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International Journal of Proteomics logoLink to International Journal of Proteomics
. 2011 Oct 10;2011:214715. doi: 10.1155/2011/214715

Urine Glycoprotein Profile Reveals Novel Markers for Chronic Kidney Disease

Anuradha Vivekanandan-Giri 1, Jessica L Slocum 1, Carolyn L Buller 1, Venkatesha Basrur 2, Wenjun Ju 1, Rodica Pop-Busui 3, David M Lubman 2, 4, 5,4,5, Matthias Kretzler 1, 4,4, Subramaniam Pennathur 1, 4,4,*
PMCID: PMC3196258  PMID: 22091387

Abstract

Chronic kidney disease (CKD) is a significant public health problem, and progression to end-stage renal disease leads to dramatic increases in morbidity and mortality. The mechanisms underlying progression of disease are poorly defined, and current noninvasive markers incompletely correlate with disease progression. Therefore, there is a great need for discovering novel markers for CKD. We utilized a glycoproteomic profiling approach to test the hypothesis that the urinary glycoproteome profile from subjects with CKD would be distinct from healthy controls. N-linked glycoproteins were isolated and enriched from the urine of healthy controls and subjects with CKD. This strategy identified several differentially expressed proteins in CKD, including a diverse array of proteins with endopeptidase inhibitor activity, protein binding functions, and acute-phase/immune-stress response activity supporting the proposal that inflammation may play a central role in CKD. Additionally, several of these proteins have been previously linked to kidney disease implicating a mechanistic role in disease pathogenesis. Collectively, our observations suggest that the human urinary glycoproteome may serve as a discovery source for novel mechanism-based biomarkers of CKD.

1. Introduction

Chronic kidney disease (CKD) affects approximately 11% of the US population with over 100,000 individuals progressing to end-stage renal disease (ESRD) annually [1, 2]. Despite this significant and growing public health problem, it remains difficult to predict which individuals will progress to ESRD. As ESRD carries a substantial increase in morbidity and mortality, it is critical to identify this high-risk patient population that would most benefit from early and aggressive therapy.

Current strategies for predicting CKD progression are limited. Pathologic examination of renal tissue provides valuable information on degree of interstitial fibrosis and predilection for ESRD. However, renal biopsy is invasive with a limited role for longitudinal followup. Quantitative measures of proteinuria have long been used as noninvasive markers of CKD progression [3], yet these largely albumin-based methods detect nonselective proteinuria and incompletely correlate with disease. With recent advances in high through-put technology and mass spectrometry techniques, urine proteomic investigation is an attractive tool in the pursuit for noninvasive and specific markers of CKD progression [4, 5].

Numerous investigators have successfully applied broad-scale urine proteomic strategies to kidney disease. The urine proteome predicts nephropathy and decline in renal function in diabetic subjects [6, 7]. It also correlates with early changes of focal segmental nephrosclerosis [8], can identify IgA nephropathy and renal allograft rejection [9, 10], and predicts treatment response and disease activity in nephrotic syndrome and lupus nephritis [11, 12]. Despite these advances, analysis of the entire urine proteome is particularly difficult in CKD. With disruption of the glomerular filtration barrier and leakage of abundant plasma proteins into the urine, a nonselective, largely albumin predominant, pattern often results [13]. To overcome this, methods to increase the detection of low-abundance proteins have been developed to provide disease specificity and clinical relevance of urine profiling and to mechanistically understand factors influencing disease progression.

Glycoprotein enrichment techniques allow depletion of albumin and other abundant plasma proteins while providing a more thorough analysis of a subfraction of the urine proteome. As glycosylated proteins are critical for cellular interactions and signaling cascades, disease states are likely to cause early and specific alterations in urinary glycoprotein excretion. Indeed, glycoproteins are now important markers of autoimmunity and malignancy [14, 15]. More recently, the plasma glycoproteome has been used to predict nephropathy in diabetic subjects [16]. Despite this promising role as a noninvasive and specific biomarker of disease, little is known about the urinary glycoproteome in CKD.

We hypothesized that the urinary glycoproteome would be altered in CKD compared to healthy controls and that specific glycoprotein alterations might be useful in predicting CKD progression. The overall goal of this study was to perform an initial exploratory analysis of the urine glycoproteins in patients with CKD compared to healthy controls. We present a comprehensive profiling of the urinary glycoproteome in control and CKD subjects utilizing a hydrazide enrichment technique combined with tandem mass spectrometry identification of the glycoproteins.

2. Methods

2.1. Sample Collection and Processing

Clean catch urine samples were obtained from six CKD subjects and six age-matched healthy controls following written informed consent approved by the University of Michigan Institutional Review Board. Samples were stored at −80°C and thawed immediately prior to proteomic analysis. An initial 5000 g centrifugation was performed at 4°C for 10 minutes to remove cellular debris. Approximately, 30–50 mL healthy control samples and 1-2 mL CKD samples were concentrated using a 3 kDa filter cut-off membrane (Vivaspin 3 kDa MWCO, GE healthcare, Buckinghamshire, UK and Amicon ultra 0.5 mL, Millipore, Ireland resp.). As CKD subjects had higher urinary protein content (Table 1), the processed volumes were lower.

Table 1.

Patient characteristics of study subjects.

Variable Healthy control (n = 6) CKD (n = 6) P
Age (years) 46.3 (13.5) 47.2 (14.2) 0.92
Sex (male/female) 2/4 2/4 1.00
Body mass index (kg/m2) 24.3 (3.0) 30.5 (4.8) 0.02
Serum creatinine (mg/dL) 0.85 (0.16) 1.75 (1.09) 0.07
eGFR (mL/min)* 83.0 (15.0) 52.0 (27.4) 0.05
Protein/creatinine ratio 0.03 (0.02) 2.15 (1.44) 0.01
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All data expressed as mean ± SD.

eGFR estimated glomerular filtration rate.

*eGFR calculated from Modification of Diet in Renal Disease formula.

Urine protein concentration was determined using Coomassie Protein Assay Reagent with BSA standard (Thermo Scientific, Rockford, Illinois). 200 μg of concentrated protein were utilized for downstream processing. Protein samples were exchanged into 50 mM ammonium bicarbonate buffer (pH 7.4). Urine creatinine concentration was determined by tandem mass spectrometry (MS/MS) as described previously by our group [17]. To determine the level of creatinine, a known amount of [2H3]creatinine was spiked into each sample. A full-scan mass spectrum revealed molecular ions of m/z 114 and 117 for authentic creatinine and [2H3]creatinine, respectively. The transitions of the m/z 114 to 44 and m/z 117 to 47 were monitored in multiple-reaction monitoring mode for authentic and [2H3]creatinine, respectively, utilizing an Agilent Technologies (New Castle, DE) 6410 Triple Quadrupole mass spectrometer system, equipped with an Agilent 1200 series HPLC system. The creatinine concentration in the urine sample was determined by comparing the peak areas for authentic and [2H3]creatinine for the above transitions.

