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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Proteomics Clin Appl. Author manuscript; available in PMC 2010 April 20.
Published in final edited form as:
PMCID: PMC2857342

Characterization of the renal cyst fluid proteome in autosomal dominant polycystic kidney disease (ADPKD) patients


Autosomal dominant polycystic kidney disease (ADPKD) is characterized by localized autonomous cellular proliferation, fluid accumulation within the cysts, and intraparenchymal fibrosis of the kidney. Little is known about the cyst fluid's protein composition. We hypothesized that the complex collection of cyst fluid proteins (cyst fluid proteome) plays a major role in cyst formation/maintenance and contains yet unknown diagnostic and mechanistic features that are common to all forms of PKD. We analyzed five kidney cyst fluids from four patients with ADPKD. Tryptic peptides from plasma-protein immunodepleted (ProteoPrep®) and undepleted cyst fluid samples were analyzed by LC-MS/MS. Proteins were identified by SEQUEST™ and validated via the Trans-Proteomic Pipeline; 391 proteins were identified with >90% confidence; 251 of them in undepleted and 362 in immunodepleted samples. Immunodepletion removed >94% of the cyst fluid protein. A surprisingly large and functionally diverse number of proteins common to most cysts were identified. These proteins may be of mechanistic interest and include Ig γ, κ, and fragments; complement components; vitronectin; orosomucoid; prostaglandin D2 synthase; vitamin D-binding protein; clusterin; SERPIN family proteins; hemopexin; and fetuin-A. Additionally, these results suggest that further prefractionation and enhanced chromatographic separation of tryptic peptides is likely to expose an even greater number of relevant proteins.

Keywords: Cyst fluid, Immunodepletion, Liquid chromatography, Mass spectrometry, Polycystic kidney disease

1 Introduction

Polycystic kidney disease (PKD) is a common genetic disorder that results in bilateral renal enlargement due to the development of fluid filled cysts. This disease afflicts 5–10% of all end-stage renal disease patients in the US and its pathology is associated with hypertension and other extra-renal complications, rendering it a considerable public health burden [1]. Two forms of PKD are diagnosed, autosomal dominant PKD (ADPKD) and autosomal recessive PKD (ARPKD). ADPKD results from mutations in a set of genes that encode the proteins polycystin-1 (PKD1) and polycystin-2 (PKD2) [2, 3], two integral membrane proteins localized in the renal cilia [4]. ADPKD (PKD1) is much more common, affects approximately 1 in 800 in the human population, and is usually diagnosed after the fourth decade of life. ARPKD is a more severe disease that afflicts neonates and children. Renal failure in ADPKD is due to the infiltration of the functional parenchyma with monocytes and fibroblasts that results in renal fibrosis [5].

Recent research has focused on the genetic defects and processes involved in cyst formation in PKD. However, little is known about the growth and maintenance of renal cysts and how these processes might relate to cyst fluid constituents. Previous studies have identified a few of the proteins present in the renal cyst fluid: EGF [6-8], TGF-α [7, 9], amphiregulin (AR) and heparin-binding (HB)-EGF [7], laminin fragments [10], nanobacterial constituents [11], α1 antitrypsin, prealbumin, hemopexin, α1 antichymotrypsin, transferrin, IgG, IgA, and alanine amino-transaminase [12]. Nevertheless, the complex protein constituency of cyst fluid remains unknown.

It is our hypothesis that the collection of cyst fluid proteins (cyst fluid proteome) is diverse and complex, that some if not many of these proteins may play a major role in liver cyst development and maintenance, and that they embody both diagnostic and mechanistic features that may be common to all forms of cystic disease, but are not yet known. An effective approach to test this hypothesis is mass spectrometric-based proteomic analysis. This approach enables one to identify the proteins contained in complex biological samples and, using various additional spectral data analyses [13], can provide relative quantitation from individuals whose protein synthesis or degradation rates are affected by the state of the organism or a tissue. Proteins that are present or absent in a disease state, such as PKD, can be used as markers for mechanistic explanation, diagnoses, or potential targets for therapeutic intervention.

The renal cyst fluid proteome is of particular interest because it has been shown to have proliferative and secretory properties that are likely to initiate cyst growth and maintain the cysts [14, 15]. Gattone et al. [16] described mRNA misexpression in organs involved in the murine BALB/c-cpk/cpk model of ARPKD. These mRNAs are known to be involved in proliferation, apoptosis, differentiation, and/or extracellular matrix (ECM). While some of the factors have been identified, it is likely that additional components of the cyst fluid, when identified, will provide further information on the elements that cause cyst formation and growth. Proteins associated with differentiation and dedifferentiation processes, specifically those related to secretory pathways and ECM, may reflect alterations in polarized protein sorting/trafficking [17, 18] and be involved in renal cyst development [19]. It is likely that these will be found in the cyst fluid.

