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Recon3D enables a three-dimensional view of gene variation in human metabolism

Nature Biotechnology volume 36, pages 272–281 (2018)Cite this article

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Abstract

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

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Figure 1: The properties and content of the Recon3D knowledge-base.
Figure 2: Linking the human metabolic network to protein structural databases, cheminformatics platforms, and the Protein Data Bank.
Figure 3: Linking the human metabolic network to gene variation and cancer knowledge-bases.
Figure 4: An example of bridging systems biology and structural biology through Recon3D.
Figure 5: Protein structure-guided discovery of mutation hotspots across structurally related genes.
Figure 6: Identification of metabolic signatures linked to drug indications.

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Acknowledgements

The results here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. This work was funded by the Novo Nordisk Foundation Center for Biosustainability and the Technical University of Denmark (grant number NNF10CC1016517), the National Institutes of Health (grant GM057089 to B.O.P.) and by the Luxembourg National Research Fund (FNR) through the National Centre of Excellence in Research (NCER) on Parkinson's disease and the ATTRACT programme (FNR/A12/01), by the European Union's Horizon 2020 research and innovation programme under grant agreement No 668738, by the Institutional Strategy of the University of Tübingen (German Research Foundation DFG, ZUK 63), and by Google Inc. (Summer of Code 2016). RCSB PDB is funded by the National Science Foundation (NSF DBI-1338415 to S.K.B.), the Department of Energy, and the National Institutes of Health (NIGMS and NCI). This research used resources of the National Energy Research Scientific Computing Center. The authors gratefully acknowledge P. Mischel and W. Zheng for experimental help and discussions on GBM, N. Lewis, A. McCammon, J. Mesirov, J.M. Thornton, J. Monk, and J. Lerman for scientific discussions and Z. King for help with Escher integration in RCSB PDB, M. Abrams for manuscript editing, V. Kohler and A.E. Kärcher-Dräger for drawing the platelet and RBC map in Escher, and F. Monteiro and M.A.P. Oliveira for help in reconstructing the dopamine subsystem.

Author information

Author notes

  1. Swagatika Sahoo

    Present address: Present address: Department of Chemical Engineering, Indian Institute of Technology, Madras, India.,

Authors and Affiliations

  1. Department of Bioengineering, University of California, San Diego, San Diego,California, USA.,

    Elizabeth Brunk, Daniel C Zielinski, Nathan Mih, Francesco Gatto, Anand Sastry & Bernhard O Palsson

  2. The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark

    Elizabeth Brunk, Jens Nielsen & Bernhard O Palsson

  3. Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg

    Swagatika Sahoo, German Andres Preciat Gonzalez, Maike Kathrin Aurich, Anna D Danielsdottir, Almut Heinken, Alberto Noronha, Ronan M T Fleming & Ines Thiele

  4. RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, USA.,

    Ali Altunkaya, Andreas Prlić, Peter W Rose & Stephen K Burley

  5. Department of Computer Science, Arizona State University, Tempe, AZ 85281, Arizona, USA

    Ali Altunkaya

  6. Applied Bioinformatics Group, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany

    Andreas Dräger

  7. Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Francesco Gatto, Avlant Nilsson & Jens Nielsen

  8. Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, and Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA

    Stephen K Burley

  9. Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Faculty of Science, University of Leiden, Leiden, the Netherlands

    Ronan M T Fleming

  10. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Bernhard O Palsson

Authors
  1. Elizabeth Brunk
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Contributions

Conceptualization: E.B., I.T., and D.C.Z.; methodology, reconstruction of metabolic network: S.S., I.T., R.M.T.F., A.D.D., A.H., and M.K.A.; reconstruction of GEM-PRO: E.B., N.M., and A.S.; 3D-hotspot analysis: E.B., A.P., A.S., and P.W.R.; machine learning: D.C.Z.; PDB visualization: A.A., A.P., A.D., R.M.T.F., and S.K.B.; atom–atom mapping: G.A.P.G. and R.M.T.F.; model testing and validation: I.T., R.M.T.F., S.S., M.K.A., D.C.Z., A.N., and F.G.; cell-specific and infant model simulations: M.K.A., A.N., and F.G.); investigation, E.B., D.C.Z., and G.A.P.G.; writing, original draft: E.B. and B.O.P.; writing, review and editing: all authors; funding acquisition: I.T., R.M.T.F., S.K.B., J.N., and B.O.P.; resources, I.T., R.M.T.F., S.K.B., J.N., and B.O.P.; supervision: I.T., R.M.T.F., S.K.B., and B.O.P.