2.2. Glycoprotein Separation and Enrichment

In order to assess recovery following the enrichment procedure, 5 μg of invertase from Saccharomyces cerevisiae (Sigma, St. Louis, MO) was spiked into 200 μg of protein in every sample. Glycoproteins were enriched from urinary proteins utilizing the hydrazide resin capture protocol as described previously by Zhang et al. [18]. Briefly, samples were oxidized with 10 mM sodium metaperiodate then incubated with hydrazide resin overnight at room temperature. Samples were then centrifuged at 3000 g for 2 minutes and the resin was washed successively with equal volumes 50 mM ammonium bicarbonate buffer (pH 7.4; Buffer A) supplemented with 8 M urea, followed by Buffer A alone and then water. The beads were resuspended in water, and the protein was reduced with 5 mm DTT followed by alkylation with 15 mM iodoacetamide. Trypsin (sequencing grade modified trypsin, Promega Corporation, Madison, WI) at 1 : 20 μg ratio was added to the samples and incubated overnight at 37°C for digestion. Following digestion, the beads were centrifuged at 3000 g for 2 minutes and the resin was then washed successively with 1.5 M NaCl, 80% acetonitrile, 100% methanol, and Buffer A. The resin was then resuspended in Buffer A and incubated with 5 units of PNGaseF (New England Biolabs, Ipswich, MA) overnight at 37°C for glycopeptide release. The glycopeptides were cleaned using a reverse phase column and eluted with 50% acetonitrile/0.1% TFA followed by elution with 80% acetonitrile/0.1% TFA. The peptides were then dried at 60°C in a vacuum centrifuge and stored for mass spectrometric analysis.

2.3. Liquid Chromatography Electrospray Ionization (ESI/LC) MS/MS Analysis

Peptide samples were resuspended in 0.1% formic acid and loaded onto an in-house packed reverse phase separation column (0.075 × 100 mm, MAGIC C18 AQ particles, 5 μm, Michrom Bioresources). The peptides were separated on a 1% acetic acid/acetonitrile gradient system (5–50% acetonitrile for 75 min, followed by a 10 min 95% acetonitrile wash) at a flow rate of ~300 nl/min. Peptides were directly sprayed onto the MS using a nanospray source. An LTQ Orbitrap XL (Thermo Fisher Scientific, Waltham, MA) was run in automatic mode collecting a high resolution MS scan (FWHM 30,000) followed by data-dependent acquisition of MS/MS scans on the 9 most intense ions (relative collision energy ~35%). Dynamic exclusion was set to collect 2 MS/MS scans on each ion and exclude it for an additional 2 min. Charge state screening was enabled to exclude +1 and undetermined charge states.

2.4. Data Processing and Statistical Analysis

The Human UniProt database (Release 2011-5) was appended with a reverse database, a common contaminant list, and yeast invertase. Raw files were converted to mzXML format and searched against the database using X!Tandem with a k-score plug-in, an open-source search engine developed by the Global Proteome Machine (http://www.thegpm.org/). The search parameters were as follows: (1) precursor mass tolerance window of 100 ppm and fragment mass tolerance of 0.8 Da; (2) allowing two missed cleavages; (3) variable modification: oxidation of methionine (+15.9949 Da), carbamidomethyl cysteine (57.0214 Da), and +0.9840 Da, reflecting the conversion of asparagine in the NxS/T motif to aspartate due to the release of the N-linked glycopeptides from their oligosaccharides. All proteins with a ProteinProphet probability of greater than 0.9 were considered as positive identifications [19]. Only proteins containing peptides with the NxS/T sequence motif were included for statistical analysis.

Baseline characteristics of the control and CKD subjects were compared using Fisher's exact test for categorical variables and Student's t-test for continuous variables. Data is presented as means (±SD). Spectral counts for individual proteins were normalized to Saccharomyces cerevisiae invertase and to urine creatinine content. Spectral counts were compared across the two subject groups using the nonparametric Mann-Whitney test, and P values were adjusted for multiple comparisons using the False Discovery Rate (FDR) with reported q-values. All statistical analyses were performed with the use of SAS software, version 9.2.

2.5. Gene Ontology Analysis

Significant proteins of interest were analyzed using the Gene Ontology Database (Gene Ontology Consortium, http://www.geneontology.org, Princeton University, New Jersey, US; [20]). For a given Gene Ontology (GO) category, the relative enrichment of genes encoding the proteins detected in CKD relative to all reference genes in that category were calculated as previously described using GO Tools made available by the Bioinformatics Group at the Lewis-Sigler Institute (Princeton University, New Jersey, US; [21]). A cutoff value of P < 0.01 was used to report a functional category as significantly overrepresented. To address the multiple comparisons problem that arises when many processes are evaluated simultaneously, the analysis included calculation of the FDR [21]. To improve statistical confidence in our results, all enriched functional categories were required to be significant using both methods (P < 0.01 and FDR < 0.05).

3. Results

3.1. Study Subject Characteristics

Urine was isolated from six subjects with CKD and six age-matched healthy controls. Baseline subject characteristics are provided in Table 1. Two important issues were considered with patient selection. First, the etiology of CKD was chosen to be diverse. This would ensure robustness of the putative markers as a CKD marker rather than a disease-specific marker. Second, we specifically targeted early Stage 3 CKD subjects to identify early disease markers that would potentially indicate pathways dysregulated early in the course of disease. This might offer mechanistic insights into disease pathogenesis and progression and have implications in therapeutic strategies. The six subjects had biopsy-proven diabetic nephropathy, lupus nephritis (n = 2), postacute tubular necrosis damage, NSAID nephropathy, and membranoproliferative glomerulonephritis, respectively. The mean estimated glomerular filtration rate (eGFR) was 83 mL/min in control subjects and 52 mL/min in CKD subjects.

3.2. Glycoprotein Spectral Count Normalization

Glycoproteins were extracted and enriched from the twelve urinary samples. To account for variations in the glycoprotein extraction efficiency, 5 μg of the yeast protein invertase from Saccharomyces cerevisiae was added to each sample prior to extraction. After addition to the database, invertase spectral count served as a surrogate marker for extraction efficiency in each individual sample. Invertase spectral counts ranged from 31 to 122 in the twelve samples with an average spectral count of 86 (±31). Each sample was normalized independently to the invertase spectral counts.

To account for intersubject urine concentration variability, spectral counts were then normalized to urine creatinine content. This provides standardization for urinary creatinine excretion and concentration differences which can vary with volume status, stress, diet, activity level, age, gender, and overall health status [22]. Indeed, this normalization is commonly followed in clinical practice where degree of urinary protein is normalized to creatinine to obtain protein excretion rates [23]. Final spectral counts were expressed per mmol creatinine.

3.3. Urine Glycoproteome Is Altered in CKD

Urinary glycoproteins were isolated from six subjects with CKD and six healthy controls using a hydrazide technique as described in Section 2. A total of 122 glycoproteins were identified, of which 35 proteins were unique to healthy control patients, 8 were unique to CKD subjects, and 79 were common proteins in both groups (Figure 1, Table 2). Unique proteins to the CKD group were Antithrombin-III (SERPINC1), Complement factor H-related 1 (CFHR1), Desmoglein-2 (DSG2), Lumican (LUM), Lymphatic vessel endothelial hyaluronic acid receptor 1 (LYVE1), Pigment epithelium-derived factor (SERPINF1), Thyroxine-binding globulin (SERPINA7), and Zinc-alpha-2-glycoprotein (AZP1).

Figure 1.

