Article

Proteomic Analysis of Urinary Microvesicles and Exosomes in Medullary Sponge Kidney Disease and Autosomal Dominant Polycystic Kidney Disease

Maurizio Bruschi,1 Simona Granata,2 Laura Santucci,1 Giovanni Candiano,1 Antonia Fabris,2 Nadia Antonucci,2 Andrea Petretto,3 Martina Bartolucci,3 Genny Del Zotto ,4 Francesca Antonini,4 Gian Marco Ghiggeri ,5 Antonio Lupo,2 Giovanni Gambaro,6 and Gianluigi Zaza 2 1Division of Nephrology, Dialysis, Abstract and Transplantation, Background and objectives Microvesicles and exosomes are involved in the pathogenesis of autosomal dominant Laboratory of polycystic kidney disease. However, it is unclear whether they also contribute to medullary sponge kidney, a Molecular sporadic kidney malformation featuring cysts, nephrocalcinosis, and recurrent kidney stones. We addressed this Nephrology, 3Laboratory of Mass knowledge gap by comparative proteomic analysis. Spectrometry—Core Facilities, Design, setting, participants, & measurements The protein content of microvesicles and exosomes isolated from 4Department of the urine of 15 patients with medullary sponge kidney and 15 patients with autosomal dominant polycystic kidney Research and Diagnostics, and disease was determined by mass spectrometryfollowedby weightedgenecoexpression network analysis,support 5 fi Division of vector machine learning, and partial least squares discriminant analysis to compare the pro les and select the Nephrology, Dialysis most discriminative proteins. The proteomic data were verified by ELISA. and Transplantation, Istituto di Ricovero e Results A total of 2950 proteins were isolated from microvesicles and exosomes, including 1579 (54%) identified in Cura a Carattere fi Scientifico, Istituto all samples but only 178 (6%) and 88 (3%) speci c for medullary sponge kidney microvesicles and exosomes, and Giannina Gaslini, 183 (6%) and 98 (3%) specific for autosomal dominant polycystic kidney disease microvesicles and exosomes, Genoa, Italy; 2Renal respectively. The weighted gene coexpression network analysis revealed ten modules comprising proteins with Unit, Department of similar expression profiles. Support vector machine learning and partial least squares discriminant analysis Medicine, University identified 34 proteins that were highly discriminative between the diseases. Among these, CD133 was upregulated HospitalofVerona, Verona, Italy; and in exosomes from autosomal dominant polycystic kidney disease and validated by ELISA. 6Division of Nephrology and Conclusions Our data indicate a different proteomic profile of urinary microvesicles and exosomes in patients Dialysis, School of with medullary sponge kidney compared with patients with autosomal dominant polycystic kidney disease. The Medicine, Columbus- fi Gemelli University urine proteomic pro le of patients with autosomal dominant polycystic kidney disease was enriched of proteins Hospital Catholic involved in cell proliferation and matrix remodeling. Instead, proteins identified in patients with medullary University, Rome, Italy sponge kidney were associated with parenchymal calcium deposition/nephrolithiasis and systemic metabolic derangements associated with stones formation and bone mineralization defects. Correspondence: Prof. CJASN 14: 834–843, 2019. doi: https://doi.org/10.2215/CJN.12191018 Gianluigi Zaza, Renal Unit, Department of Medicine, University HospitalofVerona, Introduction The hypothesis that extracellular vesicles are present Piazzale A Stefani 1, fi 37126 Verona, Italy. Extracellular vesicles, such as microvesicles (diameter in human urine (8) was con rmed by the proteomic Email: gianluigi. of 100–1000 nm) and exosomes (diameter of 30–100 identification of membrane proteins in a pellet isolated [email protected] nm), are membrane-enclosed particles released by by the ultracentrifugation of urine samples (9). Such most cells under normal and pathologic conditions urinary extracellular vesicles contain cell-specific (1–5). Microvesicles are shed directly from the plasma marker proteins from every segment of the nephron membrane, whereas exosomes are formed by the (9,10), and they offer a source of potentially valuable fusion of intracellular multivesicular bodies (also urinary biomarkers (10). The intrinsic characteristics of known as late endosomes) with the plasma membrane, extracellular vesicles also suggest that they may play leading to the release of their vesicular contents into an important role in kidney development and kidney the extracellular space. These vesicles can mobilize a disease. Accordingly, extracellular vesicles seem to be large number of biologic factors, including receptors, involved in the mechanism of cystogenesis in autoso- other proteins, nucleic acids, and lipids, thus shuttling mal polycystic kidney disease, a common hereditary information to other cells (6). The transfer of RNA and kidney disorder with a prevalence of 0.1%–0.25%. miRNA can reprogram recipient cells and modify their Autosomal polycystic kidney disease gives rise to phenotype (7). predominantly kidney symptoms, including cysts that

834 Copyright © 2019 by the American Society of Nephrology www.cjasn.org Vol 14 June, 2019 CJASN 14: 834–843, June, 2019 Urinary Proteome Analysis Differentiated MSK versus ADPKD, Bruschi et al. 835

progressively disrupt the kidney parenchyma, leading to autosomal dominant polycystic kidney disease was de- interstitial fibrosis, cellular infiltration, and the loss of pendent on the revised Ravine criteria (20). The study functional nephrons. was carried out in accordance with the Declaration of The proteomic analysis of urinary exosome-like vesicles Helsinki and approved by the institutional ethical board (particularly those containing polycystin) revealed ap- of the University Hospital of Verona (Verona, Italy; proximately 500 autosomal dominant polycystic kidney code 1312CESC) and the Independent Ethics Committee disease–associated proteins, many with signaling func- (Comitato Etico Regione Liguria) on October 14, 2014 tions (11). Furthermore, the quantitative proteomic analysis (study number 408REG2014). of urinary extracellular vesicles from patients affected by a complete spectrum of chronic kidney functional damage Isolation of Microvesicles and Exosomes highlighted 30 proteins strongly associated with the autoso- Second morning urine samples were obtained from mal dominant polycystic kidney disease phenotype, includ- patients and healthy donors. Extracellular vesicles were ing periplakin, envoplakin, villin-1, and complement C3 (12). isolated by centrifugation. Briefly, aliquots of 16 ml were In contrast to the wealth of information available for centrifuged at 16,0003g for 30 minutes at 16°C to remove autosomal dominant polycystic kidney disease, little is cells, debris, and organelles, such as mitochondria. To known about the role of extracellular vesicles in the onset obtain the microvesicle fraction, the supernatant was of medullary sponge kidney, a sporadic cystic kidney centrifuged at 22,0003g for 120 minutes at 16°C (21). The malformation that involves nephrocalcinosis and recurrent microvesicle pellet was rinsed in PBS and centrifuged again kidney stones (13). The detailed analysis of extracellular at 22,0003g; this rinse/centrifugation cycle was carried out vesicles could provide insight into the pathogenesis of this five times in total to obtain a clean microvesicle fraction. The rare disease. Despite sporadic genetic associations (14,15) 3g – supernatant was then centrifuged at 100,000 for 120 and the dysregulation of a few biologic factors (16 18), the minutes at 16°C to pellet the exosomes. The pellet was systemic and kidney biologic/cellular network underlying resuspended in 1 ml 0.25 M sucrose, loaded on a 1-ml this disease is poorly characterized, and its relationship with 30% sucrose cushion, and centrifuged at 100,0003g for other cystic diseases is unclear. 120 minutes at 16°C. The pellet was rinsed in PBS and To address this knowledge gap, we carried out a com- centrifuged again at 100,0003g for 10 minutes at 4°C, and prehensive comparative proteomic analysis of urinary this rinse/centrifugation cycle was carried out five times microvesicles and exosomes to identify differences between in total to obtain a clean exosome fraction. For each assay, medullary sponge kidney and autosomal dominant poly- we have performed the same purification procedure. Each cystic kidney disease in terms of the mechanism of cysto- pellet fraction was stored at 280°C until use. The size and genesis and identify putative diagnostic biomarkers that purity of microvesicles and exosomes isolated by ultracen- distinguish these diseases. In fact, at the moment, no trifugation were confirmed by dynamic light scattering, diagnostic biomarkers are available for both diseases. whereas the antigen profile of exosomes and microvesicles Although some urinary biomarkers for autosomal dominant was performed by Western blot as described in Supple- polycystic kidney disease (NGAL, M-CSF, and MCP-1) (19) mental Material. have been proposed, none of them have been used in clinical practice (19). Additionally, most of them are only effective in the advanced stage of the disease. Identification of Mass Spectrometry both diseases at early stages could help clinicians start The samples were processed by the in-StageTip method prevention, diet adjustment, and for selected patients, with two poly(styrene divinylbenzene) reverse phase m pharmacologic treatment. Finally, they could potentiate sulfonate disks (22). Each pellet was solubilized in 25 l diagnostic accuracy for medullary sponge kidney (this 2% sodium deoxycholate, 10 mM Tris(2-carboxyethyl) disease is often undiagnosed and confused with other cause phosphine, 40 mM chloroacetamide, and 100 mM Tris of nephrocalcinosis or papillary ductal plugging), minimize (pH 8.5). Microvesicles or exosomes were lysed, reduced, patients’ radiation and/or nephrotoxic contrast media expo- and alkylated in a single step, and then, they were loaded sure from medical imaging (e.g., intravenous urography and into the StageTip. The lysates were diluted with 25 mM Tris m CT urography), and reduce underdiagnosis of noncontrast (pH 8.5) containing 1 g of trypsin. The samples were fi m fl CT scans. acidi ed with 100 l 1% (vol/vol) tri uoroacetic acid and washed three times with 0.2% (vol/vol) trifluoroacetic acid. The proteins were eluted in 60 ml 5% (vol/vol) ammonium Materials and Methods hydroxide containing 80% (vol/vol) acetonitrile. Detailed Patients descriptions of mass spectrometry instrumentation, data The study included 15 adult patients with autosomal analysis, and biologic validation with homemade ELISA are dominant polycystic kidney disease and 15 adult patients reported in Supplemental Material. with medullary sponge kidney matched for age, sex, and geographical origin as well as a cohort of 17 healthy donors Statistical Analyses matched for age and sex (Table 1, Supplemental Figure 1). After normalization using the Normalyzer R-package The patients were followed up by the Renal Unit at the with the LOESS-G method (23), mass spectrometry data Department of Medicine, University Hospital of Verona were analyzed by unsupervised hierarchical clustering (Verona, Italy), and they were enrolled after providing using multidimensional scaling with k means and Spear- informed consent. Medullary sponge kidney diagnosis was man correlation to identify outliers and the dissimilar- performed as previously reported (15). The diagnosis of ity between samples. The normalized expression profiles 836 CJASN

Table 1. Baseline characteristics of the study participants

Medullary Sponge Autosomal Dominant Polycystic Variable Healthy Controlsa Kidney Kidney Disease

Age, yr 266426652768 Sex (men/women) 6/9 7/8 8/9 eGFR, ml/min per 1.73 m2 132615 133612 13968 Plasma calcium, mg/dl 9.560.3 9.460.4 9.460.3 Plasma phosphate, mg/dl 3.160.5 2.960.5 2.860.5 Plasma sodium, mmol/L 140621396213864 Plasma potassium, mmol/L 3.860.6 3.960.2 3.960.1 Proteinuria, g/24 h 0.0860.06 0.0760.07 0.0460.09 Urine volume, ml/d 17866212 17506581 17426526 Systolic BP, mm Hg 119641186711765 Diastolic BP, mm Hg 746576667564

Values are expressed as mean6SD. P values were determined by ANOVA except for sex, which was determined by Fisher exact test. aIncluded only in the flow cytometry analysis.

