The health and disease states of the human can be described more meaningfully by the metabolic state of human. As the most-used network tool, metabolic network plays a key role in disease research and genetic engineering. 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. Human complex disease is very tough due to its various disease mechanisms, and involvs extensive interaction between inheridity and environment. Animal models have played a key role in the study of cardiovascular disease and provided important insights into disease pathogenesis and drug development. However, the experiment outcome always belows or contradict our expectations, which may result from the function divergence between human and animal models. To address this issue, we focus on the comparative transcriptomics of heart failure. We compared the gene expression of orthologous genes between human and four animal models(mouse,rat, dog,pig). The results show that the similarity of global expression profiles between animal models and human orthologous genes are consistent with species evolutionary trees. Additionaly, we identified a large number of different expression genes between heart failure and nomal counterpart, and found that they have rare overlap among different animal models except human and mouse, whose different expression genes both participated in extracellular matrix disassemblyand cartilage condensation pathways. GO annotation results indicate that different expression genes of different animal models participated in different pathways. Finally we compared two gene expression data of mice, heart failure of which were indued by different experimental treatment methods, the results indicate that their heart failure still have large difference concerning the expression level and pathways. Our work may provide valuable information for the development of animal models of human complex diseases and personal medicine.
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