Figure 1

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Venn diagram of the total urinary glycoproteins detected in healthy controls and CKD subjects. Tryptic digests of urine glycoproteins were subjected to LC-ESI-MS/MS analysis, and the proteins were identified as described in Section 2. 35 proteins were unique to healthy control subjects while 8 proteins were unique to subjects with CKD. 79 proteins were present in both groups.

Table 2.

Urinary glycoproteins unique to CKD or healthy control subjects.

Unique proteins in healthy controls Unique proteins in CKD
70 kDa lysosomal alpha-glucosidase (GAA) Antithrombin-III (SERPINC1)
Alpha-1B-glycoprotein (A1BG) Complement factor H-related 1 (CFHR1)
Basigin (BSG) Desmoglein-2 (DSG2)
Beta-galactosidase (GLB1) Lumican (LUM)
Beta-sarcoglycan (SGCB) Lymphatic vessel endothelial hyaluronic acid receptor 1 (LYVE1)
Butyrophilin (BTN2A1) Pigment epithelium-derived factor (SERPINF1)
Carboxypeptidase M (CPM) Thyroxine-binding globulin (SERPINA7)
CD276 antigen (CD276) Zinc-alpha-2-glycoprotein (AZP1)
Complement component C4B (C4B)
Cubilin (CUBN)
Colony stimulating factor 1 (macrophage) (CSF1)
Delta and notch-like epidermal growth factor-related receptor (DNER)
Desmocollin-2 (DSC2)
Desmoglein-1 (DSG1)
Epidermal growth factor (EGFR)
Secreted frizzled-related protein-4 (SFRP4)
Fibronectin 1 (FN1)
Folate receptor alpha (FOLR1)
Golgi phosphoprotein 2 (GOLPH2)
Glutamyl aminopeptidase (ENPEP)
Hepatitis B virus receptor binding protein (Q6PYX1)
Hepatic asialoglycoprotein receptor 1 transcript variant b (ASGR1)
Heparan sulfate proteoglycan 2 (HSPG2)
Intercellular adhesion molecule 1 (ICAM1)
Kallikrein-1 (KLK1)
Kallikrein 3 (APS)
Lysosomal alpha-glucosidase (GAA)
Lysosomal-associated membrane protein 2 (LAMP2)
Maltase-glucoamylase (MGAM)
Microfibril-associated glycoprotein 4 (MFAP4)
Mucin-6 (MUC6)
Neuronal pentraxin receptor (NPTXR)
Neuropilin and tolloid-like protein 1 (NETO1)
Probable serine carboxypeptidase (CPVL)
Sex hormone binding globulin (SHBG)
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Figure 2 displays MS spectra of two individual glycopeptides with glycosylation motifs which were altered in CKD subjects. Zinc-alpha-2-glycoprotein is significantly upregulated in CKD (Figure 2(a)), while Golgi phosphoprotein is significantly downregulated in CKD (Figure 2(b)). Table 3 displays motifs and specific peptide modifications for all unique 122 proteins. Proteins were only included if the peptides contained the NxS/T motif.

Figure 2.

Figure 2

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Mass Spectra of glycopeptides derived from Zinc alpha 2 Glycoprotein (a) and Golgi phosphoprotein (b) in CKD urine. Tryptic digests of urine glycoproteins were subjected to LC-ESI-MS/MS analysis as described in Section 2. The mass spectra of peptides DIVEYYNDSNGSHVLQG from zinc alpha 2 glycoprotein which is upregulated (a) and those of peptide AVLVNNITTGER from Golgi phosphoprotein which is significantly downregulated in CKD subjects (b) are shown. The N-linked glycosylation site of each peptide is depicted in red.

Table 3.

Glycoproteins identified with peptides carrying NxS/T motif.

