ReviewJournal of inherited metabolic disease2018
Inborn errors of metabolism and the human interactome: a systems medicine approach.
Review in Journal of inherited metabolic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Exploration of Metabolic Alterations in Methylmalonic Acidemia across Distinct Genetic Backgrounds.ACS omega · 2026Article
- Peroxisomal interactome mapping enables network-based modelling of function and disease.Life science alliance · 2026Article
- Considering Metabolic Context in Enzyme Evolution and Design.Biochemistry · 2025Review
- Article
- Convergent molecular mechanisms underlying cognitive impairment in mucopolysaccharidosis type II.Metabolic brain disease · 2022Article
- Glucosylceramide Associated with Gaucher Disease Forms Amyloid-like Twisted Ribbon Fibrils That Induce α-Synuclein Aggregation.ACS nano · 2021Article
- Network Analysis Reveals Proteins Associated with Aortic Dilatation in Mucopolysaccharidoses.Interdisciplinary sciences, computational life sciences · 2021Article
- IRS-2/Akt/GSK-3Oxidative medicine and cellular longevity · 2021Article
- Article
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Authors and funding
3 authors.
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Abstract
The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.
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