of the proteins were then used to construct the coexpression Furthermore, about 40% of the proteins found in extra- network using the weighted gene coexpression network cellular vesicles were associated with one or both kidney analysis package in R (24). Additionally, to identify the hub diseases: 95% were found in the medullary sponge kidney proteins of modules that maximize the discrimination be- samples, and 100% were found in the autosomal dominant tween the selected clinical traits, we applied a nonparametric polycystic kidney disease samples (Figure 1, B and C). The Mann–Whitney U test, machine learning methods (such as cellular origins of the proteins in the exosomes were very nonlinear support vector machine learning), and partial similar in the medullary sponge kidney and autosomal least squares discriminant analysis. A complete and de- dominant polycystic kidney disease samples, with 18% of tailed description of the data analysis has been reported in proteins originating from membranes, 32% originating from Supplemental Material. the cytoplasm, 10% originating from the nucleus, and 39% originating from other organelles (Supplemental Figure 3). Similar results were observed for the microvesicle proteins, Results with 34% originating from membranes, 26% originating Characterization of Exosomes and Microvesicles from the cytoplasm, 8% originating from the nucleus, and The size and purity of microvesicles and exosomes 32% originating from other organelles. fi isolated by ultracentrifugation were con rmed by dynamic The significant overlap among the groups of proteins fi light scattering, revealing a Gaussian distribution pro le foundineachsamplewasconfirmed by constructing a 6 6 with peak means at 1000 65 or 90 5 nm, respectively, the two-dimensional scatter plot of the multidimensional typical sizes for microvesicles or exosomes, respectively scaling analysis (Supplemental Figure 4). No samples were (Supplemental Figure 2, A and B). There was no difference excluded during the quality check performed by nonhierar- in size between the microvesicles and exosomes isolated chical clustering (Supplemental Figure 5). We used weighted from patients with medullary sponge kidney and patients gene coexpression network analysis to identify proteins with autosomal dominant polycystic kidney disease. associated with each type of extracellular vesicle and disease, Western blot analysis revealed that the exosomes were revealing a total of ten modules comprising proteins with positive for CD63 and CD81 but not CD45, whereas the similar expression profiles. To distinguish between mod- fi microvesicles showed the opposite antigen pro le (Sup- ules, we chose an arbitrary color for each module (Figure plemental Figure 2C). 2A). The number of proteins included in each module ranged from 44 (gray) to 930 (turquoise). The gray, brown, Protein Composition of Exosomes and Microvesicles pink, and blue modules showed closer relationships with The protein composition of exosomes and microvesicles the medullary sponge kidney, autosomal dominant poly- from the urine of patients with medullary sponge kidney cystic kidney disease, microvesicle, and exosome groups, and patients with autosomal dominant polycystic kidney respectively (Figure 2B). disease was determined by mass spectrometry. We iden- Next, we applied the Mann–Whitney U test to identify tified 2950 proteins in total, 1579 (54%) of which were the proteins that best distinguish the type of disease in the present in all four sample types. Among the medullary microvesicles or exosomes (Figure 3, A and B) and the type sponge kidney samples, only 178 (6%) and 88 (3%) proteins of extracellular vesicle in the medullary sponge kidney or were exclusively found in the exosomes and microvesicles, autosomal dominant polycystic kidney disease samples respectively. Similarly, among the autosomal dominant (Figure 3, C and D). This revealed a total of 255 discrim- polycystic kidney disease samples, only 183 (6%) and 98 inatory proteins, 50 that distinguished between medullary (3%) proteins were exclusively found in the exosomes and sponge kidney and autosomal dominant polycystic kidney microvesicles, respectively (Figure 1A); .60% of all of the disease microvesicles, 90 that distinguished between med- extracellular vesicle proteins that we identified were present ullary sponge kidney and autosomal dominant polycystic in exosomes, and .80% were present in microvesicles. kidney disease exosomes, 150 that distinguished between CJASN 14: 834–843, June, 2019 Urinary Proteome Analysis Differentiated MSK versus ADPKD, Bruschi et al. 837

A ADPKDEx MSKMv (2375) (2020)

183 88 (6.2%) (3%) 32 145 (1.1%) 48 (4.9%) (1.6%) 178 88 74 98 (6%) (3%) (2%) (3.3%)

1579 59 (53.5%) 82 (2%) (2.8%) 52 189 (1.8%) (6.4%) 55 (1.9%) MSKEx ADPKDMv (2345) (2177)

B C Exocarta Exocarta (1649) Associated to (1649) Associated to Kidney diseases Kidney diseases Associated to (1172) (1172) MSKMv Associated to ADPKDMv MSK (22) (2020) PKD (112) (2177)

153 134 3 Vesiclepedis 32 Vesiclepedis (1948) 2 (1948) 1 29 21 27 19 21 9 35 16 40 36 11 2 10 1 36 16 1 2 1 2 2 17 5 100 35 79 3 822 88 24 23 50 795 105 33 71 518 89 45 546 110

2 1 4 8 3 5 9

336 6 7 225 289 87 244

MSKEx ADPKDEx (2345) (2372)

Figure 1. | Venn diagram of total proteins detected in exosomes and microvesicles from the urine of patients with medullary sponge kidney (MSK) and patientswith autosomal dominantpolycystickidney disease (ADPKD)identified by massspectrometry.(A) TheVenndiagramshows common and exclusive proteins in MSK and ADPKD. The numbers represent the distinct proteins in the overlapping and nonoverlapping areas. (B and C) The numbers represent the distinct proteins in the overlapping and nonoverlapping areas. The data were extracted from the Exocarta, Vesiclepedia, UniProt, Open Target, DisGeNET, and Atlas databases. The majority of the proteins identified in extracellular vesicles correspond to proteins already described as components of exosomes or microvesicles or associated with kidney disease (about 40%). We found that 95% and 100% of the proteins were associated with MSK and ADPKD, respectively. PKD, polycystic kidney disease. exosomes and microvesicles in the autosomal dominant corresponding expression profiles (Figure 4A) and polycystic kidney disease samples, and 62 that distin- prepared a graphical representation for their cluster guished between exosomes and microvesicles in the med- separation (Figure 4B). ullary sponge kidney samples (Supplemental Table 1, The diversity of expression profiles among the proteins Supplemental Figures 6 and 7). Support vector machine in this core panel indicated their association with different learning and partial least squares discriminant analysis functions, and therefore, GO analysis of functional anno- were then used to highlight the proteins that maximize the tations was used to build a scatter plot of enriched gene – discrimination between different sample types, revealing signatures on the y axis and log10 P values on the x axis a core panel of 34 proteins that allowed us to distinguish (Supplemental Figure 8). The size of scatters is proportional to the four conditions with an accuracy of 100% (Figure 4, A the number of proteins associated with each biologic pro- and B). After Z-score analysis, we built a heat map of the cess. After Z-score analysis, we built a heat map showing the 838 CJASN

A B 1.0 MEyellow -0.067 0.067 -0.081 0.081 (0.6) (0.6) (0.5) (0.5) 1

MEblue -0.51 0.51 -0.5 0.5 0.8 (4e-05) (4e-05) (4e-05) (4e-05)

MEblack -0.18 0.18 0.3 -0.3 (0.2) (0.2) (0.02) (0.02) 0.5 0.6 MEgreen -0.19 0.19 0.02 -0.02 (0.1) (0.1) (0.9) (0.9) Height -0.29 0.29 0.16 -0.16 MEturquoise 0.4 (0.03) (0.03) (0.2) (0.2) 0 -0.15 0.15 0.8 -0.8 MEpink (0.2) (0.2) (1e-14) (1e-14) 0.2 -0.42 0.46 0.55 -0.55 MEred (7e-04) (2e-04) (6e-06) (6e-06)

-0.55 0.55 0.18 -0.18 -0.5 MEbrown (6e-06) (6e-06) (0.2) (0.2) Dynamic Tree Cut: -0.51 0.51 -0.43 0.43 MEmagenta (4e-05) (4e-05) (6e-04) (6e-04)

Merged 0.55 -0.55 -0.29 0.29 MEgray -1 Dynamic: (6e-06) (6e-06) (0.02) (0.02)

MSK ADPKD Mv Ex

Figure 2. | Module identification and clinical trait relationship. (A) Dendrogram of all proteins identified in the extracellular vesicles of patients with medullary sponge kidney (MSK) and patients with autosomal dominant polycystic kidney disease (ADPKD) clustered on the basis of a dissimilarity measure with topological overlap matrix (TOM) (1-TOM). (B) Heat map of the relationships between module eigengenes and the trait indicator of samples. Module-trait weighted relationships and their P values (in parentheses) between the identified modules and trait indicator. The color scale on the right shows module-trait relationship from 21 (blue) to one (red), where blue represents a perfect negative correlation and red represents a perfect positive correlation. Mv, microvesicles; Ex, exosomes; ME, module eigengenes. expression profiles of the enriched biochemical pathways ROC analysis were 0.98 (95% CI, 0.94 to 1) and P,0.001 (Figure 4C). Interestingly, this revealed that proteins in- (patients with autosomal dominant polycystic kidney dis- volved in cell migration/adhesion were over-represented in ease versus healthy controls), 0.82 (95% CI, 0.67 to 0.97) and the microvesicles of patients with polycystic kidney disease, P=0.003 (patients with autosomal dominant polycystic whereas those involved in the regulation of the epithelial kidney disease versus patients with medullary sponge cell differentiation were over-represented in the exosomes kidney), and 0.70 (95% CI, 0.51 to 0.89) and P=0.05 (patients of patients with autosomal dominant polycystic kidney with medullary sponge kidney versus healthy controls) disease. (Figure 4E). The cutoff, sensitivity, specificity, and likelihood ratio are reported in Supplemental Table 2. ELISA for CD133 in Exosomes-Validated Proteomics A homemade ELISA for urinary CD133 was performed in exosomes from all patients and healthy controls to Discussion validate proteomic data. We found that CD133 was highly Microvesicles and exosomes are known to be involved in expressed in patients with autosomal dominant polycystic the pathogenesis of several chronic kidney disorders, but kidney disease compared with patients with medullary few studies have focused on their role in kidney cystic sponge kidney and healthy controls (Figure 4D). The diseases (9,11,25,26), and their potential involvement in medians (interquartile ranges [IQRs]) were 1.04 (IQR, medullary sponge kidney disease has not been addressed. 0.54–1.68), 0.4 (IQR, 0.22–0.76), and 0.28 (IQR, 0.16–0.34) In this study, we used mass spectrometry to identify the for patients with autosomal dominant polycystic kidney protein content of microvesicles and exosomes to gain disease, patients with medullary sponge kidney, and insight into medullary sponge kidney–related cystogenesis healthy controls, respectively, and P values were P,0.001 and its similarities and differences compared with autoso- for Kruskal–Wallis test analysis. Also, ROC analysis re- mal dominant polycystic kidney disease. By applying a vealed that the expression of CD133 in urinary exosomes layered statistical analysis approach, we found 34 core can discriminate patients with autosomal dominant poly- proteins that distinguished the microvesicles and exo- cystic kidney disease from healthy subjects and patients somes of medullary sponge kidney and autosomal dom- with medullary sponge kidney. The areas under the curve, inant polycystic kidney disease. Interestingly, most of the 95% confidence intervals (95% CIs), and P values of these proteins were assigned to a small number of specific CJASN 14: 834–843, June, 2019 Urinary Proteome Analysis Differentiated MSK versus ADPKD, Bruschi et al. 839

A B MSKMv ADPKDMv MSKEx ADPKDEx 16 10

14 8 12

10 6 P-value 8 P-value 10 10 6 4 -Log -Log 4 2 2

0 0 -9 -6 -3 0369 -9-6-30369

Log2 Fold Change Log2 Fold Change

C D ADPKDMv ADPKDEx MSKMv MSKEx 18 14 16 12 14 10 12 10 8 P-value P-value 10 8 10 6 -Log 6 -Log 4 4 2 2 0 0 -9 -6 -3 0 3 6 9 -9 -6 -3 0369

Log2 Fold Change Log2 Fold Change

Figure 3. | Volcano plots of univariate statistical analysis as applied to urinary extracellular vesicle samples. The plots are on the basis of the 2 fold change (log2) and the P-value ( log10) of all proteins identified in (A) Mv from MSK and ADPKD; (B) Ex from MSK and ADPKD; (C) Mv and Ex from ADPKD; (D) Mv and Ex from MSK. Red circles indicate proteins related to the selected clinical trait with statistically significant changes between the clinical traits selected in this study. ADPKD, autosomal dominant polycystic kidney disease; MSK, medullary sponge kidney; Mv, microvesicles; Ex, exosomes. functions, including the regulation of epithelial cell dif- confirm this hypothesis. Accordingly, the kidney pro- ferentiation, kidney development, cell migration, cell genitor cells in human kidney papillary loops of Henle can adhesion, carbohydrate metabolism, and extracellular differentiate into both neural-like and epithelial-like line- matrix organization. ages as well as producing tubules (30). An abundant One of the core proteins was prominin 1 (CD133), a population of CD133+ cells was also shown to be present pentaspan transmembrane glycoprotein that localizes to in the cystic wall and kidney tubules of patients with membrane protrusions and is often expressed on adult autosomal dominant polycystic kidney disease (31). The stem/progenitor kidney cells, where it is thought to role of these cells is not yet clear, but it would be maintain stem cell properties by suppressing differenti- interesting to evaluate more patients with autosomal ation. The high-level expression of prominin 1 is associ- dominant polycystic kidney disease at different disease ated with several types of cancer (27–29). This protein stages (from asymptomatic to the late disease stage) and was more abundant in the exosomes of patients with clarify whether CD133+ (and CD24+) cells are associated autosomal dominant polycystic kidney disease, reflecting with a better or worse prognosis. the attempted tissue repair in response to the aberrant rate of We also found that the cellular repressor of E1A stimulated proliferation and apoptosis, which would require kidney genes 1 (CREG1), a factor that interacts with the IGF2 progenitor cells. The upregulation of other proteins in- receptor to regulate cell growth, was more abundant in volved in cell migration/adhesion, such as Cadherin 4, or autosomal dominant polycystic kidney disease. This protein the epithelial cell differentiation, such as CREG1, seems to may facilitate stem cell differentiation and activity, which 840 CJASN

