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Global Analysis Reveals the Complexity of the Human Glomerular

† † Rachel Lennon,* Adam Byron,* Jonathan D. Humphries,* Michael J. Randles,* † ‡ † | Alex Carisey,* Stephanie Murphy,* David Knight, Paul E. Brenchley, Roy Zent,§ and Martin J. Humphries*

*Wellcome Trust Centre for -Matrix Research and ‡Biological Mass Spectrometry Core Facility, Faculty of Life Sciences, and †Faculty of Medical and Human Sciences, University of Manchester, Manchester, United Kingdom; §Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee; and |Department of Medicine, Veterans Affairs Hospital, Nashville, Tennessee

ABSTRACT The glomerulus contains unique cellular and extracellular matrix (ECM) components, which are required for intact barrier function. Studies of the cellular components have helped to build understanding of glomerular disease; however, the full composition and regulation of glomerular ECM remains poorly understood. We used mass spectrometry-based proteomics of enriched ECM extracts for a global analysis of human glomerular ECM in vivo and identified a tissue-specific proteome of 144 structural and regulatory ECM . This catalog includes all previously identified glomerular components plus many new and abundant components. Relative quantification showed a dominance of IV, collagen I, and isoforms in the glomerular ECM together with abundant collagen VI and TINAGL1. Protein net- work analysis enabled the creation of a glomerular ECM interactome, which revealed a core of highly connected structural components. More than one half of the glomerular ECM proteome was validated using colocalization studies and data from the Human Protein Atlas. This study yields the greatest number of ECM proteins relative to previous investigations of whole glomerular extracts, highlighting the impor- tance of sample enrichment. It also shows that the composition of glomerular ECM is far more complex than previously appreciated and suggests that many more ECM components may contribute to glomerular development and disease processes. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000456.

J Am Soc Nephrol 25: 939–951, 2014. doi: 10.1681/ASN.2013030233

The glomerulus is a sophisticated organelle com- In addition to operating as a signaling platform, prisinguniquecellularandextracellularmatrix(ECM) ECM provides a structural scaffold for adjacent cells components. Fenestrated capillary endothelial cells and has a tissue-specific molecular composition.3,4 and overlying podocytes are separated by a special- ized glomerular (GBM), and Received March 11, 2013. Accepted September 10, 2013. these three components together form the filtration Published online ahead of print. Publication date available at barrier. Mesangial cells and their associated ECM, www.jasn.org. the mesangial matrix, exist between adjacent capil- Present address: Adam Byron, Edinburgh Cancer Research lary loops and maintain the three-dimensional or- United Kingdom Centre, Institute of Genetics and Molecular ganizationof the capillary bundle.Inturn,theparietal Medicine, University of Edinburgh, Edinburgh, United Kingdom. epithelial cells and ECM of Bowman’s capsule enclose Correspondence: Dr. Rachel Lennon, Wellcome Trust Centre for this network of capillaries. Cells adhere to ECM pro- Cell-Matrix Research, Michael Smith Building, University of teins by adhesion receptors, and these interactions are Manchester, Manchester M13 9PT, United Kingdom. Email: Rachel. required to maintain intact barrier function of the [email protected] glomerulus.1,2 Copyright © 2014 by the American Society of Nephrology

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Candidate-based investigations of glomerular ECM have fo- Therefore, to interrogate this complexity effectively, a systems- cused on the GBM and shown that it resembles the typical level understanding of glomerular ECM is required. To address basal lamina found in multicellular organisms, containing a the need for a global analysis of the extracellular environment core of (collagen IV, , and nidogens) within the glomerulus, we used mass spectrometry (MS)-based and (agrin, , and colla- proteomics of glomerular ECM fractions to define the human gen XVIII).5 Mesangial and parietal cell ECMs have been less glomerular ECM proteome. well investigated; nonetheless, they are also thought to contain similar core components in addition to other glycoproteins, including fibronectin.6,7 Thus, the glomerulus consists of a RESULTS combination of condensed ECM within the GBM and Bow- man’s capsule and loose ECM supporting the mesangial cells. Isolation of Glomerular ECM The ECM compartments in the glomerulus are thought to Using purified isolates of human glomeruli (Figure 1A), we be distinct and exhibit different functional roles. The GBM is developed a fractionation approach to collect glomerular integral to the capillary wall and therefore, functionally linked ECM proteins (Figure 1B). Before homogenization and solu- to glomerular filtration.5 Mutations of tissue-restricted iso- bilization, extracted glomeruli appeared acellular, suggestive forms of collagen IV (COL4A3, COL4A4,andCOL4A5)and of successful ECM enrichment (Figure 1A). Western blotting laminin (LAMB2), which are found in the GBM, cause signif- confirmed enrichment of ECM proteins (collagen IVand lam- icant barrier dysfunction and ultimately, renal failure.8,9 Less inin) and depletion of cytoplasmic and nuclear proteins is understood about the functions of mesangial and parietal (nephrin, , and B1) in the ECM fraction (Figure cell ECMs, although expansion of the mesangial compartment 1C). Glomerular ECM fractions from three adult human kid- is a histologic pattern seen across the spectrum of glomerular neys were then extracted and analyzed by MS. disease.10 Compositional investigation of the distinct glomerular Ontology Enrichment Analysis of ECM Fractions ECM compartments is limited by the technical difficulties of All proteins identified by MS were allocated to categories separation. Early investigations of GBM constituents used the according to their (GO) assignment, and the relative insolubility of ECM proteins to facilitate separation enrichment of GO terms in the dataset was assessed using GO from cellular proteins in the glomerulus but did not separate enrichment analysis, which is described in Supplemental the GBM from mesangial and parietal cells ECMs.11,12 Re- Methods. The network of enriched GO terms clustered into cently, studies incorporating laser microdissection of glomer- three subnetworks, and the largest, most confidently identified ular sections have been coupled with proteomic analyses.13,14 subnetwork was for ECM-related GO terms (Figure 2A). A These studies report both cellular and ECM components and large majority of other proteins were included in two addi- typically require pooled material from glomerular sections to tional subnetworks representing mitochondrial and cytoskel- improve protein identification. The ability of laser microdis- etal GO terms, and the presence of these proteins in the ECM section to separate glomerular ECM compartments has not yet fractions is likely to be caused by the strength of their inter- been tested; however, this approach will be limited by the molecular interactions with ECM proteins. Spectral counting amount of protein that is possible to retrieve. To achieve was used to determine the enrichment of ECM in each of the good coverage of ECM proteins within a tissue, proteomic four fractions collected from isolated human glomeruli. There studies need to combine a reduction in sample complexity was 38% enrichment of extracellular proteins in the glomer- with maximal protein quantity. Currently, the inability to sep- ular ECM fractions (Figure 2B), and this result compares arate glomerular ECM compartments in sufficient quantity favorably with the 12%–30% enrichment of ECM proteins re- is a limitation that prohibits proteomic studies of these struc- ported in comparable proteomic studies.15 tures; however, for other tissues, proteomic analysis of ECM has been achieved by enrichment of ECM combined with sam- Relative Abundance of Glomerular ECM Proteins ple fractionation.15 To determine the relative abundance of glomerular ECM Although the composition of the ECM in other tissues has proteins, we used MS to analyze additional glomerular ECM been addressed using proteomic approaches,15 studies of glo- fractions from three human kidneys, and we quantified the merular ECM to date have used candidate-based technologies. proteins using a intensity approach,20 which is de- These studies have identified key molecular changes during scribed in Supplemental Methods. Using this analysis, the 10 development and disease and highlighted the compositional most abundant proteins were a combination of , lam- and organizational dynamics of glomerular ECM. Nonethe- inin 521, and heparan sulfate proteoglycans (Figure 2C, Sup- less, the extracellular environment within the glomerulus is plemental Table 1), which have all previously been reported in the setting for a complex series of interactions between both the glomerulus. In addition, we found collagen VI and the structural ECM proteins and ECM-associated proteins, such TINAGL1 among the 10 most abundant pro- as growth factors16–18 and proteases,19 which together pro- teins, and the detection of these two components was con- vide a specialized niche to support glomerular cell function. firmed by Western blotting (Figure 2D). Relative quantification

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further categorized as basement membrane proteins (Table 1), other structural ECM proteins (Table 2), or ECM-associated pro- teins (Supplemental Table 2). There are a number of published glomerular proteo- mic studies,21–25 and a comparison was made with protein identifications in our dataset (Figure 3). Of two published studies for which complete MS datasets were avail- able,22,25 neither study enriched for glo- merular ECM or analyzed more than one biologic replicate. We detected all 15 ECM proteins reported by Yoshida et al.22 (which represents 8% of ECM proteins detected in this study) and 76% of 91 ECM proteins reported by Cui et al.25 (which represents 39% of ECM proteins detected in this study) based on identification in one bio- logic replicate, which was reported in these published studies. Furthermore, we identi- fied12timesasmanyECMproteinsas Yoshida et al.22 and 2 times as many ECM proteins as Cui et al.25 Therefore, our study yielded the greatest number of ECM pro- teins from glomeruli to date, likely owing to the use of effective ECM enrichment be- fore state-of-the-art MS analysis.

