ArticleBioMed research international2013
Reconstruction and analysis of human kidney-specific metabolic network based on omics data.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Multi-omics approaches for revealing the complexity of cardiovascular disease.Briefings in bioinformatics · 2021Review
- Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model.Toxicology and applied pharmacology · 2021Article
- Omics databases on kidney disease: where they can be found and how to benefit from them.Clinical kidney journal · 2016Review
- A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis.Mammalian genome : official journal of the International Mammalian Genome Society · 2016Article
- Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease.World journal of gastroenterology · 2014Review
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.
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