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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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The functional characteristics of KS and HK genes in the kidney-specific metabolic model. (a) shows the distribution of predicted relative growth rates of HK and KS genes using both FBA (blue star) and linear MOMA (red circle) methods. The y axis represents growth rate ratio between deletion strain to knockout strain, and the x axis represents the 297 HK genes and 34 KS genes. (b) shows a typical example of effects of KS gene mutant strains on the network flexibility. In the mutant strain of Solute carrier family 7 member 9 (SLC7A9), the majority (99.51%) of the metabolic reactions do not change flux span compared to wild-type strain while 12 reactions have much higher (r > 2) flux span.
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fig2: The functional characteristics of KS and HK genes in the kidney-specific metabolic model. (a) shows the distribution of predicted relative growth rates of HK and KS genes using both FBA (blue star) and linear MOMA (red circle) methods. The y axis represents growth rate ratio between deletion strain to knockout strain, and the x axis represents the 297 HK genes and 34 KS genes. (b) shows a typical example of effects of KS gene mutant strains on the network flexibility. In the mutant strain of Solute carrier family 7 member 9 (SLC7A9), the majority (99.51%) of the metabolic reactions do not change flux span compared to wild-type strain while 12 reactions have much higher (r > 2) flux span.

Mentions: We performed gene essentiality analysis by categorizing genes in the human kidney metabolic model into HK genes and KS genes. We totally retrieved 55 KS genes and 2064 HK genes from previous study [33], then we mapped these genes to the kidney model. Finally, only 24 KS and 233 HK genes are present in our kidney model, the other 31 genes were discarded for they are not the component genes in kidney metabolism. If we take the information of cellular compartments into consideration, then the numbers of above genes turned out to be 34 (KS genes) and (297 HK genes). Single gene deletion experiments were performed with these genes by using both FBA and linear MOMA methods to characterize the gene deletion phenotypes. The distribution of predicted relative growth rates of knockout strains to wild-type strains for all the mapped gene deletions in the kidney model was shown in Figure 2(a). All the 34 KS genes are nonlethal, while out of 297 HK genes, 10 were considered lethal and one resulted in reduced maximal growth rate. It suggests that HK genes are mainly involved in fundamental cellular functions and knockingout these genes may cause metabolism alterations, thus result in lower growth rate or lethality. In contrast, the functions of KS genes can be complemented by other members in the same gene family, such as ATPase alpha/beta chains family and solute carrier family. The latter is a large family and transport succinate and other Krebs cycle intermediates and play an important role in the handling of citrate in kidney. Consequently, their single gene mutations do not lead to lethality in human. Furthermore, these KS genes may play a role in kidney's particular function (such as sodium ion and inorganic anion transport) other than the fundamental cell activity (Figure  S1). Through the analysis of metabolic networks, we found that these KS genes mainly belong to transport subsystem or amino acid metabolism subsystem. We listed the detailed information about these genes and the involved reactions in Table  S2.


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

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

The functional characteristics of KS and HK genes in the kidney-specific metabolic model. (a) shows the distribution of predicted relative growth rates of HK and KS genes using both FBA (blue star) and linear MOMA (red circle) methods. The y axis represents growth rate ratio between deletion strain to knockout strain, and the x axis represents the 297 HK genes and 34 KS genes. (b) shows a typical example of effects of KS gene mutant strains on the network flexibility. In the mutant strain of Solute carrier family 7 member 9 (SLC7A9), the majority (99.51%) of the metabolic reactions do not change flux span compared to wild-type strain while 12 reactions have much higher (r > 2) flux span.
© Copyright Policy
Related In: Results  -  Collection

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

fig2: The functional characteristics of KS and HK genes in the kidney-specific metabolic model. (a) shows the distribution of predicted relative growth rates of HK and KS genes using both FBA (blue star) and linear MOMA (red circle) methods. The y axis represents growth rate ratio between deletion strain to knockout strain, and the x axis represents the 297 HK genes and 34 KS genes. (b) shows a typical example of effects of KS gene mutant strains on the network flexibility. In the mutant strain of Solute carrier family 7 member 9 (SLC7A9), the majority (99.51%) of the metabolic reactions do not change flux span compared to wild-type strain while 12 reactions have much higher (r > 2) flux span.
Mentions: We performed gene essentiality analysis by categorizing genes in the human kidney metabolic model into HK genes and KS genes. We totally retrieved 55 KS genes and 2064 HK genes from previous study [33], then we mapped these genes to the kidney model. Finally, only 24 KS and 233 HK genes are present in our kidney model, the other 31 genes were discarded for they are not the component genes in kidney metabolism. If we take the information of cellular compartments into consideration, then the numbers of above genes turned out to be 34 (KS genes) and (297 HK genes). Single gene deletion experiments were performed with these genes by using both FBA and linear MOMA methods to characterize the gene deletion phenotypes. The distribution of predicted relative growth rates of knockout strains to wild-type strains for all the mapped gene deletions in the kidney model was shown in Figure 2(a). All the 34 KS genes are nonlethal, while out of 297 HK genes, 10 were considered lethal and one resulted in reduced maximal growth rate. It suggests that HK genes are mainly involved in fundamental cellular functions and knockingout these genes may cause metabolism alterations, thus result in lower growth rate or lethality. In contrast, the functions of KS genes can be complemented by other members in the same gene family, such as ATPase alpha/beta chains family and solute carrier family. The latter is a large family and transport succinate and other Krebs cycle intermediates and play an important role in the handling of citrate in kidney. Consequently, their single gene mutations do not lead to lethality in human. Furthermore, these KS genes may play a role in kidney's particular function (such as sodium ion and inorganic anion transport) other than the fundamental cell activity (Figure  S1). Through the analysis of metabolic networks, we found that these KS genes mainly belong to transport subsystem or amino acid metabolism subsystem. We listed the detailed information about these genes and the involved reactions in Table  S2.

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