The most widely used proteomic method for assessing protein expression globally is “bottom-up” proteomics. In this strategy, proteins in a body fluid or cell/tissue extract are digested proteolytically and the resulting complex peptide mixture is separated by unidimensional or multidimensional LC coupled to MS/MS for the identification of all the proteins present in the original sample. For the first time, we present a comprehensive analysis of the proteins present in human renal cyst fluid, discuss their relevance in terms of cyst growth and maintenance, and suggest additional proteomic strategies for further study.

2 Materials and methods

2.1 Materials

TFA, iodoethanol, triethylphosphine (TEP), and ammonium bicarbonate were purchased from Sigma–Aldrich (St. Louis MO, USA). Acetone was obtained from Fisher Scientific (Philadelphia, PA, USA). ACN and MS grade water were purchased from EMD Chemicals (Gibbstown, NJ, USA). Modified sequencing grade porcine trypsin was obtained from Princeton Separations (Freehold, NJ, USA). The ProteoPrep® 20 plasma immunodepletion kit was purchased from Sigma–Aldrich. The 2-D Quant Kit was purchased from Amersham Biosciences (Piscataway, NJ, USA).

2.2 Clinical specimens

Sample management of de-identified samples was performed according to the bioethical recommendations of the Institutional Review Board. Human renal cyst fluid samples were obtained from patients by needle aspiration, immediately frozen in liquid nitrogen, and stored at −80°C until use. The diagnosis of ADPKD was made clinically. More clinical specimen information is provided in Table 1.

Table 1
Sample information

2.3 Depletion of high-abundance plasma proteins

Preliminary sample evaluation revealed an excessive abundance of albumin and other plasma proteins in the cyst fluid. As discussed later in the paper, this necessitated removal of these proteins to facilitate the identification of low-abundance proteins. Therefore, a 2 mL aliquot of each sample was centrifuged at 3000 rpm, 4°C for 10 min. The supernatant concentration was determined by the Bradford protein assay [20]. A 500 μL aliquot of each centrifuged sample was precipitated by addition of nine volumes of ice-cold acetone containing 10% TFA v/v and immediately mixed by gentle vortexing. The mixture was incubated overnight at 4°C and centrifuged at 4500 rpm, 4°C for 20 min. The precipitate was mixed with 2 mL ice-cold acetone, incubated at 4°C for 30 min and centrifuged as above. The precipitate was air-dried by SpeedVac and 1 mL of 4 M urea was added to dissolve the precipitated sample. Again, protein concentration was determined by the Bradford assay.

Depletion of high-abundance plasma proteins was performed as described in the ProteoPrep 20 User Guide. Briefly, 100 μL of diluted precipitated sample was placed on the equilibrated spin column and incubated at room temperature for 20 min. The spin column and collection tube were then centrifuged at 5000 rpm for 30 s at room temperature. The flow through volume was saved in the collection tube. The spin column was then washed twice with 100 μLof equilibration buffer. Total fluid collection was 300 μL. The proteins bound to the column were eluted with 2 mL of elution solution and 1.5 mL of the first elution was collected. Each sample was processed as four technical replicates. The flow through volumes were pooled and dried via SpeedVac. These protein samples are subsequently referred to as “depleted samples.” Afterward, the bound proteins were eluted and their eluants were pooled and the concentration of this pooled solution was determined by the 2-D Quant Kit assay.

2.4 Protein reduction, alkylation, and digestion

2.4.1 Undepleted samples

Proteins in 200 μL of the diluted, TFA/acetone precipitated cyst fluid samples were reduced and alkylated by TEP and iodoethanol as described by Hale et al. [21]. Briefly, 200 μL of the reduction/alkylation cocktail was added to the protein solution. The sample was incubated at 37°C for 90 min, dried by SpeedVac, and reconstituted with 100 μL of 100 mM NH4HCO3 at pH 8.0. A 150 μL aliquot of a 20 μg/mL trypsin solution was added to the sample and incubated at 37°C for 3 h after which another 150 μL of trypsin was added, and the solution incubated at 37°C for 3 h.

2.4.2 Depleted samples

CF01 was reduced, alkylated, and digested as above. Due to their lower protein content, the other samples (CF02–CF05) were reconstituted in 100 μL of 4 M urea, reduced/alkylated by 100 μL of the cocktail, dried, reconstituted with 50 μL of NH4HCO3, and digested by two 75 μL additions of trypsin solution. Finally 50 μL of 100 mM NH4HCO3 was added to each digested depleted sample except CF01.

