Proteomic-based research strategy identified laminin subunit alpha 2 as a potential urinary-specific biomarker for the medullary sponge kidney disease
2016; Elsevier BV; Volume: 91; Issue: 2 Linguagem: Inglês
10.1016/j.kint.2016.09.035
ISSN1523-1755
AutoresAntonia Fabris, Maurizio Bruschi, Laura Santucci, Giovanni Candiano, Simona Granata, Alessandra Dalla Gassa, Nadia Antonucci, Andrea Petretto, Gian Marco Ghiggeri, Giovanni Gambaro, Antonio Lupo, Gianluigi Zaza,
Tópico(s)Kidney Stones and Urolithiasis Treatments
ResumoMedullary sponge kidney (MSK) disease, a rare kidney malformation featuring recurrent renal stones and nephrocalcinosis, continues to be diagnosed using expensive and time-consuming clinical/instrumental tests (mainly urography). Currently, no molecular diagnostic biomarkers are available. To identify such we employed a proteomic-based research strategy utilizing urine from 22 patients with MSK and 22 patients affected by idiopathic calcium nephrolithiasis (ICN) as controls. Notably, two patients with ICN presented cysts. In the discovery phase, the urine of 11 MSK and 10 controls, were randomly selected, processed, and analyzed by mass spectrometry. Subsequently, several statistical algorithms were undertaken to select the most discriminative proteins between the two study groups. ELISA, performed on the entire patients' cohort, was used to validate the proteomic results. After an initial statistical analysis, 249 and 396 proteins were identified exclusive for ICN and MSK, respectively. A Volcano plot and ROC analysis, performed to restrict the number of MSK-associated proteins, indicated that 328 and 44 proteins, respectively, were specific for MSK. Interestingly, 119 proteins were found to differentiate patients with cysts (all patients with MSK and the two ICN with renal cysts) from ICN without cysts. Eventually, 16 proteins were found to be common to three statistical methods with laminin subunit alpha 2 (LAMA-2) reaching the higher rank by a Support Vector Machine, a binary classification/prediction scheme. ELISA for LAMA-2 validated proteomic results. Thus, using high-throughput technology, our study identified a candidate MSK biomarker possibly employable in future for the early diagnosis of this disease. Medullary sponge kidney (MSK) disease, a rare kidney malformation featuring recurrent renal stones and nephrocalcinosis, continues to be diagnosed using expensive and time-consuming clinical/instrumental tests (mainly urography). Currently, no molecular diagnostic biomarkers are available. To identify such we employed a proteomic-based research strategy utilizing urine from 22 patients with MSK and 22 patients affected by idiopathic calcium nephrolithiasis (ICN) as controls. Notably, two patients with ICN presented cysts. In the discovery phase, the urine of 11 MSK and 10 controls, were randomly selected, processed, and analyzed by mass spectrometry. Subsequently, several statistical algorithms were undertaken to select the most discriminative proteins between the two study groups. ELISA, performed on the entire patients' cohort, was used to validate the proteomic results. After an initial statistical analysis, 249 and 396 proteins were identified exclusive for ICN and MSK, respectively. A Volcano plot and ROC analysis, performed to restrict the number of MSK-associated proteins, indicated that 328 and 44 proteins, respectively, were specific for MSK. Interestingly, 119 proteins were found to differentiate patients with cysts (all patients with MSK and the two ICN with renal cysts) from ICN without cysts. Eventually, 16 proteins were found to be common to three statistical methods with laminin subunit alpha 2 (LAMA-2) reaching the higher rank by a Support Vector Machine, a binary classification/prediction scheme. ELISA for LAMA-2 validated proteomic results. Thus, using high-throughput technology, our study identified a candidate MSK biomarker possibly employable in future for the early diagnosis of this disease. In the past 10 years, worldwide important research programs have been undertaken to better identify the pathologic network and the genetic bases involved in the onset and development of medullary sponge kidney (MSK) disease, a rare congenital condition (prevalence of ∼5 cases per 10,000–100,000 in the general population) typically associated with nephrocalcinosis and nephrolithiasis, urinary acidification and concentration defects, and cystic anomalies in the precalyceal ducts.1Fabris A. Anglani F. Lupo A. et al.Medullary sponge kidney: state of the art.Nephrol Dial Transplant. 