Creation of a Protein Interaction Network To visualize the components of glomerular ECM as a network of interacting proteins, the identified proteins were mapped onto a Figure 1. Isolation of enriched glomerular ECM. (A) Human glomeruli were isolated by curated protein interaction database (Sup- differential sieving, yielding .95% purity. Before homogenization, glomeruli appeared plemental Methods) to generate an inter- acellular (right). (B) A proteomic workflow for the isolation of enriched glomerular ECM by action network (Figure 4A). Statistical fractionation (details in Concise Methods). (C) Coomassie staining and Western blotting analysis showed that the network was – (WB) of fractions 1 3 and the ECM fraction probing for the extracellular proteins with more clustered than expected by chance, pancollagen IV and panlaminin probes and the intracellular proteins nephrin, actin, and indicative of preferential interaction of pro- . M, molecular mass marker. teins in a nonrandom network topology (Supplemental Table 3). Interestingly, base- ment membrane and structural ECM pro- was also performed using spectral counting, which was compared teins were involved in more interactions with other proteins in with the peptide intensity approach, and further showed that the network than ECM-associated proteins (Figure 4B). Topolog- collagen VI and TINAGL1 were among the most abundant pro- ical network analysis confirmed that basement membrane and teins detected in the glomerular ECM (Supplemental Figure 2). other structural ECM proteins formed a highly connected core subnetwork, whereas ECM-associated proteins were less clustered Defining the Glomerular ECM Proteome in the network (Supplemental Figures 3 and 4). These data sug- Using the GO classification of extracellular region proteins and gest that structural ECM proteins mediate multiple sets of pro- crossreferencing with the human matrisome project3 (de- tein–protein interactions and thus, have important roles in the scribed in Supplemental Methods), we identified 144 extracel- assembly and organization of glomerular ECM. lular proteins in the glomerular ECM, including all previously known glomerular ECM components. Only proteins identi- Localization of Glomerular ECM Proteins fied in at least two of three biologic replicate analyses were Validation of protein expression was performed by searching included in the dataset, and identified ECM proteins were the Human Protein Atlas (HPA) database26 for 144 glomerular

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Figure 2. MS analysis of enriched glomerular ECM fractions. (A) GO enrichment analysis of the full MS dataset. Nodes (circles) represent enriched GO terms, and edges (gray lines) represent overlap of proteins between GO terms. Node color indicates the significance of GO term enrichment; node diameter is proportional to the number of proteins assigned to each GO term. Edge weight is proportional to the number of proteins shared between connected GO terms. The full list of GO terms is detailed in Supplemental Figure 1. (B) All four protein fractions were analyzed by MS, and spectral counting was used to determine the enrichment of ECM proteins (identified by GO analysis). The mean ECM enrichment was 38% from three biologic replicates. (C) Relative quantification for the 10 most abundant ECM proteins detected by MS. Relative protein abundance was calculated using peptide intensity as described in Supplemental Methods. Gene names are shown for clarity, and collagen (COL) IV and laminin isoforms are combined as one value. (D) Western blotting (WB) confirmed enrichment of TINAGL1 and collagen VI in glomerular ECM.

ECM proteins identified in this study (Figure 5). Glomerular a5andlaminina5. Protein expression was confirmed for 78 immunostaining was not available for 20 proteins, including ECM components (63% of proteins with available HPA data) the known glomerular ECM proteins collagen IV a3, a4, and and reported as negative for 46 ECM components, although

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Table 1. Basement membrane proteins in the glomerular ECM proteome Basement Membrane Proteins Gene Name Molecular Mass (kDa) Abundance (nSC) Classification Agrin AGRN 215 3.075 Glycoprotein Collagen a1(XV) chain COL15A1 142 0.052 Collagen Collagen a1(XVIII) chain COL18A1 154 5.230 Collagen Collagen a1(IV) chain COL4A1 161 12.515 Collagen Collagen a2(IV) chain COL4A2 168 17.709 Collagen Collagen a3(IV) chain COL4A3 162 8.155 Collagen Collagen a4(IV) chain COL4A4 164 8.961 Collagen Collagen a5(IV) chain COL4A5 161 3.778 Collagen Collagen a6(IV) chain COL4A6 164 0.589 Collagen -1 FBLN1 77 0.029 Glycoprotein Fibrillin-1 FBN1 312 1.403 Glycoprotein FN1 263 1.893 Glycoprotein ECM protein FRAS1 FRAS1 443 0.039 Glycoprotein Hemicentin-1 HMCN1 613 0.007 Glycoprotein Perlecan HSPG2 467 7.012 Laminin subunit a2 LAMA2 343 0.043 Glycoprotein Laminin subunit a5 LAMA5 400 9.482 Glycoprotein Laminin subunit b1 LAMB1 198 0.977 Glycoprotein Laminin subunit b2 LAMB2 196 14.745 Glycoprotein Laminin subunit g1 LAMC1 178 8.818 Glycoprotein Nidogen-1 NID1 136 10.247 Glycoprotein Nidogen-2 NID2 151 1.249 Glycoprotein Tubulointerstitial nephritis antigen TINAG 55 12.873 Glycoprotein A domain–containing protein 1 VWA1 47 1.171 Glycoprotein The glomerular ECM proteome was further categorized according to GO annotation. Twenty-four basement membrane proteins were identified by MS. Relative protein abundance is shown as normalized spectral counts (nSCs).

Table 2. Other structural ECM proteins in the glomerular ECM proteome Other Structural ECM Proteins Gene Name Molecular Mass (kDa) Abundance (nSC) Classification Asporin ASPN 44 1.199 Proteoglycan BGN 42 2.268 Proteoglycan Collagen a1(XII) chain COL12A1 333 0.073 Collagen Collagen a1(I) chain COL1A1 139 0.253 Collagen Collagen a2(I) chain COL1A2 129 0.594 Collagen Collagen a1(III) chain COL3A1 139 0.143 Collagen Collagen a1(VI) chain COL6A1 109 12.685 Collagen Collagen a2(VI) chain COL6A2 109 5.889 Collagen Collagen a3(VI) chain COL6A3 344 11.145 Collagen DCN 40 0.067 Proteoglycan Dermatopontin DPT 24 0.397 Glycoprotein EMILIN-1 EMILIN1 107 0.699 Glycoprotein a-chain FGA 95 0.954 Glycoprotein Fibrinogen b-chain FGB 56 1.965 Glycoprotein g-A of fibrinogen g-chain FGG 49 2.002 Glycoprotein MGP 12 0.557 Glycoprotein Nephronectin NPNT 65 3.707 Glycoprotein Periostin, osteoblast-specific factor POSTN 90 0.191 Glycoprotein RPE-spondin RPESP 30 1.364 Glycoprotein TGF-b–induced protein ig-h3 TGFBI 75 0.369 Glycoprotein Tubulointerstitial nephritis antigen-like TINAGL1 52 20.010 Glycoprotein VTN 54 9.015 Glycoprotein von Willebrand factor A domain–containing protein 5B2 VWA5B2 133 0.024 Glycoprotein von Willebrand factor A domain–containing protein 8 VWA8 211 0.076 Glycoprotein The glomerular ECM proteome was categorized according to GO annotation. Twenty-four proteins were identified as having a structural role but without basement membrane association, and they were termed other structural ECM proteins. Relative protein abundance is shown as normalized spectral counts (nSCs).