A C MSK ADPKD ADPKD MSK ADPKD ADPKD MSK Mv – + MSK

–+ Ex Ex Mv Mv Mv Ex Ex

CARMIL3 ZSCAN32 Regulation of cell adhesion SPP1 OLR1 EEF1G MATN2 Regulation of cell migration SEMG2 PRG2 GUCA2B DPT Cell adhesion ENOSF1 CLSTN3 DAG1 Regulation of epithelial VPS4A cell differentiation involved PROM1 CREG1 in kidney development ANKFY1 FLRT3 Extracellular matrix B3GNT8 organization DNAJB6 COL14A1 VWA7 Carbohydrate metabolism ANKRD18B NAAA CPAMD8 FAT4 MAN2A2 ZFHX3 LRRC40 MAL ITIH5 PAM TSPAN9 hPEPT1-RF

B D E 20 2.5 100 MSKEx ADPKD 15 Ex MSKMv 2.0 80 ADPKD 10 Mv

5 1.5 60

0 1.0 40 -5 Sensitivity (%) CTR vs MSK Component 2 (19.5%)

Optical Density (RU/ml) AUC=0.70 P=0.047 0.5 -10 CTR vs ADPKD 20 AUC=0.98 P<0.0001 -15 0.0 MSK vs ADPKD AUC=0.82 P=0.003 -20 0 -20 -15 -10 -5 0 5 10 15 20 25 CTR MSK ADPKD 0 20 40 60 80 100 Component 1 (27%) 100-Specificity (%)

Figure 4. | Proteins and gene ontology annotation that maximize the discrimination among all conditions. (A) Heat map of 34 core proteins identified through the combined use of univariate statistical analysis, support vector machine learning, and partial least squares discriminant analysis. In the heat map, each row represents a protein, and eachcolumn corresponds to a condition. Normalized Z scores of protein abundance are depicted by a pseudocolor scale, with red indicating positive expression, white indicating equal expression, and blue indicating negative expression compared with each protein value, whereas the dendrogram displays the outcome of unsupervised hierarchical clustering analysis, placing similar proteome profile values near to each other. (B) Two-dimensional scatter plot of multidimensional scaling analysis of exosome (solid symbols) and microvesicle (open symbols) of medullary sponge kidney (MSK; red triangles) and autosomal dominant polycystic kidney disease (ADPKD; black squares) samples using the above 34 highlighted proteins. Ellipses indicates 95% confidence intervals. Visual inspection of the dendrogram and heat map shows the ability of these proteins to clearly distinguish between the different conditions. (C) The heat map shows biologic process enrichment for different extracellular vesicle samples. In the heat map, each row represents a protein, and each column Cont. CJASN 14: 834–843, June, 2019 Urinary Proteome Analysis Differentiated MSK versus ADPKD, Bruschi et al. 841

was recently shown for the differentiation of embryonic example was SPP1 (osteopontin), a protein implicated in stem cells in cardiomyocytes, improving the integration of nephrolithiasis, a major clinical condition associated with stem cell–derived cardiomyocytes into recipient hearts (32). medullary sponge kidney (13). Osteopontin is intimately The exosomes sourced from our patients with autosomal involved in the regulation of both physiologic and path- dominant polycystic kidney disease not only contained ologic mineralization. In normal bone tissue, osteopontin higher levels of the proliferation regulator CREG1 but also, is expressed by osteoclasts and osteoblasts during bone proteins required for matrix remodeling (ITIH5) and the remodeling, and osteoclast-derived osteopontin inhibits regulation of salt secretion (GUCA2B or MAL). All of these the formation of hydroxyapatite during normal minerali- mechanisms are important for cyst formation and enlarge- zation (40). Osteopontin is also involved in kidney stone ment, which in autosomal dominant polycystic kidney formation (41). This protein is synthesized in the kidney disease, involve tubular cell proliferation, abnormalities in and secreted into the urine by epithelial cells, including the the extracellular matrix, and transepithelial fluid secretion loop of Henle, distal convoluted tubule, and papillary directed toward the cyst lumen. Because cysts are anatom- epithelium (42), inhibiting the nucleation, growth, and ically separated from their source tubule (33), the intracystic aggregation of calcium oxalate crystals (43) and the binding fluid does not originate from the glomerular filtrate, but of calcium oxalate crystals to kidney epithelial cells (44). rather, it originates from transepithelial fluid secretion (34). Osteopontin knockout mice are hyperoxaluric, leading to Autosomal dominant polycystic kidney disease is also the significant intratubular deposition of calcium oxalate, characterized by the disruption of the planar cell polarity whereas wild-type mice remove calcium oxalate effectively pathway, which is required for oriented cell division and (45). Therefore, the greater abundance of osteopontin in convergent extension to establish and maintain the struc- the microvesicles of our patients with medullary sponge ture of kidney tubules (35). We found that the FAT Atypical kidney could represent a defense mechanism against Cadherin 4 protein was more abundant in the exosomes of microcalcification, and it could, at least partially, explain patients with autosomal dominant polycystic kidney dis- the bone symptoms often observed in patients with this ease. The loss of this protein disrupts oriented cell division disease. Accordingly, 58% of patients with medullary and tubule elongation during kidney development, causing sponge kidney have a dual-energy x-ray absorptiometry tubule dilation (36). profile of osteopenia, and 14% have a profile of osteoporosis Notably, none of the proteins discussed above were unrelated to the common causes of bone demineralization, upregulated in medullary sponge kidney, showing a dif- particularly hyperparathyroidism and menopause (46). ferent mechanism of cystogenesis. The specificdiagnosisof Taken together, our results have shown for the first time medullary sponge kidney requires the anatomic feature of that the urinary microvesicles and exosomes of patients papillary precalyceal ectasias, sometimes associated with with autosomal dominant polycystic kidney disease and tiny medullary cysts, and such alterations can be unilateral patients with medullary sponge kidney have distinct or even limited to a portion of a single kidney medulla. proteomic profiles. The urine of patients with autosomal Unlike autosomal dominant polycystic kidney disease, the dominant polycystic kidney disease was enriched for tubular dilations and microcysts tend to be stable in terms proteins involved in cell proliferation and matrix remodeling, of size throughout life as if they formed at the same time as probably due to pathologic tissue remodeling prompting the kidneys. Taken together, our data confirmed earlier cystic development and enlargement. In contrast, the urine of reports indicating that medullary sponge kidney is an inborn patients with medullary sponge kidney revealed a proteome malformation similar to developmental disorders, such as indicative of a systemic biochemical imbalance that could congenital hemihypertrophy and Beckwith–Wiedemann syn- explain the predisposition of such patients to parenchy- drome, and kidney developmental anomalies, such as horse- mal calcium deposition/nephrolithiasis and extrarenal shoe kidney, unilateral kidney aplasia, and contralateral complications, including bone mineralization defects. congenital small kidney (37,38), with the absence of the Although small sample size and lack of independent sequence of events leading to cyst formation. Additionally, replication are major weaknesses of the study and addi- our data showing the abundance of proteins involved in cell tional research is required for validation, some of the proliferation and extracellular matrix remodeling in patients proteins (mainly CD133) that we identified could be suitable with autosomal dominant polycystic kidney disease could in in the future as diagnostic biomarkers that could help part explain why these patients are predisposed to the clinicians to distinguish between patients with medullary development of cancer, particularly kidney carcinoma (39). sponge kidney and patients with autosomal dominant poly- In contrast, only a few proteins were highly expressed in cystic kidney disease during the early stages of the disease, medullary sponge kidney, mainly in the microvesicles. One avoiding time-consuming and expensive clinical testing.

Figure 4. | Continued. corresponds to a condition. Normalized Z scores of protein abundance are depicted by a pseudocolor scale, with red indicating positive expression, white indicating equal expression, and blue indicating negative expression compared with each protein value, whereas the dendrogram displays the outcome of unsupervised hierarchical clustering analysis, placing similar proteome profile values near to each other. Visual inspection of the dendrogram and heat map shows the ability of these biologic processes to distinguish between the different types of extracellular vesicle in the MSK and ADPKD samples. (D) ELISA for CD133. Box plot showing the median and interquartile range value of the urinary exosome CD133 in all samples. CD133 was highly expressed in patients with ADPKD compared with patients with MSK and healthy controls. (E) ROC curve analysis revealed that the expression of CD133 in urinary exosomes can discriminate patients with ADPKD from healthy controls and patients with MSK. AUC, area under the curve. ROC, Receiver Operating Characteristic; Mv, microvesicles; Ex, exosomes; CTR, healthy controls. 842 CJASN

Acknowledgments 7. Valadi H, Ekstro¨m K, Bossios A, Sjo¨strand M, Lee JJ, Lo¨tvall JO: This study was performed (in part) in the Laboratorio Uni- Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol 9: versitariodiRicercaMedicaResearchCenter,UniversityofVerona. 654–659, 2007 This study was supported by Fondazione Cariverona call 2016 8. Thongboonkerd V, McLeish KR, Arthur JM, Klein JB: Proteomic (Principal Investigator Prof. Tagliaro) and Ministero Della Salute analysis of normal human urinary proteins isolated by acetone grant GR-2011-02350438. precipitation or ultracentrifugation. Kidney Int 62: 1461–1469, 2002 Disclosures 9. Pisitkun T, Shen RF, Knepper MA: Identification and proteomic profiling of exosomes in human urine. Proc Natl Acad Sci U S A Dr. Antonini, Dr. Antonucci, Dr. Bartolucci, Dr. Bruschi, Dr. 101: 13368–13373, 2004 Candiano, Dr. Del Zotto, Dr. Fabris, Dr. Gambaro, Dr. Granata, Dr. 10. Moon PG, You S, Lee JE, Hwang D, Baek MC: Urinary exosomes Ghiggeri, Dr. Lupo, Dr. Petretto, Dr. Santucci, and Dr. Zaza have and proteomics. Mass Spectrom Rev 30: 1185–1202, 2011 nothing to disclose. 11. Hogan MC, Manganelli L, Woollard JR, Masyuk AI, Masyuk TV, Tammachote R, Huang BQ, Leontovich AA, Beito TG, Madden BJ, Supplemental Material Charlesworth MC, Torres VE, LaRusso NF, Harris PC, Ward CJ: This article contains the following supplemental material online at Characterization of PKD protein-positive exosome-like vesicles. http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/ J Am Soc Nephrol 20: 278–288, 2009 CJN.12191018/-/DCSupplemental. 12. Salih M, Demmers JA, Bezstarosti K, Leonhard WN, Losekoot M, vanKooten C, Gansevoort RT,Peters DJ, Zietse R, Hoorn EJ; DIPAK Supplemental Material. Methods. Consortium: Proteomics of urinary vesicles links plakins and Supplemental Figure 1. Age and eGFR of all study participants complement to polycystic kidney disease. J Am Soc Nephrol 27: included in the study. 3079–3092, 2016 SupplementalFigure2. Characterizationof isolated exosomesand 13. Gambaro G, Danza FM, Fabris A: Medullary sponge kidney. Curr Opin Nephrol Hypertens 22: 421–426, 2013 microvesicles. 14. Fabris A, Lupo A, Ferraro PM, Anglani F, Pei Y, Danza FM, Supplemental Figure 3. Gene Ontology annotation of urinary Gambaro G: Familial clustering of medullary sponge kidney is extracellular vesicle proteins. autosomal dominant with reduced penetrance and variable ex- Supplemental Figure 4. Multidimensional scaling analysis of pressivity. Kidney Int 83: 272–277, 2013 extracellular vesicles from the urine of patients with medullary 15. Torregrossa R, Anglani F, Fabris A, Gozzini A, Tanini A, Del Prete D, Cristofaro R, Artifoni L, Abaterusso C, Marchionna N, Lupo A, sponge kidney (MSK) and patients with autosomal dominant D’Angelo A, Gambaro G: Identification of GDNF gene sequence polycystic kidney disease (ADPKD). variations in patients with medullary sponge kidney disease. Clin J Supplemental Figure 5. Sample clustering and trait indicators. Am Soc Nephrol 5: 1205–1210, 2010 Supplemental Figure 6. Venn diagram of statistically significant 16. Fabris A, Bruschi M, Santucci L, Candiano G, Granata S, Dalla Gassa A, Antonucci N, Petretto A, Ghiggeri GM, Gambaro G, differences in protein abundance in the different types of extra- Lupo A, Zaza G: Proteomic-based research strategy identified cellular vesicles from patients with medullary sponge kidney (MSK) laminin subunit alpha 2 as a potential urinary-specific biomarker or patients with autosomal dominant polycystic kidney disease for the medullary sponge kidney disease. Kidney Int 91: 459–468, (ADPKD). 2017 Supplemental Figure 7. Proteins network interaction. 17. Ria P,Fabris A, Dalla Gassa A, Zaza G, Lupo A, Gambaro G: New non-renal congenital disorders associated with medullary sponge Supplemental Figure 8. Gene ontology enrichment analysis for kidney (MSK) support the pathogenic role of GDNF and point to core discriminatory proteins in the extracellular vesicles of patients the diagnosis of MSK in recurrent stone formers. 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Proteomic analysis of urinary microvesicles and exosomes in medullary sponge

kidney disease and autosomal dominant polycystic kidney disease

Maurizio Bruschi1*, Simona Granata2*, Laura Santucci1*, Giovanni Candiano1, Antonia

Fabris2, Nadia Antonucci2, Andrea Petretto3, Martina Bartolucci3, Genny Del Zotto3,

Francesca Antonini3, Gian Marco Ghiggeri4, Antonio Lupo2, Giovanni Gambaro2, Gianluigi

Zaza2

1 Laboratory on Molecular Nephrology, IRCCS Istituto Giannina Gaslini, Genoa, Italy. 2 Renal Unit, Department of Medicine, University-Hospital of Verona, Italy 3 Core Facilities, IRCCS Istituto Giannina Gaslini, Genoa, Italy. 4 Division of Nephrology, Dialysis and Transplantation, IRCCS Istituto Giannina Gaslini, Genoa, Italy.

