Visual Abstract
Abstract
Background Antibody-mediated rejection (AMR) accounts for >50% of kidney allograft loss. Donor-specific antibodies (DSA) against HLA and non-HLA antigens in the glomeruli and the tubulointerstitium cause AMR while inflammatory cytokines such as TNFα trigger graft injury. The mechanisms governing cell-specific injury in AMR remain unclear.
Methods Unbiased proteomic analysis of laser-captured and microdissected glomeruli and tubulointerstitium was performed on 30 for-cause kidney biopsy specimens with early AMR, acute cellular rejection (ACR), or acute tubular necrosis (ATN).
Results A total of 107 of 2026 glomerular and 112 of 2399 tubulointerstitial proteins was significantly differentially expressed in AMR versus ACR; 112 of 2026 glomerular and 181 of 2399 tubulointerstitial proteins were significantly dysregulated in AMR versus ATN (P<0.05). Basement membrane and extracellular matrix (ECM) proteins were significantly decreased in both AMR compartments. Glomerular and tubulointerstitial laminin subunit γ-1 (LAMC1) expression decreased in AMR, as did glomerular nephrin (NPHS1) and receptor-type tyrosine-phosphatase O (PTPRO). The proteomic analysis revealed upregulated galectin-1, which is an immunomodulatory protein linked to the ECM, in AMR glomeruli. Anti-HLA class I antibodies significantly increased cathepsin-V (CTSV) expression and galectin-1 expression and secretion in human glomerular endothelial cells. CTSV had been predicted to cleave ECM proteins in the AMR glomeruli. Glutathione S-transferase ω-1, an ECM-modifying enzyme, was significantly increased in the AMR tubulointerstitium and in TNFα-treated proximal tubular epithelial cells.
Conclusions Basement membranes are often remodeled in chronic AMR. Proteomic analysis performed on laser-captured and microdissected glomeruli and tubulointerstitium identified early ECM remodeling, which may represent a new therapeutic opportunity.
- acute allograft rejection
- extracellular matrix
- glomerular endothelial cells
- proximal tubule
- renal biopsy
- kidney transplantation
Kidney transplantation is the optimal treatment for ESKD, but most grafts fail prematurely. Antibody-mediated rejection (AMR) is the leading cause of kidney graft failure, accounting for >50% of graft loss.1 AMR is characterized by graft injury (i.e., acute tubular necrosis [ATN] or thrombotic microangiopathy), donor-specific antibodies (DSA), and endothelial activation, as evidenced by C4d deposition in peritubular capillaries, microvascular inflammation, or increased endothelial injury markers. ATN indicates tubular cell injury and may be observed in the context of AMR.2 Chronic AMR is associated with an aggressive lesion, transplant glomerulopathy (TG), which portends poor prognosis.3 Multilayering of peritubular capillary basement membranes (BMs) may be seen in TG, together with the duplication of glomerular BMs; these abnormalities likely arise from repeated cycles of endothelial cell injury. Caveolae formation in microvascular endothelial cells4 and development of anti-BM antibodies have been observed,5–⇓7 but their significance is unclear. It is evident from the histopathologic changes that injury to microvascular endothelial cells and BMs represent key sites of AMR-induced injury, initiated mainly by anti-HLA DSA.
Lesions in the kidney BMs are often a result of maladaptive molecular changes in the extracellular matrix (ECM).8,9 The kidney ECM behaves as a dynamic structure undergoing remodeling (partly mediated by proteases).10,11 In many kidney diseases, profibrotic changes occur in response to injury, leading to abnormal remodeling and deposition of ECM components.11 As disease progresses, persistent ECM deposition ultimately leads to irreversible fibrosis.12 In the setting of AMR, it is known that more evident ECM lesions develop at chronic stages and correlate with allograft loss.13–⇓15 However, little is known about the specific BM components involved in these chronic ECM changes. Moreover, whether the ECM remodeling starts early in AMR, and whether this remodeling is linked to the action of anti-HLA DSA, is still unknown.
Anti-HLA class I (α-HLA-I) and class II (α-HLA-II) antibodies exert pathogenic effects in human glomerular microvascular endothelial cells (HGMECs) and other cells.16,17 Endothelial cells express HLA class I constitutively, and HLA class II in injury settings (e.g., exposure to IFNγ).18–⇓20 Ligation of HLA with α-HLA-I induces cell proliferation and activation of extracellular signal–regulated kinase (ERK) and mammalian target of rapamycin.20–⇓22 These effects are enhanced by IFNγ and TNFα.20 Endothelial cells stimulated with α-HLA-I increased cytokine expression (e.g., IL-8 and C-C motif chemokine 2 [CCL2]).23 Importantly, α-HLA-I-activated endothelial cells display cytoskeletal alterations and interact with leukocytes.24–⇓⇓27 α-HLA-II induces necrosis in IFNγ-treated endothelial cells,18 and exerts prothrombotic effects in HGMECs.17
The role of tubular cells in AMR is unclear. These cells express HLA and other immunomodulatory proteins and interact with immune cells.28,29 Proximal tubular epithelial cells (PTECs) increase inflammatory cytokine (IL-6, CCL2, and C-X-C motif chemokine 10) secretion in response to α-HLA-I, IFNγ, and TNFα.30 Both IFNγ and TNFα accentuate antibody-mediated injury in AMR.31–⇓33 Moreover, tubular proteins can be targeted by non-HLA antibodies,30,34 which reinforces the rationale for studying AMR in the tubulointerstitium.