No. Protein Charge state Peptide sequence with NxS/T motif
1 155 kDa platelet multimerin (MMRN1) 2+
2+ 		LQN[115]LTLPTN[115]ASIK
FNPGAESVVLSN[115]STLK
2 70 kDa lysosomal alpha-glucosidase (GAA) 2+
2+ 		GVFITN[115]ETGQPLIGK
LEN[115]LSSSEM[147]GYTATLTR
3 Afamin (AFAM) 2+
2+/3+ 		DIENFN[115]STQK
YAEDKFN[115]ETTEK
4 Aminopeptidase N (AMPN) 3+
2+ 		KLN[115]YTLSQGHR
N[115]ATLVNEADKLR
5 Attractin (ATRN) 2+ IDSTGN[115]VTNELR
6 Apolipoprotein D
(APO D)		 2+/3+
2+ 		ADGTVNQIEGEATPVN[115]LTEPAK
C[160]IQAN[115]YSLM[147]EN[115]GK
7 Apolipoprotein F (APO F) 2+ Q[111]GGVN[115]ATQVLIQHLR
8 Apolipoprotein J (APO J) 2+
2+/3+
3+
3+ 		LAN[115]LTQGEDQYYLR
EDALN[115]ETRESETK
M[147]LN[115]TSSLLEQLNEQFNWVSR
EIRHN[115]STGC160LR
9 Alpha-1-antichymotrypsin (AACT) 3+/4+
3+/4+
2+/3+
2+/3+ 		GLKFN[115]LTETSEAEIHQSFQHLLR
YTGN[115]ASALFILPDQDKM[147]EEVEAM[147]LLPETLKR
TLN[115]QSSDELQLSM[147]GNAM[147]FVK
KLIN[115]DYVKN[115]GTR
10 Alpha-2-HS-glycoprotein (FETUA) 2+/3+
2+/3+ 		AALAAFNAQNN[115]GSNFQLEEISR
KVC[160]QDC[160]PLLAPLN[115]DTR
11 Alpha-1-acid
glycoprotein 1 (ORM1)		 2+/3+
3+
3+/4+ 		QDQC[160]IYN[115]TTYLNVQR
SVQEIQATFFYFTPN[115]KTEDTIFLR
N[115]EEYN[115]KSVQEIQATFFYFTPN[115]KTEDTIFLR
12 Alpha-1-acid
glycoprotein 2 (ORM2)		 2+/3+
3+ 		QNQC[160]FYN[115]SSYLNVQR
SVQEIQATFFYFTPN[115]KTEDTIFLR
13 Alpha-1B-glycoprotein (A1BG) 3+/4+ EGDHEFLEVPEAQEDVEATFPVHQPGN[115]YSC[160]SYR
14 Antithrombin-III (SERPINC1) 2+/3+
2+ 		LGAC[160]N[115]DTLQQLM[147]EVFKFDTISEK
SLTFN[115]ETYQDISELVYGAK
15 Basigin (BSG) 3+
3+ 		ITDSEDKALM[147]N[115]GSESR
ILLTC[160]SLN[115]DSATEVTGHR
16 Beta-galactosidase (GLB1) 2+ NNVITLN[115]ITGK
17 Beta-sarcoglycan (SGCB) 2+ ITSN[115]ATSDLNIK
18 Biotinidase (BTD) 2+/3+
2+/3+ 		NPVGLIGAEN[115]ATGETDPSHSK
DVQIIVFPEDGIHGFN[115]FTR
19 Butyrophilin,
subfamily 2, member A1 (BTN2A1)		 2+ GSVALVIHN[115]ITAQEN[115]GTYR
20 Cathepsin D heavy chain (CTSD) 2+ GSLSYLN[115]VTR
21 Cathepsin L (CTSL) 3+ YSVAN[115]DTGFVDIPKQEK
22 Carboxypeptidase
B2 (CBPB2)		 2+/3+ QVHFFVN[115]ASDVDNVK
23 Carboxypeptidase
M (CBPM)		 2+
4+		 NFPDAFEYNN[115]VSR
TVAQN[115]YSSVTHLHSIGK
24 Calcium binding protein 39 (CAB39) 2+ HN[115]FTIM[147]TK
25 CD276 antigen (CD276) 2+ VVLGAN[115]GTYSC[160]LVR
26 CD163 antigen (CD163) 2+ APGWAN[115]SSAGSGR
27 CD44 protein (CD44) 2+ AFN[115]STLPTM[147]AQM[147]EK
28 CD7 antigen (CD7) 3+ GRIDFSGSQDN[115]LTITM[147]HR
29 Cell adhesion molecule 1 (CADM1) 2+
2+ 		VSLTN[115]VSISDEGR
FQLLN[115]FSSSELK
30 Ceruloplasmin (CP) 2+/3+
3+/4+
2+ 		EHEGAIYPDN[115]TTDFQR
ELHHLQEQN[115]VSNAFLDKGEFYIGSK
EN[115]LTAPGSDSAVFFEQGTTR
31 Complement component C4B (C4B) 2+ GLN[115]VTLSSTGR
32 Complement factor H (CFH) 3+ IPC[160]SQPPQIEHGTIN[115]SSR
33 Complement factor H-related 1 (CFHR1) 2+ LQNNENN[115]ISC[160]VER
34 Complement
factor I (CFI)		 2+ FLNN[115]GTC[160]TAEGK
35 Cubilin (CUBN) 2+
2+ 		LC[160]SSVN[115]VSNEIK
AGFN[115]ASFHK
36 Corticosteroid-binding globulin (SERPINA6) 2+
3+ 		AQLLQGLGFN[115]LTER
AVLQLNEEGVDTAGSTGVTLN[115]LTSKPIILR
37 Colony stimulating factor 1 (macrophage) (CSF1) 2+ VKNVFN[115]ETK
38 Delta and notch-like epidermal growth factor-related receptor (DNER) 2+ LVSFEVPQN[115]TSVK
39 Desmocollin-2 (DSC2) 2+
2+ 		LKAIN[115]DTAAR
AN[115]YTILK
40 Desmoglein-1 (DSC1) 2+ DYNTKN[115]GTIK
41 Desmoglein-2 (DSG2) 2+
2+ 		IN[115]ATDADEPNTLNSK
YVQN[115]GTYTVK
42 DNA ligase 4 (LIG4) 2+ APN[115]LTNVNK
43 Dual specificity protein phosphatase CDC14B (CDC14B) 2+ NHN[115]VTTIIR
44 Epidermal growth factor (EGF) 2+ GN[115]NSHILLSALK
45 Epididymis secretory sperm binding protein Li 44a (SERPINA1) 2+/3+/4+
3+/4+ 		YLGN[115]ATAIFFLPDEGKLQHLENELTHDIITK
ADTHDEILEGLNFN[115]LTEIPEAQIHEGFQELLR
46 Extracellular link domain containing 1 (XLKD1) 2+/3+ KANQQLN[115]FTEAK
47 Fibrillin 1 (FBN1) 2+ TAIFAFN[115]ISHVSNK
48 Fibrinopeptide A (FGA) 2+ M[147]DGSLNFN[115]RT
49 Fibronectin type III domain-containing protein 5 (FNDC5) 2+ FIQEVN[115]TTTR
50 Frizzled protein 4 (FRP4) 2+ ISM[147]C[160]QNLGYN[115]VTK
51 Fibronectin 1 (FN1) 3+ DQC[160]IVDDITYNVN[115]DTFHK
52 Folate receptor alpha (FOLR1) 2+ GWN[115]WTSGFNK
53 Galectin-3-binding protein
(LGALS3BP)		 2+
2+
2+
2+ 		ALGFEN[115]ATQALGR
AAIPSALDTN[115]SSK
GLN[115]LTEDTYKPR
TVIRPFYLTN[115]SSGVD
54 Glutaminyl-peptide cyclotransferase (QPCT) 2+/3+
3+/4+ 		NYHQPAILN[115]SSALR
YFQN[115]YSYGGVIQDDHIPFLR
55 Golgi phosphoprotein 2 (GOLPH2) 3+
1+/2+ 		LQQDVLQFQKN[115]QTNLER
AVLVN[115]N[115]ITTGER
56 Glutamyl aminopeptidase (ENPEP) 2+ HTAEYAAN[115]ITK
57 Haptoglobin beta
chain (HP)		 2+/3+
3+/4+
2+/3+/4+
3+ 		VVLHPN[115]YSQVDIGLIK
MVSHHN[115]LTTGATLINEQWLLTTAK
NLFLN[115]HSEN[115]ATAKDIAPTLTLYVGKK
Q[111]LVEIEKVVLHPN[115]YSQVDIGLIK
58 HEG homolog 1 (HEG1) 2+ SYSESSSTSSSESLN[115]SSAPR
59 Hemopexin (HPX) 3+/4+
2+ 		GHGHRN[115]GTGHGN[115]STHHGPEYM[147]R
ALPQPQN[115]VTSLLGC[160]TH
60 Hepatitis B virus receptor binding protein (Q6YPX1) 2+ EEQYN[115]STYR
61 Hepatic asialoglycoprotein receptor 1 transcript variant b (ASGR1) 2+ ETFSN[115]FTASTEAQVK
62 Heparan sulfate proteoglycan 2 (HSPG2) 2+ ALVN[115]FTR
63 Ig alpha-1 chain C
region (IGHA1)		 3+ LAGKPTHVN[115]VSVVM[147]AEVDGTC[160]Y
64 Ig gamma-1 chain C region (IGHG1) 2+
2+/3+ 		EEQYN[115]STYR
TKPREEQYN[115]STYR
65 Ig gamma-2 chain C region (IGHG2) 2+
2+/3+ 		EEQFN[115]STFR
TKPREEQFN[115]STFR
66 Ig gamma-4 chain C region (IGHG4) 2+
2+/3+ 		EEQFN[115]STFR
TKPREEQFN[115]STFR
67 Ig mu chain C
region (IGHM)		 2+ YKN[115]NSDISSTR
68 Inducible T-cell co-stimulator ligand (ICOSLG) 2+ TVVTYHIPQN[115]SSLENVDSR
69 Insulin-like growth factor-binding
protein 3 (IGFBP3)		 2+ GLC[160]VN[115]ASAVSR
70 Intercellular adhesion molecule 1 (ICAM1) 2
2+ 		LNPTVTYGN[115]DSFSAK
AN[115]LTVVLLR
71 Intercellular adhesion molecule 2 (ICAM2) 2+ GN[115]ETLHYETFGK
72 Kallikrein-1 (KLK1) 4+ HNLFDDEN[115]TAQFVHVSESFPHPGFN[115]M[147]SLLEN[115]HTR
73 KALLIKREIN-2 (KLK2) 2+ N[115]KSVILLGR
74 Kininogen 1 (KNG1) 2+
2+
3+/4+
2+ 		LNAENN[115]ATFYFK
