The human metabolic reconstruction

The human metabolism resource [1] has been developed by the systems biology community over the past decade [1,2,3] and describes metabolic reactions and pathways known to occur in at least one cell type in the human body.

Details on the human metabolism resource, the latest updates, and the underlying reconstruction, Recon 3D, can be found in Brunk et al. [1]. In brief, this version was created by expanding the previous version of the human metabolic reconstruction, Recon 2 [2] through the addition of new metabolites, transport reactions, and catalyzing reactions guided by publically available metabolomics data. Furthermore, we added metabolites and reactions from HMR 2.0 [4], a drug module [5], a transport module [6], host-microbe reactions [7], as well as for the absorption and metabolism of dietary compounds. During the reconstruction process, we followed the established protocol [8]. In total, 13,543 reactions and 4,140 unique metabolites are represented in this resource, representing an expanded metabolic scope of 82% and 58%, respectively. The metabolic reactions are associated with 3,288 genes, representing 17% of the functionally annotated gene products in the human genome (build 38). Numerous aspects of the reconstruction content were refined and expanded in this newest release, including 2,181 gene-protein-reaction (GPR) associations, reaction/metabolite duplication, reaction directionality, and thermodynamic feasibility. Overall, the human metabolic resource is based on biochemical information that were manually retrieved from more than 2,000 scientific publications and books.

At the core of the human metabolism resource lies the human metabolic reconstruction, which is amenable to metabolic modeling. Consequently, metabolic functions without genetic evidence but with physiological evidence have been also included in this resource. Many of the reactions are associated with a confidence score indicating the evidence supporting their inclusion into the human metabolic reconstruction.

Evidence type Confidence score Examples
Biochemical data 4 Direct evidence for gene product function and biochemical reaction: Protein purification, biochemical assays, experimentally solved protein structures, and comparative gene-expression studies.
Genetic data 3 Direct and indirect evidence for gene function: Knock-out characterization, knock-in characterization, and over-expression.
Physiological data 2 Indirect evidence for biochemical reactions based on physiological data: secretion products or defined medium components serve as evidence for transport and metabolic reactions.
Sequence data 2 Evidence for gene function: Genome annotation.
Modeling data 1 No evidence is available but reaction is required for modeling. The included function is a hypothesis and needs experimental verification. The reaction mechanism may be different from the included reaction(s).
Not evaluated 0
Taken from [8].

The metabolic reactions and their metabolites are distributed over nine cellular compartments:

Compartment abbreviation Compartment
[e] Extracellular space
[c] Cytosol
[m] Mitochondria
[i] Inner mitochondrial space
[n] Nucleus
[x] Peroxisome
[g] Golgi apparatus
[r] Endoplasmic reticulum
[l] Lysosome

We are continuously updating and refining the content of the human metabolism resource. Please help us in further developing it by providing your feedback!

The computational model of human metabolism:

In addition to the human metabolism resource, and the underlying metabolic reconstruction, we also provide a simulation-ready, tissue-/cell-type unspecific model of human metabolism. Again, for details please also refer to [1].

Briefly, Recon 3D was converted into a computational model (Recon3 model) by 1. removing stoichiometrically inconsistent reactions and 2. flux inconsistent reactions, i.e., reactions that could not carry flux under the applied reaction constraints. Subsequently, we performed the standard quality-control tests [8], including ensuring leakfreeness and no energy production from nothing. Furthermore, we ensured that 1. the energy (ATP) yield from different carbon sources under oxic and anoxic conditions is consistent with the theoretical values, 2. metabolic functions describing cellular and whole body metabolism could be fulfilled, and 3. infant growth predictions were consistent with a previous study [9]. The resulting generic Recon3D model contains 10,600 reactions (78% of the reconstruction reactions) and 2,797 unique metabolites.

Recon 3D Recon 3 model*
Reactions 13,543 10,600
Metabolites 8.399 5,835
Metabolites (unique) 4,140 2,797
Compartments (unique) 9 9
Genes (unique) 3,288 1,882
Subsystems 110 102
Dead-end metabolites 882 (11%) 0 (0%)
Flux inconsistent reactions 1,582 (12%) 0 (0%)
Comparison of content of the reconstruction and its generic model, taken from [1]. Dead-end metabolites are those metabolites that are only consumed or produced in the metabolic reconstruction. *Simulation ready.

The latest version of the computational model can be obtained under the download menu.

References

  1. Brunk et al., "Recon3D enables a three-dimensional view of gene variation in human metabolism, Nature Biotechnology, 36, 272-281 (2018).
  2. Thiele, I., Swainston, N., Fleming, R.M.T., Hoppe, A., Sahoo, S., Aurich, M.K., Haraldsdottir, H., Mo, M.L., Rolfsson, O., Stobbe, M.D., Thorleifsson, S.G., Agren, R., Boelling, C., Bordel, S., Chavali, A.K., Dobson, P., Dunn, W.B., Endler, L., Hala, D., Hucka, M., Hull, D., Jameson, D., Jamshidi, N., Jonsson, J.J., Juty, N., Keating, S., Nookaew, I., Le Novere, N., Malys, N., Mazein, A., Papin, J.A., Price, N.D., Selkov Sr., E, Sigurdsson, M.I., Simeonidis, E., Sonnenschein, N., Smallbone, K., Sorokin, A., van Beek, J.H.G.M., Weichart, D., Goryanin, I., Nielsen, J., Westerhoff, H.V., Kell, D.B., Mendes, P., Palsson, B.O.,, "A community-driven global reconstruction of human metabolism", Nat Biotech, 31(5):419-25 (2013).
  3. Duarte, N.C., Becker, S.A., Jamshidi, N., Thiele, I., Mo, M.L., Vo, T.D., Srivas, R., and Palsson, B. O., "Global reconstruction of the human metabolic network based on genomic and bibliomic data.", PNAS, 104(6):1777-82 (2007).
  4. Mardinoglu, A. et al. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun. 5, 3083 (2014).
  5. Sahoo, S., Haraldsdóttir, H.S., Fleming, R.M.T. & Thiele, I. Modeling the effects of commonly used drugs on human metabolism. FEBS J. 282, 297-317 (2015).
  6. Sahoo, S., Aurich, M.K., Jonsson, J.J. & Thiele, I. Membrane transporters in a human genome-scale metabolic knowledgebase and their implications for disease. Front. Physiol. 5, 91 (2014).
  7. Heinken, A., Thiele, I., "Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework", Gut Microbes, 6 (2), 120-130 (2015).
  8. Thiele, I., Palsson, B. O., "A protocol for generating a high-quality genome-scale metabolic reconstruction.", Nat Protocols, 5(1): 93 - 121 (2010).
  9. Nilsson, A., Mardinoglu, A. & Nielsen, J. Predicting growth of the healthy infant using a genome scale metabolic model. Syst. Biol. Appl. 3, 3 (2017).

When using the human metabolism resource, the reconstruction, or the computational model, please cite:

Brunk et al., "Recon3D enables a three-dimensional view of gene variation in human metabolism", Nature Biotechnology,36, 272-281 (2018) (2018); doi:10.1038/nbt.4072