Identification of mechanisms underpinning cell-specific maladaptive responses in AMR may uncover new therapeutic targets. Transcriptomic studies show that graft injury in AMR correlates with gene expression alterations.35 However, compartment-specific molecular alterations or changes in proteome composition/expression are unknown. To address this gap, we conducted a discovery-based proteomics study in for-cause kidney biopsy specimens. We selected seven biopsy specimens with pure, early AMR, and compared them to 11 biopsy specimens with pure acute cellular rejection (ACR), and to 12 biopsy specimens with pure ATN. We isolated glomeruli and tubulointerstitium by laser-capture microdissection, and subjected them to mass spectrometry (MS)–based proteomic analysis. Working with ultra-small protein amounts, we identified >2000 proteins in each compartment. We demonstrated, for the first time, that BM and ECM proteins were significantly decreased in AMR, in both compartments. We observed that galectin-1 (LGALS1), an ECM-related immunomodulatory protein, was increased in AMR glomeruli, and that glutathione S-transferase ω-1 (GSTO1), an ECM-modifying enzyme, was increased in the AMR tubulointerstitium. These proteins represent potential therapeutic targets to prevent ECM remodeling in AMR.
Methods
Study Design and Patient Population
We carefully selected early, for-cause kidney allograft biopsy specimens with a diagnosis of acute AMR and graft age-matched cases with ACR or ATN. Patients were identified by searching the CoReTRIS registry for the period of 2008–2015.36 Inclusion criteria encompassed a kidney transplant with an allograft biopsy within the first 3 months post-transplantation, with a diagnosis of pure AMR, ACR, or ATN. Exclusion criteria were a diagnosis of mixed rejection or insufficient formalin-fixed, paraffin-embedded biopsy material to perform laser-capture microdissection. For each patient, different sections from the same 18-gauge needle biopsy core were used for histopathology evaluation, proteome analysis, and orthogonal verification studies. All biopsy specimens were assessed by a renal pathologist (R.J.) and scored according to the Banff classification (2017).37 Anti-HLA antibodies were assessed using a Luminex single-antigen bead assay and adjudicated by the local HLA laboratory, as part of clinical standard of care. This study was approved by the University Health Network institutional research ethics board (Coordinated Approval Process for Clinical Research [CAPCR] identifier 13-7261).
Histology
From the formalin-fixed, paraffin-embedded biopsy material of our study cases, sections were cut at 3 μm; subjected to periodic acid–Schiff, periodic Schiff-methenamine, trichrome, and hematoxylin and eosin stains; and examined by light microscopy. C4d was detected on frozen sections by immunofluorescence. Morphologic features were diagnosed and scored according to the updated Banff classification.37 For electron microscopy, tissue was examined following appropriate fixation using a JEOL-100S microscope. Glomerular BM thickness was examined by standard methodology on two glomeruli per biopsy specimen. Approximately seven to eight capillary loops per glomerulus and ten to 11 peritubular capillaries per biopsy specimen were studied at 8000× magnification. Subendothelial new BM formation was reported as the proportion of loops with any degree of new BM formation. Peritubular capillary BM multilayering and glomerular and peritubular capillary endothelial cell swelling were semiquantitatively assessed on a scale of zero to three: zero, none; one, mild (<25%); two, moderate (25%–50%); three, severe (>50%).
Immunostaining
For laminin subunit γ-1 (LAMC1) immunohistochemistry, antigen retrieval was performed in 5 μm sections by heating the samples with 0.01 M sodium citrate (pH 6) in a pressure cooker for 5 minutes. Sections were then incubated with rabbit anti-LAMC1 antibody (dilution 1:100, HPA001909; Atlas Antibodies). Horseradish peroxidase–conjugated anti-rabbit (BA-1000; Vector Labs) was used as secondary antibody. Binding of antibodies was detected using the Liquid DAB+ Substrate Chromogen System (Dako). Samples were counterstained with hematoxylin to visualize nuclei, and slides were digitally scanned in a ZEISS Axio Scan.Z1 system. To assess protein expression, glomerular and tubulointerstitial areas were differentially outlined, and the intensity of the positive pixels was quantified using the ImageScope software (version 12.3.2.8013).
We also assessed glomerular expression of nephrin (NPHS1) and receptor-type tyrosine-phosphatase O (PTPRO) by immunofluorescence. Deparaffinized sections were heated at pH 9 to retrieve antigens, and incubated with rabbit anti-NPHS1 antibody (dilution 1:1,000, ab216341; Abcam) and with mouse anti-PTPRO (dilution 1:50, MABS1221; Millipore). Cy3-conjugated anti-rabbit IgG (A11035; Invitrogen) and FITC-conjugated anti-mouse IgG (A11029; Life Technologies) were used as secondary antibodies to detect NPHS1 and PTPRO, respectively. 4′,6-Diamidino-2-phenylindole staining was used to visualize the nuclei, and slides were digitally scanned in a ZEISS Axio Scan.Z1 system. To assess protein expression, glomerular areas were selected and positive pixels were quantified using ZEISS Imaging Software (ZEN 2 blue edition). For our immunostaining studies, data were expressed as mean intensity of positive pixels, and normalized to the total number of pixels or the μm2 of area analyzed.