1 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Table of contents for supplemental materials

Supplemental methods

Supplemental Figure 1. Age and eGFR of all study partecipants included in the study.

Supplemental Figure 2. Characterization of isolated exosomes and microvesicles

Supplemental Figure 3. Gene Ontology annotation of urinary extracellular vesicle proteins.

Supplemental Figure 4. Multidimensional scaling analysis of extracellular vesicles from the urine of medullary sponge kidney (MSK) and autosomal dominant polycystic kidney disease (ADPKD) patients.

Supplemental Figure 5. Sample clustering and trait indicators.

Supplemental Figure 6. Venn diagram of statistically significant differences in protein abundance in the different types of extracellular vesicles from medullary sponge kidney

(MSK) or autosomal dominant polycystic kidney disease (ADPKD) patients.

Supplemental Figure 7. Proteins network interaction.

Supplemental Figure 8. Gene Ontology enrichment analysis for core discriminatory proteins in the extracellular vesicles of medullary sponge kidney (MSK) and autosomal dominant polycystic kidney disease (ADPKD) patients.

Supplemental Table 1. List of all significant proteins identified using mass spectrometry

Supplemental Table 2. ELISA cutoff, sensitivity, specificity and likelihood ratio.

References for supplemental materials

2 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental methods

Mass spectrometry: Instrumentation

The desalted peptides were dried by speed vacuum and resuspended in 2% acetonitrile containing 0.2% formic acid (FA). They were separated on a 50-cm reversed-phase Easy

Spray column (75-μm internal diameter × 50 cm; 2 μm/100 Å C18) on an Ultimate 3000

RSLCnano system (Thermo Fisher Scientific, Waltham, MA, USA) with a binary buffer system comprising buffer A (0.1% FA) and buffer B (80% acetonitrile, 5% dimethylsulfoxide, 0.1% FA). The program comprised a 70-min gradient (2–45% buffer B) at a flow rate of 250 nl per min, with the column temperature maintained at 60°C. The chromatography system was coupled to an Orbitrap Fusion Tribrid mass spectrometer

(Thermo Fisher Scientific), acquiring data in Charge Ordered Parallel Ion aNalysis

(CHOPIN) mode [1]. The precursors were ionized using an EASY-spray source held at

+2.2 kV and the inlet capillary temperature was held at 300°C. Single MS survey scans were performed over the mass window 375–1500 m/z with an AGC target of 250,000, a maximum injection time of 50 ms, and a resolution of 120,000 at 200 m/z. Monoisotopic precursor selection was enabled for peptide isotopic distributions, precursors of z = 2-5 were selected for 2 s of cycle time, and dynamic exclusion was set to 25 s with a ±10 ppm window set around the precursor. The following CHOPIN conditions were applied: a) if the precursor charge state is 2, then follow with collision-induced dissociation (CID) and scan in the ion trap with an isolation window of 1.8, CID energy of 35% and a rapid ion trap scan rate; b) if the precursor charge state is 3–5 and precursor intensity >500,000, then follow with higher-energy C-trap dissociation (HCD) and scan in the Orbitrap with an isolation window of 1.8, HCD energy of 28% and a resolution of 15000; c) if the precursor charge state is 3–5 and precursor intensity <500,000, then follow with CID as described for option

3 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

(a). For all MS2 events, the following options were set: “Injection Ions for All Available

Parallelizable Time” with an AGC target value of 4000 and a maximum injection time of

250 ms for CID, or an AGC target value of 10,000 and a maximum injection time of 40 ms for HCD.

Mass spectrometry: Data analysis

Raw MS files were processed within the MaxQuant v1.6.0.16environment [2] using the

MaxLFQ algorithm for label-free quantification and the integrated Andromeda search engine with a false discovery rate (FDR) <0.01 at the protein and peptide levels. The search included variable modifications for oxidized methionine (M), acetylation (protein

N-terminus), and fixed carbamidomethyl modifications (C). Up to two missed cleavages were allowed for protease digestion. Peptides with at least six amino acids were considered for identification, and ‘match between runs’ was enabled with a matching time window of 1 min to allow the quantification of MS1 features which were not identified in each individual measurement. Peptides and proteins were identified using the UniProt

FASTA Homo sapiens database (August 2017).

Dynamic light scattering

The size of the exosomes and microvesicles were determined by dynamic light scattering

(DLS) using a Zetasizer nano ZS90 particle sizer at a 90° fixed angle (Malvern

Instruments, Worcestershire, UK). The particle diameter was calculated using the Stokes–

Einstein equation [3]. For particle sizing in solution, exosome or microvesicle aliquots were diluted in 10% PBS and analyzed at a constant 25°C. The data were acquired and analyzed using Dispersion Technology Software (Malvern Instruments).

4 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Western blot

The protein content of microvesicles and exosomes was measured by Bicinchoninic acid assay. Samples (5 µg total protein for all type of extracellular vesicles) were separated by sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE) on an 8–16% acrylamide gradient and then transferred to a nitrocellulose membrane. The membrane was blocked, rinsed and labeled with one of the following primary human antibodies diluted in 1% (w/v) bovine serum albumin (BSA) in PBS containing 0.15% (v/v) Tween-20

(PBS-T): monoclonal anti-CD63 (Novus Biological, Littleton, CA, USA, 1:1000 clone

H5C6), monoclonal anti-CD81 (Novus Biological, 1:1000 clone 1D6), or monoclonal anti-

CD45 (LifeSpan BioSciences, Seattle, WA, USA, 1:1000 clone 3G4). After rinsing in PBS-

T, the membrane was probed with secondary antibodies conjugated to horseradish peroxidase (diluted 1:10,000 in 1% (w/v) BSA in PBS-T). Chemiluminescence was monitored using the ChemiDoc Touch Imaging System (Bio-Rad, Hercules, CA, USA) and the signal was quantified by densitometry using Image Lab software (Bio-Rad).

Flow cytometry: Instrumentation

Before exosomes acquisition, the LSRFortessa X-20 instrument (Becton Dickinson

Franklin Lakes, NJ, USA) was calibrated using CS&T beads (Becton Dickinson) and carefully washed with double-distilled water for at least 1 h. Front scatter (FSC) and side scatter (SSC) were set to log scale as recommended [4], and voltages were adjusted to the highest values to exclude background noise from PBS [4], which was purified by passing through 0.22-µm UltrafreeVR-MC/DuraporeVR-PVDF centrifugal filter units (Merk,

Darmstadt, Germany). This allowed the detection of all different dimensions (0.5, 0.24, 0.2,

0.16 µm) of SSC MegaMix beads (BioCytex, Stago Group, France). For every sample 5 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material. acquisition, Rainbow Fluorescent Particles (Spherotech, Lake Forest, IL, USA) were used to adjust all channel voltages thus maintaining voltage consistency. Monoclonal mouse anti-human CD63 (IgG1, clone H5C6), CD81 (IgG1, clone JS-81) and IgG1 isotype controls (clone MOPC-21) were sourced from Becton Dickinson whereas CD133PE-Cy7

(IgG1, clone7) was sourced from BioLegend (San Diego, CA, USA). FITC-conjugated phalloidin was provided by Sigma-Aldrich (St. Louis, MO, USA).

Flow cytometry: Analysis

Exosomes, identified as particles smaller than 0.16 µm in diameter when compared to

SSC MegaMix Beads, were gated according to their SSC, and 100,000 events for each sample were acquired at a low sample pressure and a low flow rate of 8–12 µl/min [4].

Fluorescence Minus One (FMO) analysis was used to achieve the correct size cutoff [5].

FCS files were exported from Fortessa X20 and data were evaluated using Kaluza software (Beckman Coulter, Brea, CA, USA).

CD133 ELISA assay

CD133 expression in urinary exosomes was determined by homemade ELISA. Clean exosome fraction recovered from 16 ml of urine was solubilized in 10 μl of mild detergent solution i.e. 1%(v/v) Nonident P-40 (NP-40), 0.5%(v/v) Tween-20 in PBS and stored at -

80°C until use. 96-well maxi-sorp-nunc-immuno plates (ThermoFisher Scientific, MA, USA) were coated overnight at 4°C with Anti-CD133 antibody (United States Biological, USA) diluted 1:10 in PBS. After blocking with 3%(w/v) Bovine serum Albumin (BSA) in PBS, 100

μl of diluted sample (1:10) were added per well and incubated overnight at 4°C. Samples were, then, washed five times with PBS and 0.15%(v/v) Tween-20 (PBS-T) and incubated

6 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

4 hours with anti-CD133 mouse monoclonal antibody (United States Biological, USA, clone 2F8C5) diluted 1:1000 with 1%BSA(w/v) in PBS-T. After three washes with PBS-T, conjugated HRP anti-mouse IgG diluted 1:5000 were added and incubated 1 hour.

Samples were washed again three times with PBS-T and one time with PBS before adding the peroxidase substrate (TMB, Bio-Rad). The reaction was stopped with a H2SO4 solution. Absorbance at 450 nm was measured using Mark microplate Absorbance

Spectrophotometer (Bio-Rad). To standardize the response of the antibodies, a pool of highly positive control was used. The optical density results were expressed as Relative

Unit per ml (RU/ml).

Statistical analysis

After normalization, mass spectrometry data were analyzed by unsupervised hierarchical clustering using multidimensional scaling (MDS) with k-means and Spearman’s correlation to identify outliers and the dissimilarity between samples. The normalized expression profiles of the proteins were then used to construct the co-expression network using the weighted gene co-expression network analysis (WGCNA) package in R [6]. A weighted adjacency matrix was constructed using the power function. After choosing the appropriate

β parameter of power (with the value of independence scale set to 0.8) the adjacency matrix was transformed into a topological overlap matrix (TOM), which measures the network connectivity of all the proteins. To classify proteins with co-expression profiles into protein modules, hierarchical clustering analysis was conducted according to the TOM dissimilarity with a minimum size of 30 proteins per module. To identify the relationship between each module and clinical trait, we used module eigengenes (MEs) and calculated the correlation between MEs and each clinical trait and their statistical significance

7 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material. corrected for multiple interactions. A heat map was then used to visualize the degree and significance of each relationship.

To identify the hub proteins of modules that maximize the discrimination between the selected clinical traits, we applied a non-parametric Mann–Whitney U test, machine learning methods such as non-linear support vector machine (SVM) learning, and partial least squares discriminant analysis (PLS-DA). For the Mann–Whitney U test, proteins were considered to be significantly differentially expressed between the two conditions with power of 80% and an adjusted P-value ≤0.05 after correction for multiple interactions

(Benjamini-Hochberg) and a fold change of ≥2. In addition, the proteins needed to show at least 70% identity in the samples in one of two conditions. Volcano plots were used to visualize this analysis. In SVM learning, a fourfold cross-validation approach was applied to estimate the prediction and classification accuracy. Besides, the whole matrix was randomly divided into two part. One for learning (65%) and the other one (35%) to verify the accuracy of the prediction. Finally, the resulting core panel of hub proteins was uploaded to Cytoscape to construct a protein–protein interaction network and identify the principal biological processes and pathways involved in the modules corresponding to each clinical trait. Gene Ontology (GO) annotations were extracted from the UniProt,

Reactome, KEGG and ClueGO databases and presented on a heat map and two- dimensional scatter plot.

