Research ArticleMetabolism

An atlas of human metabolism

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Science Signaling  24 Mar 2020:
Vol. 13, Issue 624, eaaz1482
DOI: 10.1126/scisignal.aaz1482
  • Fig. 1 Overview of Human1 generation and curation.

    A simplified illustration of the key steps involved in the generation of Human1 from HMR2, Recon3D, and iHsa. The bottom of the diagram represents the ongoing open-source curation of Human1 using input from databases, literature, other models, and the scientific community. The four side panels provide further detail into selected Human1 features: extensive reaction mass and charge balancing to achieve 100% stoichiometric consistency, incorporation of new enzyme complex information, mapping model components to standard database identifiers, and version-controlled and open-source model curation framework. In the bar graphs in the upper left panel, “Balanced” reactions represent the number of mass-balanced reactions, “Consistent” metabolites are the number of stoichiometrically consistent metabolites, and “R3D model” is the model version of Recon3D.

  • Fig. 2 Highlighted features provided by the Metabolic Atlas web portal.

    A collection of screen captures from Metabolic Atlas, illustrating key features such as 2D and 3D metabolic network maps. A zoomed inset shows a subset of the endoplasmic reticulum compartment map, from which further information on components such as reactions, enzymes, or metabolites can be accessed in the GEM browser. Interaction partner graphs are dynamically generated for any given enzyme or metabolite in Human1, which show the connectivity to other metabolites and enzymes based on their associated reactions.

  • Fig. 3 Structural and functional comparison of cancer- and healthy tissue–specific GEMs.

    (A) Visualization of differences in models’ reaction content using a tSNE projection to two dimensions based on the Hamming similarity. See fig. S5 for individual point labels. (B) Heat map showing pairwise comparisons of reaction content between GEMs specific to healthy liver (CHOL-NT, LIHC-NT, and Liver-GTEx), blood, and their corresponding cancers (CHOL, LIHC, and LAML). (C) Relative subsystem coverage (number of reactions present in a model that are associated with the given subsystem) compared among GEMs of liver and liver tumors. Only subsystems with at least a 10% deviation from mean subsystem coverage among the models are shown. (D) Summary of metabolic task performance by the healthy and cancerous liver models, showing only the tasks that differed in at least one of the models. (E) Comparison of relative subsystem coverage between LAML- and blood-specific GEMs, showing only subsystems with at least a 10% deviation between the two models. (F) Summary of the five metabolic tasks that could be completed by the LAML GEM but failed in the healthy blood GEM. ROS, reactive oxygen species; GSL, glycosphingolipid; FA, fatty acid; [p], peroxisomal compartment; DHA, docosahexaenoic acid.

  • Fig. 4 Predicted gene essentiality among different cell lines and human GEMs.

    (A) Schematic illustration of the generation of cell line–specific GEMs from HMR2, Recon3D, and Human1 and subsequent prediction of gene essentiality based on the GEMs’ ability to perform basic metabolic tasks. Genes predicted to be essential by the GEMs were compared to experimental measures of gene essentiality (45, 49) obtained from CRISPR knockout screens. (B) Comparison of gene essentiality predictions among the three reference GEMs and their five derivative cell line models with CRISPR screen results from Hart et al. (45). Left: Average accuracy, specificity, and sensitivity of predictions across the five cell lines for each reference GEM, with error bars representing the SE of the mean. Right: Comparison of the Matthews correlation coefficient (MCC) of the predictions for each of the reference GEMs and cell lines. The “All” category indicates genes found to be essential in all five cell lines. (C) Comparison of gene essentiality predictions among the three reference GEMs and their 621 derivative cell line models with CRISPR screen results from the DepMap database (49).

  • Fig. 5 Generation and analysis of human ecGEMs.

    (A) Graphical representation of the pipeline used to construct NCI-60 cell line–specific ecGEMs from Human1. (B) Cumulative distribution of flux variability among reactions in HOP62-GEM and ecHOP62-GEM. Only the ~3200 reactions that carried a flux of >10−8 mmol/gDW hour when optimizing biomass production in HOP62-GEM were included in the plot. Distributions for all 11 cell lines are shown in fig. S12. (C) Comparison of predicted with measured exchange fluxes (log10-transformed absolute flux values) for the 11 cell-specific ecGEMs, where only the set of metabolites present in the growth medium (Ham’s medium) was specified. Different colored markers represent the different cell lines. Metabolites whose fluxes were systematically under- or overpredicted among the different models are labeled in circles, whereas the other ~78% lie within the shaded oval. Note that metabolites along the bottom of the plot have a predicted flux of zero but are shown here as having the absolute minimum measured value to avoid logarithm of zero. (D) Boxplots showing the relative error in predicted growth rate for the 11 cell-specific ecGEMs and non-ecGEMs. “Unbounded” indicates that the solutions are effectively unbounded and therefore have unquantifiable (infinite) error. Colored markers on the x axis denote the exchange constraints that were cumulatively added to the models when making predictions. “Media” indicates that only the metabolites present in the growth medium were specified, without constraining their exchange rates. “Glucose,” “Lactate,” and “Threonine” indicate that the exchange flux for those metabolites in the model was constrained to the measured value.