ITYSIVQTN[115]C[160]SK
HGIQYFNN[115]NTQHSSLFTLNEVKR
YNSQN[115]QSNNQFVLYR
75 Leucine-rich alpha-2-glycoprotein (LRG1) 2+
2+/3+ 		MFSQN[115]DTR
KLPPGLLAN[115]FTLLR
76 Leukocyte-associated immunoglobulin-like receptor 1 (LAIR1) 2+/3+ STYN[115]DTEDVSQASPSESEAR
77 Lumican (LUM) 3+ KLHINHNN[115]LTESVGPLPK
78 Lysosomal acid phosphatase (ACP2) 2+ YEQLQN[115]ETR
79 Lysosomal alpha-glucosidase (GAA) 2+
2+ 		GVFITN[115]ETGQPLIGK
LEN[115]LSSSEM[147]GYTATLTR
80 Lysosome-associated membrane glycoprotein 1 (LAMP1) 2+ GHTLTLN[115]FTR
81 Lysosomal-associated membrane protein 2, (LAMP2) 2+ VASVININPN[115]TTHSTGSC[160]R
82 Lymphatic vessel endothelial hyaluronic acid receptor 1 (XLKD1) 2+ ANQQLN[115]FTEAK
83 Lysyl oxidase (LOX) 3+
3+ 		AEN[115]QTAPGEVPALSNLRPPSR
RDPGAAVPGAAN[115]ASAQQPR
84 Major prion protein (PRNP) 2+ Q[111]HTVTTTTKGEN[115]FTETDVK
85 Membrane protein FAM174A (FAM174A) 2+ GSEGGN[115]GSNPVAGLETDDHGGK
86 Maltase-glucoamylase (MGAM) 2+
2+ 		ILGM[147]EEPSN[115]VTVK
VILILDPAISGN[115]ETQPYPAFTR
87 Microfibril-associated glycoprotein 4 (MFAP4) 2+ VDLEDFEN[115]NTAYAK
88 Monocyte differentiation antigen CD14 2+ LRN[115]VSWATGR
89 Mucin-6 (MUC6) 2+ GC[160]M[147]AN[115]VTVTR
90 N-acetylglucosamine-6-
sulfatase (GNS)		 2+
2+ 		YYN[115]YTLSIN[115]GK
TPMTN[115]SSIQFLDNAFR
91 N-acylsphingosine amidohydrolase (ASAH1) 2+ TVLEN[115]STSYEEAK
92 Neuronal pentraxin receptor (NPTXR) 2+ ALPGGADN[115]ASVASGAAASPGPQR
93 Neuropilin and tolloid-like protein 1 (NETO1) 2+ HESEYN[115]TTR
94 Peptidase inhibitor 16 (PI16) 2+ SLPNFPN[115]TSATAN[115]ATGGR
95 Pigment epithelium-derived factor (SERPINF1) 3+ VTQN[115]LTLIEESLTSEFIHDIDR
96 Plasma protease
C1 inhibitor
(SERPING1)		 2+/3+
2+
3+ 		GVTSVSQIFHSPDLAIRDTFVN[115]ASR
VLSN[115]NSDANLELINTWVAK
VGQLQLSHN[115]LSLVILVPQNLK
97 Plasma serine protease inhibitor (SERPINA5) 2+ VVGVPYQGN[115]ATALFILPSEGK
98 Platelet-derived growth factor subunit B (PDGFB) 3+ LLHGDPGEEDGAELDLN[115]M[147]TR
99 Polytrophin (TROPH) 2+ N[115]N[115]VTEDIK
100 Probable G-protein coupled receptor 116 (GPR116) 2+
2+ 		ANEQVVQSLN[115]QTYK
YEEQQLEIQN[115]SSR
101 Probable serine carboxypeptidase (CPVL) 2+ Q[111]AIHVGN[115]QTFNDGTIVEK
102 Prosaposin (PSAP) 2+/3+ NLEKN[115]STKQEILAALEK
103 Prostaglandin D2 synthase 21 kDa (PTGDS) 2+/3+
2+ 		SVVAPATDGGLN[115]LTSTFLR
WFSAGLASN[115]SSWLR
104 Prostatic acid phosphatase (ACPP) 3+ FLN[115]ESYKHEQVYIR
105 Proteinase-activated receptor 1 (F2R) 2+ ATN[115]ATLDPR
106 Protein shisa-7 (SHISA7) 2+ LTGALTGGGGAASPGAN[115]GTR
107 RING finger protein 10 (RNF10) 2+ N[115]ESFN[115]N[115]QSR
108 Secretory component (Polymeric IG Receptor) (PIGR) 3+
2+
2+
2+ 		AN[115]LTNFPEN[115]GTFVVNIAQLSQDDSGR
Q[111]IGLYPVLVIDSSGYVNPN[115]YTGR
VPGN[115]VTAVLGETLK
YKCGLGIN[115]SR
109 Slit homolog 1 (SLIT1) 2+ LELN[115]GN[115]N[115]ITR
110 Sushi domain-containing protein 2 (SUSD2) 2+ SELVN[115]ETR
111 Sex hormone binding globulin (SHBG) 2+ LDVDQALN[115]RT
112 Transferrin (TF) 2+/3+
2+/3+ 		Q[111]QQHLFGSN[115]VTDC[160]SGNFC[160]LFR
C[160]GLVPVLAENYN[115]KSDN[115]C[160]EDTPEAGYFAVAVVK
113 Thrombin heavy
chain (F2)		 4+ YPHKPEIN[115]STTHPGADLQENFC[160]R
114 Tripeptidyl-peptidase I variant (TPP1) 3+ FLSSSPHLPPSSYFN[115]ASGR
115 Tyrosine-protein kinase receptor UFO (AXL) 2+
3+ 		SLHVPGLN[115]KT
N[115]GSQAFVHWQEPR
116 TIMP metallopeptidase inhibitor 1 (TIMP1) 2+
3+ 		FVGTPEVN[115]QTTLYQR
SHN[115]RSEEFLIAGK
117 Thyroxine-binding globulin (SERPINA7) 2+ TLYETEVFSTDFSN[115]ISAAK
118 Trypstatin (AMBP) 2+/3+ SKWN[115]ITM[147]ESYVVHTNYDEYAIFLTK
119 Transmembrane protein 108 (TMEM108) 4+ KGAGN[115]SSRPVPPAPGGHSR
120 Uromodulin
(UMOD)		 2+/3+
2+
2+/3+ 		Q[111]DFN[115]ITDISLLEHR
FALLMTNCYATPSSN[115]ATDPLK
CNTAAPMWLN[115]GTHPSSDEGIVSR
121 Vasorin (VASN) 2+ LHEITN[115]ETFR
122 Zinc-alpha-2-glycoprotein (AZGP1) 2+/3+
3+/4+
2+ 		DIVEYYN[115]DSN[115]GSHVLQGR
AREDIFM[147]ETLKDIVEYYN[115]DSN[115]GSHVLQGR
FGCEIENN[115]RS
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To test if proteins were significantly up- or downregulated in CKD, normalized spectral counts from the 6 CKD subjects were compared with those from the healthy controls. As sample size was small and spectral counts were not normally distributed, comparisons were made with the nonparametric Mann-Whitney test. As 122 proteins were being simultaneously tested, the FDR and corresponding q-values were determined to account for false positive results. Table 4 displays 23 proteins which are differentially expressed in CKD utilizing an uncorrected P value threshold of less than 0.05. These proteins include 70 kDa lysosomal alpha-glucosidase (GAA), Apolipoprotein D (APOD), Alpha-2-HS-glycoprotein chain B (FETUA), Alpha-1-acid glycoprotein 1 (ORM1), Antithrombin-III (SERPINC1), Beta-galactosidase (GLB1), Ceruloplasmin (CP), Cubilin (CUBN), Epidermal growth factor (EGF), Epididymis secretory sperm binding protein Li 44a (E9KL23), Galectin-3-binding protein (LGALS3BP), Golgi phosphoprotein 2 (GOLPH2), Haptoglobin beta chain (HP), Ig gamma-1 chain C region (IGHG1), Ig gamma-2 chain C region (IGHG2), Kininogen 1 (KNG1), Leucine-rich alpha-2-glycoprotein (LRG), Plasma protease C1 inhibitor (SERPING1), Prostaglandin D2 synthase 21 kDa (PTGDS), Transferrin (TF), Trypstatin (AMBP), Uromodulin (UMOD), and Zinc-alpha-2-glycoprotein (AZGP1). Following correction for multiple comparisons, differential expression remained significant in 12 proteins (APOD, ORM1, FETUA, E9KL23, LGALS3BP, GOLPH2, HP, KNG1, LRG, SERPING1, PTGDS, AZGP1). Incidentally, not all unique proteins to CKD or healthy control groups had statistically significant up- or down-regulation. For example, lumican was not isolated in any healthy control subjects and was found in only three of the six CKD subjects. Thus, lumican is unique to CKD; however, as it was only seen in three CKD subjects, it was not significantly upregulated in CKD via nonparametric testing.