Proteomics
Laser-Capture Microdissection and Sample Preparation for Proteomics Analysis
The workflow of our approach is summarized in Supplemental Figure 1. For each study case, remaining kidney cortical tissue from the formalin-fixed, paraffin-embedded biopsy specimen was sectioned at 8 μm, and stained with hematoxylin. The kidney glomeruli and tubulointerstitium of each section were captured with a Leica LMD 7000 laser, coupled to a Leica DM 6000B microscope, under the following conditions: magnification, 10×; power, 45; speed, 10. The compartment-specific fractions were collected in a tube with 35 μl of protein extraction buffer containing 10 mM Tris, 1 mM EDTA, and 0.002% Zwittergent in MS-grade pure water (Sigma). To obtain comparable protein amounts across all samples, in each biopsy section, we standardized the amount of captured glomeruli and tubulointerstitium to 350,000 μm2. In each biopsy section, this area included an average of 23 captured glomeruli (glomerular compartment) or four to five captured tubulointerstitial areas (tubulointerstitial compartment). The total protein amount per sample was below the limit of detection of standard protein quantification assays. Samples were vortexed for 2 minutes and centrifuged at 12,000 × g for 2 minutes. To reverse formaldehyde crosslinking, samples were heated for 90 minutes at 98°C, with vortexing every 15 minutes. Samples were then centrifuged at 12,000 × g for 2 minutes and subjected to sonication for 1 hour. After another centrifugation at 12,000 × g for 2 minutes, proteins were then digested into peptides with 0.5 μg of MS-grade trypsin (Promega) diluted in 50 mM ammonium bicarbonate, overnight at 37°C. After tryptic digestion, peptides were reduced by adding 2 μl of 100 mM dithiothreitol in ammonium bicarbonate (final concentration 5.13 mM) and heating the samples at 95°C for 5 minutes. Samples were then acidified by addition of 10 μl of 0.5% v/v trifluoroacetic acid with 0.15% v/v formic acid in MS-grade pure water (Sigma). Peptides were extracted and desalted with 10 μl OMIX C18 MB tips (Agilent), eluted in 3 μl of 65% v/v acetonitrile, and diluted to 41 μl with 0.1% v/v formic acid in MS-grade pure water. This workflow was previously developed38 and was also successfully used in prior proteomic studies of microdissected formalin-fixed, paraffin embedded samples.39–⇓⇓⇓⇓⇓45
Tandem MS
For each study biopsy section, the glomerular and tubulointerstitial fractions were randomized and subjected to MS on a Thermo Scientific EASY-nLC1000 system, coupled to a Q-Exactive Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer using a nano-electrospray ionization source (Thermo Scientific). Samples were run on a 60-minute gradient of increasing concentrations of Buffer B (100% acetonitrile) in 0.1% formic acid / 99.9% MS-grade water (Thermo Scientific). The method started at 1% Buffer B, and the concentration was increased to 5% at 2 minutes, with subsequent increases to 35% (49 minutes), 65% (52 minutes), and 100% (53 minutes). For each sample, 18 μl of eluted peptides were injected onto a 3.3 cm C18 preanalytic column (inner diameter, 75 μm; bead size, 5 μm; IntegraFrit capillary, New Objective) and then passed through a C18 resolving analytical column (PicoTip emitter; inner diameter, 15 cm × 75 μm; tip, 8-μm; bead size, 3 μm; Agilent Technologies). The spectra were obtained under data-dependent acquisition mode, consisting of full MS1 scans (m/z range, 400–1500; resolution, 70,000) followed by MS2 scans of the top 15 parent ions (resolution, 17,500).
Protein Identification and Quantification
For protein identification, the RAW files of each MS run were generated by XCalibur software version 3.0.63 (Thermo Scientific). Raw data were analyzed by MaxQuant software (version 1.5.3.28) and searched in Andromeda against the human UniProt FASTA database (HUMAN5640_sProt–072016, update of July 20, 2016). Proteins and peptides were identified with a false discovery rate of 1%. For peptide identification, a minimum length of six amino acids was selected. The false positive rate was determined using reverse mode. Trypsin/P was selected as digestion enzyme, and a maximum of two missed cleavages (default setting) was enabled. Cysteine carbamidomethylation was selected as a fixed modification, and methionine oxidation and N-terminal acetylation were set as variable modifications. The initial peptide tolerance against a “human-first-search” database was set to 20 ppm. The main search peptide mass tolerance was 40 ppm, and the fragment mass tandem MS tolerance was 0.5 Da. Matching between runs was selected. Normalized label-free quantification (LFQ) of proteins was derived from extracted ion current information from razor and unique peptides, with a minimum ratio count of one. We adopted less-restrictive search settings to increase proteome coverage, keeping in mind that our inclusive approach may have led to more false positive hits. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository,46 with the data set identifier PXD017580.
Proteomics data were analyzed using Perseus software (version 1.5.6.0). Reverse hits and contaminants were manually checked and removed. We examined the distribution of log2-transformed LFQ intensity values of all proteins quantified in each sample (Supplemental Figure 2). In the glomerular compartment, 28 of the 30 samples had protein intensity values that followed a normal distribution. One ACR and one ATN case were excluded due to poor protein recovery, and non-normal distribution (Supplemental Figure 2A). Meanwhile, the protein intensity values of all 30 tubulointerstitial fractions followed a normal distribution (Supplemental Figure 2B). We then subjected the zero-value intensities to imputation (assuming low abundance values were missing), keeping a normal distribution, with a downshift of 1.8 SDs, and a width of 0.3. After imputation, we determined the differentially expressed proteins between the AMR and the ACR and ATN groups in glomeruli and tubulointerstitium, by comparing their mean log2-transformed LFQ intensities using the two-tailed independent t test (P<0.05). In both compartments, >99% of proteins were identified with at least one unique peptide, and >90% were identified with at least two unique peptides. To increase the probability of focusing on true hits, we applied a quality filter to further analyze only those proteins identified in at least 50% of the samples in each study group. Proteins identified with at least five unique peptides were selected for further verification/validation. In particular, we further studied LAMC1 (53 unique peptides in the glomeruli, and 29 unique peptides in the tubulointerstitium), NPHS1 (13 unique peptides in the glomeruli), PTPRO (five unique peptides in the glomeruli), LGALS1 (nine unique peptides in the glomeruli), and GSTO1 (eight unique peptides in the tubulointerstitium). All five selected proteins were present in all biopsy samples, except PTPRO, which was identified in 27/28 glomerular samples.
Bioinformatics Analyses
Gene Ontology and Pathway Enrichment Analysis
For the proteins differentially expressed in each AMR compartment, Gene Ontology (GO) enrichment was performed using clusterProfiler_3.16.047 in R (version 4.0.0). Enrichments were generated for each comparison (AMR versus ACR; AMR versus ATN) and direction of change (upregulated in AMR; downregulated in AMR) in each kidney compartment. Redundant GO terms were removed using REViGO,48 and remaining terms were used to generate plots with ggplot (version 2 3.3.0). Terms related to biologic processes, molecular function, and cell compartment were considered in the analysis. Similarly, pathDIP49 database 3.0 (http://ophid.utoronto.ca/pathDIP) was used for pathway enrichment analysis. For the statistical analyses of GO and pathway enrichment, the hypergeometric test with Benjamini–Hochberg correction was used. Q<0.05 was considered significant in the GO analysis, whereas the significance cutoff was set to Q<0.01 in the pathway enrichment analysis.
