A community-driven global reconstruction of human metabolism

Abstract

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

Figure 1

Overview of the community-driven reconstruction approach to assemble Recon 2. Comp. EHMN, compartmentalized EHMN; and hs_sIEC611, small intestinal enterocyte reconstruction.

Figure 2

Pathway coverage in Recon 1 and Recon 2. Only pathways with added reaction content in Recon 2 are shown. The 29 pathways that have identical reaction content in both reconstructions are not shown.

Figure 3

Predicted biomarkers for IEMs. (a) Comparison of the prediction accuracy of Recon 1 and Recon 2 against the gold standard. (b) Correct and incorrect predictions. IEMs and biomarkers are sorted by subsystem. Bright yellow, amino-acid metabolism; green, central metabolism; blue, hormones; yellow, lipid metabolism; pink, nucleotide metabolism; lilac, vitamin and cofactor metabolism. Blue and red shading corresponds to predicted increase and decrease in biomarker, respectively. Blue and red lines represent reported increase and decrease of the biomarker in plasma, respectively. 34dhphe, 3,4-dihydroxy-L-phenylalanine; 3mlda, 3-methylimidazoleacetic acid; 5htrp, 5-hydroxy-L-tryptophan; bhb, (R)-3-hydroxybutanoate; tetdec2crn, tetradecadienoyl carnitine; tetdece1crn, tetradecenoyl carnitine. (See Supplementary Table 5 for complete names of the IEMs.)

Figure 4

Comparison of metabolomic data with the extracellular metabolites present in Recon 2 metabolites. (a) Comparison with the cancer exometabolome (see Supplementary Table 3 for a breakdown of the 18 cancer exometabolites that are absent from Recon 2, the 31 for which no exchange reaction is present, and the 91 for which uptake or release profile comparisons could be performed). (b) Comparison with the HMDB (see Supplementary Fig. 6).

Figure 5

Summary properties of the 65 draft cell type–specific models. (a) Distribution of reaction (left) and genes (right) across the models. (b) The protein expression data captured proteins from all subsystems almost evenly. In contrast, most of reactions in the core reaction set, consisting of 593 reactions, belonged to the transport subsystem. The second largest subsystem in Recon 2, lipid metabolism, represented only a small fraction of the core reaction set.