Supplementary Materials

  • stke.sciencemag.org/cgi/content/full/13/624/eaaz1482/DC1

    Materials and Methods

    Fig. S1. The evolution of generic human GEMs.

    Fig. S2. Replication of infant growth simulation using Human1.

    Fig. S3. Memote report screenshot for Human1.

    Fig. S4. Human1 quality and performance over the curation process.

    Fig. S5. Labeled 2D tSNE projection of tissue- and tumor-specific GEM reaction content comparison based on Hamming similarity.

    Fig. S6. Visualization of altered proline metabolism in CHOL using Metabolic Atlas.

    Fig. S7. Visualization of increased expression in fatty acid beta oxidation subsystems for LAML using Metabolic Atlas.

    Fig. S8. Enrichment of true positives in model-predicted essential genes.

    Fig. S9. Comparison of gene essentiality predictions among the three reference GEMs and their 621 derivative cell line models with CRISPR knockout screen results from the DepMap database.

    Fig. S10. Impact of gene essentiality threshold on DepMap gene essentiality analysis results.

    Fig. S11. Gene essentiality predictions when considering only biomass production compared to considering the activity of 57 different metabolic tasks.

    Fig. S12. Effect of enzyme constraints on GEM flux variability.

    Table S1. Comparison of generic human GEM statistics.

    Table S2. Issue-guided model curation workflow implemented on the Human-GEM GitHub repository.

    Table S3. Summary of model changes associated with each version of Human-GEM.

    Data file S1. Composition of the generic human cell biomass reaction.

    Data file S2. Average fatty acid composition for the curation of lipid metabolism.

    Data file S3. Metabolic tasks required for cellular viability.

    Data file S4. FVA of ecGEMs.

    Data file S5. NCI-60 cell line experimental exchange fluxes.

    References (5672)

  • The PDF file includes:

    • Materials and Methods
    • Fig. S1. The evolution of generic human GEMs.
    • Fig. S2. Replication of infant growth simulation using Human1.
    • Fig. S3. Memote report screenshot for Human1.
    • Fig. S4. Human1 quality and performance over the curation process.
    • Fig. S5. Labeled 2D tSNE projection of tissue- and tumor-specific GEM reaction content comparison based on Hamming similarity.
    • Fig. S6. Visualization of altered proline metabolism in CHOL using Metabolic Atlas.
    • Fig. S7. Visualization of increased expression in fatty acid beta oxidation subsystems for LAML using Metabolic Atlas.
    • Fig. S8. Enrichment of true positives in model-predicted essential genes.
    • Fig. S9. Comparison of gene essentiality predictions among the three reference GEMs and their 621 derivative cell line models with CRISPR knockout screen results from the DepMap database.
    • Fig. S10. Impact of gene essentiality threshold on DepMap gene essentiality analysis results.
    • Fig. S11. Gene essentiality predictions when considering only biomass production compared to considering the activity of 57 different metabolic tasks.
    • Fig. S12. Effect of enzyme constraints on GEM flux variability.
    • Table S1. Comparison of generic human GEM statistics.
    • Table S2. Issue-guided model curation workflow implemented on the Human-GEM GitHub repository.
    • Table S3. Summary of model changes associated with each version of Human-GEM.
    • Legends for data files S1 to S5
    • References (5672)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Data file S1 (Microsoft Excel format). Composition of the generic human cell biomass reaction.
    • Data file S2 (Microsoft Excel format). Average fatty acid composition for the curation of lipid metabolism.
    • Data file S3 (Microsoft Excel format). Metabolic tasks required for cellular viability.
    • Data file S4 (Microsoft Excel format). FVA of ecGEMs.
    • Data file S5 (Microsoft Excel format). NCI-60 cell line experimental exchange fluxes.

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