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Metabolomic and Proteomic Techniques for Establishing Biomarkers and Improving Our Understanding of Pathophysiology in Diabetic Nephropathy

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Molecular studies of the proteome and metabolome in readily available body fluids such as urine and blood performed in a comprehensive qualitative and quantitative way are a valuable source of information for kidney disease research. They provide potential biomarkers of disease progression, markers of efficacy of interventions, as well as information on the underlying pathophysiology. Identified proteins and metabolites may point to dysregulated biological pathways and this knowledge may be useful in the identification of new treatment targets.Many studies, focusing on chronic kidney disease as well as diabetic nephropathy, demonstrate that peptidome and metabolome analysis can substantially contribute to early detection and prediction of disease progression, but also stratification of kidney disease in clinical practice. An innovative, well-explored application of urinary peptidome analysis is the back-translation of results obtained in humans to animals, for animal model validation and improvement of the preclinical readouts. In this chapter, we provide an overview of urinary proteomic analysis with the CE-MS analytical platform, a strategy that has been successfully employed in several studies for the identification and validation of biomarkers in kidney diseases. We describe how to obtain the orthology between the animal model and humans. We also deliver an overview of the analysis of the metabolome with the GC×GC-TOF-MS and UHPLC-Q-TOF-MS analytical platforms for blood and serum as new methods being applied in kidney disease.It is expected that a systems medicine approach to kidney disease including multiple omics methods will provide us with the best way to understand and treat diabetic kidney disease in the future.

OriginalsprogEngelsk
TidsskriftMethods in molecular biology (Clifton, N.J.)
Vol/bind2067
Sider (fra-til)287-306
ISSN1064-3745
DOI
StatusUdgivet - nov. 2019

ID: 58329070