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Abstract
Chronic kidney disease (CKD) is the gradual decrease in renal function. Currently available biomarkers are effective only in detecting late stage CKD. Biomarkers of early stage CKD and prognostic biomarkers are required. We review the major findings in urinary proteomics in CKD during the last five years. Significant progress has been made and today urinary proteomics is applied in large randomized trials, and in patient management. Many of the biomarkers indicate altered protease activity. We therefore also review the literature on proteases associated with renal function loss. We anticipate in silico prediction tools of protease activity and additional system biology studies may contribute to biomarker discovery and elucidate the role of proteases in CKD development and progression. These approaches will enable the deciphering of the molecular pathophysiology of CKD, and hence definition of the most appropriate therapeutic targets in the future. Together with stable biomarker panels available today, this will significantly improve patient management.
Financial & competing interests disclosure
The authors were supported by the grant agreements ‘Improvement of tools and portability of MS-based clinical proteomics as applied to chronic kidney disease’ (Protoclin, PEOPLE-2009-IAPP, GA 251368), ‘Clinical and system – omics for the identification of the Molecular Determinants of established Chronic Kidney Disease’ (iMODE-CKD, PEOPLE-ITN-GA-2013–608332), ‘Systems Biology to Identify Molecular Targets for Vascular Disease Treatment’ (SysVasc, HEALTH-2013 603288), and ‘Systems biology toward novel chronic kidney disease diagnosis and treatment’ (SysKID HEALTH–F2–2009–241544). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.
Key issues
There is a pressing clinical need for chronic kidney disease (CKD) biomarkers.
High-throughput proteomics and peptidomics studies coupled with robust statistical analysis have already resulted in the discovery of novel CKD biomarkers.
A classifier based on 273 urinary peptides was demonstrated as a useful composite biomarker for CKD in several independent studies.
Main proteomic/peptidomic urinary biomarkers are collagen type 1 fragments (reduced in CKD) and α-1-antitrypsin fragments (increased in CKD).
Age, disease etiology and medical history may have an effect on biomarker concentration.
One of the most important classes of proteins involved in CKD may be proteases, specifically matrix metalloproteinases, ADAM family members, dipeptidyl peptidase-4 and aminopeptidase N. However, the elucidation of their role in CKD has not been achieved yet.
In silico prediction of proteases based on urinary peptide data may contribute to identification of novel proteases related to CKD.
Large validation studies that demonstrated value of urinary proteomic biomarkers in CKD appear to be the essential next step to enable implementation of proteomics as a routine tool in CKD management.