Corresponding authors

Correspondence to Ines Thiele or Bernhard O Palsson.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Iterative model building of Recon 3

The published version Recon 236 was significantly expanded by addition of new reactions, metabolites, and genes. Simultaneously, the network content was refined for its gene-protein-reaction associations, thermodynamic infeasibility of reactions and reaction directionality, leading to Recon 3. The model was subjected to an expanded metabolic objective tests to ensure broader coverage of biochemical functions. Such iterative model building method greatly improved the content of all the models involved.

Supplementary Figure 2 Statistics of Recon 3.

A. The reaction content of Recon 3 was categorized as per the major metabolic category. For the corresponding metabolic subsystem, follow Figure S3. B. The new reactions that led to assembly of Recon 3 are shown with their corresponding major metabolic category involved. C. The highest number of newly added metabolites are shown with their corresponding major metabolism involved. D. The newly added genes in Recon 3 are shown with their corresponding major metabolism. Interestingly, lipid metabolism was top scorer in each category.

Supplementary Figure 3 Subsystem comparison between Recon 2 and Recon 3.

Shown is the gain in the reaction content per metabolic subsystem. Additionally, ten new subsystems were introduced to include metabolic pathways of aminoacyl-tRNA biosynthesis, hippurate metabolism, leukotriene metabolism, N-glycan metabolism, nucleotide metabolism, peptide metabolism, protein assembly, protein degradation, protein modification, and vitamin K metabolism. On the other hand, cysteine metabolism of Recon 2 was merged with ‘Methionine and cysteine metabolism’ in Recon 3.

Supplementary Figure 4 Simulations of infant growth on human breast milk.

Good agreement between the simulated growth curves using Recon3D and HMR 2.00 and with the growth standards from the World Health Organization (WHO). The predicted growth curves show the cumulative weight gain from 180 growth simulations with age dependent nutrient intake, biomass composition and activity level. The discrepancy observed at 3-6 months is due to differences in the Kcal to ATP conversion factor between fat and glucose.

Supplementary Figure 5 GEM-PRO workflow for mapping gene identifiers to the UniProt, RefSeq, and Ensembl databases when considering isoforms.

The example shown here is for Entrez gene ID 314, with two isoforms, 314.1 and 314.2. Taking the gene ID without the Recon 3 isoform IDs, we are able to map it directly to the UniProt database which contains 2 annotated isoforms, and then map them back to the Entrez gene IDs. A separate workflow maps the gene ID (without isoform ID) to the RefSeq database, and transcript names are utilized to assign isoforms. Once a UniProt identifier has been found, we query the PDB database for all corresponding protein structures.

Supplementary Figure 6 Distribution of total energy-related (PSQS) scores for all 3D protein structures.

In (a), distribution of X-ray structure resolution for all PDBs mapped to genes in Recon 3. In light green are all PDB IDs, while in dark green are the selected structures that are best representative of each gene after the QC/QA steps. In (b), total PSQS scores for all homology models in Recon 3. A lower PSQS score indicates higher quality.

Supplementary Figure 7 Predictive accuracy of algorithmically derived atom mappings versus manually curated atom mappings.

A reaction is accurately predicted if each substrate atom is mapped to the correct product atom. Metabolic reactions can be classified by the enzyme that catalyses a reaction using four digits known as EC numbers. The top level EC number indicate the type of reaction that an enzyme carries out, therefore, to test the accuracy of the Reaction Decoder Tool, DREAM, and CLCA algorithms for different reaction types, we illustrate the predictive accuracy for the 512 curated reactions according to the reaction type as defined by their top level EC number.

Supplementary Figure 8 SBGN-PD map

SBGN-PD map view of the SBML Level 3 Version 1 file with Layout and Render extension that was generated from the manually drawn CellDesigner file of Recon 2.01. This map can be downloaded from https://vmh.life/#downloadview.

Supplementary Figure 9 Escher map view of Recon 2.01.

This map can be downloaded from https://vmh.life/#downloadview.

Supplementary Figure 10 Escher map of the human red blood cell

Escher map of the human red blood cell, redrawn from iAB-RBC-28343,127.

Supplementary Figure 11 Escher map of the human platelet cell

Escher map of the human platelet cell, redrawn from iAT-PLT-636127.

Supplementary Figure 12 Disease Networks in Recon3D

(a) (Left) All metabolic subsystems that map to damaging or potentially damaging variants in Recon 3D.

