Application of systems biology to the study of chronic kidney disease.
- Author:
Yu-Han CAO
1
;
Lin-Li LÜ
;
Jian-Dong ZHANG
;
Bi-Cheng LIU
Author Information
1. Institute of Nephrology, Zhong Da Hospital, Southeast University, Nanjing, Jiangsu 210009, China.
- Publication Type:Journal Article
- MeSH:
Computational Biology;
Gene Expression Profiling;
Genome-Wide Association Study;
Humans;
Renal Insufficiency, Chronic;
genetics;
metabolism;
Systems Biology;
methods
- From:
Chinese Medical Journal
2012;125(14):2603-2609
- CountryChina
- Language:English
-
Abstract:
Chronic kidney disease (CKD) is a major public health problem that affects about 10% of the general population. Current approaches to characterize the category and progression of CKD are normally based on renal histopathological results and clinical parameters. However, this information is not sufficient to predict CKD progression risk reliably or to guide preventive interventions. Nowadays, the appearance of systems biology has brought forward the concepts of "-omics" technologies, including genomics, transcriptomics, proteomics, and metabolomics. Systems biology, together with molecular analysis approaches such as microarray analysis, genome-wide association studies (GWAS), and serial analysis of gene expression (SAGE), has provided the framework for a comprehensive analysis of renal disease and serves as a starting point for generating novel molecular diagnostic tools for use in nephrology. In particular, analysis of urinary mRNA and protein levels is rapidly evolving as a non-invasive approach for CKD monitoring. All these systems biological molecular approaches are required for application of the concept of "personalized medicine" to progressive CKD, which will result in tailoring therapy for each patient, in contrast to the "one-size-fits-all" therapies currently in use.