Table 4.

Differentially regulated proteins identified in CKD subjects.

Protein Code Name of the protein identified P value q-value Direction of change in CKD subjects
APOD Apolipoprotein D 0.0022 0.0224 Up
FETUA Alpha-2-HS-glycoprotein chain B 0.0022 0.0224 Up
ORM1 Alpha-1-acid glycoprotein 1 0.0022 0.0224 Up
E9KL23 Epididymis secretory sperm binding protein Li 44a 0.0022 0.0224 Up
LGALS3BP Galectin-3-binding protein 0.0022 0.022 Up
GOLPH2 Golgi phosphoprotein 2 0.0022 0.0224 Down
HP Haptoglobin beta chain 0.0022 0.0224 Up
KNG1 Kininogen 1 0.0022 0.0224 Up
LRG Leucine-rich alpha-2-glycoprotein 0.0022 0.0224 Up
SERPING1 Plasma protease C1 inhibitor 0.0022 0.0224 Up
PTGDS Prostaglandin D2 synthase 21kDa 0.0022 0.0224 Up
AZGP1 Zinc-alpha-2-glycoprotein 0.0022 0.0224 Up
GAA 70 kDa lysosomal alpha-glucosidase 0.0152 0.13 Down
SERPINC1 Antithrombin-III 0.0152 0.103 Up
GLB1 Beta-galactosidase 0.0152 0.103 Down
CUBN Cubilin 0.0152 0.103 Down
EGF Epidermal growth factor 0.0152 0.13 Down
UMOD Uromodulin 0.0152 0.103 Up
TF Transferrin 0.0216 0.1387 Up
AMBP Trypstatin 0.0411 0.18 Up
CP Ceruloplasmin 0.0433 0.18 Up
IGHG1 Ig gamma-1 chain C region 0.0433 0.18 Up
IGHG2 Ig gamma-2 chain C region 0.0433 0.18 Up
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3.4. Gene Ontology Analysis Reveals Enrichment for Distinct Biological Functions of Differentially Expressed Urinary Glycoproteins

The 23 proteins with differential expression in CKD were subjected to a GO Database search and further analyzed with GO Tools [20, 21]. GO Term Finder (http://go.princeton.edu/cgi-bin/GOTermFinder) allowed for clustered identification of proteins annotated to specific GO biological process, location, and function classifications. A subsequent GO Term Mapper (http://go.princeton.edu/cgi-bin/GOTermMapper) analysis of significantly altered proteins was performed to bin the proteins to GO parent terms or GO Slim terms (http://www.geneontology.org/GO.slims.shtml).

GO analysis (Figure 3) for biological processes demonstrated that 16 of the 23 proteins were linked to immune/stress response and biological process regulation (P < 1 × 10−4). 9 of the 23 were acute-phase and inflammatory response proteins (P < 1 × 10−3). Six proteins were regulators of hemostasis, platelet degranulation and coagulation (P < 1 × 10−4), and 10 were involved in localization, transport, and secretion (P < 1 × 10−4). Other processes involved include metal ion homeostasis (4 proteins) and cell death (3 proteins).

Figure 3.

Figure 3

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Global view of biological processes of differentially expressed urinary glycoproteins in CKD. Urinary glycoproteins that were differentially detected in CKD subjects were associated with biological functions using GO process annotations. This approach demonstrated significant overrepresentation of proteins involved in several categories, including regulation of response to stress, platelet activation/hemostasis/coagulation, acute-phase response, regulation of biological processes, localization, secretion, transport, and cell death. A bipartite network (generated using Cytoscape [24]) showing the relationship between GO process annotations (yellow hexagon nodes) and differentially regulated proteins in CKD subjects (white/red/green circular nodes). The size of the GO nodes is proportional to the number of edges (lines) that connect them to proteins. The 10 proteins that are altered with q-value of < 0.05 are depicted in red (up-regulation) and green (down-regulation) in CKD subjects. GAA, 70 kDa lysosomal alpha-glucosidase; APOD, Apolipoprotein D; FETUA: Alpha-2-HS-glycoprotein chain B; ORM1, Alpha-1-acid glycoprotein 1; SERPINC1, Antithrombin-III; GLB1, Beta-galactosidase; CP, Ceruloplasmin; CUBN, Cubilin; EGF, Epidermal growth factor; E9KL23, Epididymis secretory sperm binding protein Li 44a; LGALS3BP, Galectin-3-binding protein; GOLPH2, Golgi phosphoprotein 2; HP, Haptoglobin beta chain; IGHG1, Ig gamma-1 chain C region; IGHG2, Ig gamma-2 chain C region; KNG1, Kininogen 1; LRG, Leucine-rich alpha-2-glycoprotein; SERPING1,Plasma protease C1 inhibitor; PTGDS, Prostaglandin D2 synthase 21 kDa; TF, Transferrin; AMBP, Trypstatin; UMOD, Uromodulin; AZGP1, Zinc-alpha-2-glycoprotein.

Table 5 displays function and location for the 23 proteins which were differentially expressed in CKD. 18 out of the 23 proteins localized to the extracellular region consistent with possible extracellular matrix remodeling that typifies renal disease. The analysis also revealed 2 major clusters of molecular function: 20 out of the 23 proteins were involved in binding and protein-protein interactions (P = 5 × 10−4). 5 proteins were endopeptidase inhibitors (P < 1 × 10−6). Collectively, these observations implicate the inflammatory/acute-phase response and extracellular matrix remodeling in CKD. They also strongly support the proposal that glycoproteomic analysis of urine might reveal mechanisms underpinning CKD.