2.5 LC-MS/MS

The digested samples were analyzed using a Thermo-Finnigan linear ion-trap (LTQ) mass spectrometer coupled with a Surveyor autosampler and MS HPLC system (Thermo-Finnigan). Tryptic peptides were injected onto the C18 microbore RP column (Zorbax SB-C18, 1.0 mm × 150 mm) at a flow rate of 50 μL/min. The mobile phases A, B, and C were 0.1% formic acid in water, 50% ACN with 0.1% formic acid in water, and 80% ACN with 0.1% formic acid in water, respectively. The gradient elution profile was as follows: 10% B (90% A) for 5 min; 10–95% B (90–5% A) for 120 min; 100% C for 5 min; and 10% B (90% A) for 12 min. The data were collected in the “Triple-Play” (MS scan, Zoom scan, and MS/MS scan) mode with the ESI interface using a normalized collision energy of 35%. Dynamic exclusion settings were set to repeat count 1, repeat duration 30 s, exclusion duration 120 s, and exclusion mass width 0.75 m/z (low) and 2.0 m/z (high).

2.6 Data analysis

The acquired data were searched against the International Protein Index (IPI) human database (ipi.HUMAN.v3.34) using SEQUEST (v. 28 rev. 12) algorithms in Bioworks (v. 3.3). General parameters were set to: peptide tolerance 2.0 amu, fragment ion tolerance 1.0 amu, enzyme limits set as “fully enzymatic – cleaves at both ends”, and missed cleavage sites set at 2. The searched peptides and proteins were validated by PeptideProphet [22] and ProteinProphet [23] in the Trans-Proteomic Pipeline (TPP, v. 3.3.0) ( Each protein identification was reported and compared using an in-house developed platform named LASPAP (LArge-Scale Shotgun Proteomics data Analysis Platform). Gene ontology analysis was completed using the Generic GO Term Finder [24] developed by the Bioinformatics Group at the Lewis-Sigler Institute at Princeton (

3 Results and discussion

3.1 Sample preparation

In view of our preliminary results that indicated the predominance in cyst fluid of albumin and other prominent plasma proteins in cyst fluid and our interest in desalting the fluid prior to proteolysis/LC, we employed a two-stage sample cleanup strategy. This included TFA/acetone precipitation followed by immunodepletion. Protein precipitation is typically used to remove salts and concentrate proteins. However, it is also capable of depleting at least a portion of the albumin contained in a sample. In a previous study, TCA, HCl, CH3COOH, and H3PO4 were added to acetone individually to determine the most effective precipitation solution. In addition to providing excellent precipitation, TCA/acetone also was found to be the most efficient method for albumin removal [25]. Though TFA/acetone was not evaluated in that experiment, we tested it in the present study. Our results indicated that 24.2, 26.8, 47.4, 45.6, and 34.0% of the original protein (albumin) was depleted from each sample (CF01-CF05, respectively) using nine volumes of TFA/acetone. The low-abundance protein concentration increased from 3.0% in unprecipitated CF01 to 21.0% after precipitation. These data confirm that the TFA/acetone approach, like TCA/acetone, is an efficient way to remove albumin, as applied in this study. Hereafter, “low-abundance proteins” will refer to those proteins present in the cyst fluid after removal of albumin and the other high-abundance plasma proteins by TFA/acetone precipitation and immunodepletion.

In addition to albumin, both PKD renal cyst fluid and plasma contain large fractions of IgG, apolipoproteins, trypsin inhibitors, and other high-abundance proteins. Therefore, the depletion of the high-abundance plasma proteins in the cystic fluid was deemed a prerequisite to increase our analytical “depth of field.” The ProteoPrep 20 plasma immunodepletion kit [26], which removes 20 of the top high-abundance proteins using conventional antibodies coupled to small recombinant immunoaffinity ligands, was applied in this experiment to remove them from the cyst fluid. After depletion, protein assay confirmed that 79.0, 99.0, 98.4, 97.9, and 97.9% of the proteins in the original samples (CF01, CF02, CF03, CF04, and CF05, respectively) were removed by the immunodepletion column. These results clearly emphasize the necessity of high-abundance protein depletion to facilitate identification of the remaining cyst fluid proteome.

The depletion of high-abundance proteins, that can mask the presence of low-abundance proteins, increases the relative concentration of low-abundance proteins and therefore improves detection sensitivity [27]. As our data in Fig. 1 demonstrate, the number of peptides identified with ≥90% confidence for each sample (technical replicates A and B) increased dramatically after immunodepletion. Likewise, proteins identified from these peptides reveal the presence of 112, 71, 60, 64, and 72 low-abundance proteins in the depleted samples CF01–CF05, respectively, compared to 56, 45, 29, 38, and 38 from the undepleted samples (see Fig. 2).