The significantly enriched pathways emerging from PathDIP analysis in each compartment were classified using a reduction algorithm. Through this algorithm, pathway categories were mapped to high-level pathway terms in two ontologies: PathwayOntology (https://bioportal.bioontology.org/ontologies/PW) and Reactome50 (https://reactome.org/). First, each pathway category was mapped to ontology terms based on the similarity of pathway names and ontology terms using Python (version 3.6.8). Next, each pathway category was mapped to an ancestor of its associated ontology term, at the first (Reactome) or second (PathwayOntology) level of the ontology. When there were multiple ancestors, the best one was manually selected. Ancestor terms not pertinent to this work were grouped in the category “other.”
Hierarchical Clustering Analysis
For each of the two kidney compartments (glomeruli and tubulointerstitium) and group comparisons (AMR versus ACR and AMR versus ATN), all of the differentially expressed proteins (P<0.05) were depicted by heatmaps using R (version 3.6.1),51 with hierarchical clustering of proteins and samples.
Protein-Protein Interaction and Network Analysis
Physical protein-protein interactions of LGALS1 were collected using the Integrated Interactions Database (version 2018-11).52 We next focused on the interactions between LGALS1 and proteins that were also differentially expressed in the AMR glomeruli. Interactions experimentally validated or predicted, and annotated as present in the human kidney, were retained and visualized using NAViGaTOR 3.9.113 (http://ophid.utoronto.ca/navigator).53 The color of the nodes reflects the category to which each protein was computationally assigned. If a protein was annotated with pathways from multiple categories, only the category with the highest number of pathways (“other” excluded) was used for annotation.
Analysis of External Data Set
We examined a publicly available transcriptome data set (GSE36059).35 We identified all of the GSM files corresponding to the microarray gene expression data from control (n=281) and AMR (n=65) kidney biopsy specimens and downloaded them from the Gene Expression Omnibus public functional genomics data repository (https://www.ncbi.nlm.nih.gov/geo/). We then conducted a differential gene expression analysis of AMR versus control in R software (version 3.5.2), using a limma-moderated t test followed by Benjamini–Hochberg adjustment (Q<0.05). We crossreferenced our list of proteins differentially expressed in AMR in the glomeruli and tubulointerstitium with the 1275 genes significantly differentially expressed in AMR versus control biopsies, and studied the overlap between data sets.
Protease Prediction Analysis
The MEROPS54 database (https://www.ebi.ac.uk/merops/) was used to identify proteases predicted to cleave the ECM-related proteins in each AMR compartment. Briefly, the UniProt accession numbers corresponding to the proteins significantly decreased in AMR versus ACR (39 in the glomeruli and 92 in the tubulointerstitium) and in AMR versus ATN (66 in the glomeruli and 137 in the tubulointerstitium) were evaluated in MEROPS to identify cleavage sites by known proteases. Proteases were ranked manually in descending order, according to the number of targets in our data they were predicted to cleave.
Cell Culture
HGMECs
Primary HGMECs (Cell Systems) were cultured in Endothelial Cell Growth Media MV (Promocell), supplemented with a ready-to-use kit containing 5% v/v dialyzed FCS, 10 ng/ml EGF, 1 μg/ml hydrocortisone, and 90 μg/ml heparin. We then added 50 U/ml penicillin and 50 g/ml streptomycin. All of the experiments were performed at passage 5. After reaching approximately 70% confluence, cells were serum-starved for 16 hours in medium containing 0.5% FCS. Cells were then treated with 1, 5, or 10 μg/ml of mouse anti-human HLA class I antibody (clone W6/32, ab23755; Abcam) for 7.5, 15, 30, 60, or 120 minutes (signaling experiments), or for 18 or 24 hours (gene/protein expression studies). The same concentration of mouse IgG2α (ab18413; Abcam) was administered as isotype control, and PBS was used as vehicle. To study response to cytokines, HGMECs were also exposed to 10 ng/µl of TNFα (Sigma) or 1000 U/ml of IFNγ (Sigma) for 24 hours. For these experiments, 0.1% BSA in PBS was used as vehicle. All treatments were prepared in FCS-free media. After stimulation, cells were washed with PBS, and harvested with 0.25% trypsin-EDTA (Life Technologies) for 5 minutes at 37°C. Cell pellets were then snap frozen and stored at −80°C until further analysis. Cell supernatants were also collected, cleared at 1000 × g at 4°C for 10 minutes to remove cell debris, and stored at −80°C until further analysis.
Human PTECs
Primary human PTECs (Lonza) were cultured in DMEM containing 5.55 mM d-glucose, 4 mM l-glutamine, and 1 mM sodium pyruvate, and supplemented with 10% v/v dialyzed FBS, 10 ng/ml EGF, 1× of Transferrin/Insulin/Selenium (Invitrogen), 0.05 M hydrocortisone, 50 U/ml penicillin, and 50 μg/ml streptomycin, as previously described.55 All of the experiments were performed at passage 5. After reaching approximately 70% confluence, cells were serum starved for 16 hours and treated with 20 ng/µl of TNFα, 1000 U/ml of IFNγ, or 1 ng/µl of LPS (all from Sigma) for 24 hours. BSA (0.1%) in PBS was used as vehicle. At the end of the treatment, cells were washed with PBS and harvested with 0.25% trypsin-EDTA (Life Technologies) for 5 minutes at 37°C. Cell pellets were then snap frozen and stored at −80°C until further analysis.
