The application of multi-omics and systems biology to identify therapeutic targets in chronic kidney disease

被引:84
作者
Cisek, Katryna [1 ]
Krochmal, Magdalena [2 ,3 ]
Klein, Julie [4 ,5 ]
Mischak, Harald [1 ,6 ]
机构
[1] Mosa Diagnost GmbH, Hannover, Germany
[2] Acad Athens, Biomed Res Fdn, Div Biotechnol, Athens, Greece
[3] Univ Klinikum RWTH Aachen, Inst Mol Cardiovasc Res, Aachen, Germany
[4] INSERM, U1048, Toulouse, France
[5] Univ Toulouse III Paul Sabatier, Toulouse, France
[6] Univ Glasgow, BHF Glasgow Cardiovasc Res Ctr, Glasgow, Lanark, Scotland
关键词
chronic kidney disease; data integration; omics; systems biology; therapeutic target; MOLECULAR-MECHANISMS; INTEGRATION; PROTEOMICS; NETWORK; IDENTIFICATION; TRANSCRIPTOME; PREDICTION; PATHWAY; MODELS; GENES;
D O I
10.1093/ndt/gfv364
中图分类号
R3 [基础医学]; R4 [临床医学];
学科分类号
1001 ; 1002 ; 100602 ;
摘要
The quest for the ideal therapeutic target in chronic kidney disease (CKD) has been riddled with many obstacles stemming from the molecular complexity of the disease and its co-morbidities. Recent advances in omics technologies and the resulting amount of available data encompassing genomics, proteomics, peptidomics, transcriptomics and metabolomics has created an opportunity for integrating omics datasets to build a comprehensive and dynamic model of the molecular changes in CKD for the purpose of biomarker and drug discovery. This article reviews relevant concepts in omics data integration using systems biology, a mathematical modelling method that globally describes a biological system on the basis of its modules and the functional connections that govern their behaviour. The review describes key databases and bioinformatics tools, as well as the challenges and limitations of the current state of the art, along with practical application to CKD therapeutic target discovery. Moreover, it describes how systems biology and visualization tools can be used to generate clinically relevant molecular models with the capability to identify specific disease pathways, recognize key events in disease development and track disease progression.
引用
收藏
页码:2003 / 2011
页数:9
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