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Large-scale in silico modeling of metabolic interactions between cell types in the human brain

Nature Biotechnology volume 28, pages 1279–1285 (2010)Cite this article

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Abstract

Metabolic interactions between multiple cell types are difficult to model using existing approaches. Here we present a workflow that integrates gene expression data, proteomics data and literature-based manual curation to model human metabolism within and between different types of cells. Transport reactions are used to account for the transfer of metabolites between models of different cell types via the interstitial fluid. We apply the method to create models of brain energy metabolism that recapitulate metabolic interactions between astrocytes and various neuron types relevant to Alzheimer's disease. Analysis of the models identifies genes and pathways that may explain observed experimental phenomena, including the differential effects of the disease on cell types and regions of the brain. Constraint-based modeling can thus contribute to the study and analysis of multicellular metabolic processes in the human tissue microenvironment and provide detailed mechanistic insight into high-throughput data analysis.

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Figure 1: A workflow for bridging the genotype-phenotype gap with the use of high-throughput data and manual curation for the construction of multicellular models of metabolism.
Figure 2: General structure of the models.
Figure 3: Decrease in AKGDm activity associated with Alzheimer's disease (AD) shows cell-type and regional effects in silico consistent with experimental data.
Figure 4: Analysis of metabolic pathways in different regions of brains affected by Alzheimer's.
Figure 5: Singular value decomposition (SVD) of feasible pathways elucidates potential pathways that allow for coupling of mitochondria acetyl-CoA metabolism and cytosolic acetylcholine production.
Figure 6: Model-aided prediction of cholinergic contribution is consistent with experimental acetylcholine production.

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Acknowledgements

The authors thank G. Gibson at Cornell University, I. Thiele at the University of Iceland and M. Abrams, M. Mo and C. Barrett at UCSD for suggestions pertaining to this work. This work was funded in part by a Fulbright fellowship, a National Science Foundation IGERT Plant Systems Biology training grant (no. DGE-0504645), US National Institutes of Health grants 2R01GM068837_05A1 and RO1 GM071808 and the Helmholtz Alliance on Systems Biology and the BMBF by the NGFN+ neuroblastoma project ENGINE.

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Authors and Affiliations

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

    Nathan E Lewis, Aarash Bordbar, Michael P Andersen, Jeffrey K Cheng, Nilam Patel, Alex Yee & Bernhard Ø Palsson

  2. Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology and Bioquant, University of Heidelberg, Heidelberg, Germany

    Gunnar Schramm, Roland Eils & Rainer König

  3. Bioinformatics Program, University of California, San Diego, La Jolla, California, USA.,

    Jan Schellenberger

  4. Department of Economics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Randall A Lewis

  5. Department of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Gunnar Schramm, Roland Eils & Rainer König

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Contributions

N.E.L., J.K.C., A.Y., N.P., M.P.A. and B.O.P. conceived and designed the model. N.E.L., J.K.C., G.S., R.K., R.E., J.S., A.B. and R.A.L. performed data analyses. The manuscript was written by N.E.L., G.S., J.S., A.B. and B.O.P.

Corresponding author

Correspondence to Bernhard Ø Palsson.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Notes (PDF 1971 kb)

Supplementary Model 1

All neuron types normal (ZIP 72 kb)

Supplementary Model 2

All neuron types normal elderly (ZIP 72 kb)

Supplementary Model 3

All neuron types Alzheimer's (ZIP 72 kb)

Supplementary Table 1

Model rxns (XLS 317 kb)

Supplementary Table 2

Model metabolites (XLS 109 kb)

Supplementary Table 3

Model genes (XLS 50 kb)

Supplementary Table 4

Model S. matrix (ZIP 904 kb)

Supplementary Table 5

Model parameters (XLS 24 kb)

Supplementary Table 6

Significant Pathwave pathways (XLS 25 kb)

Supplementary Table 7

Protein data accession numbers (XLS 18 kb)

Supplementary Table 8

Comparison with other models (XLS 18 kb)

Supplementary Table 9

Pathwave pathway classes (XLS 42 kb)

Supplementary Table 10

Pathwave exception metabolites (XLS 15 kb)

Supplementary Table 11

Significant Pathwave features (XLS 168 kb)

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Lewis, N., Schramm, G., Bordbar, A. et al. Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat Biotechnol 28, 1279–1285 (2010). https://doi.org/10.1038/nbt.1711

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