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Figure 3. Comparison of the glomerular ECM proteome with published glomerular proteomic datasets. The glomerular ECM proteome identified in this study was compared with other glomerular proteomic studies for which full datasets were available (studies by Cui et al.25 and Yoshida et al.22). Numbers of proteins in each intersection set of the area proportional Euler diagram are in bold italics. ECM proteins were categorized as basement membrane, other structural ECM, or ECM-associated proteins, and they were colored and arranged accordingly. Nodes (circles) are labeled with gene names for clarity. ECM proteins detected in any of the three biologic replicates reported in this study were included in the comparison with other proteomic datasets; these published datasets each re- ported one biologic replicate. Large node size indicates proteins detected in at least two biologic replicates in this study. there were notable false negatives, including collagen IV a2, more than one ECM compartment (Figure 5B), suggesting a collagen XVIII, and laminin b1. Immunohistochemistry relies common core of protein components between the glomerular on antibody specificity, and therefore, combining expression ECM compartments. data from antibody-based investigations with MS data has the potential to significantly increase the number of protein identi- Colocalization of Novel Glomerular ECM Proteins fications. Indeed, our proteomic dataset increased the number of To confirm the expression of new glomerular ECM proteins, ECM proteins detected in the glomerulus by 59% compared which were either abundant in our MS analysis or not detected with HPA data alone. We also evaluated the HPA database to in the HPA database, we conducted colocalization studies. determine the pattern of immunostaining as GBM, mesangial Collagen VI and TINAGL1 were among the most abundant matrix, Bowman’s capsule, or a combination of compartments proteins detected in this study (Supplemental Figure 2), and (Supplemental Methods). Most components were present in they were validated by Western blotting (Figure 2D). Collagen

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Figure 4. Interaction network analysis of human glomerular ECM. (A) Protein interaction network constructed from enriched glomerular ECM proteins identified by MS. Nodes (circles) represent proteins, and edges (gray lines) represent reported protein–protein inter- actions. ECM proteins were categorized as basement membrane, other structural ECM, or ECM-associated proteins, and they were colored and arranged accordingly. Nodes are labeled with gene names for clarity. (B) Distribution of degree (number of protein– protein interactions per protein) for basement membrane, other structural ECM, or ECM-associated proteins. Data points are shown as circles; outliers are shown as diamonds. **P,0.01. NS, P$0.05.

VI was also present in the highly connected subnetwork of known glomerular ECM proteins in addition to many poten- ECM proteins (Supplemental Figures 3 and 4). Immunohisto- tially novel components. Moreover, using immunohistochem- chemical and correlation intensity analysis of human renal cortex istry, we have confirmed the MS identification of TINAGL1, revealed colocalization of TINAGL1 with collagen IV a1, which collagen VI, nephronectin, and vitronectin within specific has a mesangial pattern of immunostaining, whereas collagen VI glomerular compartments. These findings show that the com- overlapped with both collagen IV a1andcollagenIVa3/lam- position of glomerular ECM is far more complex than previously inin, which both have a GBM pattern of immunostaining (Figure appreciated and imply that many more ECM components may 6, A and C). MS analysis also detected nephronectin and vitro- contribute to glomerular development and disease processes. nectin (Table 2). Correlation intensity analysis showed that neph- This unbiased, global approach to define the glomerular ronectin localized in the GBM and mesangial matrix, whereas ECM proteome shows that this specialized ECM has a core of vitronectin localized in the mesangial matrix alone (Figure 6, B highly connected extracellular components. In adult human and D). This objective and quantitative method of colocalization glomeruli, 144 ECM proteins, including all previously de- allows protein expression to be correlated with reliable markers scribed components, were identified. In addition, we found predominating in distinct ECM compartments. With antibodies many more structural and regulatory ECM proteins, revealing of suitable specificity, this approach could be extended to map the complexity of the glomerular ECM. This proteome is the full proteome into glomerular ECM compartments. comparable in size with ECM profiles recently reported for vasculature-, lung-, and bowel-derived ECMs.3,27,28 In our analysis, several novel glomerular proteins were highly abun- DISCUSSION dant, including collagen VI and TINAGL1. Collagen VI is a structural component that forms microfibrils, and it is impor- We used an ECM enrichment strategy coupled with an tant for muscle function29,30; however, its role in the glomer- unbiased proteomics approach to confirm the presence of all ulus has not been investigated. Our study shows that collagen

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Figure 5. Localization of glomerular ECM proteins in the HPA database. (A) The HPA was searched for glomerular ECM proteins identified in at least two biologic replicates in this study. (Left) Glomerular immunostaining was reviewed (+, detected; 2, not detected; N/A, data not available in the HPA), and (right) localization was determined as GBM, mesangial matrix (MM), Bowman’s capsule (BC), or a combination of these ECM compartments. (B) ECM proteins were categorized as basement membrane, other structural ECM, or ECM-associated proteins, and (right) they were colored and arranged accordingly. Proteins not detected or without data in glomeruli in the HPA are shown separately. Nodes (circles) are labeled with gene names for clarity. (Left) Node diameter (proteins localized in glomeruli in the HPA only) is proportional to the intensity of glomerular immunostaining in the HPA.

VI is present in a highly connected core network of proteins in angiogenesis.33 However, its function within the glomerulus is the glomerular ECM, and it is localized within both the GBM undefined. Both of these highly abundant proteins may have and mesangial matrix. TINAGL1 was also abundantly ex- roles in barrier function or glomerular disease. pressed in glomerular ECM, where it predominantly localized To assess the relative abundance of ECM proteins, we used to the mesangial matrix. TINAGL1 (also known as TIN-Ag_RP, two distinct methods of quantification (peptide intensity lipocalin-7, oxidised LDL-responsive gene 2 and androgen reg- analysis20 and spectral counting), and both approaches gave ulated gene 1) is a glycoprotein, and it is structurally related to very similar results. Peptide intensity analysis revealed collagen TINAG, a tubular basement membrane component, which is IV and laminin isoforms to be the most abundant proteins in the antigenic target in autoimmune anti–tubular basement the glomerular ECM, and both analyses revealed abundant membrane disease.31 TINAGL1 has a proteolytically inactive collagen VI and TINAGL1. There were some differences between domain,32 and it has been shown to have a role in the methods for the quantification of collagen isoforms, but the

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Figure 6. Colocalization of novel and known glomerular ECM proteins. (A and B) Immunohistochemistry of human renal cortex was used to examine the colocalization of collagen VI, TINAGL1, nephronectin, and vitronectin with laminin, (A) collagen IV a3, and (B) collagen IV a1. (C and D) Bar charts show intensity correlation quotients calculated from immunohistochemistry images (n=6–10 images for each analysis), showing (C) colocalization of laminin, collagen VI, and nephronectin with collagen IV a3 and (D) colocalization of collagen VI, TINAGL1, nephronectin, and vitronectin with collagen IV a1. stoichiometry of different proteins is difficult to determine by discovery of unknown ECM proteins. Therefore, the findings any global proteomic methodology, including antibody-based presented in this study provide relative patterns of glomerular techniques, which rely on probe immunoaffinity. To perform ECM protein enrichment and pave the way for additional in- absolute protein quantification by MS, labeled peptide standards depth studies of the ECM components identified. for each protein could be used; however, it would be a significant Compared with other large-scale glomerular proteomic studies, undertaking for a large protein dataset and would not permit the which have identified up to a total of 1800 proteins,22,25 this