Gene Expression
Total RNA was extracted from cell pellets using the RNeasy Mini Kit (Qiagen). After quantifying the RNA concentration using Nanodrop (Thermo Scientific), 300 ng of RNA was retrotranscribed to cDNA using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Gene levels of CDH5, platelet endothelial cell adhesion molecule-1 (PECAM1), VWF, ACTA2, CCL2, IL-6, C-X-C motif chemokine 8 (CXCL8), C-X-C motif chemokine 10 (CXCL10), VCAM1, LGALS1, antigen peptide transporter 1 (TAP1), HLA-C, cathepsin-V (CTSV), cathepsin-L (CTSL), cathepsin-S (CTSS), legumain (LGMN), matrix metalloprotease 2 (MMP2), and GSTO1 were measured by real-time quantitative PCR using Power SYBR Green PCR Master Mix (Applied Biosystems) in a StepOnePlus System (Applied Biosystems). Human lung fibroblasts (Hel299 cell line), used as positive control for ACTA2 gene expression, were kindly supplied by Terrance Ku (Humar Laboratory, University Health Network). In each experiment, gene expression data were normalized to the most stable of the following four housekeeping genes, across conditions: ACTB, glyceraldehyde-3-phosphate dehydrogenase, VCL, or 60S ribosomal protein L31. All primer sequences are summarized in Supplemental Table 1.
Protein Expression
Cell pellets from HGMECs and PTECs were solubilized in lysis buffer (#9803; Cell Signaling), and total protein was extracted by mechanical homogenization. Protein concentration was determined using a Micro BCA Protein Assay Kit (Thermo). To study the protein expression of HLA molecules in HGMECs by Western blot, 10 µg of protein were loaded onto 10% acrylamide gels, separated by SDS-PAGE, and transferred to a polyvinylidene difluoride membrane (Millipore). Membranes were then blocked with 5% milk and incubated with mouse monoclonal α-HLA-I (1:4000, ab23755; Abcam) or rabbit monoclonal α-HLA-II (1:3000, ab157210; Abcam). For signaling experiments in HGMECs, blots were performed on 20 µg of protein, and membranes were incubated with primary antibodies against phospho-p44/42 ERK (1:1000, #9102; Cell Signaling) and total p44/42 ERK (1:2000, #9102; Cell Signaling). To measure GSTO1 protein expression in PTECs, blots were performed on 40 µg of protein, and membranes were incubated with 1/500 of anti-GSTO1 rabbit polyclonal antibody (HPA037603; Atlas Antibodies). Control for protein loading in HGMECs and PTECs was performed by reblotting membranes using a rabbit polyclonal antibody for α-tubulin (ab176560; Abcam) or a mouse mAb for glyceraldehyde-3-phosphate dehydrogenase (CB1001; Sigma), respectively. The secondary antibodies were anti-rabbit (A0545; Sigma) and anti-mouse (P0447; Dako). After detection in a Gel-Imaging System (Bio-Rad, Hercules, CA), bands were quantified by densitometry using ImageJ software.
Cytokine and LGALS1 Secretion
Frozen aliquots of supernatants from HGMECs were thawed and subjected to a custom multiplex bead kit (R&D Systems) to assess the levels of IL-4, IL-8, and CCL2. Samples were prepared in a 1:1 dilution with assay buffer, as suggested by the manufacturer. Diluted samples and standards were run in duplicate. Biomarker concentrations were obtained using a Bio-Plex MAGPIX Multiplex Reader (Bio-Rad). For all of the analyzed cytokines, any value falling below the lower limit of detection was assigned a concentration of 0 ng/ml.
To determine the secreted levels of LGALS1, cell supernatants from HGMECs were analyzed in duplicate using the colorimetric LGALS1 Human ELISA Kit (KA5065; Abnova). Supernatants were diluted 1:15 in sample diluent and added to the plate wells, which were precoated with an anti-LGALS1 capture antibody. After an incubation of 90 minutes at 37°C, plates were washed with PBS and a biotinylated anti-LGALS1 detection antibody was added. Samples were then incubated for 60 minutes at 37°C, and washed again with PBS. Detection was performed by adding an avidin-biotin–horseradish peroxidase complex and incubating the plates with color-developing reagent for 15 minutes at 37°C. The emitted colorimetric signal was recorded at 450 nm in a Cytation 5 plate reader (BioTek).
Cellular Metabolic Function
Metabolic function was assessed in HGMECs and PTECs by measuring the extracellular acidification rate (indicator of glycolysis) and oxygen consumption rate (OCR) in a Seahorse XFe96 analyzer (Agilent). At confluence, cells were detached with 0.25% trypsin-EDTA for 5 minutes at 37°C, and subsequently seeded in a Seahorse XF96 Cell Culture Microplate at a density of 15,000 cells/well in 100 μl of complete medium. After letting them adhere for 4–6 hours, cells were serum-starved and exposed to the treatment of interest. At 1 hour before the assay, starvation medium was removed, cells were washed with phenol-free basal medium (Agilent), and then exposed to 150 μl of minimal substrate assay medium (made by adding 2 mM glutamine, 1 mM pyruvate, and 5.55 mM glucose to the basal medium). The same treatment concentrations were maintained during this acclimatization step. During the Seahorse assay, extracellular acidification rate and OCR were recorded at baseline and after metabolic stress. To induce metabolic stress, 25 µl of oligomycin, p-trifluoromethoxy carbonyl cyanide phenylhydrazone, 2-deoxyglucose, and rotenone with antimycin A were sequentially injected into the microplate wells. After optimization, the following working concentrations for each metabolic drug were stablished: oligomycin, 1 µM; p-trifluoromethoxy carbonyl cyanide phenylhydrazone, 0.6 µM for HGMECs and 0.3 µM for PTECs; 2-deoxyglucose, 100 mM; rotenone, 1 µM; antimycin A, 1 µM.