2013; 28: 1111-1119Crossref PubMed Scopus (50) Google Scholar MSK disease, although rare in the general population, is relatively frequent in renal stone formers, reaching a high degree of association in some published medical records (as high as 20%), and it has been also linked to developmental disorders (e.g., congenital hemihypertrophy and Beckwith-Wiedemann syndrome) and kidney developmental anomalies (e.g., horseshoe kidney, unilateral renal aplasia, contralateral congenital small kidney).2Cameron S. Medullary sponge kidney.in: Davison A.M. Cameron J.S. Grunfeld J.-P. Oxford Textbook of Clinical Nephrology. 3rd ed. Oxford University Press, Oxford, UK2004: 2495-2501Google Scholar, 3Lambrianides A.L. John D. Medullary sponge disease in horseshoe kidney.Urology. 1987; 29: 426-427Abstract Full Text PDF Scopus (15) Google Scholar, 4Gambaro G. Fabris A. Citron L. et al.An unusual association of contralateral congenital small kidney, reduced renal function and hyperparathyroidism in sponge kidney patients: on the track of the molecular basis.Nephrol Dial Transplant. 2005; 20: 1042-1047Crossref PubMed Scopus (31) Google Scholar, 5Rommel D. Pirson Y. Medullary sponge kidney–part of a congenital syndrome.Nephrol Dial Transplant. 2001; 16: 634-636Crossref PubMed Scopus (12) Google Scholar MSK disease occurrence in childhood and its relationship with systemic congenital malformations support the hypothesis of an inherited condition.6Indridason O.S. Thomas L. Berkoben M. Medullary sponge kidney associated with congenital hemihypertrophy.J Am Soc Nephrol. 1996; 7: 1123-1130Google Scholar As recently reported by our group, familial clustering of MSK disease is identified in several cases with autosomal dominant inheritance with reduced penetrance and variable expressivity.7Fabris A. Lupo A. Ferraro P.M. et al.Familial clustering of medullary sponge kidney is autosomal dominant with reduced penetrance and variable expressivity.Kidney Int. 2013; 83: 272-277Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar Additionally, recent evidence showed that mutations or polymorphisms of the glial cell line–derived neurotrophic factor and receptor tyrosine kinase genes, disrupting the "ureteric bud–metanephric mesenchyme" interface, could be responsible for the disease pathogenesis.8Diouf B. Ka E.H. Calender A. et al.Association of medullary sponge kidney disease and multiple endocrine neoplasia type IIA due to RET gene mutation: is there a causal relationship?.Nephrol Dial Transplant. 2000; 15: 2062-2063Crossref PubMed Scopus (13) Google Scholar, 9Torregrossa R. Anglani F. Fabris A. et al.Identification of GDNF gene sequence variations in patients with medullary sponge kidney disease.Clin J Am Soc Nephrol. 2010; 5: 1205-1210Crossref PubMed Scopus (39) Google Scholar Despite the aforementioned associations and the latest progress in understanding the biological mechanisms associated with MSK disease, the pathogenesis of this disorder is only partially defined, and further studies are necessary to achieve better understanding of the mechanisms. Furthermore, at the moment, no diagnostic biomarkers are available for clinical purposes. In fact, because of its silent manifestation, currently MSK disease can only be clinically and laboratory suspected. Demonstration of nephrocalcinosis and multiple small calcium concretions seen on the papillary medulla on a radiography of the kidney or on a computed tomography scan without contrast or hyperechoic papillae on ultrasound is diagnostic.10Gambaro G. Danza F.M. Fabris A. Medullary sponge kidney.Curr Opin Nephrol Hypertens. 