J Am Soc Nephrol 25: 939–951, 2014 Proteome of the Glomerular Matrix 947 BASIC RESEARCH www.jasn.org investigation had the greatest yield of ECM proteins, thus high- were chosen to reduce the influence of age and sex. Normal renal lighting the importance of sample fractionation for improving functionwas determined by prenephrectomy serum creatinine values. protein identification. However, the large-scale requirement At 4°C, renal cortex (2.5 g) was finely diced and pressed onto a 250-mm for the analysis prohibited the separation of glomerular ECM sieve (Endecotts, London, UK) using a 5-ml syringe plunger. Glomer- components, and therefore, protein localization was deter- uli were rinsed through sieves (250 and 150 mm) with cold PBS and mined by immunohistochemistry. Although laser microdissec- separated from tubular fragments by collection on both 150- and 100-mm tion studies have the ability to precisely define the anatomic sieves. Retained glomeruli were retrieved into 10 ml PBS and washed structure for analysis, the total number of proteins identified in another three times with PBS and interval centrifugation. Glomerular the most recent of these studies, using a variety of tissue sam- purity was consistently .95% as determined by counting whole glo- ples, ranged from 114 to 340, which is fewer proteins than the meruli and nonglomerular fragments using phase-contrast light large-scale glomerular studies and significantly fewer ECM pro- microscopy. teins compared with this study.13,34,35 Future developments in technologies, which may couple tissue imaging with improved Isolation of Enriched Glomerular ECM sensitivity of molecular analysis, may allow direct compositional This method was adapted from previously published methods38 and analysis of ECM within distinct tissue compartments.36,37 used to reduce the complexity of protein samples for MS analysis by The profile of glomerular ECM that we have described is removing cellular components and enriching for ECM proteins. All likely to represent a snapshot of a highly dynamic extracellular steps were carried out at 4°C to minimize proteolysis. Pure glomer- environment that changes under the influence of cellular, ular isolates from three human kidneys were incubated for 30 min- physical, and chemical environmental cues. Nonetheless, the utes in extraction buffer (10 mM Tris, 150 mM NaCl, 1% [vol/vol] datasets provide a valuable resource for additional investigation Triton X-100, 25 mM EDTA, 25 mg/ml leupeptin, 25 mg/ml aprotinin, of the composition and complexity of glomerular ECM. Our and 0.5 mM 4-(2-aminoethyl)-benzenesulfonyl fluoride hydrochlo- methodology can now be applied to the comparison of ride) to solubilize cellular proteins, and samples were then centri- glomerular ECMs in vivo in the context of development or fuged at 14,0003g for 10 minutes to yield fraction 1. The remaining disease. Systems-level analysis will be integral to the down- pellet was incubated for 30 minutes in alkaline detergent buffer (20 stream interrogation of data to identify informative, predic- mM NH4OH and 0.5% [vol/vol] Triton X-100 in PBS) to further tive networks of ECM composition and function. Combined solubilize cellular proteins and disrupt cell–ECM interactions. Sam- with the methodologies and datasets described herein, such ples were then centrifuged at 14,0003g for 10 minutes to yield frac- analyses will enable the construction of a dynamic glomerular tion 2. The remaining pellet was incubated for 30 minutes in a ECM interactome and help to build understanding of how deoxyribonuclease buffer (10 mg/ml deoxyribonuclease I [Roche, this network may alter during glomerular development and Burgess Hill, UK] in PBS) to degrade DNA. The sample was centri- disease. fuged at 14,0003g for 10 minutes to yield fraction 3, and the final pellet was resuspended in reducing sample buffer (50 mM TriszHCl, pH 6.8, 10% [wt/vol] glycerol, 4% [wt/vol] SDS, 0.004% [wt/vol] CONCISE METHODS bromophenol blue, and 8% [vol/vol] b-mercaptoethanol) to yield the ECM fraction. Samples were heat denatured at 70°C for 20 Antibodies minutes. Monoclonal antibodies used were against actin (clone AC-40; Sigma- Aldrich, Poole, UK), nephronectin (ab64419; Abcam, Cambridge, Western Blotting UK), TINAGL1 (ab69036; Abcam), vitronectin (ab11591; Abcam), After SDS-PAGE, resolved proteins were transferred to nitrocellulose and collagen IV chain–specific antibodies (provided by B. Hudson, membrane (Whatman, Maidstone, UK). Membranes were blocked Vanderbilt University Medical Center, Nashville, TN). Polyclonal anti- with casein blocking buffer (Sigma-Aldrich) and probed with primary bodies used were against pancollagen IV (ab6586; Abcam), panlaminin antibodies diluted in blocking buffer containing 0.05% (vol/vol) (ab11575; Abcam), pancollagen VI (ab6588; Abcam), lamin B1 Tween 20. Membranes were washed with Tris-buffered saline (10 mM (ab16048; Abcam), and nephrin (ab58968; Abcam). Secondary antibod- TriszHCl, pH 7.4, and 150 mM NaCl) containing 0.05% (vol/vol) ies against rabbit IgG conjugated to tetramethylrhodamineisothiocyanate Tween 20 and incubated with species-specific fluorescent dye– and mouse or rat IgG conjugated to FITC (Jackson ImmunoResearch conjugated secondary antibodies diluted in blocking buffer contain- Laboratories, Inc., West Grove, PA) were used for immunofluorescence; ing 0.05% (vol/vol) Tween 20. Membranes were washed in the dark secondary antibodies conjugated to Alexa Fluor 680 (Life Technologies, and then scanned using the Odyssey infrared imaging system (LI- Paisley, UK) or IRDye 800 (Rockland Immunochemicals, Glibertsville, COR Biosciences, Cambridge, UK) to visualize bound antibodies. PA) were used for Western blotting. MS Data Acquisition Isolation of Human Glomeruli Protein samples were resolved by SDS-PAGE and visualized by Normal renal cortex from human donor kidneys technically unsuit- Coomassie staining. Gel lanes were sliced and subjected to in-gel trypsin able for transplantation was used with full ethical approval (reference digestion asdescribed previously39 Liquid chromatography–tandem MS 06/Q1406/38). Three adult men donors between 37 and 63 years of age analysis was performed using a nanoACQUITY UltraPerformance

948 Journal of the American Society of Nephrology J Am Soc Nephrol 25: 939–951, 2014 www.jasn.org BASIC RESEARCH liquid chromatography system (Waters, Elstree, UK) coupled online through MetaVue software (Molecular Devices, Wokingham, UK). to an LTQVelos mass spectrometer (Thermo Fisher Scientific, Waltham, Images were collected using a CoolSnap HQ camera (Photometrics, MA) or offline to an Orbitrap Elite analyzer (Thermo Fisher Scientific) Tucson, AZ) and separate 49,6-diamidino-2-phenylindole, FITC, and for experiments incorporating analysis with Progenesis (Supple- Cy3 filters (U-MWU2, 41001, and 41007a, respectively; Chroma, mental Methods). were concentrated and desalted on a Olching, Germany) to minimize bleed through between the differ-

Symmetry C18 preparative column (20-mm length, 180-mminner ent channels. Images were processed and analyzed using Fiji/ImageJ diameter, 5-mm particle size, 100-Å pore size; Waters). Peptides were software (version 1.46r; National Institutes of Health, Bethesda, separated on a bridged ethyl hybrid C18 analytical column (250-mm MD). Raw images were subjected to signal rescaling using linear length, 75-mm inner diameter, 1.7-mm particle size, 130-Å pore size; transformation for display in the figures. For calculation of the Waters) using a 45-minute linear gradient from 1% to 25% (vol/vol) Pearson’scorrelationcoefficient, a region of interest was drawn, acetonitrile in 0.1% (vol/vol) formic acid at a flow rate of 200 nl/min. and a threshold was set to restrict analysis to a single glomerulus. Peptides were selected for fragmentation automatically by data- The coefficient was measured using the Intensity Correlation Analysis dependent analysis. plugin for Fiji/ImageJ,44 and the subsequent plotting steps were per- formed using MATLAB (version R2012a; MathWorks, Natick, MA). MS Data Analyses Tandem mass spectra were extracted using extract_msn (Thermo Statistical Analyses 6 Fisher Scientific) executed in Mascot Daemon (version 2.2.2; Matrix All measurements are shown as mean SEM. Box plots indicate 25th 3 Science, London, UK). Peak list files were searched against a modified and 75th percentiles (lower and upper bounds, respectively), 1.5 in- version of the International Protein Index Human Database (version terquartile range (whiskers), and median (black line). Numbers of pro- – – 3.70; release date, March 4, 2010) containing 10 additional contam- tein protein interactions were compared using Kruskal Wallis one-way inant and reagent sequences of nonhuman origin using Mascot ANOVA tests with post hoc Bonferroni correction. GO enrichment anal- fi ’ – (version 2.2.03; Matrix Science).40 Carbamidomethylation of cysteine yses were compared using modi ed Fisher sexacttestswithBenjamini , fi was set as a fixed modification; oxidation of and hydrox- Hochberg correction. P values 0.05 were deemed signi cant. ylation of proline and were allowed as variable modifications. Only tryptic peptides were considered, with up to one missed cleav- age permitted. Monoisotopic precursor mass values were used, and ACKNOWLEDGMENTS only doubly and triply charged precursor ions were considered. Mass tolerances for precursor and fragment ions were 0.4 and 0.5 Da, Mass spectrometry was performed in the Biological Mass Spec- respectively. MS datasets were validated using rigorous statistical trometry Core Facility, Faculty of Life Sciences, University of Man- algorithms at both the peptide and protein levels41,42 implemented chester, and we thank Stacey Warwood for advice and technical in Scaffold (version 3.00.06; Proteome Software, Portland, OR). Pro- support. We thank Julian Selley for bioinformatic support. Micro- tein identifications were accepted on assignment of at least two scopy was performed in the Bioimaging Core Facility, Faculty of Life unique validated peptides with $90% probability, resulting in Sciences, University of Manchester. The collagen IV chain-specific $99% probability at the protein level. These acceptance criteria re- antibodies were kindly provided by the Billy Hudson research group, sulted in an estimated protein false discovery rate of 0.1% for all Department of Medicine, Vanderbilt Medical Center. datasets. This work was supported by Wellcome Trust Intermediate Fel- lowship Award Reference 090006 (to R.L.) and Wellcome Trust Grant MS Quantification and Proteomic Analyses 092015(to M.J.H.). R.Z. is supported byVeteransAffairs Merit Awards MS quantification and proteomic data analyses were performed as 1I01BX002196-01, DK075594, DK069221, and DK083187, and an previously described39,43 with the modifications described in Supple- American Heart Association Established Investigator Award. The mass mental Methods. The mass spectrometry proteomics data have been spectrometer and microscopes used in this study were purchased with deposited to the ProteomeXchange Consortium with the dataset grantsfromtheBiotechnologyandBiologicalSciencesResearchCouncil, identifier PXD000456 (http://www.ebi.ac.uk/pride). the Wellcome Trust, and the University of Manchester Strategic Fund.