For ELISA data analysis, the Kruskal-Wallis test was used to assess differences in CD133 protein levels among the three study groups, and the results were expressed as medians and IQ ranges. A value of P < 0.05 was considered to be statistically significant. Received operating characteristic (ROC) curves were generated to assess the diagnostic efficiency of each assay. Youden's index was used to identify the cutoff [7]. All statistical tests were performed using the latest version of software package R available at the time of the experiments. 8 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Figure 1. Age and eGFR of the study participants.

A B p=0.96 p=0.35 40 170

160

35 ) 2 150

30 140

130 25

Age (years) 120

20 eGFR m (ml/min/1.73 110

15 100 MSK ADPKD HC MSK ADPKD HC

Dot plots represent (A) age and (B) eGFR measured by CKD-EPI Equations of all study participants. Full triangles, squares and circles indicate male subjects while triangles, squares and circles with diagonal stripes indicate female patients. The line represents the mean value.

9 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Figure 2. Characterization of isolated exosomes and microvesicles.

A B C CD63 CD81 CD45 1.0 10 MSK MSK 200 ADPKD ADPKD 150

0.8 8 100 75 0.6 6 Mr kDa 50

0.4 4 37

25 Intensity [Arbitrary unit] [Arbitrary Intensity Intensity [Arbitrary Unit] [Arbitrary Intensity 0.2 2 20

15 0.0 0 0.1 1 10 100 1000 10000 0.1 1 10 100 1000 10000 1 2 3 4 5 6 7 8 9 10 11 12 Exosome Size [nm] Microvesicle Size [nm]

Plot of exosomes (A) and microvesicles (B) size distribution, as evaluated by dynamic light scattering. The plot shows a Gaussian distribution profile with a mean peak at 90 ± 5 nm or 1000 ± 70 nm respectively for exosomes or microvesicles. No statistical differences were observed between the exosomes or microvesicles isolated from the urine of medullary sponge kidney (MSK) and autosomal dominant polycystic kidney disease (ADPKD) patients. (C) Representative western blot analysis of exosomes and microvesicles isolated from the urine of medullary sponge kidney (MSK) and autosomal dominant polycystic kidney disease (ADPKD) patients. Whole exosomes (lanes 1–2, 5–6 and 9–10) and microvesicles (lanes 3–4, 7–8 and 11–12) from MSK (lanes 1, 3, 5, 7, 9 and 11) and ADPKD (lanes 2, 4, 6, 8, 10 and 12) patients were analyzed by detecting CD63 (lanes 1–4), CD81 (lanes 5–8) and CD45 (lanes 9–12). Stain-free technology was used as loading control.

10 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Figure 3. Gene Ontology annotation of urinary extracellular vesicle proteins.

MSK ADPKD

Nucleus 10% Secreted 0.6% Nucleus 10% Secreted 0.5%

Membrane 18% Membrane 18% Ex

Organelle 39% Organelle 39%

Cytoplasm 32% Cytoplasm 32%

Nucleus 8% Secreted 0.4% Nucleus 8% Secreted 0.3%

Membrane 34% Membrane 34% Mv

Organelle 32% Organelle 32%

Cytoplasm 26% Cytoplasm 26%

The pie charts show the distribution of cellular component annotations in the different conditions. The percentage distribution of cellular component categories is similar between the exosomes of medullary sponge kidney (MSK) and autosomal dominant polycystic kidney disease (ADPKD) patients, and between the microvesicles of medullary sponge kidney (MSK) and autosomal dominant polycystic kidney disease (ADPKD) patients.

11 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Figure 4. Multidimensional scaling analysis of extracellular vesicles from the urine of medullary sponge kidney (MSK) and autosomal dominant polycystic kidney disease (ADPKD) patients.

Two-dimensional scatter plot of MDS analysis of exosomes (solid symbol) and microvesicles (open symbol) of medullary sponge kidney (MSK) (red triangle) and autosomal dominant polycystic kidney disease (ADPKD) (black square) samples. Ellipsis indicates 95% confidence interval. No outliers were detected.

12 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Figure 5. Sample clustering and trait indicators.

In the upper panel, the clustering of samples was based on the label-free quantification of proteins identified by mass spectrometry. In the lower panel, the color intensity was proportional to the trait indicator classification, i.e. the type of pathology and extracellular vesicle. No outliers were detected.

13 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Figure 6. Venn diagram of statistically significant differences in protein abundance in the different types of extracellular vesicles from medullary sponge kidney (MSK) or autosomal dominant polycystic kidney disease (ADPKD) patients.

ADPKDMv vs ADPKDEx MSKEx vs ADPKDEx (151) (90)

MSK vs MSK MSKMv vs ADPKDMv Mv Ex (50) (62)

Total (255)

Venn diagram shows common and exclusive peptides. The numbers represent the distinct proteins in the overlapping and non-overlapping areas.

14 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Figure 7. Network of proteins interaction.

Exosome ADPKD Microvesicle ADPKD Exosome MSK Microvesicle MSK Protein interaction

Network of proteins interaction. The diagram report all the interaction between the 255 statistically significant proteins. Circles and grey lines correspond respectively, to proteins and their interactions. Circles size and colours are related respectively, to the number of protein interactions and the relative expression, after Z-score, in the four conditions i.e. ADPKD exosomes (dark red) and microvesicles (light red), and MSK exosome (dark blue) and microvesicles (light blue). Besides, the proteins are grouped into four classes (dotted ellipses) in function of the type of proteins interaction and their ability to distinguish the four conditions. These classes correspond from outside to inside, respectively, to proteins: 1) linked by only co-expression and co-localization interactions, 2) with also physical interactions, 3) also linked by biochemical pathway and 4) that maximize the discrimination between the four conditions.

15 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Figure 8. Gene Ontology enrichment analysis for core discriminatory proteins in the extracellular vesicles of medullary sponge kidney (MSK) and autosomal dominant polycystic kidney disease (ADPKD) patients.

The –log10 (P value) of each term is shown on the x-axis and the enriched GO terms are shown on the y-axis. The size of each circle is proportional to the number of proteins associated with each GO term.

16 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Table 1. List of all significant proteins identified using mass spectrometry. The symbol “+” identify the significant proteins in each experimental comparison. Fold change value is reported as mean and standard deviation.

Mv

- Mv Ex Mv

- - - Mv Ex

Mv - - Mv - Mv Ex - Mv - - - Mv -

Ex vs MSK vs Ex - Ex vs MSK vs Ex Mv vs MSK vs Mv - - Ex vs MSK vs Ex Ex vs ADPDK vs Ex Mv vs MSK vs Mv Ex vs MSK vs Ex - - - Ex vs ADPDK vs Ex Ex vs MSK Ex - - MSK vs Mv - - Ex vs ADPDK vs Ex Ex vs MSK Ex vs - - Uniprot ID Uniprot Gene name Gene Protein name Protein value MSK value - value ADPDK value - value ADPDK value P - value ADPDK value P - Significant MSK Significant P Significant ADPDK Significant Fold Change MSK Change Fold P Fold Change ADPDK Change Fold Significant ADPDK Significant Significant ADPDK Significant Fold Change ADPDK Change Fold Fold Change ADPDK Change Fold

1.72±3.91 0.09±1.27 -7.29±4.17 10.31 -5.65±4.51 + A0A075B7C1 Cyclic AMP-dependent transcription factor ATF-1 FUS -1.11±1.68 1.96±1.95 2.42 3.57±2.21 7.62 0.5±1.96 + + A0A087WTQ1 Solute carrier family 15 member 1 hPEPT1-RF 2.12±2.84 3.13±2.56 3.63 0.68±1.72 -0.33±2.88 + A0A087WU36 Plexin-B2 PLXNB2 -0.47±1.15 1.25±2.95 3.21±2.51 4 1.5±2.55 + A0A087WU85 Major facilitator superfamily domain-containing protein 12 MFSD12 0.91±1.81 0.66±2.24 -2.05±1.77 3.22 -1.8±2.66 + A0A087WV23 SH3 domain-binding glutamic acid-rich-like protein 3 SH3BGRL3 -1.34±1.66 0.66±2.16 4.72±2.96 7.23 2.72±2.39 + A0A087WV34 Glycosylated lysosomal membrane protein GLMP 0.92±3 -0.07±2.79 -3.32±3.5 2.16 -2.32±2.91 A0A087WWC + 0 Mucosal addressin cell adhesion molecule 1 MADCAM1 -0.84±1.62 0.38±2.19 -3.73±2.78 4.48 -4.94±3.05 7.66 A0A087WWY + + 3 Filamin-A FLNA 1.77±2.11 3.99±2.91 4.73 0.77±2.23 -1.45±1.9 + A0A087WXB8 Type 2 lactosamine alpha-2,3-sialyltransferase ST3GAL6 -0.33±1.21 1.27±1.07 3.43 0.73±1.02 -0.87±1.2 + A0A087WY93 Alpha-1-antichymotrypsin SERPINA3 -0.33±1.21 1.27±1.07 3.43 0.73±1.02 -0.87±1.2 + A0A087WY93 Alpha-1-antichymotrypsin SERPINA3 1.72±4.11 -0.12±2.6 -6.28±4.06 6.57 -4.43±4.63 + A0A087WYG6 LYNX1

17 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

4.36±2.63 4.51±3.48 4.11 0.27±2.08 0.13±2.09 + A0A087WZ31 Mannosyl-oligosaccharide 1,2-alpha-mannosidase IC MAN1C1 1.8±3.42 2.37±3.3 -3.54±3.35 2.67 -4.11±4.11 + A0A087WZG7 Cadherin-13 CDH13 -0.41±1.41 3.59±3.66 4.78±3.53 4.6 0.79±2.37 Endoplasmic reticulum mannosyl-oligosaccharide 1,2-alpha- + A0A087X064 mannosidase MAN1B1 0.02±1.17 -0.04±1.32 -1.65±1.7 2.24 -1.59±1.23 4.09 + + A0A087X0I2 Complement factor I CFI 0.63±1.45 1.71±1.68 2.46 -0.7±1.5 -1.78±1.65 2.78 + + A0A087X0S5 Collagen alpha-1 COL6A1 1.23±2.21 -1.46±2.37 -5.44±3.36 7.67 -2.74±2.84 + A0A087X137 Leukocyte-associated immunoglobulin-like receptor 1 LAIR1 4.62±3.43 4.7 3.53±3.67 -1.73±2.9 -0.64±2.94 + A0A087X1J7 Glutathione peroxidase 3 GPX3 5.73±3.2 1.54±2.71 -0.86±1.41 3.33±3.06 2.83 + A0A087X1K9 Acyl-protein thioesterase 1 LYPLA1 -0.22±2.14 -0.09±2.19 -2.81±2.54 2.92 -2.94±2.65 2.93 + + A0A087X1L8 ICOS ligand ICOSLG 2±2.51 0.24±2.77 -3.48±3.07 3.07 -1.73±2.77 + A0A087X1M4 V-set domain-containing T-cell activation inhibitor 1 VTCN1 -0.38±1.61 4±2.92 4.72 4.22±2.7 6.8 -0.16±2.06 + + A0A096LP62 Inter-alpha-trypsin inhibitor heavy chain H5 ITIH5 -1.15±1.93 4.61±3.08 5.99 4.87±3.17 6.48 -0.89±1.89 + + A0A0A0MQT7 Collagen alpha-1 COL14A1 0.45±1.94 1.19±2 -2.69±2.53 2.7 -3.43±2.43 5.1 + + A0A0A0MQX7 Testican-1 SPOCK1 1.03±1.79 2±1.77 3.03 -0.81±1.61 -1.77±1.83 Basement membrane-specific heparan sulfate proteoglycan core + A0A0A0MRS3 protein HSPG2 0.48±1.57 0.57±2.25 -2.52±2.1 3.47 -2.62±2.51 2.58 + + A0A0A0MSA9 Poliovirus receptor PVR 1.21±2.47 3.55±2.57 4.84 2.01±2.69 -0.33±1.73 + A0A0A0MSK1 Angiopoietin-related protein 1 ANGPTL1 0.06±1.53 1.32±3.41 4.62±3.25 5.19 3.36±3.37 + A0A0A6YY98 Transient receptor potential cation channel subfamily V member 6 TRPV6 0.31±1.56 2.44±2.72 -0.33±2.08 -2.45±2.34 2.63 + A0A0B4J288 Cadherin-16 CDH16 3.25±2.54 -0.72±2.72 0.46±1.99 4.42±3.46 4.01 + A0A0C4DFP6 Cartilage acidic protein 1 CRTAC1 1.97±2.18 2.52±2.46 2.49 -0.12±1.73 -0.67±2.28 + A0A0C4DFZ2 Arylsulfatase A ARSA 1±1.94 2.06±2.87 1.85±1.88 2.31 0.79±2.83 + A0A0C4DGG1 Protein kinase C and casein kinase substrate in neurons protein 3 PACSIN3 0.05±1.84 3.88±2.98 4.17 4.62±3.17 5.58 0.79±1.99 + + A0A0C4DGL1 Alpha-mannosidase 2x MAN2A2 1.23±2.97 0.5±2.25 -2.25±2.31 2.28 -1.53±3.15 + A0A0C4DGN4 Zymogen granule protein 16 homolog B ZG16B 0.25±1.32 0.38±1.66 -2.58±2.2 3.27 -2.7±1.81 5.91 + + A0A0C4DGV7 Retinol-binding protein 4 RBP4

