Further analysis of the differentially expressed unigenes in Y-organ after eyestalk ablation reveals that 191 genes in the network were up-regulated and 283 were down-regulated. The unigenes associated with the ecdysone synthetic pathway were all up-regulated, which is consistent with the report on the increasing secretion of ecdysone after eyestalk ablation. Besides, we compared the Y-organ GSMN with that of E. sinensis eyestalk and hepatopancreas, and we analyzed the specific metabolisms in each organ. The specific metabolisms and pathways of these three networks are closely related to their corresponding metabolic functions. The GSMN reconstructed in this work provides a new method and many novel clues for further understanding the physiological function of Y-organ. It also supplies a new platform for the investigation of the interactions among different organs in the growth process of E. sinensis.Genome-scale metabolic network (GSMN) has been proven to be a useful tool for the system analysis of organism metabolism and applied to deeply explore the metabolic functions or mechanisms in many organisms, including model or non-model organisms. However, the systematic studies on the metabolisms of aquatic animals are seldom reported, especially the aquatic crustaceans. In this work, we reconstructed an  Eriocheir sinensis (Chinese mitten crab) is an important economic aquatic animal in China. The growth of E. sinensis undergoes multiple molting processes, which is regulated by the ecdysone and molt-inhibiting hormone. The main function of Y-organ is to secrete the precursor of ecdysone, which enters the blood and transforms to active ecdysone catalyzing by 20-hydroxyecdysone (E. sinensis Y-organs and found that it has the highest expressions in Y-organ and muscle. E. sinensis ecdysone regulation protein (ERP) using homologous reorganization and obtained the purified ERP. However, it is noticed that the metabolism or regulation in cells interacted in a complex biological system, in which the function of each gene is influenced by other genes or proteins. Therefore, a systematic view is necessary for the function analysis and related regulation studies in Y-organ.ecdysone . The resE. sinensis eyestalk  were reconstructed for more than 100 organisms including archaea, bacteria, and lots of eukaryotes. GSMN has been applied to metabolic engineering, bioenergy development, and biomedicine. The standard protocol of the reconstruction of GSMN was reported by eyestalk  and hepaeyestalk  were recsearches , transcrsearches . HoweverE. sinensis Y-organ and analyzed its metabolic features. The differentially expressed unigenes (DEUs) in Y-organ after eyestalk ablation were further analyzed based on the GSMN to explore the interaction between Y-organ and eyestalk. We investigated the common and unique metabolisms of the three organs, compared with the previously reconstructed GSMN of E. sinensis eyestalk and hepatopancreas. The GSMN reconstructed in this work provides a new method and novel clues for further understanding the physiological function and mechanism of Y-organ.In this work, we reconstructed a GSMN of Healthy crabs (body weight 5–6 g) with good vitality were held in fresh water at 18–20°C for 3–5 days. Sixty crabs were randomly selected and equally divided into two groups. The eyestalk of experimental group (C1) was ablated, and the Y-organ was taken as a sample 48 h later. The eyestalk of control group (C0) was not treated. The sample was stored in −80°C liquid nitrogen until RNA was extracted. The total RNA from the Y-organ was extracted using the TRIzol method  according to the manufacturer’s protocol.TM2000 with single-end sequencing.After extraction of the total RNA from the samples, mRNA was enriched by using the oligo magnetic beads. By adding the fragmentation buffer, the mRNA was interrupted to short fragments (about 200 bp), and then the first-strand cDNA was synthesized by random hexamer-primer using the mRNA fragments as templates. Buffer, dNTPs, RNase H, and DNA polymerase I were added to synthesize the second strand. The double-stranded cDNA was purified with QiaQuick PCR extraction kit and washed with elution buffer (EB) for end repair and single nucleotide A (adenine) addition. Finally, sequencing adaptors were ligated to the fragments. The required fragments were purified by agarose gel electrophoresis and enriched by PCR amplification. The cDNA libraries of two samples were sequenced using Illumina HiSeqPRJEB4541) . The clean reads obtained with sequencing were mapped to reference sequences obtained from transcriptome sequencing (ENA accession number: JEB4541)  using SOJEB4541)  to get tJEB4541) . A KO asThe quality of the sequencing was further assessed by sequencing saturation analysis and distribution of reads on reference genes as well as clean read filter. Sequence saturation analysis is used to measure the sequencing data of a sample. When the number of reads increases, the number of detected genes also increases. However, when the number of reads reaches a certain value, the growth rate of detected