Immunohistochemistry and Image Analysis DISCLOSURES Formalin-fixed, paraffin-embedded tissue blocks were sectioned at 5 mm. Sections were dewaxed and treated with recombinant protein- None. ase K (Roche Diagnostics, Indianapolis, IN) for 15 minutes. Sections were blocked with 5% (vol/vol) donkey serum (Sigma-Aldrich) and REFERENCES 1.5% (vol/vol) BSA (Sigma-Aldrich) for 30 minutes and primary antibodies overnight at 4°C. Sections were washed three times with PBS, incubated with secondary antibodies, mounted with polyvinyl 1. Pozzi A, Jarad G, Moeckel GW, Coffa S, Zhang X, Gewin L, Eremina V, Hudson BG, Borza DB, Harris RC, Holzman LB, Phillips CL, Fassler R, alcohol mounting medium (Fluka 10981; Sigma-Aldrich), and im- Quaggin SE, Miner JH, Zent R: Beta1 expression by podocytes aged using a BX51 upright microscope (Olympus, Southend on Sea, is required to maintain glomerular structural integrity. Dev Biol 316: UK) equipped with a 203 UPlan Fln 0.50 objective and controlled 288–301, 2008

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J Am Soc Nephrol 25: 939–951, 2014 Proteome of the Glomerular Matrix 951 SUPPLEMENTAL INFORMATION

Global analysis reveals the complexity of the human glomerular extracellular matrix

Rachel Lennon,1,2 Adam Byron,1,* Jonathan D. Humphries,1 Michael J.

Randles,1,2 Alex Carisey,1 Stephanie Murphy,1,2 David Knight,3 Paul E.

Brenchley,2 Roy Zent,4,5 and Martin J. Humphries.1

1Wellcome Trust Centre for Cell-Matrix Research, Faculty of Life Sciences,

University of Manchester, Manchester, UK; 2Faculty of Medical and Human

Sciences, University of Manchester, Manchester, UK; 3Biological Mass

Spectrometry Core Facility, Faculty of Life Sciences, University of Manchester,

Manchester, UK; 4Division of Nephrology, Department of Medicine, Vanderbilt

University Medical Center, Nashville, TN, USA; and 5Veterans Affairs Hospital,

Nashville, TN, USA.

*Present address: Edinburgh Cancer Research UK Centre, Institute of

Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.

Corresponding author:

Dr Rachel Lennon, Wellcome Trust Centre for Cell-Matrix Research, Michael

Smith Building, University of Manchester, Manchester M13 9PT, UK.

Phone: 0044 (0) 161 2755498. Fax: 0044 (0) 161 2755082.

Email: [email protected] Supplementary methods

MS data deposition

MS data were converted using PRIDE Converter 2 (version 2.0.3)1 and deposited in the PRIDE database (http://www.ebi.ac.uk/pride)2 under accession numbers XXXXX–XXXXX. Details of identified ECM proteins are provided in Tables 1 and 2 and Supplemental Table S2.

MS data quantification and statistical analysis

Relative protein abundance was calculated using the unweighted spectral count of a given protein normalized to the total number of spectra observed in the entire sample and to the molecular weight of that protein (normalized spectral count). Mean normalized spectral counts were calculated using data from enriched glomerular ECM samples from three human kidneys. Peptide intensity was used as an alternative method of protein quantification, as described by Silva et al.3 Orbitrap MS data were entered into Progenesis LC-

MS (Non Linear Dynamics Ltd, Newcastle upon Tyne, UK) and aligned.

Spectra were extracted using extract_msn (Thermo Fisher Scientific, Waltham,

MA, USA) executed in Mascot Daemon (version 2.4; Matrix Science, London,

UK) and imported back into Progenesis to acquire intensity data. Peptides assigned to proteins with fewer than three unique peptides per protein were removed, and median normalized intensity was calculated for all peptides.

Peptides with multiple entries were reduced to the most intense median peptide, and the three peptides with the most intense median values were retained and summed. The sum value is used to compare approximate stoichiometries between proteins in the same sample.3

Functional annotation and enrichment analysis

Proteins identified in at least two of the three biological replicates were included for further analysis. Gene ontology (GO) annotations were downloaded using the online resource DAVID.4 5 The GO cellular compartment annotation chart (GOTERM_CC_FAT) was selected and the protein list was imported into Cytoscape (version 2.8.1)6 and analyzed using the Enrichment Map plugin.7 The following criteria were used to generate the network: P value < 0.005, FDR (Benjamini–Hochberg) cut-off < 0.01, similarity cut-off > 0.6. The network was then analyzed using the Markov Cluster

Algorithm (MCL) to generate distinct clusters. Proteins annotated in the extracellular region cluster were further cross-referenced with the human matrisome project,8 and cytoplasmic proteins annotated as extracellular region (ACTN1, ACTN2, ACTN4, CALM1, CSNK2B, FLNA, GLIPR2, HINT2,

HSPD1, RNH1, TLN1, TTN, TUB4A4, and VCL) were excluded. The final list of 144 proteins was termed the glomerular ECM proteome, and this was used for further analysis.

Protein interaction network analysis

Protein interaction network analysis was performed using Cytoscape (version

2.8.1).6 Proteins identified in at least two biological replicates were mapped onto a merged human interactome built from the Protein Interaction Network

Analysis platform Homo sapiens network (release date, 28 June 2011) and

Mus musculus network (release date, 28 June 2011),9 the ECM interactions database MatrixDB (release date, 26 August 2010),10 and a literature-curated database of integrin-based adhesion–associated proteins.11 Topological parameters were computed using the NetworkAnalyzer plug-in.12

Analysis of Human Protein Atlas immunohistochemistry data

The Human Protein Atlas (HPA) (version 11.0) was searched for the 144 ECM proteins identified in this study, and glomerular immunohistochemistry was reviewed. Proteins were categorized as being positive (strong, moderate, weak), negative or not available according to the HPA report. Proteins with positive immunostaining were further allocated to one or more ECM compartments (GBM, mesangial matrix, Bowman’s capsule) according to our assessment of the histological pattern of staining. Negative immunostaining controls were not present for analysis in the HPA database, limiting our independent evaluation of protein expression. In addition, the HPA report indicated protein expression in cells of the glomerulus, rather than ECM compartments. Both of these factors could influence false negative or false positive reporting.

References

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2008;24(2):282-4.

