The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

in Nucleic Acids Research (2018)

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic ... [more ▼]

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community. [less ▲]

Modeling the effects of commonly used drugs on human metabolism

in FEBS Journal (2014)

Metabolism contributes significantly to the pharmacokinetics and pharmacodynamics of a drug. In addition, diet and genetics have a profound effect on cellular metabolism under health and disease. Herein ... [more ▼]

Metabolism contributes significantly to the pharmacokinetics and pharmacodynamics of a drug. In addition, diet and genetics have a profound effect on cellular metabolism under health and disease. Herein, we assembled a comprehensive, literature-based drug metabolic reconstruction of the 18 most highly prescribed drug groups including statins, antihypertensives, immunosuppressants, and analgesics. This reconstruction captures in detail our current understanding of their absorption, intra-cellular distribution, metabolism, and elimination. We combined this drug module with the most comprehensive reconstruction of human metabolism, Recon 2, yielding Recon2_DM1796, which accounts for 2803 metabolites and 8161 reactions. By defining 50 specific drug objectives that captured the overall drug metabolism of these compounds, we investigated effects of dietary composition and inherited metabolic disorders on drug metabolism and drug-drug interactions. Our main findings include (i) shift in dietary patterns significantly affect statins and acetaminophen metabolism, (ii) disturbed statin metabolism contributes to the clinical phenotype of mitochondrial energy disorders, and (iii) the interaction between statins and cyclosporine can be explained by several common metabolic and transport pathways other than the previously established CYP3A4 connection. This work holds the potential for studying adverse drug reactions and designing patient-specific therapies. [less ▲]

A community-driven global reconstruction of human metabolism.

in Nature Biotechnology (2013), 31

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 ... [more ▼]

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/. [less ▲]

A compendium of inborn errors of metabolism mapped onto the human metabolic network

in Molecular Biosystems (2012), 8(10), 2545-2558

Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through ... [more ▼]

Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases. [less ▲]