genes flattens, and it means that the number of detected genes tends to saturation. Distribution of reads locating on the genes is used to evaluate the randomness. During the RNA-Seq experiment, mRNAs are firstly broken into short segments by chemical methods and then sequenced. If the randomness is poor, read preference from a specific gene region will directly affect subsequent bioinformatics analysis. Since reference genes have different lengths, the read location on gene is standardized to a relative position , and then the number of reads in each position is counted. If the randomness is good, the reads in every position would be evenly distributed.SRX8639987 for sample C01 and SRX8639988 for sample C12. The unigene information of C0 sample obtained from differential gene expression (DGE) and transcriptome sequencing were used for the subsequent draft reconstruction of the GEM.The RNA-Seq data have been submitted to the Sequence Read Archive (SRA) database, and the accession number is Eriocheir sinensis Y-organ. Two criterions, false discovery rate (FDR) ≤ 0.001 and |log2 ratio(C1/C0)| > 1, were used to screen the DEUs. The differential expression data have been submitted to Gene Expression Omnibus (GEO) database, and the accession number is GSE1535523.The unigene expression is calculated by the number of reads mapped to the reference sequence and every unigene. The unigene expression level was calculated by reads per kb per million (RPKM)  and usedThe reconstruction of the GSMN almost follows the standard protocol of GSMN proposed by PRJEB4541) and information from the KEGG database were used as the data source or draft reconstruction. With KO as a bridge, the candidate metabolic reactions  and corresponding metabolites (including metabolite names and identifiers) from the KEGG database were matched with unigenes. With these reactions and metabolites, the draft reconstruction network was assembled.The reaction list, metabolite list, reaction–KO relationships, and reaction_mapformula files were downloaded from the KEGG database. The unigene–KO relationship obtained from transcriptome data . A reaction has only one pathway in our network to facilitate the subsequent gap filling step.The enzyme–KO information was obtained from the KEGG database. In the daft reconstruction, the unigene–reaction relationship was determined by KO numbers. In this step, also taking KO number as a bridge, the unigene–enzyme–reaction relationships were identified. As unigene is not exactly the gene, the relationships between unigenes were difficult to be identified. In this work, all the unigenes matching to the same enzyme were considered as “and” relationship, and the unigenes matching to different enzymes of a reaction were considered to function independently, i.e., “or” relationship.According to the confidence rules in the paper of The healthy crabs obtained from aquatic farm were cultured in a plastic incubator at 18–20°C for 5 days. During collection of the samples, crabs with average weight of 8–9 g were put on the ice plate for low-temperature anesthesia. The Y-organ of each crab was quickly cut off, placed in liquid nitrogen immediately, and then transferred to −80°C refrigerator for preservation. The biomass constituents in the samples were tested experimentally by the Beijing Institute of Nutritional Resources. The contents of 20 amino acids, 30 fatty acids, 10 trace elements, 2 carbohydrates, and 5 nucleotides were determined with the national standard testing method.iCHOv1 of Cricetulus griseus  to form the stoichiometry coefficient in the biomass reaction. The coefficients of ATP, ADP, and phosphate were set to be 29.8303 mmol/(g dry weight) for ATP maintenance referenced to  griseus .E. sinensis. Exchange reactions are the unbalanced network reactions that allow the accumulation of metabolites or provide the network with metabolites. Therefore, the transport and exchange reactions for the components composing the biomass were added, as well as H2O, H+, and O2, which are essential to most living beings.The transport and exchange reactions were added according to the nutrient requirement of In the draft reconstruction network, dead-end metabolites were unavoidable due to the incomplete understanding of gene annotation and metabolic system of an organism. The existence of dead-end metabolites may cause the breakpoints in some metabolic pathways, which leads to the failure of some metabolic functions. In order to reduce the dead-end metabolites and the breakpoints in the network, we try to fill the gaps by taking all the reactions in the KEGG database but not in the draft reconstruction network as the “metabolic environment.” The gaps were filled based on the graph theory and carried out in two scales: pathway scale  and glob–1 h–1, and that for irreversible reactions is  mmol gDW–1 h–1. The flux of transport reactions was set to be  mmol gDW–1 h–1. The exchange reactions for non-essential amino acid were set to be  mmol gDW–1 h–1, which means that these amino acids do not need to be ingested from the environment. For the fatty