18 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

2.11±2.23 2.21 1.57±2.5 -1.35±2.45 -0.82±1.97 + A0AV65 Neural cell adhesion molecule L1 L1CAM 4.01±3.26 3.78 3.73±2.94 3.92 0.95±2.11 1.23±2.72 + + A0AV84 Leucine-rich repeat transmembrane protein FLRT2 FLRT2 -1.56±2.27 0.74±2.36 -3.45±3.05 3.06 -5.76±3.51 8.02 + + A0AVK2 Cilia- and flagella-associated protein 91 MAATS1 0.06±2.36 2.53±2.23 3.07 0.01±1.66 -2.46±2.77 + A1L3U4 Cadherin-related family member 2 CDHR2 1.05±1.76 0.89±2.4 -2.5±1.86 4.49 -2.35±2.8 + A2A2V1 Major prion protein PRNP 1.12±1.69 5.43±3.91 4.89 4.81±3.71 4.15 0.5±1.56 + + A2A2Z9 Ankyrin repeat domain-containing protein 18B ANKRD18B 0.69±2.25 2.86±2.56 2.98 2.83±2.82 2.39 0.66±1.9 + + A2BEX8 von Willebrand factor A domain-containing protein 7 VWA7 -1.46±2.13 -0.11±2.3 -2.34±2.64 -3.7±2.67 4.85 + A2N6W9 Low affinity immunoglobulin gamma Fc region receptor III-A FCGR3A 3.38±2.67 4.06 3.73±3.35 2.96 -0.25±2.55 -0.61±2.27 + + A3KFJ8 Fibrinogen C domain-containing protein 1 FIBCD1 0.5±1.66 2.97±3.07 3.62±2.44 5.81 1.15±2.77 + A4D108 Transmembrane protein 106B TMEM106B -1.64±2.36 1.51±2.16 5.16±3.55 5.5 2.01±2.31 + A4D201 Protein tweety homolog 3 TTYH3 3.18±2.62 3.67 4.48±3.16 5.19 0.3±1.34 -1±2.7 + + A4D205 7,8-dihydro-8-oxoguanine triphosphatase NUDT1 0.07±2.46 2.65±2.5 2.67 2.05±2.05 2.38 -0.53±2.7 + + A4D232 DnaJ homolog subfamily B member 6 DNAJB6 0.87±2.1 2.54±2.78 2.79±2.03 4.75 1.11±2.9 + A6NC20 Neutral alpha-glucosidase AB GANAB 1.88±2.3 4.05±2.78 5.55 -2.6±2.32 2.97 -4.77±3.18 6.01 + + + A6NDN2 Protein FAM3C FAM3C 1.09±2.25 0.3±1.47 3.7±2.56 5.42 4.5±2.99 6.08 + + A6NEH4 Tetraspanin-3 TSPAN3 1.29±1.78 3.98±3.45 3.18 -0.28±1.82 -2.98±3.08 + A6NGE3 Hemicentin-1 HMCN1 0.87±1.59 -0.73±1.78 -2.01±1.99 2.42 -0.4±1.69 + A6NH94 Matrilin-4 MATN4 1.85±2.34 3.31±3.04 -2.05±2.62 -3.51±2.87 3.63 + A6NHI3 Leukocyte immunoglobulin-like receptor subfamily A member 5 LILRA5 2.59±2.11 3.79 2.57±2.17 3.37 -0.29±1.51 -0.27±1.83 + + A6NJS5 Alpha-amylase 2B AMY2B 2.54±1.94 4.17 0.24±1.72 -3.66±2.43 6.11 -1.36±1.77 + + A6NMP3 Mitochondrial enolase superfamily member 1 ENOSF1 2.86±2.59 3.01 3.7±2.93 3.89 -0.33±1.57 -1.18±2.71 + + A6NMR0 Peptidyl-glycine alpha-amidating monooxygenase PAM 4.27±2.78 6.17 0.14±1.17 -4.18±2.7 6.64 -0.05±1.37 + + A6XMW0 Bone marrow proteoglycan PRG2 1.91±2.78 4.27±3.05 5.01 1.21±2.27 -1.15±2.59 + A7VJG6 Galectin-9 LGALS9

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1.04±1.72 1.43±2.31 -1.91±2.13 -2.3±2.27 2.45 + A8K052 Alpha-1B-glycoprotein A1BG 0.91±1.95 3.43±3.27 2.61 -1.97±2.35 -4.49±3.44 4.19 + + A8K0H9 Contactin-1 CNTN1 -0.4±1.61 -3.74±3.37 2.93 -0.11±1.26 3.23±3.37 2.2 + + A8K106 Matrilin-2 MATN2 1.5±2.72 0.29±2.21 2.08±2.8 3.29±2.96 2.96 + A8K125 Vacuolar protein-sorting-associated protein 36 VPS36 1.44±2.27 1.45±2.58 -2.53±2.7 2.11 -2.54±2.57 + A8K154 Roundabout homolog 4 ROBO4 2.21±2.63 1.15±2.72 -2.33±2.39 2.28 -1.27±2.93 + A8K1D5 Integral membrane protein GPR180 GPR180 0.5±1.27 1.49±2.83 2.27±2.01 3.05 1.28±2.58 + A8K305 Succinate receptor 1 SUCNR1 1.22±1.31 2.16 2.8±2.01 4.93 -0.12±1.29 -1.7±1.54 2.88 + + + A8K458 Mannan-binding lectin serine protease 2 MASP2 0.65±1.25 0.96±1.47 -1.79±1.6 3 -2.1±1.66 3.92 + + A8K474 Kininogen-1 KNG1 2.95±2.53 -0.29±2.29 0.08±2.08 3.32±2.81 3.35 + A8K4V9 Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 PIN1 -0.78±1.07 0.62±1.36 1.68±1.12 5.98 0.28±1.48 + A8K4X5 Lysosome-associated membrane glycoprotein 2 LAMP2 1.72±2.09 2.61±2.56 2.47 0.16±1.87 -0.72±2.23 + A8K5E2 N-sulphoglucosamine sulphohydrolase SGSH -0.17±1.26 1.93±2.97 5.89±3.18 14.19 3.79±3.47 + A8K5L7 ATP-binding cassette sub-family D member 4 ABCD4 0.05±2.07 4.28±4.23 2.44 5.17±3.98 4.15 0.94±2.96 + + A8K5Z6 Protocadherin Fat 4 FAT4 1.81±2.05 -0.3±1.7 -3.5±2.63 4.38 -1.4±1.78 + A8K6J5 Macrophage colony-stimulating factor 1 CSF1 3.27±3.28 2.43 3.72±2.95 3.87 -2±2.82 -2.45±2.73 + + A8K6K8 Endonuclease domain-containing 1 protein ENDOD1 3.1±2.59 3.56 1.77±3.32 -3.09±3.08 2.41 -1.77±2.83 + + A8K6M7 Dystroglycan DAG1 1.84±3.07 -0.14±1.73 -4.42±3.32 4.39 -2.44±2.71 + A8K6R7 Vascular cell adhesion protein 1 VCAM1 -0.7±1.67 1.11±1.8 2.62±2.1 3.8 0.8±1.75 + A8K762 Uroporphyrinogen decarboxylase UROD 1.98±2.09 2.26±2.93 1.97±2.04 2.24 1.69±2.85 + A8K796 Glucosylceramidase GBA 3.15±2.67 3.44 3.05±2.8 2.83 -1.69±1.9 -1.59±2.76 + + A8K7R4 EGF-containing fibulin-like extracellular matrix protein 2 EFEMP2 0.96±1.12 1.46±1.39 2.6 -0.02±0.85 -0.51±1.35 + A8K7V2 Dipeptidyl peptidase 1 CTSC -2.23±2.31 2.2 0.87±1.91 0.24±1.75 -2.86±2.58 2.93 + + A8K7V9 Oxidized low-density lipoprotein receptor 1 OLR1 -0.67±1.88 1.39±2.58 4.13±2.66 6.69 2.07±2.81 + A8K8Z6 High affinity copper uptake protein 1 SLC31A1

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2.03±1.89 2.33 0.05±1.33 -2.76±2.29 3.47 -0.78±1.34 + + A8K981 Dermatopontin DPT 5.29±3.35 7.16 4.68±2.78 8.76 -1.24±1.82 -0.62±1.77 + + A8KA14 Beta-galactoside alpha-2,6-sialyltransferase 1 ST6GAL1 1.7±1.84 4.2±3.3 3.94 3.01±2.84 2.67 0.51±1.8 + + A8KA65 Rabankyrin-5 ANKFY1 5.99±3.58 3.81±3.36 3.07 1.62±2.34 3.8±3.12 + A8KAJ1 Microfibril-associated glycoprotein 4 MFAP4 0.1±2.45 -2.09±2.52 -3.42±2.43 5.09 -1.23±2.93 + A8MTF8 Protein FAM3B FAM3B 0.38±1.29 2.39±2.35 2.46 -0.8±1.33 -2.81±2.47 3.09 + + A8MVI4 Collagen alpha-1 COL18A1 3.01±2.75 2.97 1.14±3.26 -1±2.73 0.87±2.87 Ectonucleotide pyrophosphatase/phosphodiesterase family + A8UHA1 member 2 ENPP2 0.41±1.23 -0.93±3.01 -3.94±2.98 4.33 -2.6±2.7 + B0YIW2 Apolipoprotein C-III APOC3 1.91±3.61 2.76±2.45 -4.91±3.39 5.43 -5.76±4.42 + B1AMP9 Extracellular sulfatase Sulf-2 SULF2 2.92±2.71 2.82 -0.57±1.09 -7.58±4.04 15.66 -4.09±3 4.64 + + + B2R4G0 Stromal cell-derived factor 1 CXCL12 0.55±1.33 0.26±1.36 -2.64±1.73 6.38 -2.34±1.94 3.54 + + B2R579 Apolipoprotein D APOD 2.12±2.72 -1.1±2.42 -2.94±2.92 2.42 0.28±2.61 + B2R5F2 Uteroglobin SCGB1A1 0.58±1.46 2.49±2.98 -2.38±2.29 2.58 -4.29±3.21 4.46 + + B2R5J9 Cystatin-C CST3 0.57±2.74 -1.72±3.47 -3.77±3.21 3.31 -1.48±3.75 + B2R5L2 Alpha-1-acid glycoprotein 2 ORM2 -0.01±1.57 1.7±1.96 2.71±2.2 3.65 1±1.66 + B2R5W7 Tetraspanin-7 TSPAN7 -0.64±1.32 0.5±2.68 3.76±2.67 5.06 2.62±2.66 + B2R692 Rho-related GTP-binding protein RhoB RHOB -0.26±2.15 0.45±2.34 -2.24±2.31 2.24 -2.95±2.94 + B2R699 Ganglioside GM2 activator GM2A 0.35±1.48 1.27±2.93 4.9±3.47 5.12 3.99±3.02 4.32 + + B2R6W6 Procollagen-lysine,2-oxoglutarate 5-dioxygenase 3 PLOD3 0.66±1.07 2.82±2.62 2.76 0.69±1.26 -1.47±2.21 + B2R710 Beta-1,4-galactosyltransferase 1 B4GALT1 1.7±1.43 3.33 1.9±2.39 -0.81±1.34 -1.01±2.18 + B2R7H0 Secreted and transmembrane protein 1 SECTM1 2.08±2.64 1.73±2.71 -2.64±2.36 2.98 -2.29±3.13 + B2R7L5 Cell adhesion molecule 4 CADM4 3.13±2.43 4.22 1.95±2.35 -1.74±2.57 -0.57±1.42 + B2R8F7 Cartilage intermediate layer protein 1 CILP 0.76±2.84 1.49±2.22 -3.02±2.99 2.43 -3.75±3.02 3.75 + + B2R9E1 Procollagen C-endopeptidase enhancer 1 PCOLCE 0.33±1.58 0.56±2.56 -2.34±2.52 2.07 -2.57±2.4 2.74 + + B2R9M3 Beta-2-glycoprotein 1 APOH