Figure 1
Comparison of peptides identified with ≥90% confidence between the depleted and undepleted samples. Each sample underwent two injections (A and B). 90D refers to the depleted samples and 90U to the undepleted samples. These data demonstrate the ...
Figure 2
The “low-abundance protein” distribution comparison for each individual sample, where proteins were detected in two technical replicates. Low-abundance proteins are those present in the cyst fluid after removal of albumin and the other ...

3.2 Protein identification

The peptides and proteins identified by SEQUEST were evaluated with the PeptideProphet and ProteinProphet to determine their identification probabilities (Table 2). The validated proteins from the various samples were then reported and compared with LASPAP. The complete validated protein identification list and the protein comparisons between all the samples appear in the Supporting Information. Figure 3 illustrates a total and low-abundance protein comparison between the depleted and undepleted samples, combining data from all individuals. A total of 391 proteins were identified with ≥90% confidence. Of those, 362 were from the depleted samples and 251 from the undepleted samples (Fig. 3A). Low-abundance proteins comprised 225 out of the 391 identified proteins (Fig. 3B). Among these, 199 were from the depleted samples and 106 from the undepleted samples.

Figure 3
Venn diagrams of the relationship between sets of proteins identified in depleted samples and undepleted samples. (A) Total proteins. (B) Low-abundance proteins. This figure extends the data presented in Fig. 2 to the group by illustrating total and low-abundance ...
Table 2
Human renal cyst fluid proteins common to at least three out of five renal cysts studied

3.3 Comparative analysis of different cyst samples

The five cysts from which the kidney fluid samples were obtained were variable in diameter, appearance, protein concentration, and protein composition. Table 1 lists the cysts in the order of random assignment and lists the origin and characteristics of each cyst. Cyst 1 had an unusually low protein concentration of 1.58 mg/mL and cyst 2 had an unusually high concentration of 41.36 mg/mL. In comparison, plasma protein concentration is typically 39–50 mg/mL and glomerular filtrate protein ranges from 0.06 to 0.11 mg/mL ( gram/section7/7ch04/7ch04p10.htm). Previously, PKD renal cyst fluid protein concentration was shown to range from 7 to 28 mg/mL [28]. CF01 fluid from cyst 1 had other unusual characteristics that included a brown, cloudy appearance and an abundance of mucin and ribosomal isoforms. One possible explanation for these unusual characteristics is that cyst 1 may have contained an adenoma [29].

Aside from the described differences in cyst 1, the relatively large size of cyst 5, and the elevated protein concentration in cyst 2, the cysts were similar in patient origin, color, fluid consistency, fluid volume, and protein concentration. Because PKD cysts typically range from barely visible to 500 mm, our sample cysts easily fall into that range (

3.4 Gene ontology annotation

The IPI numbers of all identified proteins common to at least three out of five cysts were entered into the Lewis-Sigler GO Term Finder. As such, the focus of this discussion will be on the 150 proteins common to at least 3/5 cysts. The IPI numbers corresponding to these 150 proteins were submitted to the GO Term Finder that recognized all but a few. In the individual ontological categories or “aspects”, some IPI numbers were recognized but were not annotated relative to any aspect in the database.

A review of unannotated IPI numbers yielded protein fragments, complement isoforms, and Ig fragments. In addition, due to the redundancy in the reporting of sub-categories by the web tool and our desire to visually simplify the histograms, some categories have been combined or nonspecific parent terms eliminated. This editing was done by consulting AmiGO (the official tool for searching and browsing the Gene Ontology database) to insure correct attribution of subcategories and to respect the gene ontology dendogram [30]. These annotated proteins grouped by sub-categories are included in a graphical display in Figs. Figs.44--6.6. Ontologies are explained fully on the Gene Ontology website: (

Figure 4
The results of the GO Term Finder query of the “Molecular Function” aspect using 150 proteins from renal cyst fluid common to at least three out of five samples analyzed. The ordinate value is the actual number of IPI numbers that match ...
Figure 6
The results of the GO Term Finder query of the “Cellular Component” aspect using 150 proteins from renal cyst fluid common to at least three out of five samples analyzed. The ordinate value is the actual number of IPI numbers that match ...

