Limits...
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.

Show MeSH

Related in: MedlinePlus

Biological processes enrichment analysis for PDG in comparison of kidney-specific metabolic network to heart-specific metabolic network. (a) shows the cellular metabolic processes overrepresented by kidney-PDG compared to the model of heart; it indicates that the kidney metabolic genes are largely involved in various processes, like amine metabolic process, indolalkylamine biosynthetic process, and hormone biosynthetic process. (b) shows the cellular metabolic processes overrepresented by heart-PDG compared to the model of kidney, it indicates that the heart metabolic genes are largely involved in other cellular process, such as cofactor metabolic process, coenzyme metabolic process, and glutathione biosynthetic process. GO annotation was performed by using BINGO. The yellow to orange color of the circles represents enriched GO categories, and the darkness of color is proportional to the significance level; the size of circle is proportional to the number of gene cluster annotated to that node. Only categories with a low P value (<0.01) were considered as enriched in the network. P value is determined by Hypergeometric statistical test employing the Benjamini and Hochberg false discovery rate correction.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3814056&req=5

fig4: Biological processes enrichment analysis for PDG in comparison of kidney-specific metabolic network to heart-specific metabolic network. (a) shows the cellular metabolic processes overrepresented by kidney-PDG compared to the model of heart; it indicates that the kidney metabolic genes are largely involved in various processes, like amine metabolic process, indolalkylamine biosynthetic process, and hormone biosynthetic process. (b) shows the cellular metabolic processes overrepresented by heart-PDG compared to the model of kidney, it indicates that the heart metabolic genes are largely involved in other cellular process, such as cofactor metabolic process, coenzyme metabolic process, and glutathione biosynthetic process. GO annotation was performed by using BINGO. The yellow to orange color of the circles represents enriched GO categories, and the darkness of color is proportional to the significance level; the size of circle is proportional to the number of gene cluster annotated to that node. Only categories with a low P value (<0.01) were considered as enriched in the network. P value is determined by Hypergeometric statistical test employing the Benjamini and Hochberg false discovery rate correction.

Mentions: Tissues execute their functions via different gene sets and metabolic pathways, so tissues may show characteristic metabolic features that make them different from other tissues. Thus, comparing the components of their metabolic models is essential for understanding the tissue-specific metabolic behavior. We compared our constructed kidney metabolic model with two other tissue-specific models obtained from previous work [6, 9], including heart specific model and liver specific model. Then four different small subnetworks, consisting of genes, reactions and metabolites, were generated corresponding to different tissues. We extracted the above genes (named Pair-Different-Genes, PDG) and performed GO enrichment analysis, respectively. We found that different metabolic processes were detected corresponding to different tissues. Here we only elaborated the comparison of kidney and heart in detail. The cellular processes overrepresented by PDG of kidney comparing with those of heart are shown in Figure 4(a). The kidney-PDG were largely involved in several processes, like, indolalkylamine biosynthetic process, hormone biosynthetic process and amine metabolic process, which is important for kidney to filter and eliminate the byproducts of metabolism and regulate many important body functions, especially the urea excretion functions during nitrogen metabolism process. Beyond these processes, other metabolic processes were also found to be significantly enriched in kidney, including oxidation reduction, cellular aromatic compound metabolic processes, cellular ketone metabolic process and the generation of precursor metabolites and energy. The top fifteen overrepresented metabolic processes in kidney with P value < 0.01 are listed in Table 1. Consistent with the above finding, for heart metabolic model, its heart-PDG were involved in other cellular processes (Figure 4(b)), including cofactor metabolic process, coenzyme metabolic process, and glutathione biosynthetic process. These processes were corresponding to heart's function of working like a pumping machine to provide the power needed for life. Similar results for comparison between metabolic modes of kidney and liver are also shown in Figure  S3. The detailed information about gene clusters and the corresponding significantly enriched GO categories can be found in Table  S3.