# GO accession GO term Extracellular region 1 GO:0005615 EXTRACELLULAR SPACE 23 2 GO:0048770 PIGMENT Extracellular region 22 5 3 GO:0030934 ANCHORING COLLAGEN 1 12 4 GO:0031983 VESICLE LUMEN 7 29 30 5 GO:0005581 COLLAGEN 15 25 6 GO:0030141 SECRETORY GRANULE 20 19 24 7 GO:0005604 BASEMENT MEMBRANE 11 8 4 14 8 GO:0060205 CYTOPLASMIC MEMBRANE-BOUNDED VESICLE LUMEN 9 13 9 GO:0031988 MEMBRANE-BOUNDED VESICLE 6 26 16 10 GO:0005606 LAMININ-1 COMPLEX 27 11 GO:0031410 CYTOPLASMIC VESICLE 28 3 10 2 18 12 GO:0043256 LAMININ COMPLEX 17 13 GO:0031012 EXTRACELLULAR MATRIX 14 GO:0043260 LAMININ-11 COMPLEX 15 GO:0044421 EXTRACELLULAR REGION PART 39 35 16 GO:0005576 EXTRACELLULAR REGION 36 21 17 GO:0044433 CYTOPLASMIC VESICLE PART 40 57 38 18 GO:0031982 VESICLE 34 45 58 50 63 19 GO:0016023 CYTOPLASMIC MEMBRANE-BOUNDED VESICLE 52 42 44 20 GO:0042470 MELANOSOME 33 55 61 21 GO:0009986 CELL SURFACE 41 31 46 64 22 GO:0030935 SHEET-FORMING COLLAGEN 32 62 56 70 23 GO:0005587 COLLAGEN TYPE IV 65 43 68 24 GO:0031091 59 25 GO:0044420 EXTRACELLULAR MATRIX PART 37 51 60 26 GO:0043259 LAMININ-10 COMPLEX 47 49 Mitochondrial 54 27 GO:0005605 BASAL LAMINA 67 72 48 28 GO:0005577 FIBRINOGEN COMPLEX 73 Other 66 29 GO:0031093 PLATELET ALPHA GRANULE LUMEN 74 69 53 30 GO:0005578 PROTEINACEOUS EXTRACELLULAR MATRIX 71 Mitochondrial 77 Cytoskeletal 31 GO:0009295 NUCLEOID 76 32 GO:0031967 ORGANELLE ENVELOPE 33 GO:0005743 MITOCHONDRIAL INNER MEMBRANE 75 34 GO:0019866 ORGANELLE INNER MEMBRANE 35 GO:0005759 36 GO:0044429 MITOCHONDRIAL PART −25 −2 37 GO:0031965 NUCLEAR MEMBRANE 1×10 1×10 38 GO:0031966 MITOCHONDRIAL MEMBRANE 39 GO:0042645 MITOCHONDRIAL NUCLEOID 40 GO:0031980 MITOCHONDRIAL LUMEN Corrected P value Number of proteins Degree of overlap Cytoskeletal 41 GO:0031975 ENVELOPE 42 GO:0045111 43 GO:0044430 CYTOSKELETAL PART 44 GO:0045095 FILAMENT 45 GO:0001740 BARR BODY 46 GO:0030018 Z DISC 47 GO:0044449 CONTRACTILE FIBER PART 48 GO:0032432 ACTIN FILAMENT BUNDLE 49 GO:0001725 STRESS FIBER 50 GO:0000786 NUCLEOSOME 51 GO:0005938 CELL CORTEX 52 GO:0031674 I BAND 53 GO:0042641 ACTOMYOSIN 54 GO:0005856 CYTOSKELETON 55 GO:0044445 CYTOSOLIC PART 56 GO:0043228 NON-MEMBRANE-BOUNDED ORGANELLE 57 GO:0032993 PROTEIN-DNA COMPLEX 58 GO:0000785 CHROMATIN 59 GO:0030016 MYOFIBRIL 60 GO:0033279 RIBOSOMAL SUBUNIT 61 GO:0030529 RIBONUCLEOPROTEIN COMPLEX 62 GO:0043292 CONTRACTILE FIBER 63 GO:0022625 CYTOSOLIC LARGE RIBOSOMAL SUBUNIT 64 GO:0005840 RIBOSOME 65 GO:0005912 ADHERENS JUNCTION 66 GO:0015629 ACTIN CYTOSKELETON 67 GO:0070161 ANCHORING JUNCTION 68 GO:0022626 CYTOSOLIC RIBOSOME 69 GO:0016323 BASOLATERAL PLASMA MEMBRANE 70 GO:0005882 INTERMEDIATE FILAMENT 71 GO:0042995 CELL PROJECTION 72 GO:0005916 FASCIA ADHERENS 73 GO:0016459 COMPLEX Other 74 GO:0005579 MEMBRANE ATTACK COMPLEX 75 GO:0046930 PORE COMPLEX 76 GO:0048471 PERINUCLEAR REGION OF 77 GO:0042383 SARCOLEMMA

Supplemental Figure S1. Gene ontology (GO) enrichment analysis of the full MS dataset. Nodes (circles) represent enriched GO terms and edges (gray lines) represent overlap of proteins between GO terms. Node color indicates the significance of GO term enrichment; node diameter is proportional to the number of proteins assigned to each GO term; edge weight is proportional to the number of proteins shared between connected GO terms. The network from Figure 2 is annotated (left panel); nodes are numbered according to their GO term (right panel). 80 40 Peptide intensity ) 6 nSC

10 60 30 × nSC 40 20

20 10 (peptide intensity Relative protein abundance 0 0

COL1 HSPG NID1 COL6 AGRN COL18 TINAGL1 COL4A1/2 COL4A3/4/5 LAMA5/B2/G1

Supplemental Figure S2. Relative quantification of the ten most abundant ECM proteins detected by MS. Quantification was calculated using peptide intensity as described in the Supplementary Methods. Quantification by peptide intensity was compared to normalized spectral counts (nSC). Gene names are shown for clarity, and collagen IV and laminin isoforms are combined as one value. CD59 C8G C8A

C9

C8B

C6 C7

CLU C5

CPB2 HP

APOA1 LRP2

APOE

CFHR5 RBP4 TTR C3 VWA1 ALB

SERPINA3 COL15A1 HSPG2 AMBP LAMA2 SERPINA1 SERPINA5 CTSG FGB ANXA2 ANXA11 FGA FBLN1 FBN1 PRELP NID2 S100A6 ANXA5 FGG NID1 COL18A1 FLT1 AGT TGM2 BGN B2M FN1 ELANE KNG1 COL1A1 TGFBI LAMC1 CTSD COL1A2 VTN CST3 PRSS23 COL6A3 SERPING1 TIMP3 LGAS3BP DPT COL4A4 PPIA LAMA5 MMP9 COL4A3 LGALS1 DCN LAMB1 COL4A1 S100A8 MUC16 COL4A5 APCS LGALS3 COL6A1 COL6A2 COL4A6 COL4A2 SERPINE2 Degree GNAS SNX13 CA2 AZU1 PTGDS COPA MPO CNP SERPINH1 DST TF INHBE C1QC Clustering coefficient HSD17B12 SEMA3B DCD LYZ ITIH3 LIPC F9 PRG4 ANXA7 GSN SERPINC1 UMOD APOA4 ITIH1 0 1

TNFSF13 GPI S100A18 DEFA1 WNT7B IGHG4 PEBP1 SORD MGP VWA5B2 NPNT EMILIN1 COL3A1 Basement membrane Other structural ECM TINAGL1 TINAG LAMB2 AGRN ECM associated

Supplemental Figure S3. Glomerular ECM interaction network analysis. Proteins identified by MS and classified as extracellular region according to Gene Ontology annotation were converted to a protein–protein interaction network model. The interaction network was clustered, and topological parameters were computed. Self-interactions were excluded from the analysis. Nodes are colored according to their clustering coefficient, and node diameter is proportional to number of interaction partners (degree). Nodes are labeled with gene names. Proteins classified as basement membrane are displayed with red node borders; other structural ECM proteins are displayed with orange node borders; and ECM-associated proteins are displayed with green node borders. 30

20 Degree 10

0

FN1 VTN DCN LAMC1 COL4A1COL1A1 COL4A2 COL1A2 COL6A2 COL6A1 Protein

Supplemental Figure S4. The most connected proteins in the ECM interaction network. The ten proteins with the highest number of neighbors (degree) in the glomerular ECM interaction network are plotted as a bar chart. The most connected proteins (known as hubs), which incorporated the top 20% of the degree distribution, are indicated by dark blue bars. Self-interactions were excluded from the analysis. Proteins are labeled with gene names. Supplemental Table S1. Most abundant basement membrane and other structural ECM proteins in the glomerular ECM proteome. Proteins identified by MS were quantified using normalized spectral counts (nSC) or peptide intensity (PI). The ten most abundant components are shown (including component chains). Proteins were categorized according to GO annotation as basement membrane or other structural ECM proteins.

Basement membrane protein IPI Accession UniProtKB Gene ID Gene name MW (kDa) Total peptides Abundance (nSC) Abundance (PI) Classification Agrin IPI00374563 O00468 375790 AGRN 215 41 4.802 9.773 Glycoprotein Collagen alpha-1(IV) chain IPI00743696 P02462 1282 COL4A1 161 51 1.157 61.212 Collagen Collagen alpha-2(IV) chain IPI00306322 P08572 1284 COL4A2 168 66 3.261 55.512 Collagen Collagen alpha-3(IV) chain IPI00010360 Q01955 1285 COL4A3 162 16 1.692 17.256 Collagen Collagen alpha-4(IV) chain IPI00478572 P53420 1286 COL4A4 164 35 1.768 43.006 Collagen Collagen alpha-5(IV) chain IPI00021715 P29400 1287 COL4A5 161 29 0.898 22.209 Collagen Collagen alpha-1(XVIII) chain IPI00022822 P39060 80781 COL18A1 154 38 3.445 7.929 Collagen Perlecan IPI00943326 P98160 3339 HSPG2 467 328 8.330 20.940 Proteoglycan Laminin subunit alpha-5 IPI00783665 O15230 3911 LAMA5 400 311 10.032 26.222 Glycoprotein Laminin subunit beta-2 IPI00296922 P55268 3913 LAMB2 196 211 13.368 22.008 Glycoprotein Laminin subunit gamma-1 IPI00298281 P11047 3915 LAMC1 178 193 10.214 22.570 Glycoprotein Nidogen-1 IPI00026944 P14543 4811 NID1 136 80 9.444 18.768 Glycoprotein Other structural ECM protein IPI Accession UniProtKB Entrez Gene ID Gene name MW (kDa) Total peptides Abundance (nSC) Abundance (PI) Classification Collagen alpha-1(I) chain IPI00297646 P02452 1277 COL1A1 139 3 0.820 30.541 Collagen Collagen alpha-1(VI) chain IPI00291136 P12109 1291 COL6A1 109 96 11.166 16.483 Collagen Collagen alpha-2(VI) chain IPI00304840 P12110 1292 COL6A2 109 25 8.295 8.498 Collagen Collagen alpha-3(VI) chain IPI00022200 P12111 1293 COL6A3 344 94 14.742 16.688 Collagen Tubulointerstitial nephritis antigen-like IPI00005563 Q9GZM7 64129 TINAGL1 52 26 9.103 13.877 Glycoprotein Supplemental Table S2. The glomerular ECM proteome. Enriched fractions of human glomerular ECM were isolated and analysed by MS-based proteomics as described in the methods sections. A proteome of 144 components was identified and categorized as basement membrane, other structural ECM proteins or ECM-associated proteins according to GO annotation. Proteins were further classified by subtype, dominant cellular component or function. Relative protein abundance is shown as normalized spectral counts (nSC).