Figure 4 illustrates proteins categorized by the gene ontology Molecular Function aspect. A large number of identified cyst proteins function as protease inhibitors, specifically serine-type endopeptidase inhibitors. Serine-type endopeptidases include trypsin, thrombin, and most notably numerous coagulation-pathway enzymes. The identified inhibitors of interest include antichymotrypsin, angiotensinogen, antithrombin III, α-2-antiplasmin, and C1 inhibitor. Antigen binding is also a function of many of the proteins identified in the sample. A review of the antigen binding proteins shows that they are predominantly Ig heavy and κ-chains, as would be expected. Isoprenoid binding proteins appear in the sample as vitamin A binding proteins. It is important to note that vitamin A has a role in growth and differentiation [30] and its presence in the fluid may reflect such an effect on the expanding cyst.

Figure 5 illustrates the number of proteins categorized according to the Biological Process aspect. This category includes a high proportion of proteins that are involved in responses to external stimulus, wounding, and pathogens. Defense response proteins restrict damage to an organism caused by an infection. In the present study, most of these proteins are likely to have been proteolyzed to active enzymes such as complement components. Complement activation proteins enable the direct killing of microbes, the disposal of immune complexes, and the regulation of other immune processes [31]. Regulation of body fluids is another category of interest. These proteins include antithrombin, β-2-glycoprotein, angiotensinogen, and complement inhibitors [30].

Figure 5
The results of the GO Term Finder query of the “Bio-logical Process” aspect using 150 proteins from renal cyst fluid common to at least three out of five samples analyzed. The ordinate value is the actual number of IPI numbers that match ...

In the subcategories comprising the cellular component aspect, all identified and annotated proteins fell into the extracellular region category. A more specific designation suggests that many of these proteins are secreted into the extracellular space. This would be expected of cyst fluid, and emphasizes the importance of concomitantly analyzing the proteome of epithelial cells generating this extracellular fluid in future studies.

3.5 Notable proteins

Over 350 unique proteins were identified in PKD cyst fluid spanning all five samples studied. A significant fraction of these were recognized as Ig fragments, protein precursors, and isoforms. Nonetheless, many distinct protein identifications were made, representing an abundant source of protein molecules potentially involved in cyst maintenance, growth, and structural formation.

For instance, vitronectin is a protein of interest because it serves as a receptor for the integrin α-v-β3, an adhesion, and angiogenesis molecule. A study by Bello-Reuss et al. demonstrated neovascularization in PKD cyst walls along with expression of α-v-β3. Neovascularization may be necessary for cyst growth and contribute to increased vascular permeability and fluid secretion [32].

A study by Woo [33] showed that, in addition to cyst enlargement and interstitial fibrosis, apoptosis is a pathological feature of PKD. Apoptotic cells are phagocytized within a few hours by neighboring cells or by phagocytes in a process involving the vitronectin receptor or the phosphatidylserine receptor. In human PKD, where nephron loss is slow, apoptosis was nevertheless detected before the onset of uremia.

The sulfated glycoprotein clusterin (Apo J) has been detected in significant quantity in hepatic cyst fluid [34] and is a potential biomarker of PKD [35, 36]. Urinary clusterin has been found to be elevated in (cy/+) rat model of ADPKD rats with progressive PKD [37]. Clusterin also interacts with complement components and vitronectin [38], where they bind to the membrane attack complex (MAC) and prevent cytolysis. Despite this apparent inhibitory effect, patients in that study diagnosed with lupus nephritis were noted to have more renal pathology with elevated serum vitronectin and clusterin. It is interesting that many components of the complement cascade were identified in our cyst fluid samples, but it is unknown if the complement system is involved in the pathology of PKD [38].

The renal cyst fluid contains a large number of proteins from the SERPIN (serine-protease inhibitor) family. This protein family inhibits the proteases that function as coagulation factors, complement system components, and digestive enzymes. Although there is no described mechanism of how this class may contribute to PKD, we cannot ignore the consistent identification of multiple, unique SERPINs in the various human cyst fluid samples studied here. Many of these proteins may be leaking into cysts from a plasma source, as about 10% of plasma proteins are SERPINs [39]. Nevertheless, these proteins are excellent candidates for the further experimental investigation.

β-2-Glycoprotein has been associated with renal disease, but more as a marker than as an etiologic agent. Other proteins in our study, such as α-1-microglobulin, retinol binding protein, and β-2-microglobulin have also been identified as protein markers of tubular malfunction. These proteins are low molecular weight markers that are freely filtered and normally are reabsorbed by renal tubule cells [40]. These proteins may have a pathological role in PKD, or, alternatively, could be markers for disease severity or cyst stage.