(Right) The landscape of protein motifs or domain types after filtering missense mutations using a 3D protein domain hotspot analysis. (b) (Left) We found that 13 of the 26 genes with 3D localized mutation hotspots catalyze metabolic reactions in Recon 3D. Using information from the metabolic network, protein structural domains, and disease associations, we laid out a subset of genes in a disease connectivity network. Visualization of this network reveals the diversity in metabolic roles and biological assemblies among this set of genes. Red outlined ovals indicate the number of 3D localized mutations in a given domain, the green squares indicate the number of representative domains linked to a given gene, the beige rectangles indicate the metabolic subsystems associated with a given gene, and the red triangles represent disease-associated SNPs that overlap with a given missense cancer mutation. In several cases, mutation positions without known effects are found to be associated with other diseases (e.g., Cowden syndrome 1 and Bannayan-Riley-Ruvalcaba syndrome in PTEN). (Right) Striking similarities were found in protein structure within the same subset of genes using a structure-based connectivity network, where links represent the degree of structural overlap (a significant score is typically > 0.4, determined from structural alignment98). The protein chain is indicated by a green square and whether mutations have known effects (K) or unknown effects (U) is annotated in the red outlined oval. These findings enable future studies that compare mutations with known effects to those with unknown effects in structurally similar (but not identical) regions.

Supplementary Figure 13 Single gene deletion simulations for GBM-specific cell line models.

Supplementary Figure 14 3D hotspot analysis statistics

Extension of the 3D hotspot analysis was performed on the metabolic SNP database (Table S19; Supplementary Data S3.xlsx). This dataset contains 1,385 unique genes with 3,649 SNPs; of these, 604 SNPs map to protein structures and are considered “deleterious” and 270 map to structures and are found to be “tolerated” (based on their SIFT predictors145). We found that deleterious mutations were much more likely to have co-occurring deleterious mutations in 5 and 10 Angstrom spheres than mutations that are tolerated (using a two-tailed t-test, p <0.5 and p> 0.1 for deleterious and tolerated mutations, respectively).

Supplementary Figure 15 Basic and detailed workflows for identifying drug-induced perturbed pathways and linking them to their indication.

(a) Basic workflow for identifying drug-induced perturbed pathways and linking them to their indication. (b) Detailed workflow for the Genetic Algorthm: 1) Inputs to the algorithm are a set of response variables for each gene expression set (either MetCHANGE scores or gene expression changes), a binary presence/absence vector for whether each sample was treated with a drug that has the side effect or indication, and the desired maximum number of predictor variables desired. The latter was set based on the number of treated gene expression sets in order to minimize the potential for overfitting. 2) At initiation, the genetic algorithm generates a ‘population’ of random guesses at the predictor variables, termed ‘individuals’, and assigns them either a value of -1, 0, or 1. For each individual, all gene expression samples are scored as the response variables (MetCHANGE or gene expression changes) multiplied by the candidate signature. 3) Each gene expression sample is then ranked and a receiver operator characteristic (ROC) curve is generated and area under the curve (AUC) is calculated using the input presence/absence vector for the side effect or indication. The sample AUCs are the maximization objective of the genetic algorithm. 4) The genetic algorithm subroutines are then used to generate a new population, biasing towards higher AUCs. Best solutions are maintained without modification, and lower scoring individuals are combined (‘crossed over’) and modified (‘mutated’) to search the solution space in a heuristic fashion. The termination criteria is typically a number of generations without improvement; however, we applied a simple maximum time termination criteria, as obtaining a global optimum was not deemed essential to gain biological insight. 5) The signature yielding the highest prediction AUC is considered the best predictor set. In the example case, the resultant AUC is 1.0, a perfect predictor for the sample set. 6) To assess overfitting and hence the predictive potential of the metabolic signature, 10-fold cross validation is performed by generating 10 partitions of 90% of the data to train signatures and predict the remaining 10 partitions of 10% of the data. To find signatures that have constant predictive power, the cross validation signatures were summed, and high scoring metabolites were considered the conserved metabolic response signature for the side effect or indication.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15 (PDF 2987 kb)

Life Sciences Reporting Summary (PDF 129 kb)

Supplementary Tables and Supplementary Notes

Supplementary tables1–9 and Supplementary notes1–6 (PDF 1987 kb)

Supplementary Datafiles 1-10

Reconstruction; Recon3D (XLSX 2106 kb)

Supplementary Datafiles 11-14

File contains all GEM-PRO related content for Recon3D.Contains Supplementary Data Files 11-14. (XLSX 3195 kb)

Supplementary Datafiles 15-26

File contains all mappings to variant disease SNPs/somatic mutations, FATCAT representative domain annotations and drug indication analyses. Contains Supplementary Data Files 15-26. (XLSX 2972 kb)

Supplementary Datafile 27

Recon 3D GEM-PRO has been consolidated into a shareable JSON file, which can be used to start structural analyses. (ZIP 444 kb)

Supplementary Software

IndiFinder.m (ZIP 3 kb)

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Brunk, E., Sahoo, S., Zielinski, D. et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 36, 272–281 (2018). https://doi.org/10.1038/nbt.4072

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