2013; 22: 421-426Crossref PubMed Scopus (24) Google Scholar The essential characteristic of the diagnostic image should be the identification of ectatic precalyceal papillary collecting ducts that represent the anatomic landmark of MSK disease.11Gambaro G. Feltrin G.P. Lupo A. et al.Medullary sponge kidney (Lenarduzzi-Cacchi-Ricci disease): a Padua medical school discovery in the 1930s.Kidney Int. 2006; 69: 663-670Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar Because of its high spatial resolution, i.v. urography represented the gold standard for diagnosing MSK disease,12Forster J.A. Taylor J. Browning A.J. et al.A review of the natural progression of medullary sponge kidney and a novel grading system based on intravenous urography findings.Urol Int. 2007; 78: 264-269Crossref Scopus (15) Google Scholar, 13Teichman J.M. Clinical practice. Acute renal colic from ureteral calculus.N Engl J Med. 2004; 350: 684-693Crossref PubMed Scopus (291) Google Scholar but unfortunately this test has been largely abandoned, revealing the necessity to identify MSK disease molecular biomarkers that could help clinicians to easily identify patients with this disease and to start preventive therapies to minimize renal and systemic complications. Therefore, in our research project, a proteomic high-throughput methodology was used for the first time to identify new biological elements involved in the pathophysiology of MSK disease and to select specific MSK disease–associated proteins that, in future, whether validated in a large cohort, could represent valuable new diagnostic disease biomarkers for use in daily clinical practice. This will represent a significant step forward beyond current state of the art. Multidimensional scaling (MDS) performed using the whole patients' urinary proteomic profile obtained by mass spectrometer analysis was able to clearly discriminate MSK disease from ICN (Figure 1). Interestingly, ICN with renal cysts are located on the edge of the two cohorts revealing an intermediate phenotype of these subjects. Concordantly to MDS, correlogram (Figure 2a ) was able to differentiate the 2 study groups. The average Spearman coefficient (R2) was higher in the 2 study groups (0.45 ± 0.85). The mean of the coefficient of variation for the 21 biological replicates was 0.43, and no relationship was found between individual proteomes. A total of 1529 proteins were identified by mass spectrometry (MS) analysis, and 884 (58%) overlapped the 2 study groups. Instead, 249 proteins (16%) and 396 (26%) were exclusive for ICN and MSK disease, respectively (Figure 2b) (Supplementary Tables S1 and S2). To better describe differences between the 2 cohorts of patients and to restrict the number of MSK disease–associated proteins, we used a volcano plot and receiver-operating characteristic (ROC) curve analysis. The 2 methods revealed that 328 (Figure 3a ) and 44 (Supplementary Table S3) proteins, respectively, were specific for MSK disease. Interestingly, then, 119 proteins (Supplementary Table S4) differentiated patients with cysts (a group including all MSK disease + 2 ICN patients with renal cysts) from ICN patients without cysts (Figure 3b). In particular, a total of 22 and 15 proteins were simultaneously up-regulated or down-regulated in the comparisons between MSK disease versus ICN and cysts versus no cysts. Sixteen proteins were common in the 3 methods, as shown in the Venn diagram (Figure 4a ) and listed in Table 1. The proteome profile (Figure 4b) summarized results and reported the name of the 16 highlighted proteins.Table 1Highly significant proteins discriminating medullary sponge kidney disease patients from idiopathic calcium nephrolithiasis patientsProtein IDProtein namesGene symbolMolecular weight, DaSequence coverage, %PeptidesMS/MS countRanks MSK/ICNExpression MSK/ICNLog2 fold change MSK/ICNLog2 fold change SD−Log10 P value: MSK/ICNExpression cysts/no cysts−Log10 P value: cysts/no cystsROC AUC MSK/ICN95% CI MSK/ICN−Log10 P value: ROC MSK/ICNP24043Laminin subunit alpha 2LAMA2343,9051.2320Up5.53527510.84895553.84485Up2.28083580.90.7451–1.0552.708409P35052Glypican-1GPC161,687.94624Up2.535594070.396752862.21179Up2.002198590.88180.7186–1.0452.506542C9JWQ3Plexin domain-containing protein 1PLXDC155,7613.1347Up3.39472Up2.157862140.90.7451–1.0552.708409H3BS10Beta-hexosaminidaseHEXA60,7035.93149Down−2.64740850.005693612.90072Up2.958012110.89090.7328–1.0492.606425H0YA68Alpha-mannosidaseMAN2B2113,9799.172145Up2.278857750.099298071.90534Up2.544225740.90.7502–1.052.708409P15941-7Mucin-1MUC1122,10225.152494Down−3.2000440.370328711.79053Down1.615289950.76820.5453–0.9911.422278J3QS03Twisted gastrulation protein homolog 