Proteomic profile‑based screening of potential protein biomarkers in the urine of patients with nephrotic syndrome

Wang,Yu; Zheng,Chunxia; Wang,Xia; Zuo,Ke; Liu,Zhihong

doi:10.3892/mmr.2017.7329

Proteomic profile‑based screening of potential protein biomarkers in the urine of patients with nephrotic syndrome

Authors:
- Yu Wang
- Chunxia Zheng
- Xia Wang
- Ke Zuo
- Zhihong Liu
View Affiliations

Affiliations: National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu 210002, P.R. China
Published online on: August 22, 2017 https://doi.org/10.3892/mmr.2017.7329
Pages: 6276-6284

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Nephrotic syndrome is not a single disease; rather, it is a term for numerous diseases and pathological types. Renal biopsy is of use in determining the diagnosis and prognosis, and for guiding treatment; however, the use of this intervention is limited due to its invasive nature. Abnormal kidney‑derived proteins in the urine of patients provide useful information regarding numerous pathological processes that occur in the kidneys, and may be considered a potential non‑invasive biomarker for kidney disease. Proteomic analysis exhibits the advantage of being high‑throughput and has previously been used to identify biomarkers of disease. The present study aimed to identify abnormal kidney‑derived proteins in the urine of patients with nephrotic syndrome using a novel proteomic strategy. Urine samples from 5 patients with nephrotic syndrome were subjected to acetone precipitation and albumin/immunoglobulin G depletion prior to analysis by two‑dimensional liquid chromatography tandem mass spectrometry. The resulting data were compared to a publicly available proteomic database of normal human plasma/urine and normal human kidney in PeptideAtlas, and of normal human kidney in the Human Protein Atlas. Candidate biomarkers were validated using ELISA analysis in 60 patients with nephrotic syndrome: 30 with focal segmental glomerulosclerosis (FSGS) and 30 with minimal change disease (MCD), as well as in 30 healthy controls. The initial screening identified 809 proteins in the urine of patients with nephrotic syndrome. A total of 13/809 proteins were additionally present in the kidney proteome of PeptideAtlas and the Human Protein Atlas, although not in normal human urine and normal human plasma according to PeptideAtlas; these were referred to as ‘kidney‑derived disease‑associated proteins’. One of the kidney‑derived disease‑associated proteins, ubiquitin‑60S ribosomal protein L40 (UBA52) was observed to be increased in the urine of patients compared with normal controls [Creatinine, 637 ng/mg (216‑1,851) vs. 1.89 ng/mg (1.37‑3.33), P<0.001; and 18.58 ng/mg (11.11‑46.25) vs. 1.89 ng/mg (1.37‑3.33), P<0.001)], and the urinary UBA52 levels were significantly increased in patients with FSGS compared with in patients with MCD (P<0.001). In conclusion, the present study identified potential novel urinary protein biomarkers for nephrotic syndrome, in addition to an extensive urinary proteomic profile of patients with nephrotic syndrome.