Table 5.

Component location and function associated with significantly up- or downregulated proteins identified in CKD patients.

Location Function
Gene ontology term Cluster frequency P-value FDR Proteins annotated to the GO Term Gene ontology term Cluster frequency P value FDR Proteins annotated to the GO term
Extracellular region 18 of 23 proteins, 78.3% 7.36E−15 0.00% EGF, IGHG1, LRG, AMBP, AZGP1, KNG1, SERPING1, TF, LGALS3BP, FETUA, CP, HP, SERPINC1, IGHG2, ORM1, APOD, PTGDS, UMOD Binding 20 of 23 proteins, 87.0% 0.00051 0.89% EGF, IGHG1, AMBP, CUBN, AZGP1, KNG1, SERPING1, TF, LGALS3BP, GAA, FETUA, CP, SERPINC1, HP, IGHG2, GLB1, ORM1, APOD, PTGDS, UMOD
Extracellular space 11 of 23 proteins, 47.8% 5.47E−11 0.00% EGF, LGALS3BP, CP, FETUA, SERPINC1, APOD, ORM1, PTGDS, UMOD, KNG1, SERPING1 Protein binding 15 of 23 proteins, 65.2% 0.00066 0.80% TF, EGF, IGHG1, LGALS3BP, CP, FETUA, AMBP, HP, SERPINC1, APOD, ORM1, GLB1, CUBN, KNG1, SERPING1
Extracellular region part 11 of 23 proteins, 47.8% 1.13E−09 0.00% EGF, LGALS3BP, CP, FETUA, SERPINC1, APOD, ORM1, PTGDS, UMOD, KNG1, SERPING1 Enzyme regulator activity 6 of 23 proteins, 26.1% 0.00024 0.00% EGF, SERPINC1, FETUA, KNG1, AMBP, SERPING1
Cytoplasmic vesicle 6 of 23 proteins, 26.1% 6.81E−05 0.20% TF, EGF, CUBN, UMOD, KNG1, SERPING1 Endopeptidase inhibitor activity 5 of 23 proteins, 21.7% 3.78E−07 0.00% SERPINC1, FETUA, KNG1, AMBP, SERPING1
Vesicle 6 of 23 proteins, 26.1% 8.57E−05 0.18% TF, EGF, CUBN, UMOD, KNG1, SERPING1 Endopeptidase regulator activity 5 of 23 proteins, 21.7% 4.29E−07 0.00% SERPINC1, FETUA, KNG1, AMBP, SERPING1
Cell fraction 5 of 23 proteins, 21.7% 0.00405 2.29% EGF, IGHG2, IGHG1, CUBN, AMBP Peptidase inhibitor activity 5 of 23 proteins, 21.7% 4.84E−07 0.00% SERPINC1, FETUA, KNG1, AMBP, SERPING1
Cytoplasmic membrane-bounded vesicle 5 of 23 proteins, 21.7% 0.00055 0.15% TF, EGF, CUBN, KNG1, SERPING1 Peptidase regulator activity 5 of 23 proteins, 21.7% 1.10E−06 0.00% SERPINC1, FETUA, KNG1, AMBP, SERPING1
Membrane-bounded vesicle 5 of 23 proteins, 21.7% 0.00062 0.29% TF, EGF, CUBN, KNG1, SERPING1 Enzyme inhibitor activity 5 of 23 proteins, 21.7% 8.50E−06 0.00% SERPINC1, FETUA, KNG1, AMBP, SERPING1
Cytoplasmic vesicle part 4 of 23 proteins, 17.4% 0.00024 0.17% EGF, CUBN, KNG1, SERPING1 Transporter activity 5 of 23 proteins, 21.7% 0.00527 2.88% CUBN, APOD, AZGP1, PTGDS, AMBP
Stored secretory granule 4 of 23 proteins, 17.4% 5.33E−05 0.22% TF, EGF, KNG1, SERPING1 Carbohydrate binding 3 of 23 proteins, 13.0% 0.00656 2.50% SERPINC1, GAA, KNG1
Membrane fraction 4 of 23 proteins, 17.4% 0.00796 2.69% IGHG2, IGHG1, CUBN, AMBP Lipid binding 3 of 23 proteins, 13.0% 0.0092 2.82% APOD, AZGP1, PTGDS
Insoluble fraction 4 of 23 proteins, 17.4% 0.00897 2.81% IGHG2, IGHG1, CUBN, AMBP Serine-type endopeptidase inhibitor activity 3 of 23 proteins, 13.0% 9.99E−05 0.00% SERPINC1, AMBP, SERPING1, E9KL23
Apical plasma membrane 3 of 23 proteins, 13.0% 0.00069 0.27% TF, CUBN, UMOD Hemoglobin binding 2 of 23 proteins, 8.7% 1.36E−05 0.00% HP, CUBN
Perinuclear region of cytoplasm 3 of 23 proteins, 13.0% 0.00565 2.58% TF, GLB1, PTGDS Fatty acid binding 2 of 23 proteins, 8.7% 0.00076 0.73% AZGP1, PTGDS
Apical part of cell 3 of 23 proteins, 13.0% 0.00146 0.94% TF, CUBN, UMOD Cysteine-type endopeptidase inhibitor activity 2 of 23 proteins, 8.7% 0.00084 0.83% FETUA, KNG1
Lytic vacuole 3 of 23 proteins, 13.0% 0.0026 1.89% CUBN, GLB1, GAA Monocarbox-ylic acid binding 2 of 23 proteins, 8.7% 0.00161 0.92% AZGP1, PTGDS
Lysosome 3 of 23 proteins, 13.0% 0.0026 1.79% CUBN, GLB1, GAA Hydrolase activity, hydrolyzing O-glycosyl compounds 2 of 23 proteins, 8.7% 0.00354 2.29% GLB1, GAA
Platelet alpha granule lumen 3 of 23 proteins, 13.0% 2.55E−05 0.00% EGF, KNG1, SERPING1 Protein kinase regulator activity 2 of 23 proteins, 8.7% 0.00418 2.13% EGF, FETUA
Secretory granule lumen 3 of 23 proteins, 13.0% 1.37E−05 0.00% EGF, KNG1, SERPING1 Kinase regulator activity 2 of 23 proteins, 8.7% 0.00541 2.71% EGF, FETUA
Cytoplasmic membrane-bound vesicle lumen 3 of 23 proteins, 13.0% 1.55E−05 0.00% EGF, KNG1, SERPING1 Hydrolase activity, acting on glycosyl bonds 2 of 23 proteins, 8.7% 0.0056 2.78% GLB1, GAA
Vesicle lumen 3 of 23 proteins, 13.0% 1.74E−05 0.00% EGF, KNG1, SERPING1 Tetrapyrrole binding 2 of 23 proteins, 8.7% 0.00841 2.67% CUBN, AMBP
Platelet alpha granule 3 of 23 proteins, 13.0% 2.55E−05 0.00% EGF, KNG1, SERPING1 Heparin binding 2 of 23 proteins, 8.7% 0.00608 2.63% SERPINC1, KNG1
Vacuole 3 of 23 proteins, 13.0% 0.00423 2.27% CUBN, GLB1, GAA
Extrinsic to membrane 2 of 23 proteins, 8.7% 0.00532 2.61% CUBN, UMOD
Lysosomal membrane 2 of 23 proteins, 8.7% 0.00699 2.64% CUBN, GAA
Endocytic vesicle 2 of 23 proteins, 8.7% 0.00332 2.10% TF, CUBN
Coated pit 2 of 23 proteins, 8.7% 0.00113 0.88% TF, CUBN
Cell projection membrane 2 of 23 proteins, 8.7% 0.00899 2.71% CUBN, UMOD
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4. Discussion