Oxidative Stress
Oxidative stress was assessed in HGMECs and PTECs by measuring the intracellular levels of superoxide ion with the Cellular Reactive Oxygen Species (ROS) Assay Kit (Red) (Abcam) following the manufacturer’s instructions. At confluence, cells were detached with 0.25% trypsin-EDTA for 5 minutes at 37°C, and subsequently seeded in black 96-well microplates at a density of 15,000 cells/well in 100 μl of complete medium. After letting the cells adhere for 4–6 hours, they were starved and exposed to the treatment of interest. After each treatment, starvation medium was removed and cells were washed with PBS. Cells were stained with 100 μl of ROS Red Working Solution for 45 minutes at 37°C. Changes in fluorescence intensity were recorded at an excitation/emission wavelength of 520/605 nm in a Cytation 5 plate reader (BioTek).
Intracellular Levels of DNA and ATP
Intracellular levels of DNA and ATP in HGMECs and PTECs were measured with a CyQUANT Assay Kit (Thermo Scientific) and a CellTiter-Glo 2.0 Assay Kit (Promega), respectively, following manufacturer’s instructions. At confluence, cells were detached with 0.25% trypsin-EDTA for 5 minutes at 37°C, and subsequently seeded in black (for DNA) or white (for ATP) 96-well microplates at a density of 15,000 cells/well in 100 μl of complete medium. After letting the cells adhere for 4–6 hours, they were starved and exposed to the treatment of interest. After each treatment, medium was removed and cells were washed with PBS. To measure DNA levels, cells were exposed to 200 µl of CyQUANT dye in cell lysis buffer for 5 minutes at room temperature, and fluorescence was recorded at an excitation/emission wavelength of 480/520 nm in a Cytation 5 plate reader (BioTek). To measure ATP levels, cells were exposed to 100 µl of CellTiter-Glo 2.0 Reagent and contents were mixed for 2 minutes on an orbital shaker to induce cell lysis. Plates were then incubated at room temperature for 10 minutes to stabilize the luminescent signal, which was recorded in a Cytation 5 plate reader (BioTek).
Statistical Analyses
Normal distribution of each study variable was examined using the Shapiro–Wilk normality test. We assessed differences between groups using the independent t test for variables following a normal distribution, and the Wilcoxon–Mann–Whitney nonparametric test for variables not following a normal distribution. P<0.05 was considered significant. Data are reported as mean±SEM. For GO and pathway enrichment analyses, the Benjamini–Hochberg correction was used for multiple-hypothesis testing. Q<0.05 (enriched GO terms) or Q<0.01 (enriched pathways) were considered significant.
Results
Study Population
Seven patients with pure AMR were compared with 23 patients with other forms of allograft injury, namely ACR and ATN (Table 1). Median graft age for all groups was approximately 10 days, indicating we were studying early cases. Four patients with AMR had DSA before transplant. None of the ATN or ACR cases showed histopathologic signs of AMR. ACR cases showed the highest scores for interstitial inflammation, tubulitis, and total inflammation. The AMR cases showed microvascular inflammation, which was mostly absent in the other groups. We observed mild tubular atrophy and arterial fibrous intimal thickening in ACR and ATN, but not in AMR cases (Table 2). There was no evidence of chronic glomerulopathy in any of the groups (Supplemental Figure 3A, Table 2). The lack of evident chronic lesions was confirmed by electron microscopy (Supplemental Figure 3, B–G). The degree of foot process effacement (Supplemental Figure 3C), glomerular BM thickness (Supplemental Figure 3, B and D), new BM formation (Supplemental Figure 3E), and endothelial cell swelling in glomeruli (Supplemental Figure 3F) and peritubular capillaries (Supplemental Figure 3G) did not significantly differ between groups.
Clinical parameters of the patient cohort
Kidney biopsy specimen findings of the patient cohort
Identification of Compartment-Specific Proteome Changes in Kidney AMR
We identified 2026 proteins in the glomeruli and 2399 proteins in the tubulointerstitium (Figure 1A). Among the proteins quantified in >50% of samples per group (1299 in glomeruli and 1842 in tubulointerstitium), we confirmed compartment-specific enrichment of expected proteins. Podocyte markers (e.g., NPHS1 and podocalyxin) and endothelial cell proteins (e.g., platelet endothelial cell adhesion molecule-1 and PDGF receptor β) were enriched in the glomeruli, whereas tubule-specific markers (e.g., megalin, cubilin, and uromodulin) were found only in the tubulointerstitium (Figure 1B). We determined that 107 proteins were differentially expressed in AMR compared with ACR in the glomeruli, and 112 in the tubulointerstitium. In turn, 112 proteins were differentially expressed in AMR compared with ATN in the glomeruli, and 181 in the tubulointerstitium (Figure 1C, Supplemental Table 2).
Identified, quantified, and differentially expressed proteins. (A) Proteins identified and quantified in each renal compartment. (B) Compartment-specific enrichment of expected proteins. Whereas podocyte and endothelial cell markers were exclusively detected in glomerular fractions, tubular cell markers were only found in the tubulointerstitium. For each of the proteins of interest, the heatmap represents the median log2-transformed LFQ intensity in each compartment. (C) Compartment-specific, differentially expressed proteins in AMR versus ACR and in AMR versus ATN. For each comparison, the overlap of significantly differentially expressed proteins between the glomerular and the tubulointerstitial compartments is represented. CUBN, cubilin; FLT1, vascular endothelial growth factor receptor-1; KIRREL, Kin of IRRE-like protein; LRP2, megalin; NPHS2, podocin; PDGFRB, PDGF receptor β; PECAM1, platelet endothelial cell adhesion molecule-1; PODXL, podocalyxin; SRGAP, SLIT-ROBO ρ GTPase-activating protein 2; UMOD, uromodulin.