Fetuin- A (α-2-HS glycoprotein) has been correlated with epithelial cell toxicity in the nephron and has been detected in urinary exosomes. Zhou et al. [41] hypothesize that diseased and sloughing proximal tubular cells generate fetuin-A. They note that immature rat renal cells synthesize fetuin-A, and this protein likely plays a role in cell differentiation and tissue transformation during the initial histogenesis [42]. The identification of fetuin-A in our cyst fluid samples strongly suggests a degree of dedifferentiation in PKD renal epithelial cells [41].

Hemopexin is secreted into the plasma in response to inflammation (acute phase reactant) and it binds heme with high affinity [43]. As mentioned above, this protein has been identified previously in renal cyst fluid studies [12]. Hemopexin has been shown to cause proteinuria after direct renal infusion. This pathology is due to a protease action and results in microscopic changes in glomerular histology [44]. It is unknown if this contributes to the pathological damage in observed in PKD.

A preponderance of Ig and complement molecules are identified in the cyst fluid. It is not clear if these Ig proteins are simply diffusing into the cyst from plasma or if they are locally produced by B-cells. An intriguing explanation is that there may be an immunological component to this disease. Perhaps those cells expressing altered forms of polycystin or other unrecognized protein are inducing an immunologic response. With variable phenotypic expression and progression to renal failure in ADPKD, it would seem that an immunologic mechanism could be at play here [1]. A recent study shows high levels of complement component 3 in models of ARPKD and suggests abnormal activation of complement is a key component in cystic disease progression [45].

Several proteins of interest in PKD, such as polycystin 1 and 2, fibrocystin, TGF, and EGF are absent from our samples. The polycystin protein complex would not be expected to be detected by mass-spectrometry in the end-stage cyst fluid per se. An epithelial cell or exosomal proteomic study would be expected to yield polycystin and other intracellular/membrane-bound proteins [46]. It is also noted that TGF and EGF are absent from our sample. In a study by Quigley et al. [47], it was found that filtrated EGF is bound to albumin with a free fraction of 0.31 ± 0.04%. These proteins may part of the albumin bound peptidome in our sample and therefore depleted. Future experiments to find low-abundance proteins/polypeptides attached to albumin will be conducted in our laboratory.

4 Concluding remarks

Our analyses of human cyst fluid identified a large number distinct proteins along with hundreds of protein precursors and isoforms. Depletion of common plasma proteins yielded additional protein identifications. A query of the gene ontology databank demonstrated that our proteins are located in the extracellular region; the main protein functions include serine protease inhibition and antigen binding; the protein processes include organism defense, reaction to stimulus, and regulation of body fluids. Proteins of specific interest include vitronectin, clusterin, SERPIN family proteins, hemopexin, fetuin-A, and complement components. These identified proteins may offer mechanistic explanations for cyst development and maintenance, serve as markers for diagnosis and monitoring, or serve as potential targets for therapeutic intervention of PKD. Future studies should include quantitative comparisons of hepatic and renal cyst fluid from rodent models, comparison of human cyst samples to these rodent models, and a survey of the albuminome [48, 49] depleted from our cyst fluid samples.


The authors gratefully recognize the support of the Air Force Office of Scientific Research through grant #FA9550-06-1-0083.


autosomal dominant polycystic kidney disease
autosomal recessive polycystic kidney disease
International Protein Index
polycystic kidney disease
Trans-Proteomic Pipeline


The authors have declared no conflict of interest.