CKD is a growing public health problem with dramatic increases in morbidity and mortality following progression to ESRD. Given this, there is a tremendous need for the development of biomarkers to predict CKD progression and allow for early therapeutic intervention. Urine proteomic strategies are now at the forefront of this search due to the sensitivity of MS/MS analysis and the ability to develop noninvasive biomarkers from a readily available biofluid. Significant progress has been made, particularly in diabetes, where urine proteomic analysis can predict nephropathy [6, 25, 26]. Despite these developments, the majority of proteomic studies have relied on two-dimensional (2D) differential in-gel electrophoresis for protein separation. Resulting samples, particularly in CKD subjects, contain large amounts of highly abundant plasma proteins due to nonspecific leakage through the glomerular filtration barrier. Targeted analyses of low-abundance proteins will likely lead to more disease-specific and clinically relevant protein biomarkers.

We therefore focused our attention on the urinary N-linked glycoproteome. Glycoproteins are an important protein subfraction accounting for up to 50% of the human proteome at any given time [27]. Due to their critical role in cell-cell interactions and signaling cascades, glycoproteins are promising markers for identifying kidney disease activity and progression. In this study we present an initial examination of the urinary N-linked glycoproteome in CKD subjects compared to healthy control subjects. We successfully isolated N-linked glycoproteins from twelve urine samples utilizing a hydrazide capture technique. 122 unique glycosylated proteins were detected amongst the twelve subjects (Table 3). This number is similar to other recent glycoproteome analyses. Ahn et al. recently reported isolating 164–174 unique proteins from human diabetic plasma using a multi-lectin column enrichment technique [16]. Yang et al. isolated 265 urinary glycoproteins from bladder cancer subjects and healthy controls also utilizing a multi-lectin column for enrichment, but larger sample sizes were used than in our current study [15]. These results support a successful hydrazide based technique for glycoprotein isolation in human urine. Further studies are required to identify optimal extraction strategies.

We detected 8 glycoproteins unique to CKD subjects and 35 unique to healthy controls (Table 2). Additionally, of the 122 total proteins identified, 23 glycoproteins were differentially expressed in CKD subjects versus healthy controls. 18 were upregulated in CKD while 5 were downregulated (Table 4). Many of the differentially expressed proteins have been previously linked to kidney disease supporting a potential role as a CKD biomarker. Two of the most significantly upregulated proteins in our CKD samples were AZGP1 and LRG, both of which are established inflammatory mediators. Alteration of AZGP1 and LRG expression is predictive of acute kidney injury in postsurgical patients [28]. AZGP1 has also been shown to be increased in diabetes and diabetic nephropathy [13, 29]. PTGDS, a known extracellular transporter for lipophilic molecules, is formed de novo in renal tubules [30]. PTGDS is upregulated in early diabetes [31] and is a marker of hypertension and latent renal injury [32]. SERPING1, an extracellular matrix regulator, is increased in acute renal allograft rejection perhaps suggesting an important role for collagen remodeling [33]. KNG1, a bradykinin precursor, has also been shown to be upregulated in acute renal allograft rejection [34], and gene variation induces altered aldosterone sensitivity in hypertensive subjects [35]. Interestingly, LUM, a proteoglycan, is a protein unique to CKD but without statistically significant up-regulation. Altered regulation of LUM has been linked with abnormal collagen fibril morphology as a mediator of fibrotic disease in diabetic nephropathy [36, 37]. CUBN, an apical protein in proximal tubule cells, was unique and downregulated in CKD. Recent investigation supports a role of CUBN in albumin reabsorption with genetic variance at this locus predicting microalbuminuria [38]. The decreased urinary CUBN excretion found in our CKD population may represent a dysfunctional variant or potentially a causative factor responsible for increasing proteinuria.

We used annotations by the GO Consortium and GO Tools to connect the complex array of proteins identified in CKD subjects to biological processes, protein function, and cellular location. Many of the multiprotein pathways differentially expressed in CKD are involved in coagulation, inflammation, and acute-phase response (Table 5, Figure 3). Twenty proteins were linked to protein-protein interactions and binding. Remarkably, there were altered levels of proteins that were involved in acute-phase response and immune/stress response proteins (18 out of 23), implicating a possible mechanistic role for these pathways in CKD. Our detection of the several extracellular proteins and matrix remodeling proteases likely reflects matrix remodeling that occurs in CKD. These findings are consistent with previous literature, as CKD is known to have increased propensity for atherosclerosis, endothelial dysfunction, increased basal inflammation, and altered stress response [39, 40].

In this study, we have established normalization techniques which will be essential to future urine glycoproteome analyses. To account for variations in the glycoprotein extraction efficiency of individual samples, yeast invertase (yeast glycoprotein with several glycopeptides) was added to each sample prior to extraction. In this way, glycopeptides derived from invertase serve as an internal marker for the extraction efficiency in each sample. Our samples were also normalized for urine creatinine content. This is of particular importance as marked intersubject variability can exist in creatinine excretion in random urine specimens consistent with different concentrations due to hydration status. Indeed, such normalization would be essential to extrapolate net excretion rates of a given protein in 24 hours and is commonly employed in clinical practice to quantify albumin excretion rates [23].

In summary, we have utilized a hydrazide-based approach to enrich the urinary glycoproteome with subsequent identification of the urinary glycoproteins in a human CKD population for the first time. Our results indicate that urine carries a distinct population of glycoproteins that function in proteinase inhibition, protein binding, and the acute-phase/immune-stress response in subjects with CKD. It will be of interest to study a larger number of subjects to determine whether urinary levels of these proteins might be useful indicators of CKD and to investigate the proposal that these proteins could be markers of disease progression.

Acknowledgments

This work is supported in part by Grants from the National Institutes of Health (HL094230, DK079912, HL102334, GM49500, CA108597, DK082841, and K12HD001438), the Doris Duke Foundation Clinical Scientist Development Award (S. Pennathur), the American Diabetes Association (1-08-CR-48; R. Pop-Busui), the George O'Brien Kidney Center (DK089503), and the Molecular Phenotyping Core of the Michigan Nutrition and Obesity Research Center (DK089503). A. Vivekanandan-Giri and J. L. Slocum contributed equally to this work.

Abbreviations

CKD:

Chronic kidney disease

ESRD:

End-stage renal disease

FDR:

False discovery rate

GO:

Gene ontology

LC-ESI-MS/MS:

Liquid chromatography-electrospray ionization tandem MS analysis

MS:

Mass spectrometry.

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