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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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

    • Swagatika Sahoo

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

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

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Ines Thiele or Bernhard O Palsson.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–15

  2. 2.

    Life Sciences Reporting Summary

  3. 3.

    Supplementary Tables and Supplementary Notes

    Supplementary tables1–9 and Supplementary notes1–6

Excel files

  1. 1.

    Supplementary Datafiles 1-10

    Reconstruction; Recon3D

  2. 2.

    Supplementary Datafiles 11-14

    File contains all GEM-PRO related content for Recon3D.Contains Supplementary Data Files 11-14.

  3. 3.

    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.

Zip files

  1. 1.

    Supplementary Datafile 27

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

  2. 2.

    Supplementary Software

    IndiFinder.m

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DOI

https://doi.org/10.1038/nbt.4072