1. Torres K, Harris P, Pirson Y. Autosomal dominant polycystic kidney disease. The Lancet. 2007;369:1287–1301. [PubMed]
2. Hughes J, Ward CJ, Peral B, Aspinwall R, et al. The polycystic kidney disease 1 (PKD1) gene encodes a novel protein with multiple cell recognition domains. Nat. Genet. 1995;10:151–160. [PubMed]
3. Mochizuki T, Wu G, Hayashi T, Xenophontos SL, et al. PKD2, a gene for polycystic kidney disease that encodes an integral membrane protein. Science. 1996;272:1339–1342. [PubMed]
4. Yoder BK, Hou X, Guay-Woodford LM. The polycystic kidney disease proteins, polycystin-1, polycystin-2, polaris, and cystin, are co-localized in renal cilia. J. Am. Soc. Nephrol. 2002;13:2508–2516. [PubMed]
5. Grantham JJ. The etiology, pathogenesis, and treatment of autosomal dominant polycystic kidney disease: Recent advances. Am. J. Kidney Dis. 1996;28:788–803. [PubMed]
6. Dell KM, Nemo R, Sweeney WE, Jr., Levin JI, et al. A novel inhibitor of tumor necrosis factor-alpha converting enzyme ameliorates polycystic kidney disease. Kidney Int. 2001;60:1240–1248. [PubMed]
7. MacRae Dell K, Nemo R, Sweeney WE, Jr., Avner ED. EGF-related growth factors in the pathogenesis of murine ARPKD. Kidney Int. 2004;65:2018–2029. [PubMed]
8. Munemura C, Uemasu J, Kawasaki H. Epidermal growth factor and endothelin in cyst fluid from autosomal dominant polycystic kidney disease cases: Possible evidence of heterogeneity in cystogenesis. Am. J. Kidney Dis. 1994;24:561–568. [PubMed]
9. Lowden DA, Lindemann GW, Merlino G, Barash BD, et al. Renal cysts in transgenic mice expressing transforming growth factor-alpha. J. Lab. Clin. Med. 1994;124:386–394. [PubMed]
10. Slade MJ, Kirby RB, Pocsi I, Jones JK, Price RG. Presence of laminin fragments in cyst fluid from patients with autosomal dominant polycystic kidney disease (ADPKD): Role in proliferation of tubular epithelial cells. Biochim. Biophys. Acta. 1998;1401:203–210. [PubMed]
11. Hjelle JT, Miller-Hjelle MA, Poxton IR, Kajander EO, et al. Endotoxin and nanobacteria in polycystic kidney disease. Kidney Int. 2000;57:2360–2374. [PubMed]
12. Everson GT, Emmett M, Brown WR, Redmond P, Thickman D. Functional similarities of hepatic cystic and biliary epithelium: Studies of fluid constituents and in vivo secretion in response to secretin. Hepatology. 1990;11:557–565. [PubMed]
13. Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B. Quantitative mass spectrometry in proteomics: A critical review. Anal. Bioanal. Chem. 2007;389:1017–1031. [PubMed]
14. Yamaguchi T, Nagao S, Takahashi H, Ye M, Grantham JJ. Cyst fluid from a murine model of polycystic kidney disease stimulates fluid secretion, cyclic adenosine monophosphate accumulation, and cell proliferation by MadinDarby canine kidney cells in vitro. Am. J. Kidney Dis. 1995;25:471–477. [PubMed]
15. Ye M, Grant M, Sharma M, Elzinga L, et al. Cyst fluid from human autosomal dominant polycystic kidneys promotes cyst formation and expansion by renal epithelial cells in vitro. J. Am. Soc. Nephrol. 1992;3:984–994. [PubMed]
16. Gattone VH, Ricker JL, Trambaugh CM, Klein RM. Multiorgan mRNA misexpression in murine autosomal recessive polycystic kidney disease. Kidney Int. 2002;62:1560–1569. [PubMed]
17. Lee DB, Huang E, Ward HJ. Tight junction biology and kidney dysfunction. Am. J. Physiol. Renal Physiol. 2006;290:F20–F34. [PubMed]
18. Mostov K, Su T, ter Beest M. Polarized epithelial membrane traffic: Conservation and plasticity. Nat. Cell Biol. 2003;5:287–293. [PubMed]
19. Charron AJ, Nakamura S, Bacallao R, Wandinger-Ness A. Compromised cytoarchitecture and polarized trafficking in autosomal dominant polycystic kidney disease cells. J. Cell Biol. 2000;149:111–124. [PMC free article] [PubMed]
20. Bradford MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 1976;72:248–254. [PubMed]
21. Hale JE, Butler JP, Gelfanova V, You JS, Knierman MD. A simplified procedure for the reduction and alkylation of cysteine residues in proteins prior to proteolytic digestion and mass spectral analysis. Anal. Biochem. 2004;333:174–181. [PubMed]
22. Keller A, Nesvizhskii AI, Kolker E, Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 2002;74:5383–5392. [PubMed]
23. Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 2003;75:4646–4658. [PubMed]
24. Boyle EI, Weng S, Gollub J, Jin H, et al. GO:: Term-Finder – – open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics. 2004;20:3710–3715. [PMC free article] [PubMed]