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

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

Biological processes enrichment analysis for PDG in comparison of kidney-specific metabolic network to heart-specific metabolic network. (a) shows the cellular metabolic processes overrepresented by kidney-PDG compared to the model of heart; it indicates that the kidney metabolic genes are largely involved in various processes, like amine metabolic process, indolalkylamine biosynthetic process, and hormone biosynthetic process. (b) shows the cellular metabolic processes overrepresented by heart-PDG compared to the model of kidney, it indicates that the heart metabolic genes are largely involved in other cellular process, such as cofactor metabolic process, coenzyme metabolic process, and glutathione biosynthetic process. GO annotation was performed by using BINGO. The yellow to orange color of the circles represents enriched GO categories, and the darkness of color is proportional to the significance level; the size of circle is proportional to the number of gene cluster annotated to that node. Only categories with a low P value (<0.01) were considered as enriched in the network. P value is determined by Hypergeometric statistical test employing the Benjamini and Hochberg false discovery rate correction.
© Copyright Policy
Related In: Results  -  Collection

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

fig4: Biological processes enrichment analysis for PDG in comparison of kidney-specific metabolic network to heart-specific metabolic network. (a) shows the cellular metabolic processes overrepresented by kidney-PDG compared to the model of heart; it indicates that the kidney metabolic genes are largely involved in various processes, like amine metabolic process, indolalkylamine biosynthetic process, and hormone biosynthetic process. (b) shows the cellular metabolic processes overrepresented by heart-PDG compared to the model of kidney, it indicates that the heart metabolic genes are largely involved in other cellular process, such as cofactor metabolic process, coenzyme metabolic process, and glutathione biosynthetic process. GO annotation was performed by using BINGO. The yellow to orange color of the circles represents enriched GO categories, and the darkness of color is proportional to the significance level; the size of circle is proportional to the number of gene cluster annotated to that node. Only categories with a low P value (<0.01) were considered as enriched in the network. P value is determined by Hypergeometric statistical test employing the Benjamini and Hochberg false discovery rate correction.
Mentions: Tissues execute their functions via different gene sets and metabolic pathways, so tissues may show characteristic metabolic features that make them different from other tissues. Thus, comparing the components of their metabolic models is essential for understanding the tissue-specific metabolic behavior. We compared our constructed kidney metabolic model with two other tissue-specific models obtained from previous work [6, 9], including heart specific model and liver specific model. Then four different small subnetworks, consisting of genes, reactions and metabolites, were generated corresponding to different tissues. We extracted the above genes (named Pair-Different-Genes, PDG) and performed GO enrichment analysis, respectively. We found that different metabolic processes were detected corresponding to different tissues. Here we only elaborated the comparison of kidney and heart in detail. The cellular processes overrepresented by PDG of kidney comparing with those of heart are shown in Figure 4(a). The kidney-PDG were largely involved in several processes, like, indolalkylamine biosynthetic process, hormone biosynthetic process and amine metabolic process, which is important for kidney to filter and eliminate the byproducts of metabolism and regulate many important body functions, especially the urea excretion functions during nitrogen metabolism process. Beyond these processes, other metabolic processes were also found to be significantly enriched in kidney, including oxidation reduction, cellular aromatic compound metabolic processes, cellular ketone metabolic process and the generation of precursor metabolites and energy. The top fifteen overrepresented metabolic processes in kidney with P value < 0.01 are listed in Table 1. Consistent with the above finding, for heart metabolic model, its heart-PDG were involved in other cellular processes (Figure 4(b)), including cofactor metabolic process, coenzyme metabolic process, and glutathione biosynthetic process. These processes were corresponding to heart's function of working like a pumping machine to provide the power needed for life. Similar results for comparison between metabolic modes of kidney and liver are also shown in Figure  S3. The detailed information about gene clusters and the corresponding significantly enriched GO categories can be found in Table  S3.

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