Basement membrane protein IPI Accession UniProtKB Entrez Gene ID Gene name MW (kDa) Abundance (nSC) Classification Agrin IPI00374563 O00468 375790 AGRN 215 3.075 Glycoprotein Collagen alpha-1(XV) chain IPI00295414 P39059 1306 COL15A1 142 0.052 Collagen Collagen alpha-1(XVIII) chain IPI00022822 P39060 80781 COL18A1 154 5.230 Collagen Collagen alpha-1(IV) chain IPI00743696 P02462 1282 COL4A1 161 12.515 Collagen Collagen alpha-2(IV) chain IPI00306322 P08572 1284 COL4A2 168 17.709 Collagen Collagen alpha-3(IV) chain IPI00010360 Q01955 1285 COL4A3 162 8.155 Collagen Collagen alpha-4(IV) chain IPI00478572 P53420 1286 COL4A4 164 8.961 Collagen Collagen alpha-5(IV) chain IPI00021715 P29400 1287 COL4A5 161 3.778 Collagen Collagen alpha-6(IV) chain IPI00472200 Q14031 1288 COL4A6 164 0.589 Collagen Fibulin-1 IPI00218803 P23142 2192 FBLN1 77 0.029 Glycoprotein Fibrillin-1 IPI00328113 P35555 2200 FBN1 312 1.403 Glycoprotein Fibronectin IPI00022418 P02751 2335 FN1 263 1.893 Glycoprotein Extracellular matrix protein FRAS1 IPI00943593 Q86XX4 80144 FRAS1 443 0.039 Glycoprotein Hemicentin-1 IPI00871227 Q96RW7 83872 HMCN1 613 0.007 Glycoprotein Perlecan IPI00943326 P98160 3339 HSPG2 467 7.012 Proteoglycan Laminin subunit alpha-2 IPI00942809 P24043 3908 LAMA2 343 0.043 Glycoprotein Laminin subunit alpha-5 IPI00783665 O15230 3911 LAMA5 400 9.482 Glycoprotein Laminin subunit beta-1 IPI00013976 P07942 3912 LAMB1 198 0.977 Glycoprotein Laminin subunit beta-2 IPI00296922 P55268 3913 LAMB2 196 14.745 Glycoprotein Laminin subunit gamma-1 IPI00298281 P11047 3915 LAMC1 178 8.818 Glycoprotein Nidogen-1 IPI00026944 P14543 4811 NID1 136 10.247 Glycoprotein Nidogen-2 IPI00028908 Q14112 22795 NID2 151 1.249 Glycoprotein Tubulointerstitial nephritis antigen IPI00099386 Q0UJW2 5975177 TINAG 55 12.873 Glycoprotein von Willebrand factor A domain-containing protein 1 IPI00396383 Q6PCB0 64856 VWA1 47 1.171 Glycoprotein Other structural ECM protein IPI Accession UniProtKB Entrez Gene ID Gene name MW (kDa) Abundance (nSC) Classification Asporin IPI00418431 Q9BXN1 54829 ASPN 44 1.199 Proteoglycan Biglycan IPI00010790 P21810 633 BGN 42 2.268 Proteoglycan Collagen alpha-1(XII) chain IPI00329573 Q99715 1303 COL12A1 333 0.073 Collagen Collagen alpha-1(I) chain IPI00297646 P02452 1277 COL1A1 139 0.253 Collagen Collagen alpha-2(I) chain IPI00304962 P08123 1278 COL1A2 129 0.594 Collagen Collagen alpha-1(III) chain IPI00021033 P02461 1281 COL3A1 139 0.143 Collagen Collagen alpha-1(VI) chain IPI00291136 P12109 1291 COL6A1 109 12.685 Collagen Collagen alpha-2(VI) chain IPI00304840 P12110 1292 COL6A2 109 5.889 Collagen Collagen alpha-3(VI) chain IPI00022200 P12111 1293 COL6A3 344 11.145 Collagen Decorin IPI00012119 P07585 1634 DCN 40 0.067 Proteoglycan Dermatopontin IPI00292130 Q07507 1805 DPT 24 0.397 Glycoprotein EMILIN-1 IPI00013079 Q9Y6C2 11117 EMILIN1 107 0.699 Glycoprotein IPI00021885 P02671 2243 FGA 95 0.954 Glycoprotein Fibrinogen beta chain IPI00298497 P02675 2244 FGB 56 1.965 Glycoprotein IPI00219713 P02679 2266 FGG 49 2.002 Glycoprotein Matrix Gla protein IPI00028714 P08493 4256 MGP 12 0.557 Glycoprotein Nephronectin IPI00910888 Q6UX19 255743 NPNT 65 3.707 Glycoprotein Periostin, osteoblast specific factor IPI00410241 B1ALD8 10631 POSTN 90 0.191 Glycoprotein RPE-spondin IPI00063877 Q8IVN8 157869 RPESP 30 1.364 Glycoprotein Transforming growth factor-beta-induced protein IPI00018219 Q15582 7045 TGFBI 75 0.369 Glycoprotein Tubulointerstitial nephritis antigen-like 1 IPI00005563 Q9GZM7 64129 TINAGL1 52 20.010 Glycoprotein Vitronectin IPI00298971 P04004 7448 VTN 54 9.015 Glycoprotein von Willebrand factor A domain-containing protein 5B2 IPI00038139 Q9BVH8 90113 VWA5B2 133 0.024 Glycoprotein von Willebrand factor A domain-containing protein 8 IPI00946725 E2QRD0 23078 VWA8 211 0.076 Glycoprotein ECM-associated protein IPI Accession UniProtKB Entrez Gene ID Gene name MW (kDa) Abundance (nSC) Classification Angiotensinogen IPI00032220 P01019 183 AGT 53 0.327 Protease inhibitor Serum albumin IPI00745872 P02768 213 ALB 69 11.448 Secreted Protein AMBP IPI00022426 P02760 259 AMBP 39 8.382 Secreted A11 IPI00909703 E9PDK5 311 ANXA11 46 0.371 Annexin IPI00418169 P07355 302 ANXA2 40 1.621 Annexin IPI00329801 P08758 308 ANXA5 36 0.283 Annexin Annexin A7 IPI00002460 P20073 310 ANXA7 53 0.062 Annexin Serum amyloid P-component IPI00022391 P02743 325 APCS 25 19.195 Secreted Apolipoprotein A-I IPI00021841 P02647 335 APOA1 31 4.906 Apolipoprotein Apolipoprotein A-IV IPI00304273 P06727 337 APOA4 45 5.896 Apolipoprotein Apolipoprotein A-V IPI00465378 Q6Q788 116519 APOA5 41 0.055 Apolipoprotein Apolipoprotein C-III variant 1 IPI00657670 B0YIW2 345 APOC3 13 3.204 Apolipoprotein Apolipoprotein E IPI00021842 P02649 348 APOE 36 16.039 Apolipoprotein Azurocidin IPI00022246 P20160 566 AZU1 27 0.194 Protease Beta-2-microglobulin IPI00004656 P61769 567 B2M 14 2.157 Secreted -associated gene TD26 IPI00183480 Q6UXH0 55908 C19orf80 28 0.256 Secreted Complement C1q subcomponent subunit C IPI00022394 P02747 714 C1QC 26 0.352 Complement Complement C3 (Fragment) IPI00783987 P01024 718 C3 187 4.768 Complement Complement C5 IPI00032291 P01031 727 C5 188 0.559 Complement Complement component 6 precursor IPI00879709 P13671 729 C6 106 0.458 Complement Complement component C7 IPI00296608 P10643 730 C7 94 0.445 Complement Complement component C8 alpha chain IPI00011252 P07357 731 C8A 65 0.151 Complement Complement component C8 beta chain IPI00294395 P07358 732 C8B 67 0.276 Complement Complement component C8 gamma chain IPI00011261 P07360 733 C8G 22 6.669 Complement UPF0670 protein C8orf55 IPI00171421 Q8WUY1 51337 C8orf55 24 0.134 Secreted Complement component C9 IPI00022395 P02748 735 C9 63 2.713 Complement Carbonic anhydrase 2 IPI00218414 P00918 760 CA2 29 1.506 Enzyme CD59 glycoprotein IPI00011302 P13987 966 CD59 14 3.179 Cell surface Complement factor H-related 5 IPI00006543 Q5VYL6 81494 CFHR5 67 