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Reconstruction and analysis of human kidney-specific metabolic network based on omics data.

Zhang AD, Dai SX, Huang JF - Biomed Res Int (2013)

Bottom Line: Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model.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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China ; Graduate School of the Chinese Academy of Sciences, Kunming 650223, China.

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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Related in: MedlinePlus

The flux variability analysis of DEG in DKD. The mutation effects of six DEG on the metabolic network flexibility are shown. It indicates that the flux spans of the metabolic reactions evidently fluctuate for the six genes, especially LPL (Entrez gene ID: 4023). The y axis represents reaction count, and the x axis represents the flux span ratio of knockout strains to wild-type strains. The genes are represented by Entrez gene ID as shown in Table 2.
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fig5: The flux variability analysis of DEG in DKD. The mutation effects of six DEG on the metabolic network flexibility are shown. It indicates that the flux spans of the metabolic reactions evidently fluctuate for the six genes, especially LPL (Entrez gene ID: 4023). The y axis represents reaction count, and the x axis represents the flux span ratio of knockout strains to wild-type strains. The genes are represented by Entrez gene ID as shown in Table 2.

Mentions: To further investigate how metabolic genes affect the disease process in human, we illustrated DKD as one case to illuminate this question. We obtained the DEG from a recent DKD study between the disease samples and the normal counterpart. The expression levels of these genes demonstrate remarkable change, revealing that they play a key role in DKD by affecting the involved pathways [4]. Totally 24 of 330 DEG genes mapped to our constructed kidney model. FVA were performed to characterize the gene deletion phenotypes. For most of the genes, the flux spans of their reactions were demonstrated fluctuated distinctly. We listed several genes and their related information in Table 2, and the corresponding FVA analysis results were demonstrated in Figure 5.


Reconstruction and analysis of human kidney-specific metabolic network based on omics data.

Zhang AD, Dai SX, Huang JF - Biomed Res Int (2013)

The flux variability analysis of DEG in DKD. The mutation effects of six DEG on the metabolic network flexibility are shown. It indicates that the flux spans of the metabolic reactions evidently fluctuate for the six genes, especially LPL (Entrez gene ID: 4023). The y axis represents reaction count, and the x axis represents the flux span ratio of knockout strains to wild-type strains. The genes are represented by Entrez gene ID as shown in Table 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814056&req=5

fig5: The flux variability analysis of DEG in DKD. The mutation effects of six DEG on the metabolic network flexibility are shown. It indicates that the flux spans of the metabolic reactions evidently fluctuate for the six genes, especially LPL (Entrez gene ID: 4023). The y axis represents reaction count, and the x axis represents the flux span ratio of knockout strains to wild-type strains. The genes are represented by Entrez gene ID as shown in Table 2.
Mentions: To further investigate how metabolic genes affect the disease process in human, we illustrated DKD as one case to illuminate this question. We obtained the DEG from a recent DKD study between the disease samples and the normal counterpart. The expression levels of these genes demonstrate remarkable change, revealing that they play a key role in DKD by affecting the involved pathways [4]. Totally 24 of 330 DEG genes mapped to our constructed kidney model. FVA were performed to characterize the gene deletion phenotypes. For most of the genes, the flux spans of their reactions were demonstrated fluctuated distinctly. We listed several genes and their related information in Table 2, and the corresponding FVA analysis results were demonstrated in Figure 5.

Bottom Line: Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model.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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China ; Graduate School of the Chinese Academy of Sciences, Kunming 650223, China.

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.

Show MeSH
Related in: MedlinePlus