AMR Is Associated with Changes in ECM and BM Proteins
We determined the dominant GO terms and pathways governing compartment-specific differences in AMR. In the glomeruli, we observed a significant enrichment of GO terms and pathways related to the immune system. The majority of these proteins (e.g., TAP1, HLA-B, HLA-C, IGKV, IGHV) were increased in the AMR glomeruli, compared with ACR and ATN. “Regulation of humoral immune response” and “antigen processing and presentation of peptide via MHC class I” processes, which are intrinsically linked to HLA-mediated antigen presentation, were also enriched among the proteins increased in the AMR glomeruli (Figure 2A, Supplemental Table 3). Processes involved in metabolism (e.g., fatty and amino acid metabolism, tricarboxylic acid cycle) were also significantly enriched among the proteins upregulated in the AMR glomeruli (Supplemental Table 3). We observed a predominant enrichment of terms and pathways linked to cytoskeleton, cell/focal adhesion, ECM, and glomerular BM, among proteins decreased in AMR (Figure 2B, Supplemental Table 3). Glomerular BM proteins, including nidogen (NID1), collagens (COL4A1, COL4A4), laminin subunits (LAMC1, LAMA5, LAMB2), and podocyte-specific proteins NPHS1 and PTPRO, were significantly decreased in AMR (Figure 2B). All type IV collagen chains were decreased in AMR, compared with ACR and ATN (Supplemental Figure 4).
Significantly enriched GO terms among the proteins differentially expressed in the AMR glomeruli. The bubble maps illustrate the top enriched GO terms among proteins differentially (A) increased or (B) decreased in the glomeruli of patients with AMR, as compared with ACR and ATN (P<0.05). For each comparison, each term is represented by one node. The intensity of the node color is proportional to the gene ratio (percentage of the total input genes in the given GO term), whereas the size of the node is inversely proportional to the P value of the enriched term. The mean expression levels of the proteins that belong to key enriched terms are represented by dot plots on the right. The corresponding GO terms are shown below the names of the corresponding proteins. ER, endoplasmic reticulum. #P<0.05 AMR versus ACR, *P<0.05 AMR versus ATN.
We also identified enriched GO terms and pathways in the AMR tubulointerstitium, including a significant enrichment of IFNγ-mediated signaling, antigen processing and presentation, and metabolism (Figure 3A, Supplemental Table 4). Several proteins significantly increased in the AMR tubulointerstitium (e.g., ETFA, GSTO1) are key enzymes involved in maintaining cellular redox homeostasis.56 Accordingly, metabolism-related pathways (e.g., fatty acid β-oxidation) were predominantly enriched among the proteins increased in the AMR tubulointerstitium (Supplemental Table 4). Terms associated with cytoskeleton, ECM, and BM were predominantly enriched among the proteins decreased in the AMR tubulointerstitium (Figure 3B, Supplemental Table 4). Compared with the glomeruli, we observed a higher representation of collagens and proteoglycans. Tubular BM components such as NID1, LAMA5, and LAMC1, which were downregulated in the AMR glomeruli, were also significantly decreased in the AMR tubulointerstitium.
Significantly enriched GO terms among the proteins differentially expressed in the AMR tubulointerstitium. The bubble maps illustrate the top enriched GO terms among proteins differentially (A) increased or (B) decreased in the tubulointerstitium of patients with AMR, as compared with ACR and ATN (P<0.05). For each comparison, each term is represented by one node. The intensity of the node color is proportional to the gene ratio (percentage of the total input genes in the given GO term), whereas the size of the node is inversely proportional to the P value of the enriched term. The mean expression levels of the proteins that belong to key enriched terms are represented using dot plots. The corresponding GO terms are shown below the names of the corresponding proteins. #P<0.05 AMR versus ACR, *P<0.05 AMR versus ATN.
Using a reduction algorithm as an unbiased approach, we confirmed the enrichment in ECM proteins in both AMR compartments. “ECM and cell communication” was the main category among the significantly enriched pathways decreased in AMR, in both compartments (Figure 4A, Supplemental Table 5), and pathways falling into this category showed the highest significance (Supplemental Figure 5). Interestingly, “immune system” was the main category among pathways increased in the AMR glomeruli, whereas “metabolism” was predominant among pathways increased in the AMR tubulointersitium. Given the predominance of ECM proteins and processes decreased in AMR, we examined whether ECM proteins clustered together and were coexpressed. In the glomeruli, podocyte markers NPHS1 and PTPRO (both decreased in AMR versus ACR) clustered together (Figure 4B). We also found, among proteins decreased in the AMR versus ACR glomeruli, a cluster of seven ECM components related to the glomerular BM: LAMC1, LAMA5, LAMB2, COL4A1, GNAS, NID1, and TINAGL1. Four of these BM components (LAMA5, LAMB2, COL4A1, and TINAGL1) also clustered together among proteins differentially expressed in the AMR versus ATN glomeruli (Figure 4B). Glomerular LAMC1 expression levels directly and strongly correlated with NID1 (R2=0.62; P<0.00001), LAMA5 (R2=0.84; P<0.00001), and LAMB2 (R2=0.87; P<0.00001), suggesting coexpression of these ECM proteins in AMR. In the tubulointerstitium, we identified four clusters of ECM-related proteins decreased in AMR versus ACR, and two clusters of ECM-related proteins decreased in AMR versus ATN. In both comparisons, BM laminin subunits LAMC1 and LAMA5 clustered together and with other BM components, such as NID1 (Figure 4C). LAMC1 expression in the tubulointerstitium correlated with NID1 (R2=0.84; P<0.00001), LAMA5 (R2=0.89; P<0.00001), LMNA (R2=0.77; P<0.00001), LMNB2 (R2=0.62; P<0.00001), PRELP (R2=0.50; P<0.001), and COL3A1 expression (R2=0.54; P<0.00001). Altogether, computational analyses pointed to ECM and BM as the key alterations to be further studied in AMR.
“ECM” represents the most enriched pathway category in both AMR compartments. For each compartment, the significantly enriched pathways from PathDIP were classified using a reduction algorithm. (A) Pathway enrichment and reduction analysis revealed the main category among the proteins significantly increased in AMR was immune system in the glomerular compartment, and metabolism in the tubulointerstitium. In turn, ECM and cell communication was the most predominant pathway category among the proteins significantly decreased in both AMR compartments. Heatmaps in panels (B and C) illustrate the hierarchical clustering analyses of the proteins significantly differentially expressed in the AMR glomeruli and tubulointerstitium, respectively. In both compartments, LAMC1 clustered with other ECM proteins significantly downregulated in AMR.