1TWSG125,0178.21420Down−4.86826550.151275113.51703Down3.904184670.80450.6056–1.0031.736127Q01459Di-N-acetylchitobioseCTBS43,76023.982190Down−2.84817990.46433473.29016Down2.462045880.80910.62–0.99821.777544Q496F6CMRF35-like molecule 2CD300E22,91811.22219Down5.56590Down3.263992530.86360.6933–1.0342.312382K7ES72CalcyphosinCAPS20,9678.51253Down3.35984Down2.439001760.86360.6933–1.0342.312382Q9HBR0Putative sodium-coupled neutral amino acid transporter 10SLC38A10119,7626.15615Down−4.04901610.410025825.80300Down2.732612350.90910.7666–1.0522.812479Q5VSP4Putative lipocalin 1–like protein 1LCN1P117,9181321475Down−4.1587020.34080552.99451Down2.839343490.82730.6524–1.0021.948076P41222Prostaglandin-H2 D-isomerasePTGDS21,02963.71624995Down−2.28932050.163846023.00454Down2.505520560.78180.5835–0.98011.536256Q14315-2Filamin-CFLNC291,022472957Down−3.83494770.16387033.46704Down2.523938580.83640.6533–1.0192.036118Q08499-7cAMP-specific 3′,5′-cyclic phosphodiesterase 4DPDE4D91,1153.71621Down5.64435Down2.092347630.86360.6933–1.0342.312382H0Y4H3CD99 antigen-like protein 2CD99L227,98614.6155Down5.62626Down2.432426730.90910.7666–1.0522.812479 Open table in a new tab To have another classification method to establish the priority and relevance of proteins and to further reduce the choice of the highlighted potential biomarkers identified by means of statistical analysis, we also used a Support Vector Machine. A Support Vector Machine is a nonprobabilistic machine-learning method of binary classification/prediction proposed by Vapnik.14Vapnik V.N. An overview of statistical learning theory.IEEE Trans Neural Netw. 1999; 10: 988-999Crossref PubMed Scopus (4556) Google Scholar It constructs a hyperplane or a set of hyperplanes in a high- or infinite-dimensional space by different kernel functions to achieve a high accuracy of classification. Here, we used the nonlinear kernel function to establish a ranked proteins list. Using Support Vector Machine, the majority of 44 proteins resulting from the ROC curve analysis and all of the 16 proteins obtained from the combination of the 3 statistic methods were lower than the hundredth position of the ranked proteins list. This result confirms the high ability of these proteins to distinguish MSK disease from ICN (Figure 4). Five proteins have been consistently selected by all 4 methods: glypican-1, plexin domain–containing protein 1, beta-hexosaminidase, epididymis-specific alpha-mannosidase, and laminin subunit alpha 2 (LAMA-2). This last protein was the most significant biomarker identified by the 4 methods because it was statistically upregulated in both univariate analyses, had a high area under the curve, and had the lowest rank of the 16 highlighted biomarkers. Notably, major renal stone inhibitors, although not present at the top of the list of the discriminative proteins between MSK disease and ICN, were present in the urine of our patients. In particular, inter-alpha-trypsin inhibitor heavy chain 3 and inter-alpha-trypsin inhibitor heavy chain 4 isoform-2 were significantly downregulated in MSK disease (Supplementary Figure S1 and Supplementary Table S5) compared with ICN. Inter-alpha-trypsin inhibitor heavy chain 2, inter-alpha-trypsin inhibitor heavy chain 4, secreted phosphoprotein 1, and coagulation factor II thrombin showed a good trend for downregulation in MSK disease (although not statistically significant). The specific diversity of the proteins identified in MSK disease /ICN could imply a different function of these proteins. Therefore, we performed a gene ontology analysis based on biological process annotation using Cytoscape software. Gene ontology (GO) analysis identified 166 significant top gene signatures (data not shown). To quickly visualize the top biological processes that distinguish MSK disease from ICN, the results of the GO analysis were shown using the MDS and volcano plots. An MDS scatterplot shows 3 different clusters characterized by biological process common and exclusive of MSK disease and ICN (Figure 5). To test whether the tendency toward specific biochemical pathways in MSK and ICN in relation to the difference in protein abundance, we estimated the relative functional GO enrichment using a volcano plot. In this graph, the means of the fold change of proteins associated with each biological process were plotted against their probability to be active in MSK or ICN (Figure 6). We show a bubble diagram of significant top gene signatures to summarize the results of both analyses (Figure 7).Figure 7Two-dimensional bubble plot of gene ontology annotation analysis. The graph shows the main biological processes (circles) plotted against the −Log10 P value after false discovery rate correction for proteins identified in medullary sponge kidney disease and idiopathic calcium nephrolithiasis patients. The sizes of circles are proportional to the numbers of proteins associated with each statistically significant biological process. Colors have only an esthetic purpose.