View Figures

View References

1	Sancho-Martínez SM, Prieto-García L, Blanco-Gozalo V, Fontecha-Barriuso M, López-Novoa JM and López-Hernández FJ: Urinary proteomics in renal pathophysiology: Impact of proteinuria. Proteomics Clin Appl. 9:636–640. 2015. View Article : Google Scholar : PubMed/NCBI
2	Somparn P, Hirankarn N, Leelahavanichkul A, Khovidhunkit W, Thongboonkerd V and Avihingsanon Y: Urinary proteomics revealed prostaglandin H(2)D-isomerase, not Zn-α2-glycoprotein, as a biomarker for active lupus nephritis. J Proteomics. 75:3240–3247. 2012. View Article : Google Scholar : PubMed/NCBI
3	Zhang X, Jin M, Wu H, Nadasdy T, Nadasdy G, Harris N, Green-Church K, Nagaraja H, Birmingham DJ, Yu CY, et al: Biomarkers of lupus nephritis determined by serial urine proteomics. Kidney Int. 74:799–807. 2008. View Article : Google Scholar : PubMed/NCBI
4	Rossing K, Mischak H, Dakna M, Zürbig P, Novak J, Julian BA, Good DM, Coon JJ, Tarnow L and Rossing P: PREDICTIONS Network: Urinary proteomics in diabetes and CKD. J Am Soc Nephrol. 19:1283–1290. 2008. View Article : Google Scholar : PubMed/NCBI
5	Candiano G, Musante L, Bruschi M, Petretto A, Santucci L, Del Boccio P, Pavone B, Perfumo F, Urbani A, Scolari F and Ghiggeri GM: Repetitive fragmentation products of albumin and alpha1-antitrypsin in glomerular diseases associated with nephrotic syndrome. J Am Soc Nephrol. 17:3139–3148. 2006. View Article : Google Scholar : PubMed/NCBI
6	Varghese SA, Powell TB, Budisavljevic MN, Oates JC, Raymond JR, Almeida JS and Arthur JM: Urine biomarkers predict the cause of glomerular disease. J Am Soc Nephrol. 18:913–922. 2007. View Article : Google Scholar : PubMed/NCBI
7	Siwy J, Zoja C, Klein J, Benigni A, Mullen W, Mayer B, Mischak H, Jankowski J, Stevens R, Vlahou A, et al: Evaluation of the zucker diabetic fatty (ZDF) rat as a model for human disease based on urinary peptidomic profiles. PLoS One. 7:e513342012. View Article : Google Scholar : PubMed/NCBI
8	Pieper R, Su Q, Gatlin CL, Huang ST, Anderson NL and Steiner S: Multi-component immunoaffinity subtraction chromatography: An innovative step towards a comprehensive survey of the human plasma proteome. Proteomics. 3:422–432. 2003. View Article : Google Scholar : PubMed/NCBI
9	Pernemalm M, Lewensohn R and Lehtiö J: Affinity prefractionation for MS-based plasma proteomics. Proteomics. 9:1420–1427. 2009. View Article : Google Scholar : PubMed/NCBI
10	Björhall K, Miliotis T and Davidsson P: Comparison of different depletion strategies for improved resolution in proteomic analysis of human serum samples. Proteomics. 5:307–317. 2005. View Article : Google Scholar : PubMed/NCBI
11	Desrosiers RR, Beaulieu E, Buchanan M and Béliveau R: Proteomic analysis of human plasma proteins by two-dimensional gel electrophoresis and by antibody arrays following depletion of high-abundance proteins. Cell Biochem Biophys. 49:182–195. 2007. View Article : Google Scholar : PubMed/NCBI
12	Echan LA, Tang HY, Ali-Khan N, Lee K and Speicher DW: Depletion of multiple high-abundance proteins improves protein profiling capacities of human serum and plasma. Proteomics. 5:3292–3303. 2005. View Article : Google Scholar : PubMed/NCBI
13	Roche S, Tiers L, Provansal M, Seveno M, Piva MT, Jouin P and Lehmann S: Depletion of one, six, twelve or twenty major blood proteins before proteomic analysis: The more the better? J Proteomics. 72:945–951. 2009. View Article : Google Scholar : PubMed/NCBI
14	Mebazaa A, Vanpoucke G, Thomas G, Verleysen K, Cohen-Solal A, Vanderheyden M, Bartunek J, Mueller C, Launay JM, Van Landuyt N, et al: Unbiased plasma proteomics for novel diagnostic biomarkers in cardiovascular disease: Identification of quiescin Q6 as a candidate biomarker of acutely decompensated heart failure. Eur Heart J. 33:2317–2324. 2012. View Article : Google Scholar : PubMed/NCBI
15	Wang C, Wei LL, Shi LY, Pan ZF, Yu XM, Li TY, Liu CM, Ping ZP, Jiang TT, Chen ZL, et al: Screening and identification of five serum proteins as novel potential biomarkers for cured pulmonary tuberculosis. Sci Rep. 5:156152015. View Article : Google Scholar : PubMed/NCBI
16	Kurono S, Kaneko Y, Matsuura N, Oishi H, Noguchi S, Kim SJ, Tamaki Y, Aikawa T, Kotsuma Y, Inaji H and Matsuura S: Identification of potential breast cancer markers in nipple discharge by protein profile analysis using two-dimensional nano-liquid chromatography/nanoelectrospray ionization-mass spectrometry. Proteomics Clin Appl. 10:605–613. 2016. View Article : Google Scholar : PubMed/NCBI
17	Cao TH, Quinn PA, Sandhu JK, Voors AA, Lang CC, Parry HM, Mohan M, Jones DJ and Ng LL: Identification of novel biomarkers in plasma for prediction of treatment response in patients with heart failure. Lancet. 385 Suppl 1:S262015. View Article : Google Scholar : PubMed/NCBI
18	Jia L, Zhang L, Shao C, Song E, Sun W, Li M and Gao Y: An attempt to understand kidney's protein handling function by comparing plasma and urine proteomes. PLoS One. 4:e51462009. View Article : Google Scholar : PubMed/NCBI
19	Farrah T, Deutsch EW, Omenn GS, Sun Z, Watts JD, Yamamoto T, Shteynberg D, Harris MM and Moritz RL: State of the human proteome in 2013 as viewed through peptideatlas: Comparing the kidney, urine, and plasma proteomes for the biology- and disease-driven human proteome project. J Proteome Res. 13:60–75. 2014. View Article : Google Scholar : PubMed/NCBI
20	Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, Zwahlen M, Kampf C, Wester K, Hober S, et al: Towards a knowledge-based human protein atlas. Nat Biotechnol. 28:1248–1250. 2010. View Article : Google Scholar : PubMed/NCBI
21	Zhang L, Wen Q, Mao HP, Luo N, Rong R, Fan JJ and Yu XQ: Developing a reproducible method for the high-resolution separation of peritoneal dialysate proteins on 2-D gels. Protein Expr Purif. 89:196–202. 2013. View Article : Google Scholar : PubMed/NCBI
22	Wiśniewski JR, Zougman A, Nagaraj N and Mann M: Universal sample preparation method for proteome analysis. Nat Methods. 6:359–362. 2009. View Article : Google Scholar : PubMed/NCBI
23	Thongboonkerd V, Chutipongtanate S and Kanlaya R: Systematic evaluation of sample preparation methods for gel-based human urinary proteomics: Quantity, quality, and variability. J Proteome Res. 5:183–191. 2006. View Article : Google Scholar : PubMed/NCBI
24	Beeken M, Lindenmeyer MT, Blattner SM, Radón V, Oh J, Meyer TN, Hildebrand D, Schlüter H, Reinicke AT, Knop JH, et al: Alterations in the ubiquitin proteasome system in persistent but not reversible proteinuric diseases. J Am Soc Nephrol. 25:2511–2525. 2014. View Article : Google Scholar : PubMed/NCBI