0.477 Complement IPI00400826 P10909 1191 CLU 58 4.587 Secreted CNPI of 2',3'-cyclic- 3'-phosphodiesterase IPI00220993 P09543 1267 CNP 45 0.743 Enzyme Coatomer subunit alpha IPI00295857 P53621 1314 COPA 138 0.128 Secreted Carboxypeptidase B2 IPI00329775 Q96IY4 1361 CPB2 48 0.198 Enzyme Cystatin-C IPI00032293 P01034 1471 CST3 16 1.315 Protease inhibitor Cathepsin D IPI00011229 P07339 1509 CTSD 45 0.110 Protease ECM-associated protein, cont. IPI Accession UniProtKB Entrez Gene ID Gene name MW (kDa) Abundance (nSC) Classification Cathepsin G IPI00028064 P08311 1511 CTSG 29 1.188 Protease Dermcidin isoform 2 IPI00847793 A5JHP3 117159 DCD 12 0.075 Secreted 1 IPI00005721 P59665 1667 DEFA1 10 15.387 Secreted Dystonin IPI00642259 Q03001 667 DST 857 0.002 Plakin Neutrophil elastase IPI00027769 P08246 1991 ELANE 29 0.779 Protease factor IX IPI00296176 P00740 2158 F9 52 0.821 Coagulation -2 IPI00397801 Q5D862 388698 FLG2 248 0.041 Cytoskeleton Flt1 of Vascular endothelial growth factor receptor 1 IPI00018335 P17948 2321 FLT1 151 0.037 Secreted FRAS1-related extracellular matrix protein 2 IPI00180707 Q5SZK8 341640 FREM2 351 0.071 Cell surface Gamma-glutamyl IPI00023728 Q92820 8836 GGH 36 0.146 Enzyme Guanine nucleotide-binding protein G(s) subunit alpha IPI00095891 Q5JWF2 2778 GNAS 111 0.262 Cell surface -6-phosphate isomerase IPI00027497 P06744 2821 GPI 63 0.482 Enzyme Glutathione peroxidase 3 IPI00026199 P22352 2878 GPX3 26 8.232 Enzyme IPI00646773 P06396 2934 GSN 81 1.039 Cytoskeleton HLA class I histocompatibility antigen, Cw-7 alpha chain IPI00940896 F5GXA6 3105 HLAC 44 0.267 Cell surface IPI00641737 P00738 3240 HP 47 0.163 Secreted IPI00022488 P02790 3263 HPX 52 1.231 Secreted Estradiol 17-beta-dehydrogenase 12 IPI00007676 Q53GQ0 51144 HSD17B12 34 0.170 Enzyme Ig gamma-4 chain C region IPI00930442 P01861 3503 IGHG4 52 1.806 Secreted Inhibin beta E chain IPI00011036 P58166 83729 INHBE 39 0.536 Secreted Inter-alpha-trypsin inhibitor heavy chain H1 IPI00292530 P19827 3697 ITIH1 101 0.032 Protease inhibitor Inter-alpha-trypsin inhibitor heavy chain H3 IPI00028413 Q06033 3699 ITIH3 100 0.035 Protease inhibitor Kininogen-1 IPI00215894 P01042 3827 KNG1 48 0.674 Protease inhibitor Galectin-1 IPI00219219 P09382 3956 LGALS1 15 4.983 Secreted Galectin-3 IPI00465431 P17931 3958 LGALS3 26 2.965 Secreted Galectin-3-binding protein IPI00023673 Q08380 3959 LGAS3BP 65 0.550 Secreted Hepatic triacylglycerol lipase IPI00003882 P11150 3990 LIPC 56 0.265 Enzyme Low-density lipoprotein receptor-related protein 2 (megalin) IPI00024292 P98164 4036 LRP2 522 0.101 Cell surface C IPI00019038 P16126 4069 LYZ 17 1.547 Secreted Matrix metalloproteinase-9 IPI00027509 P14780 4318 MMP9 78 0.111 Protease IPI00007244 P05164 4353 MPO 84 0.868 Enzyme -16 IPI00103552 Q8WX17 94025 MUC16 2353 0.001 Cell surface Phosphatidylethanolamine-binding protein 1 IPI00219446 P30086 5037 PEBP1 21 0.506 Protease inhibitor Peptidyl-prolyl cis-trans isomerase A IPI00419585 P62937 5478 PPIA 18 4.879 Enzyme Prolargin IPI00020987 P15888 1252098 PRELP 44 0.198 Secreted IPI00656092 Q92954 10216 PRG4 146 0.016 Secreted 23 IPI00026941 O95084 11098 PRSS23 43 0.299 Protease Prostaglandin D2 synthase 21kDa IPI00513767 Q5SQ09 5730 PTGDS 23 0.564 Enzyme Retinol binding protein 4, plasma IPI00480192 Q5VY30 50950 RBP4 23 20.718 Secreted Hornerin IPI00398625 Q86YZ3 388697 S100A18 282 0.012 Cytoplasmic Protein S100-A6 IPI00027463 P06703 6277 S100A6 10 1.590 Cell surface Protein S100-A8 IPI00007047 P05109 6279 S100A8 11 3.701 Secreted Serum amyloid A protein IPI00552578 P02735 6288 SAA1 14 0.675 Secreted Semaphorin-3B IPI00012283 Q13214 7869 SEMA3B 83 0.032 Secreted Alpha-1-antitrypsin IPI00553177 P01009 5265 SERPINA1 47 5.590 Protease inhibitor Alpha-1-antichymotrypsin IPI00550991 P01011 12 SERPINA3 51 0.481 Protease inhibitor Plasma serine protease inhibitor IPI00007221 P05154 5104 SERPINA5 46 2.071 Protease inhibitor Antithrombin-III IPI00032179 P01008 462 SERPINC1 53 0.583 Protease inhibitor Glia-derived nexin IPI00914848 P07093 5270 SERPINE2 44 0.164 Protease inhibitor Plasma protease C1 inhibitor IPI00291866 P05155 710 SERPING1 55 0.890 Protease inhibitor H1 IPI00032140 P50454 871 SERPINH1 46 0.401 Protease inhibitor Small inducible cytokine B14 precursor IPI00396257 Q9Y5W8 23161 SNX13 13 0.782 Cytoplasmic Sorbitol dehydrogenase IPI00216057 Q00796 6652 SORD 38 2.804 Enzyme Secreted phosphoprotein 24 IPI00011832 Q13103 6694 SPP2 24 1.527 Secreted Serotransferrin IPI00022463 P02787 7018 TF 77 0.096 Secreted Protein-glutamine gamma-glutamyltransferase 2 IPI00294578 P21980 7052 TGM2 77 1.302 Enzyme Metalloproteinase inhibitor 3 IPI00218247 P35625 7078 TIMP3 24 0.928 Protease inhibitor Gamma of ligand superfamily member 13 IPI00218937 O75888 8741 TNFSF13 27 0.135 Secreted Transthyretin IPI00022432 P02766 7276 TTR 16 3.880 Secreted Uromodulin IPI00640271 E9PEA4 7369 UMOD 74 26.901 Secreted Protein Wnt IPI00876998 A8K0G1 7477 WNT7B 39 0.058 Secreted Supplemental Table S3. Glomerular ECM interaction network statistics. The interaction network generated from ECM proteomics data (real) was compared to random interaction networks. Nodes represent proteins and edges represent protein–protein interactions. Average degree is the mean number of protein–protein interactions per protein in the network. Random networks were generated by 10,000 degree-preserving random shuffles of the corresponding real ECM networks. Clustering coefficients for random networks were computed for 10,000 rounds of network randomization and are reported as mean ± standard deviation. Two-tailed P-value was calculated using a Z-test. P-value indicates significantly higher clustering coefficient for the real ECM network compared to the corresponding random networks.

Glomerular ECM Metric Real Random Number of nodes 126 126 Number of edges 210 210 Average degree 3.33 3.33 Clustering coefficient 0.187 0.108 (P < 0.0001) ± 0.0154