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Interestingly, selected proteins were implicated in proteolysis, endocytosis, extracellular matrix organization, complement and coagulation cascades, epidermal development, tissue homeostasis, and the glycosaminoglycan catabolic process. To select a potential protein to be used as a disease biomarker, we performed an enzyme-linked immunosorbent assay (ELISA) by using the most discriminating protein based on statistical analysis of proteomic data. As shown in Figure 8a, LAMA-2 (the selected protein) was analyzed by ELISA of the entire cohort of patients included in the study. Statistical analysis revealed that this protein was upregulated in MSK compared with ICN patients (median/interquartile range, 3.67/1.63–8.52 vs. 1.70/0.31–3.70). However, although able to clearly differentiate MSK from ICN (see ROC curve in Figure 8b), the difference between the 2 study groups was less marked as in a MS analysis. This could be probably due to the different performance of the 2 techniques. The sensitivity of MS is very high, and it has a significant ability to accurately distinguish different isoforms or modified forms of proteins.15Hale J.E. Advantageous uses of mass spectrometry for the quantification of proteins.Int J Proteomics. 2013; 2013: 219452Crossref Google Scholar ICN patients with cysts showed intermediate levels of LAMA-2 (median/interquartile range, 2.31/2.01–2.60). At the moment, MSK disease continues to be diagnosed by using complex, expensive, and time-consuming clinical and instrumental tests. In particular, as mainly described, urography is the available gold standard test for MSK diagnosis.12Forster J.A. Taylor J. Browning A.J. et al.A review of the natural progression of medullary sponge kidney and a novel grading system based on intravenous urography findings.Urol Int. 2007; 78: 264-269Crossref Scopus (15) Google Scholar, 13Teichman J.M. Clinical practice. Acute renal colic from ureteral calculus.N Engl J Med. 2004; 350: 684-693Crossref PubMed Scopus (291) Google Scholar Unfortunately, this methodology has been mainly abandoned, making MSK diagnosis more difficult. Therefore, it is unquestionable that the recognition of new molecular biomarkers could represent an important objective in nephrology, contributing to a rapid, accurate, and less expensive diagnosis of MSK and avoiding misclassification. To achieve this objective, we decided to use a well-standardized, high-throughput methodology to identify the urinary proteomic profile highly specific for MSK and able to clearly discriminate these patients from other cohorts of different diseases. After bioinformatics, 16 proteins were highly associated with MSK, but a more conservative selection identified LAMA-2 (merosin) as the biological element to best discriminate between MSK and ICN patients. LAMA-2 is a well-described subunit of laminin, a family of at least 15 αβγ heterotrimeric proteins of extracellular matrix, that represents a major component of the basement membrane.16Colognato H. Yurchenco P.D. Form and function: the laminin family of heterotrimers.Dev Dyn. 2000; 218: 213-234Crossref PubMed Scopus (1044) Google Scholar It is thought to mediate the attachment, migration, and organization of cells into tissues during embryonic development by interacting with other extracellular matrix components. Deposition and/or formation of laminin into the extracellular matrix is regulated by cell-surface receptors, including integrins, dystroglycan, and syndecans.17Hamill K.J. Kligys K. Hopkinson S.B. et al.Laminin deposition in the extracellular matrix: a complex picture emerges.J Cell Sci. 2009; 122: 4409-4417Crossref PubMed Scopus (94) Google Scholar, 18O'Brien L.E. Jou T.S. Pollack A.L. et al.Rac1 orientates epithelial apical polarity through effects on basolateral laminin assembly.Nat Cell Biol. 2001; 3: 831-838Crossref PubMed Scopus (373) Google Scholar As reported in the literature, laminin may have a role in cyst formation. O'Brien et al.,18O'Brien L.E. Jou T.S. Pollack A.L. et al.Rac1 orientates epithelial apical polarity through effects on basolateral laminin assembly.Nat Cell Biol. 2001; 3: 831-838Crossref PubMed Scopus (373) Google Scholar using the Madin-Darby canine kidney epithelial cell line, demonstrated that this protein is responsible for apical pole orientation during cyst formation. In particular, they observed that Rac 1 (a GTPase that belongs to the renin-angiotensin system superfamily of small guanosine triphosphate–binding proteins) regulates extracellular laminin assembly, and then laminin directs the orientation of the apical pole. In contrast, the absence of laminin causes an inversion of the apical pole. Likewise laminin seems to have a pivotal role in the development of cysts in polycystic kidney disease (PKD), the most frequent inherited disorder in which clusters of cysts develop in the kidneys. This condition is due to germline and somatic PKD1 or PKD2 gene mutations. The main clinical feature is the progressive appearance and growth of multiple renal cysts, resulting in frequent end-stage renal failure.19Wilson P.D. Polycystic kidney disease.N Engl J Med. 2004; 350: 151-164Crossref PubMed Scopus (615) Google Scholar In these patients, cysts originate from the nephrons and collecting tubules; microdissection reveals that the cysts communicate directly with the nephrons and collecting tubules. Islands of normal parenchymal renal tissue are interspaced between the cysts. Epithelial cells of different nephron areas are characterized by increased proliferation, abnormality in cell polarity, fluid secretion, and extracellular matrix production.19Wilson P.D. Polycystic kidney disease.N Engl J Med. 2004; 350: 151-164Crossref PubMed Scopus (615) Google Scholar In PKD, the basement membranes of the cysts are markedly thickened with an aberrant accumulation of laminin-332 that contributes to aberrant proliferation of epithelial cells and cyst growth.20Shannon M.B. Patton B.L. Harvey S.J. et al.A hypomorphic mutation in the mouse laminin alpha5 gene causes polycystic kidney disease.J Am Soc Nephrol. 2006; 17: 1913-1922Crossref PubMed Scopus (96) Google Scholar, 21Joly D. Berissi S. Bertrand A. et al.Laminin 5 regulates polycystic kidney cell proliferation and cyst formation.J Biol Chem. 2006; 281: 29181-29189Crossref PubMed Scopus (36) Google Scholar, 22Vijayakumar S. Dang S. Marinkovich M.P. et al.Aberrant expression of laminin-332 promotes cell proliferation and cyst growth in ARPKD.Am J Physiol Renal Physiol. 2014; 306: F640-F654Crossref Scopus (14) Google Scholar In addition, cystic cells of PKD showed that upregulation of laminin compared with control kidneys23Joly D. Morel V. Hummel A. et al.Beta4 integrin and laminin 5 are aberrantly expressed in polycystic kidney disease: role in increased cell adhesion and migration.Am J Pathol. 2003; 163: 1791-1800Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar has a role in regulating cellular proliferation and cyst growth.21Joly D. Berissi S. Bertrand A. et al.Laminin 5 regulates polycystic kidney cell proliferation and cyst formation.J Biol Chem. 2006; 281: 29181-29189Crossref PubMed Scopus (36) Google Scholar In particular, laminin-5 is overexpressed in autosomal dominant PKD, whereas it is not expressed in normal adult tubules.24Mizushima H. Miyagi Y. Kikkawa Y. et al.Differential expression of laminin-5/ladsin subunits in human tissues and cancer cell lines and their induction by tumor promoter and growth factors.J Biochem. 1996; 120: 1196-1202Crossref PubMed Scopus (94) Google Scholar Moreover, laminin-5 is a preferential adhesion substrate for cyst-lining epithelial cells, mainly through α6β4 interaction, and integrin α6β4–laminin-5 interactions mediate both adhesion and migration of cyst-derived cells.23Joly D. Morel V. Hummel A. et al.Beta4 integrin and laminin 5 are aberrantly expressed in polycystic kidney disease: role in increased cell adhesion and migration.Am J Pathol. 