acids that can be synthesized by the model, the related exchange reactions were set to be  mmol gDW–1 h–1. The flux of other nutrient absorption was set to be  mmol gDW–1 h–1, except for trace elements, which is set to be  mmol gDW–1 h–1. The flux of H2O, H+, and O2 was set to be  mmol gDW–1 h–1. Finally, the refined network was converted into a Systems Biology Markup Language (SBML) formatted file, which can be read by the COBRA toolbox. The SBML file is valid by checking by MEMOTE  in he feeds . The exc–1 h–1, which means that the precursors are only allowed to be synthesized and not absorbed from the feeds. Then the exchange reaction of a precursor was set to be the objective function, and its maximum flux was calculated. When the precursor cannot be synthesized, the missing reactions were found by backtracking the pathways in the KEGG database and added to the model. The precursors were checked one by one until the biomass can be synthesized.For the biomass evaluation, biomass synthesis reaction was set to be the objective function. If the biomass cannot be synthesized (flux is 0), the synthesis of precursors used for biomass synthesis was checked. The transport and exchange reactions for the precursors were added firstly, and the flux of their exchange reactions was set to be  mmol gDWGap reactions obtained from the evaluation step were added to the model, and the feature of the network was counted and listed as the final reconstruction results.+, H2O, and H+, which are generally only transfer carriers of electron or some functional group and participate in a large number of reactions but do not directly participate in product synthesis  are 0, which means that these two amino acids cannot be synthesized by the network. By checking the cysteine metabolic pathway in the network and comparing it with that in the KEGG database, we found that the L-cysteine synthesis reaction R00897 (O-acetyl-l-serine + hydrogen sulfide <=> L-cysteine + acetate) was missed. Therefore, R00897 was added to the network. Furthermore, the transport and exchange reactions of sulfide were also added to ensure the positive flux of R00897. The synthesis of L-cysteine was simulated again, and the flux value increased from 0 to 5, indicating that cysteine can be successfully synthesized by the network. In the same way, R01728 [prephenate + NAD+ <=> 3-(4-hydroxyphenyl) pyruvate + CO2 + NADH + H+] were added to revise the tyrosine synthesis route in the network.By investigating the synthesis capability of non-essential amino acids in –1 h–1. With the revision of fatty acid routes, the biomass was synthesized successfully, and the flux of biomass is 2.304 g h–1.In the biomass simulation, the biomass was unable to be synthesized by the model at first. Therefore, the synthesis of all the precursors was checked. The problem occurs in the synthesis of some fatty acids because the fatty acid synthesis pathways in the network are incomplete. We checked the related KEGG pathways and added 29 metabolic reactions to complete the fatty acid synthesis route, including 26 reactions in “biosynthesis of unsaturated fatty acids” pathway and three reactions in “fatty acid biosynthesis” pathway. In addition, the transport and exchange reactions for 23 precursors of the fatty acids were added to check their synthesis capability, and the flux of exchange reactions was set to be  mmol gDW–1 h–1. In order to simulate the ecdysone synthesis, the transport and exchange reactions of ecdysone were also added with the flux of exchange reactions set to be  mmol gDW–1 h–1. Then the exchange reaction of ecdysone was set to be the objective function, and the flux was calculated. The flux is not zero, which indicates that the ecdysone can be successfully synthesized by the network.As ecdysone secretion is an important function of Y-organ, the quality of the model was further evaluated by the ecdysone synthesis. Since crab cannot synthesize cholesterol by itself but absorb it from the feeds , the traThe final reconstructed GSMN consists of 1,645 reactions, 902 enzymes, 1,524 metabolites, and 1,885 unigenes distributed in 100 pathways and 11 subsystems , S3. TheIn all the 1,645 reactions in Y-organ GSMN, the currency metabolites and exchange reactions were firstly removed from the network, and some reactions with main reactions containing only currency metabolites were also removed. The finally reactions were 1,354, and the topological features of the network were calculated . The GSMThe 1,071 reactions in the largest WCC are distributed in 11 metabolic subsystems. Among these subsystems, amino acid metabolism contains the most reactions, followed by carbohydrate, lipid, and nucleic acid metabolisms . These fL-tyrosine → dopamine → noradrenaline → L-adrenaline).Tyrosine was a precursor of catecholamines , which enables the cell to make emergency responses to the external environment. Therefore, tyrosine played an essential role in the metabolism of animals . In the 3), and diacylglycerol (DAG), from phospholipase C (K01114). The main function of IP3 is to regulate the release of