21 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

2.54±3.01 1.2±2.82 -3.59±3.33 2.76 -2.24±2.95 + B2RAY2 Hepatitis A virus cellular receptor 2 HAVCR2 1.11±1.88 1.51±1.84 2.15±1.76 3.6 1.75±2.18 + B2RBB1 Choline transporter-like protein 2 SLC44A2 1.71±2.2 2.51±2.94 1.36±1.32 2.53 0.56±3.13 + B2RBZ1 Charged multivesicular body protein 4c CHMP4C 2.1±1.96 2.62 2.2±2.43 -1.28±1.53 -1.38±2.47 + B2RC40 Cathepsin Z CTSZ 1.9±2.04 2.41±2.36 2.47 1.33±1.33 2.38 0.82±2.46 + + B2RCB7 Vacuolar protein sorting-associated protein 4A VPS4A -0.92±2.62 1.03±4.55 -3.9±4.06 2.21 -5.85±4.81 3.56 + + B2RDA1 Osteopontin SPP1 1.34±2.13 2.99±2.49 3.46 1.74±1.77 2.33 0.09±2.36 + + B2RDD4 Protein CREG1 CREG1 -0.27±1.76 2.23±2.7 5.62±3.12 11.78 3.12±3.14 + B2RDK2 Transmembrane protein 176B TMEM176B 2.51±2.4 2.47±3.21 3.47±3.13 2.93 3.51±3.02 3.24 + + B2RNX7 Sortilin-related receptor SORL1 0.68±3.09 2.53±2.54 -3.81±3.51 2.8 -5.66±3.76 6.08 + + B2RUU0 Fibrillin-1 [Cleaved into: Asprosin] FBN1 2.1±2.45 2.5±3.13 2.29±2.43 2.14 1.89±3.04 + B2RXK3 Serine/threonine-protein kinase ULK3 ULK3 2.14±1.99 2.67 2.17±2.2 2.33 -0.12±1.26 -0.15±2.22 + + B3KMF3 Group XV phospholipase A2 PLA2G15 -2.69±2.81 1.33±2.98 5.78±3.62 7.28 1.76±3.26 + B3KMZ3 Transmembrane 7 superfamily member 3 TM7SF3 0.92±2.91 0.48±1.89 -2.77±2.99 2.07 -2.34±2.61 + B3KNY4 Kinesin light chain 3 KLC3 2.67±2.06 4.15 2.76±2.44 3.06 -0.78±1.62 -0.87±2.02 + + B3KPB0 Sialate O-acetylesterase SIAE 2.56±2.13 3.61 2.29±2.15 2.7 -0.42±1.6 -0.15±1.86 + + B3KTI1 Alpha-amylase 2B AMY2B 2.56±2.13 3.61 2.29±2.15 2.7 -0.42±1.6 -0.15±1.86 + + B3KTI1 Alpha-amylase 2B AMY2B 0.67±1.36 1.16±1.83 -1.25±1.84 -1.75±1.59 2.9 + B3KTR6 Matrix remodeling-associated protein 8 MXRA8 1.99±2.65 -0.29±1.31 -8.68±4.62 15.47 -6.4±4.06 6.98 + + B3KUZ0 Interleukin-18-binding protein IL18BP 1.9±1.61 1.95±1.97 2.34 1.26±1.38 1.21±1.83 + B3KVB2 Abscission/NoCut checkpoint regulator ZFYVE19 -0.17±1.42 1.94±2.6 3.66±2.71 4.54 1.54±2.25 + B3KWK6 Alpha-L-iduronidase IDUA 2.11±2.43 1.99±2.17 1.67±1.69 2.34 1.79±2.63 + B3KY69 Serine incorporator 1 SERINC1 -0.7±1.4 1.4±2.48 4.7±2.74 9.46 2.6±2.71 + B3Y612 Toll-like receptor 2 TLR2 3.31±2.46 4.65 2.18±2.44 -1.4±2.55 -0.26±1.36 + B4DDP7 Semaphorin-7A SEMA7A

22 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

-0.43±1.42 0.88±2.38 3.84±2.69 5.24 2.53±2.38 + B4DET3 Niemann-Pick C1 protein NPC1 0.53±1.8 1.83±3.22 4.41±2.99 5.77 3.12±3.35 + B4DFN3 Polycystin-2 PKD2 -2.8±2.29 1.28±3.02 6.41±3.87 8.29 2.32±2.99 + B4DI85 ADP-ribosylation factor-like protein 8B ARL8B 3.05±2.46 3.87 2.32±3.33 -0.67±2.52 0.06±2.63 + B4DJ54 Soluble calcium-activated nucleotidase 1 CANT1 -0.92±1.25 1.11±2.25 2±1.7 3.33 -0.03±2.06 + B4DKD8 Lysosome membrane protein 2 SCARB2 1.88±2.29 4.3±2.93 5.66 -0.73±1.46 -3.15±2.93 2.76 + + B4DLP9 Butyrophilin subfamily 2 member A1 BTN2A1 1.39±2.18 2.14±2.73 -2.68±2.21 3.54 -3.42±3.22 2.7 + + B4DPZ2 Urokinase-type plasminogen activator PLAU -8.3±4.55 14.08 0.49±1.54 0.95±1.61 -7.84±4.25 13.62 + + B4DR24 Zinc finger and SCAN domain-containing protein 32 ZSCAN32 0.29±2.56 -1.63±3.86 -4.19±3.62 3.19 -2.27±3.67 + B4DRR9 Phosphoinositide-3-kinase-interacting protein 1 PIK3IP1 -2.69±2.56 2.67 -1.97±2.07 2.23±2.29 1.51±2.12 2.06 + + B4DTG2 Elongation factor 1-gamma EEF1G 2.41±2.24 1.7±1.61 2.66 1.29±1.85 2±1.87 + B4DTY5 Guanine deaminase GDA 1.67±1.72 2.34 2.49±2.27 2.85 0.18±1.76 -0.63±1.64 + + B4DV35 Deoxyribonuclease-1 DNASE1 -0.23±1.88 1±2.78 -3.24±3 2.78 -4.47±3.15 5.18 + + B4DV76 Complement factor D CFD 0.04±1.28 4.32±2.98 5.46 4.83±2.76 10.36 0.54±2.05 + + B4DVL2 N-acylethanolamine-hydrolyzing acid amidase NAAA -0.01±1.67 -0.22±1.36 -2.76±2.27 3.56 -2.55±1.97 + B4DVN8 Endothelial cell-selective adhesion molecule ESAM 0.22±1.96 1.24±2.25 1.86±2 2.09 0.85±2.34 Insulin-like growth factor-binding protein complex acid labile + B4DZY8 subunit IGFALS -1.88±1.89 1.33±2.61 3.96±2.84 4.94 0.76±2.25 + B4E2V0 Heparan-alpha-glucosaminide N-acetyltransferase HGSNAT 2.33±2.37 2.36 1.55±2.89 -1.19±2.19 -0.41±2.72 + B4E3Q2 Adipocyte enhancer-binding protein 1 AEBP1 1.44±2.22 2.8±2.51 2.96 -0.84±2.14 -2.2±2.33 + B5A970 Ephrin type-B receptor 4 EPHB4 1.97±1.95 2.5 1.72±2.8 -1.22±2.28 -0.98±2.28 + B5MBX2 Transcobalamin-2 TCN2 1.48±1.64 3.81±2.61 5.58 1.77±2.07 -0.56±1.34 + B5MCZ9 Glutaminyl-peptide cyclotransferase QPCT -1.2±1.48 2.69±2.7 2.37 4.55±2.92 6.78 0.67±2.03 + + B5MD23 Tetraspanin-9 TSPAN9 -0.02±1.26 0.08±5.24 7.53±4.98 6.16 7.42±5.75 4.08 + + B7ZBX4 FRAS1-related extracellular matrix protein 1 FREM1 2.55±2.05 3.76 1.3±2.18 -1.93±2.16 -0.68±1.8 + B7ZKM9 Neogenin NEO1

23 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

4.92±3.16 6.91 3.91±2.81 4.93 -0.86±1.92 0.14±1.99 + + C9J8S2 Retinoic acid receptor responder protein 2 RARRES2 1.53±2.1 1.02±2.46 -2.61±2.49 2.61 -2.09±2.53 + C9J8Z4 Immunoglobulin superfamily member 8 IGSF8 3.49±2.74 2.78±2.39 3.24 0.88±1.35 1.59±2.64 + C9JA99 Protocadherin alpha-7 PCDHA7 0.14±0.79 1.88±1.58 3.42 0.85±1.18 -0.89±1.08 + C9JB49 Hyaluronidase-1 HYAL1 1.22±2.28 3.35±3.05 2.87 0.14±1.59 -1.99±3.09 + C9JLE3 Glypican-3 GPC3 0.67±2.87 3.03±2.62 3.2 -2.52±3.43 -4.88±2.91 8.7 + + C9JMY1 Insulin-like growth factor-binding protein 2 IGFBP2 1.27±1.48 1.69±2.65 -2.8±1.96 5.22 -3.22±2.97 2.79 + + C9JQ15 Vitelline membrane outer layer protein 1 homolog VMO1 0.77±1.2 1.46±1.66 1.44±1.34 2.76 0.75±1.54 + D2DFB0 G-protein coupled receptor family C group 5 member B GPRC5B 3.72±2.52 3.73±2.9 4.04 1.65±2.25 1.64±1.94 Alpha-1,6-mannosylglycoprotein 6-beta-N- + D3DP70 acetylglucosaminyltransferase A MGAT5 0.89±2.93 1.18±2.86 2.85±2.99 2.18 2.56±3.46 + D3DQ14 Tetraspanin-1 TSPAN1 0.79±2.05 2.23±2.29 -3.09±2.48 3.77 -4.52±3.11 5.51 + + D3DQ23 EF-hand calcium-binding domain-containing protein 14 EFCAB14 -0.02±1.8 1.36±2.27 1.76±1.67 2.64 0.38±2.43 + D3DQN6 CD82 antigen CD82 3.54±2.55 4.09 1.64±2.36 -2.08±2.88 -0.17±1.39 + D3DSD5 A disintegrin and metalloproteinase with thrombospondin motifs 1 ADAMTS1 2.75±2.5 2.95 1.42±2.5 -1.99±1.91 2.59 -0.66±2.72 + + D3DUT6 Calsyntenin-3 CLSTN3 0.65±2.08 3.13±2.64 3.39 2.24±2.13 2.64 -0.25±2.3 + + D3DW20 Leucine-rich repeat transmembrane protein FLRT3 FLRT3 1.47±2.01 2.35±2.15 2.87 -0.42±1.31 -1.31±2.3 + D3DW77 Chondroitin sulfate proteoglycan 4 CSPG4 2.2±2.03 2.85 2.11±2.21 -0.31±1.64 -0.22±1.98 + D3DX28 Reticulon-4 receptor RTN4R 1.53±2.59 2.97±3 2.34 -2.13±2.55 -3.57±3.31 2.78 + + D6RAX3 Protocadherin-1 PCDH1 3.4±1.3 9.79 3.93±1.85 9.6 1.92±1.34 6.34 1.39±1.81 2.47 + + D6RBI0 Prominin-1 PROM1 1.64±3.36 3.5±3.83 -4.38±4.28 2.49 -6.24±4.32 5.42 Transmembrane anterior posterior transformation protein 1 + + D6RBK3 homolog TAPT1 -0.47±1.88 0.9±2.14 4.26±2.66 7.32 2.89±2.75 2.63 + + D6W543 Solute carrier family 35 member F6 SLC35F6 0.36±1.53 4.98±3.16 6.98 4.87±3.09 6.98 0.25±1.56 + + E5RG79 Zinc finger homeobox protein 3 ZFHX3 4.26±2.68 0.7±2.05 -0.31±1.49 3.25±2.66 3.6 + E5RGD9 Elongin-C TCEB1 0.46±1.35 1.99±1.85 2.78 1.19±1.54 -0.34±1.46 + E7ER45 Maltase-glucoamylase, intestinal [Includes: Maltase MGAM