2003; 163: 1791-1800Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar It is plausible that a similar mechanism could be activated in MSK, and the overexpression of LAMA-2 could have a central role in the development and growth of kidney cysts in this patient population. Therefore, we believe that in the future this protein could be a good biomarker candidate, but additional studies definitely need to be performed in a large and perhaps multicenter cohort of patients to validate our results and to make it accessible as a clinically useful diagnostic biomarker. The role of LAMA-2 in cell polarization is interesting from a functional standpoint as well. Actually, the hypothesis was advanced that the many tubular dysfunctions observed in MSK patients could be the expression of abnormal tubular cell polarization and mistargeting of carriers.11Gambaro G. Feltrin G.P. Lupo A. et al.Medullary sponge kidney (Lenarduzzi-Cacchi-Ricci disease): a Padua medical school discovery in the 1930s.Kidney Int. 2006; 69: 663-670Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar Notably, our research proteomic-based approach identified other proteins overexpressed in MSK patients such as epididymis-specific alpha-mannosidase (MAN2B2), plexin domain–containing protein 1 (PLXDC1), beta-hexosaminidase (HEXA), and glypican-1 (GPC1). None of them has been previously associated with MSK or other cystic diseases. Among them, GPC1, a member of the family of heparan sulfate proteoglycans that are linked to the cell surface by a glycosyl-phosphatidylinositol anchor,25David G. Integral membrane heparan sulfate proteoglycans.FASEB J. 1993; 7: 1023-1030Crossref PubMed Scopus (373) Google Scholar, 26Weksberg R. Squire J.A. Templeton D.M. Glypicans: a growing trend.Nat Genet. 1996; 12: 225-227Crossref PubMed Scopus (60) Google Scholar interacts and regulates the activity of fibroblast growth factor-2, vascular endothelial growth factor, and other growth factors27Gengrinovitch S. Berman B. David G. et al.Glypican-1 is a VEGF165 binding proteoglycan that acts as an extracellular chaperone for VEGF165.J Biol Chem. 1999; 274: 10816-10822Crossref PubMed Scopus (158) Google Scholar, 28Steinfeld R. Van Den Berghe H. David G. Stimulation of fibroblast growth factor receptor-1 occupancy and signaling by cell surface-associated syndecans and glypican.J Cell Biol. 1996; 133: 405-416Crossref PubMed Scopus (230) Google Scholar, 29Bonneh-Barkay D. Shlissel M. Berman B. et al.Identification of glypican as a dual modulator of the biological activity of fibroblast growth factors.J Biol Chem. 1997; 272: 12415-12421Crossref PubMed Scopus (102) Google Scholar, 30Kleef J. Ishiwata T. Kumbasar A. et al.The cell surface heparan sulfate proteoglycan glypican-1 regulates growth factor in pancreatic carcinoma cells and is overexpressed in human pancreatic cancer.J Clin Invest. 1998; 102: 1662-1673Crossref PubMed Scopus (313) Google Scholar modulating biological activities. Thus, glypicans play an important regulatory role in the control of cellular growth, differentiation, and morphogenesis.26Weksberg R. Squire J.A. Templeton D.M. Glypicans: a growing trend.Nat Genet. 1996; 12: 225-227Crossref PubMed Scopus (60) Google Scholar Speculatively, its hyperexpression in MSK could reflect a possible increased cellular turnover/proliferation rate and orchestrate cellular processes leading to cyst formation. Case functional studies are also needed to better understand this point. In addition, the levels of GPC1 and PLXDC1 evaluated by ELISA, although not statistically significant, were higher in MSK compared with ICN (Supplementary Figures S2 and S3). Further studies with large patient populations could be useful in future to better study these selected proteins. Other MSK-related proteins are associated with important biological processes including proteolysis, endocytosis, extracellular matrix organization, complement and coagulation cascades, epidermal development, tissue homeostasis, and the glycosaminoglycan catabolic process, as revealed by analysis using Cytoscape software. These polyfunctional networks denote tremendous cell activation that includes matrix remodeling and immune-inflammatory activation and underscored the histologic and tissue changes that make
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