Ca2+, whereas DAG regulates the activity of protein kinase K, which played an important role in cell proliferation, differentiation, contraction, and metabolism , 6-phosphofructokinase-1 , and pyruvate kinase  in the glycolysis pathway; and citrate synthase , isocitrate dehydrogenase , and α-ketoglutarate dehydrogenase  in the TCA circle pathway. Reactions catalyzed by these enzymes constitute the core part of the carbohydrate metabolism. Besides, in the carbohydrate metabolism subsystem, inositol phosphate metabolism accounts for 14.4% of the carbohydrate metabolism. This pathway is about a series of reactions to generate two second messengers, inositol triphosphate (IPtabolism .nvd) gene (spook (related to CYP307A1), spookier (related to CYP307A2), and spookiest (related to CYP307B1) genes , spook (spo), spookier (spok), spookiest (spokest), phantom (phm), disembodied (dib), shadow (sad), and shade were a series of genes named by Nüsslein and his colleagues for investigating the mutation trait of Drosophila. They were called “Halloween Family Gene” and play significant role in the synthesis of ecdysone (disembodied (dib), spook (spo), and shadow (sad) genes from Portunus trituberculatus  gene . Then 7D1) genes . The 5β-ow genes . Finallyow genes  from ecdecdysone . In crusrculatus , and ChaE. sinensis Y-organ, we found six genes (enzymes)  and their related reactions in the ecdysis pathway  contained only one metabolic subsystem, and most of the modules (15/27) contained less than three metabolic subsystems . It indi2O <=> L-cysteine + glycine) is the reaction with the largest degree, which belongs to amino acid metabolism subsystem in module 20. Cys-Gly is the hydrolyzed product of glutathione (GSH), which is a tripeptide substance composed of glutamate, cysteine, and glycine. GSH has antioxidant effects and plays an important role in scavenging free radicals  and C1 , according to the selection criterions described in the  section . Among t section . The reaWe extracted the modules with the most differential reactions in each subsystem, and their related metabolic pathways were analyzed. The differential reactions of the amino acid metabolism, carbohydrate metabolism, and lipid metabolism were mainly in modules 14 and 27, including valine, leucine, and isoleucine degradation; inositol phosphate metabolism; and glycerophospholipid metabolism. Module 22 contains most of the differential reactions in nucleic acid metabolism and all the differential reactions in purine metabolism. The differential reactions of cofactor and vitamin metabolism, energy metabolism, and translate subsystem were mainly in modules 3 (porphyrin and chlorophyll metabolism), 22 (sulfur metabolism), and 18 (aminoacyl tRNA synthesis). These modules and pathways show the most responsive metabolic processes in Y-organ influenced by the eyestalk ablation.Penaeus chinensis ; R08132 , R08133, R08134 , and R08135 . The expression of genes corresponding to ecdysone production from 7-dehydrocholesterol was all up-regulated, indicating that eyestalk ablation accelerated the synthesis of ecdysone and the process of ecdysis. This phenomenon was reported by Zeleny firstly , and manhinensis , Macrobrpponense , Procamb clarkia , Sinopotenanense , and E. sinensis . After eD-glutamine and glutamic acid metabolisms exist only in the eyestalk network. The basic features of the three networks are shown in We compared the Y-organ GSMN with the eyestalk and hepatopancreas GSMNs reconstructed in our previous study . The numThe topological features of the three GSMNs were further compared . The scaThe metabolic reactions of Y-organ, eyestalk, and hepatopancreas GSMNs were compared, and 1,182 common reactions were found . There aThe common metabolic reactions in the three networks are distributed in 11 subsystems. The subsystems that contain the most reactions are amino acid, lipid, carbohydrate, and nucleic acid metabolisms. This result is consistent with the subsystems that have the most reactions in the largest WCC of Y-organ GSMN. The pathways contain more than 30 reactions that belong to nucleic acid metabolism, amino acid metabolism, lipid metabolism, xenobiotics biodegradation, and metabolism subsystems. These common reactions in the three networks are mainly associated with the basic metabolic processes in cells.There are 36 specific metabolic reactions in the Y-organ network after excluding exchange and transport reactions, which are distributed in 16 pathways. The pathway that contains the most unique reactions is the glycerol phospholipid metabolism, which contains seven unique reactions. These reactions include the lecithin synthesis reaction from diglyceride and phosphatidylserine synthesis reaction from lecithin catalyzed by phosphatidylserine synthase 1.L-amino acid decarboxylase DDC (EC: 4.1.1.28) (R02701). In phenylalanine metabolism pathway, the main specific reactions of Y-organ are the decomposition of L-phenylalanine to phenylethylamine (R00699) and the formation of 2-hydroxyphenylacetic acid from phenylpyruvate (R01372). In the alanine, aspartic acid, and