25. Chen YY, Lin SY, Yeh YY, Hsiao HH, et al. A modified protein precipitation procedure for efficient removal of albumin from serum. Electrophoresis. 2005;26:2117–2127. [PubMed]
26. Schuchard MD, Melm CD, Crawford AS, Chapman HA, et al. One step depletion of twenty high abundance human plasma proteins and concomitant molecular size fractionation of low abundance proteins. Mol. Cell. Proteomics. 2006;5:S203–S203.
27. Sitnikov D, Chan D, Thibaudeau E, Pinard M, Hunter JM. Protein depletion from blood plasma using a volatile buffer. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2006;832:41–46. [PubMed]
28. Ye M, Grantham JJ. The secretion of fluid by renal cysts from patients with autosomal dominant polycystic kidney disease. N. Engl. J. Med. 1993;329:310–313. [PubMed]
29. Wang KL, Weinrach DM, Luan C, Han M, et al. Renal papillary adenoma – a putative precursor of papillary renal cell carcinoma. Hum. Pathol. 2007;38:239–246. [PubMed]
30. Ashburner M, Ball CA, Blake JA, Botstein D, et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 2000;25:25–29. [PMC free article] [PubMed]
31. Paul WE. Fundamental Immunology. Lippincott William & Wilkins; Philadelphia: 2003.
32. Bello-Reuss E, Holubec K, Rajaraman S. Angiogenesis in autosomal-dominant polycystic kidney disease. Kidney. Int. 2001;60:37–45. [PubMed]
33. Woo D. Apoptosis and loss of renal tissue in polycystic kidney diseases. N. Engl. J. Med. 1995;333:18–25. [PubMed]
34. Muchatuta M, Witzmann F, Gattone V, Blazer-Yost BL. Functional analysis of ion transport in liver cyst epithelia combined with proteomic analysis of cyst fluid isolated from the BALB/c-cpk/+ mouse model of polycystic kidney disease. FASEB J. 2007;21:A505–A505.
35. Chevalier RL. Obstructive nephropathy: Lessons from cystic kidney disease. Nephron. 2000;84:6–12. [PubMed]
36. Ricker JL, Gattone VH, II, Calvet JP, Rankin CA. Development of autosomal recessive polycystic kidney disease in BALB/c-cpk/cpk mice. J. Am. Soc. Nephrol. 2000;11:1837–1847. [PubMed]
37. Hidaka S, Kranzlin B, Gretz N, Witzgall R. Urinary clusterin levels in the rat correlate with the severity of tubular damage and may help to differentiate between glomerular and tubular injuries. Cell. Tissue Res. 2002;310:289–296. [PubMed]
38. Chauhan AK, Moore TL. Presence of plasma complement regulatory proteins clusterin (Apo J) and vitronectin (S40) on circulating immune complexes (CIC) Clin. Exp. Immunol. 2006;145:398–406. [PubMed]
39. Luke CJ, Pak SC, Askew YS, Naviglia TL, et al. An intracellular serpin regulates necrosis by inhibiting the induction and sequelae of lysosomal injury. Cell. 2007;130:1108–1119. [PMC free article] [PubMed]
40. Lapsley M, Sansom PA, Marlow CT, Flynn FV, Norden AG. Beta 2-glycoprotein-1 (apolipoprotein H) excretion in chronic renal tubular disorders: Comparison with other protein markers of tubular malfunction. J. Clin. Pathol. 1991;44:812–816. [PMC free article] [PubMed]
41. Zhou H, Pisitkun T, Aponte A, Yuen PS, et al. Exosomal Fetuin-A identified by proteomics: A novel urinary biomarker for detecting acute kidney injury. Kidney Int. 2006;70:1847–1857. [PMC free article] [PubMed]
42. Terkelsen OB, Jahnen-Dechent W, Nielsen H, Moos T, et al. Rat fetuin: Distribution of protein and mRNA in embryonic and neonatal rat tissues. Anat. Embryol. 1998;197:125–133. [PubMed]
43. Tolosano E, Altruda F. Hemopexin: Structure, function, and regulation. DNA Cell Biol. 2002;21:297–306. [PubMed]
44. Bakker WW, Borghuis T, Harmsen MC, van den Berg A, et al. Protease activity of plasma hemopexin. Kidney Int. 2005;68:603–610. [PubMed]
45. Mrug M, Zhou J, Woo Y, Cui X, et al. Overexpression of innate immune response genes in a model of recessive polycystic kidney disease. Kidney Int. 2008;73:63–76. [PubMed]
46. Pisitkun T, Shen RF, Knepper MA. Identification and proteomic profiling of exosomes in human urine. Proc. Natl. Acad. Sci. USA. 2004;101:13368–13373. [PubMed]
47. Quigley R, Baum M. Effects of epidermal growth factor and transforming growth factor-alpha on rabbit proximal tubule solute transport. Am. J. Physiol. 1994;266:F459–F465. [PubMed]
48. Gundry RL, Fu Q, Jelinek CA, Van Eyk JE, Cotter RJ. Investigation of an albumin-enriched fraction of human serum and its albuminome. Proteomics Clin. Appl. 2007;1:73–88. [PMC free article] [PubMed]
49. Gundry RL, Miller J, Van Eyk JE. The albuminome as a tool for biomarker discovery; application for differentiation of stable angina and myocardial infarction. Mol. Cell. Proteomics. 2006;5:S321–S321.