24 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

1.16±1.11 2.7 2.97±2.39 3.76 1.15±1.27 -0.65±1.76 + + E7EU32 Mannosyl-oligosaccharide 1,2-alpha-mannosidase IA MAN1A1 0.79±0.97 1.5±1.68 1.56±1.56 2.39 0.85±1.17 + E9PC35 Low-density lipoprotein receptor-related protein 2 LRP2 3.69±3.04 3.62 2.3±3.27 0.32±2.52 1.72±3.06 + E9PCD7 Epididymis-specific alpha-mannosidase MAN2B2 0.18±1.73 0.98±2.98 -2.39±2.18 2.86 -3.19±3.29 + E9PHK0 Tetranectin CLEC3B 3.34±2.94 2.61±2.24 3.25 0.39±2.09 1.13±2.25 + E9PHS0 LanC-like protein 1 LANCL1 -0.9±2.51 1.67±2 2.22±2.24 2.35 -0.34±2.35 + E9PQN9 Interferon-induced transmembrane protein 2 IFITM2 0.81±1.76 3.73±2.71 4.75 1.55±2.15 -1.37±1.8 + E9PRA7 N-acyl-aromatic-L-amino acid amidohydrolase ACY3 -0.78±2.95 2.37±2.6 2.81±2.7 2.57 -0.34±2.92 + E9PS44 Interferon-induced transmembrane protein 1 IFITM1 0.62±2.76 0.53±1.7 -4.15±3.1 4.46 -4.06±3.05 4.39 + + F5GYS9 Tumor necrosis factor receptor superfamily member 19L RELT 0.08±1.39 1.99±3.19 3.03±2.71 2.99 1.12±2.55 + F5H6E2 Unconventional myosin-Ic MYO1C 0.84±1.4 0.24±1.58 -1.94±1.93 2.41 -1.35±1.65 Beta-2-microglobulin [Cleaved into: Beta-2-microglobulin form pI + F5H6I0 5.3] B2M 0.84±1.4 0.24±1.58 -1.94±1.93 2.41 -1.35±1.65 Beta-2-microglobulin [Cleaved into: Beta-2-microglobulin form pI + F5H6I0 5.3] B2M 2.21±2.66 0.59±1.74 -3.87±2.87 4.54 -2.25±2.44 + F6X2W2 Neuronal growth regulator 1 NEGR1 -0.16±1.04 0.84±1.29 1.63±1.24 4.22 0.63±1.34 + F8VNT9 CD63 antigen CD63 -0.75±1.88 1.82±2.84 3.11±2.47 3.86 0.54±2.66 + F8VPC7 Endoplasmin HSP90B1 0.26±1.14 2.74±2.78 2.32 3.21±2.4 4.43 0.73±2.01 C3 and PZP-like alpha-2-macroglobulin domain-containing protein + + F8W7D1 8 CPAMD8 0.87±1.85 1.44±2.58 2.36±2.47 2.19 1.78±2.34 + F8W922 Multivesicular body subunit 12B MVB12B 2.94±2.91 2.52 2.5±3 -0.37±2 0.07±3.06 + G3V1D7 Reticulon-4 receptor-like 2 RTN4RL2 3.45±2.43 5.42 2.53±2.53 -1.84±2.54 -0.92±1.42 + G3V3X5 Latent-transforming growth factor beta-binding protein 2 LTBP2 1.16±1.11 2.7 2.97±2.39 3.76 1.15±1.27 -0.65±1.76 + + H0Y543 Mannosyl-oligosaccharide 1,2-alpha-mannosidase IB MAN1A2 0.5±1.44 2.71±3.3 3.06±3.04 2.41 0.86±2.17 + H0YBK2 Protocadherin Fat 2 FAT2 5.04±3.04 8.69 2.49±3.08 -0.88±2.39 1.68±2.35 + H0YKB2 Furin FURIN 1.13±1.27 1.84±1.85 2.38 0.23±1.33 -0.48±1.44 + I3L0S5 Lysosomal alpha-glucosidase GAA 0.94±1.81 2.86±2.73 2.61 0.15±1.21 -1.76±2.77 + J3KRC4 5' NT5C

25 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

-0.23±1.57 3.07±3.27 4.05±2.73 5.84 0.75±2.76 + J3KTE6 Proton-coupled folate transporter SLC46A1 1.14±1.96 1.78±3.49 -2.99±2.77 2.79 -3.64±3.58 2.46 + + J3KTI2 Migration and invasion enhancer 1 MIEN1 0.94±2.37 1.74±2.37 -4.03±3.1 4.16 -4.83±3.27 5.76 + + J3QLC7 T-cell antigen CD7 CD7 -0.23±2.13 1.03±2.69 2.29±2.43 2.14 1.03±2.71 + K7EJ46 Small integral membrane protein 22 SMIM22 3.63±3.03 1.82±2.82 1.84±1.86 2.33 3.65±3.66 + K7EJY5 Coiled-coil and C2 domain-containing protein 1A CC2D1A 3.26±2.5 4.31 2.69±2.99 -2.19±2.74 -1.61±2.19 + K7EK47 Adhesion G protein-coupled receptor L1 ADGRL1 1.48±1.62 1.76±1.89 1.09±1.07 2.44 0.8±2.01 + K7EKZ3 Vacuolar protein sorting-associated protein 4B VPS4B 3.7±2.91 4.11 5.11±3.71 4.76 -2.81±2.82 2.38 -4.22±3.23 4.21 + + + + K7EPJ4 Cartilage intermediate layer protein 2 CILP2 0.16±3.31 3.36±3.13 2.74 0.06±3.01 -3.14±3.3 + M0R318 Kallikrein-1 KLK1 1.79±2.36 1.25±1.97 -4.25±3.1 4.74 -3.71±2.65 + O00546 Secreted frizzled-related protein 1 SFRP1 -1.83±1.86 1.72±2.36 5.08±2.8 12.51 1.52±2.6 + O14493 Claudin-4 CLDN4 -0.13±1.33 1.11±1.93 2.17±1.68 4.09 0.93±1.94 + O43490 PROM1 0.54±1 2.1±2.02 2.56 0.53±1.21 -1.02±1.65 + O43505 Beta-1,4-glucuronyltransferase 1 B4GAT1 3.28±2.22 5.92 1.03±2.51 -1.06±2.01 1.19±2.16 + O60319 Endoplasmic reticulum resident protein 44 ERP44 0.8±1.82 0.03±1.56 -2.88±2.07 4.92 -2.11±2.14 + O94798 Ubiquilin-2 UBQLN2 0.12±1.98 -0.66±1.72 -2.29±2 3.13 -1.51±2.13 + O94858 Adhesion G protein-coupled receptor F5 GPR116 1.36±2.62 3.58±2.97 3.5 2.2±3.22 -0.03±1.6 + O95903 Polypeptide N-acetylgalactosaminyltransferase 18 GALNT18 -1.62±2.14 0.61±2.7 -6.72±4.24 7.09 -8.95±5.04 11.13 + + P01042 KNG1 -2.68±5.05 -4.97±5.05 2.32 1.71±3.69 4.01±5.83 + P04279 Semenogelin-1 SEMG1 -5.91±5.02 3.39 0.69±2.43 -0.31±2.57 -6.9±5.22 4.33 + + P10451 SPP1 -2.04±5.12 -1.2±4.15 -3.98±3.88 2.51 -4.82±6.03 + P10451 SPP1 1.52±2.04 -0.57±2.63 -3.73±2.6 5.32 -1.63±2.8 + P12110 COL6A2 0.43±1.29 2.07±1.8 3.14 0.92±1.34 -0.72±1.51 + P14543 Nidogen-1 NID1 -0.03±1.3 2.52±2.53 2.37 4.81±3.01 7.29 2.26±2.16 + + P19021 Peptidyl-glycine alpha-amidating monooxygenase PAM

26 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

1.74±2.22 2.66±2.28 3.26 0.23±1.1 -0.69±2.5 + P19835 Bile salt-activated lipase CEL -0.39±1.35 4.63±3.17 5.57 5.13±3.3 6.71 0.12±1.53 + + P21145 Myelin and lymphocyte protein MAL 1.59±2.69 -1.71±2.13 -5.26±3.22 7.92 -1.96±2.84 + P22492 Histone H1.4 HIST1H1E 0.74±2.4 4.84±3.19 6.22 -0.18±2.57 -4.29±2.78 6.55 + + P26992 Ciliary neurotrophic factor receptor subunit alpha CNTFR 0.49±2.16 -2.5±2.03 -4.08±2.52 7.65 -1.08±2.29 + P34096 Ribonuclease 4 RNASE4 0.97±2.36 3.8±3.56 2.71 1.93±2.16 -0.9±3.26 + P53990 IST1 homolog IST1 0.3±1.54 1.97±2.37 2.69±2.11 3.98 1.02±2.17 + P78369 Claudin-10 CLDN10 -4.45±5.36 -8.3±6.17 4.51 0.93±3.21 4.78±6.22 1.48 + + Q02383 Semenogelin-2 SEMG2 6.08±3.39 9.85 3.69±2.88 4 -1.39±2.21 1.01±1.85 + + Q02487 Desmocollin-2 DSC2 1.03±2.54 3.22±3.19 2.44 0.67±2.2 -1.52±3.05 + Q14314 Fibroleukin FGL2 0.76±3.4 -0.23±2.3 -4.24±3.63 3.27 -3.24±3.36 + Q15828 Cystatin-M CST6 4.54±2.71 3.54±3.05 3.24 1.2±2.05 2.2±2.36 + Q15848 Adiponectin ADIPOQ 3.17±2.35 3.63 0.02±1.46 -3.88±2.88 4.56 -0.73±1.42 Guanylate cyclase activator 2B [Cleaved into: Guanylate cyclase + Q16661 C-activating peptide 2 GUCA2B + 1.67±2.56 3.84±3.8 2.44 1.85±2.81 -0.33±3.04 + Q4QQG1 FRAS1-related extracellular matrix protein 2 FREM2 2.47±2.8 1.83±2.91 -3.84±3.61 2.7 -3.2±2.66 + Q4VC04 ProSAAS PCSK1N 1.87±2.58 0.3±1.84 2.99±2.95 2.44 4.55±2.8 7.74 + + Q5SZ76 Sialin SLC17A5 1.42±1.93 0.26±2.38 -3.23±2.56 3.89 -2.08±2.46 + Q695G9 C-type lectin domain family 14 member A CLEC14A 0.94±2.27 0.05±1.7 -2.24±2.41 2.08 -1.35±2.12 + Q6UXB8 Peptidase inhibitor 16 PI16 0.26±1.14 3.2±2.41 4.39 2.41±2.05 3.31 -0.53±1.38 + + Q7Z7M8 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 8 B3GNT8 0.41±2.03 2.65±3.06 -1.71±2.38 -3.96±3.26 3.55 + Q8IYS5 Osteoclast-associated immunoglobulin-like receptor OSCAR -4.15±3.04 4.87 -1.32±2.27 -0.88±1.67 -3.72±3.2 3.24 + Q8ND23 CARMIL3 + 2.02±2.81 0.35±1.55 -5.34±3.18 8.76 -3.68±3.13 + Q96FA3 E3 ubiquitin-protein ligase pellino homolog 1 PELI1 0.67±1.36 1.16±1.83 -1.25±1.84 -1.75±1.59 2.9 + Q9BRK3 Matrix remodeling-associated protein 8 MXRA8 0.13±1.63 5.19±3.51 5.75 4.87±3.49 4.94 -0.19±1.38 + + Q9BTR7 Leucine-rich repeat-containing protein 40 LRRC40

27 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

1.82±2.55 -1.33±2.05 -3.77±2.72 4.85 -0.61±2.36 + Q9HCN6 Platelet glycoprotein VI GP6 1.11±2.84 4.56±3.22 5.15 -1.34±2.74 -4.79±3.39 5.12 + + Q9UJU2 LEF1 50 90 151 61 Total

28 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Table 2. Relative Operating Characteristic curve analysis value of CD133 ELISA assay for the comparison of autosomal dominant polycystic kidney disease (ADPKD) vs medullary sponge kidney (MSK), ADPKD vs Healthy Controls and MSK vs Healthy Controls.

ADPKD vs Healthy MSK vs Healthy ADPKD vs MSK Control Control AUC 0.82 0.98 0.7 AUC (95%CI) (0.67-0.97) (0.94-1) (0.51-0.89) P-values 0.003 <0.0001 0.047 Cutoff (RU/ml) > 0.9850 > 0.4250 > 0.3850 Sensitivity% 53.33 93.33 53.33 Sensitivity% (95% CI) (26.59-78.73) (68.05-99.83) (26.59-78.73) Specificity% 93.33 94.44 88.89 Specificity% (95% CI) (68.05-99.83) (72.71-99.86) (65.29-98.62) Likelihood ratio 8 16.8 4.8

29 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

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