glutamate metabolism pathways, the specific reactions are mainly concentrated in the pathway related to aspartic acid synthesis and metabolism .In the tryptophan metabolism pathway in the Y-organ network, the important neurotransmitter, serotonin, was found to be synthesized from 5-hydroxytryptophan catalyzed by aromatic P. trituberculatus can significantly promote the growth and molting of animals by supplementing 20 g/kg of lecithin every day for 50 consecutive days.The related reactions of lecithin synthesis and metabolism  were found specific in Y-organ. Lecithin (phosphatidylcholine) is the main lipid of cell membrane , which pL-amino acid decarboxylase DDC (EC: 4.1.1.28) was found in Y-organ as a specific reaction, which indicates that the Y-organ of E. sinensis has the ability to synthesize 5-HT.5-Hydroxytryptamine (5-HT) is widely distributed in the central and peripheral nerve tissues of crustaceans. As a neurotransmitter, neuromodulator, or hormone, it plays an important role in regulating the behavior, reproduction, molting, digestion, absorption, and metabolism of animals . Sun et Litopenaeus vannamei. The results showed that CYP4V18 down-regulation could lead to the increase of ecdysterone concentration, which is helpful to the ecdysis process in crustaceans.The Y-organ GSMN also contains the metabolic pathway of cytochrome P450 . Cytochrome P450 enzyme system (CYP) is a kind of metabolic enzyme system widely existing in various organisms , which ispook (CYP307A1) and spookier (CYP307A2) is also found as a specific reaction in Y-organ.In addition, the important reaction (R08132) synthesizing diketol from 7-dehydrocholesterol (7DC) catalyzed by There are 22 specific reactions in eyestalk GSMN distributed in 11 different pathways. The glycine, serine, and threonine metabolisms contain more than five unique reactions, which are all reactions producing betaine aldehyde from choline . These five reactions are catalyzed by four different enzymes including choline monooxygenase (EC:1.14.15.7), choline oxidase (EC:1.1.3.17), choline dehydrogenase (EC:1.1.99.1), and alcohol dehydrogenase (EC:1.1.1.1). The produced betaine aldehyde can be further oxidized to betaine, which is an effective osmoregulation substance, helping to reduce the osmolality caused by the change of salt concentration and improve the survival rate of fish in the process of migration from fresh water to sea water. N-acetyl-D-glucosamine 6-phosphoric acid (GlcNAc-6P). Sialic acid is a general term of derivatives of 9-carbomonosaccharide family  were found. These reactions generate sialic acid from e family , which ue family . Among tericanus , Macrobrenbergii , Cancer ennarius , Scylla  serrate , and Pen monodon . Denis eTrypanosoma cysteine synthesis and metabolism. Trypanosoma cysteine is a dimer of two GSH molecules linked by spermidine. It was first found in Leishmania, Trypanosoma, and other Protozoa (in vivo. In addition, it also has the function of biological detoxification and is the biological reductant of Cr6+ in cells (L. vannamei can improve the digestibility of the feed and the growth rate and survival rate. In addition, GSH plays an important role in improving the activity of antioxidant enzymes, regulating the immune system, and enhancing the anti-stress ability of aquatic animals (There are 18 metabolic reactions specific to hepatopancreas, which are distributed in 12 pathways. Among them, there are four reactions in the GSH metabolic pathway, all of which are related to Protozoa . GSH is in cells . GSH can animals .Eriocheir sinensis was reconstructed and validated according to the standard protocol. Module analysis and differential unigene analysis of the network revealed the main function of Y-organ. The comparison of the GSMNs of Y-organ, eyestalk, and hepatopancreas brings insight into the specific metabolic pathways in each organ. Due to the lack of experimental data, the validation of the E. sinensis GSMN cannot be as sufficient as many model organisms, but the establishment of the GSMN still provides an important platform for the study of the metabolic process of E. sinensis and even aquatic crustaceans, which is an important step in the process from in vivo to in silico experiment in aquatic crustacean research. With the help of the GSMN, the in vivo experiment will develop more rapidly, which will further promote the development of the aquatic crustaceans GSMNs and improve the quality of the networks.The GSMNs have been established for many organisms and applied to solve many metabolic issues. In this work, a GSMN of Y-organ in The datasets generated for this study can be found in the SRA database: SRX8639987, SRX8639988; GEO database: GSE153552.The animal study was reviewed and approved by the Tianjin Normal University.BW drafted the manuscript and performed the network reconstruction and analysis. JY and CG prepared the database and participated in the data analysis. JL performed the biomass composition analysis. TH and JS contributed to the guideline and revision of the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.