Glomerular BMs are known to be injured in chronic AMR.3,4 Given the clinical implications of the ECM perturbations detected in the AMR glomeruli, we selected key glomerular BM components for orthogonal verification. LAMC1 has a prominent role as a representative ECM protein decreased in both AMR compartments, and we verified changes in LAMC1 expression, using immunohistochemistry. Consistent with the proteomic findings, numerically lower LAMC1 staining was detected in the AMR compared with ACR and ATN glomeruli and tubulointerstitium (Figure 5A). Because podocytes directly communicate with the glomerular BM, the impairment in podocyte-specific proteins identified in the AMR glomeruli may relate to the early ECM changes and glomerular BM remodeling (Figure 2, Supplemental Table 2). Supporting our proteomics data, glomerular PTPRO expression was significantly decreased in AMR, compared with ACR, and numerically reduced when comparing AMR to ATN. We also verified reduced NPHS1 immunofluorescence in the AMR glomeruli. AMR cases also showed a decrease in the merged signal of the two podocyte proteins, compared with ACR and ATN (Figure 5B).
Orthogonal verification of representative BM proteins. (A) Decreased glomerular and tubulointerstitial protein levels of LAMC1 in AMR (n=6) versus ACR (n=7) and versus ATN (n=11) were verified by immunohistochemistry in new sections of formalin-fixed, paraffin-embedded biopsy specimens used in the discovery study. The solid arrows indicate lower LAMC1 expression in the tubular BMs of the AMR cases, compared with ACR and ATN, whereas the dotted arrows indicate stronger cytoplasmic staining in the tubules of the ACR and ATN cases. Original magnification, 20×. Scale bar, 200 µm. (B) Decreased glomerular protein expression of NPHS1 and PTPRO in AMR (n=3) versus ACR (n=5) and versus ATN (n=5) was demonstrated using immunofluorescence. Original magnification, 40×. Scale bar, 50 µm. *P<0.05 versus AMR. AU, arbitrary units.
LGALS1 and Cathepsins Are Linked to ECM Alterations in AMR
We investigated the potential causes of ECM remodeling in each AMR compartment, considering two hypotheses: (1) proteins decreased in AMR have decreased transcription, and (2) ECM-related proteins decreased in AMR are cleaved by proteases with increased expression/activity in AMR.
We took advantage of the biggest (to our knowledge) publicly available transcriptomic data set (GSE36059) comparing AMR biopsy specimens to stable controls. We crossreferenced our proteins dysregulated in each AMR compartment with the 1275 genes differentially expressed in AMR biopsies (Q<0.05 AMR versus stable control). Among the 178 proteins altered in AMR versus ACR and/or AMR versus ATN glomeruli, 20 were also modulated at the gene level in this external data set. Among them, we found HLA class I–mediated antigen presentation proteins (HLA-B, TAP1, TAP2), as well as five proteins that were commonly dysregulated in the glomeruli of AMR versus ACR and in AMR versus ATN. One of them was an important immunomodulatory protein increased in AMR, and linked to ECM remodeling: LGALS1 (Figure 6A).57,58 In the tubulointerstitium, a smaller proportion of differentially expressed proteins (17/244) was also altered in AMR in the external data set; ten of those were increased in AMR at the gene and protein level (Figure 6B).
Comparison of our data set to a relevant transcriptional data set and protease cleavage prediction. Our findings at the proteome level were compared with the GSE36059 data set. (A and B) Venn diagrams represent the overlap between genes significantly differentially expressed in AMR kidney biopsy specimens compared with control biopsy specimens, and proteins significantly differentially expressed in the AMR (A) glomeruli or (B) tubulointerstitium in our study. As shown in the tables, 20 proteins differentially expressed in the AMR glomeruli in our study were also dysregulated at the level of gene expression in GSE36059. In the tubulointerstitium, 17 proteins differentially expressed in AMR were also dysregulated at the gene level in GSE36059. ECM-related proteins dysregulated in each compartment in AMR were subjected to the MEROPS protease prediction tool. The dot plots represent the count of proteins decreased in the AMR (C) glomeruli or (D) tubulointerstitium which are predicted to be cleaved by a specific protease.
We next used MEROPS54 to identify proteases predicted to cleave the proteins decreased in each AMR compartment, compared with ACR or ATN. Cathepsins (CTSL, CTSS, and CTSV) and MMPs (MMP2, MMP3) were among the top proteases expressed in the kidney and predicted to cleave the highest number of proteins decreased in the AMR glomeruli (Figure 6C). Together with LGMN, cathepsins (CTSL, CTSK, and CTSS), MMP2 and MMP3 were also predicted to cleave the highest number of proteins decreased in the AMR tubulointerstitium (Figure 6D). Importantly, LGMN was increased in the AMR tubulointerstitium and linked to IFNγ-mediated signaling (Figure 3A, Supplemental Tables 2 and 4). LGMN and cathepsins can cleave ECM and cytoskeletal proteins, and promote peptide presentation by HLA molecules, thus relating to our proteomics findings in AMR.59–⇓61
α-HLA-I Antibodies Increased LGALS1 in HGMECs
We studied the effects of α-HLA-I antibodies and two prototypical cytokines associated with AMR, IFNγ, and TNFα, on our proteins and proteases of interest, in primary HGMECs. HGMECs exhibited the expected signaling, proliferative, and inflammatory responses to α-HLA-I, IFNγ, and TNFα (Supplemental Figure 6). LGALS1 was predicted to interact with 21 other differentially expressed proteins in the AMR glomeruli, seven of them in the ECM, such as LAMC1 (Figure 7A). LGALS1 is at the nexus between cell stress, apoptosis, the immune system, and ECM, and may represent a critical regulatory protein in AMR. Consistent with the proteomics and transcriptomics findings, α-HLA-I induced a significant increase in LGALS1 protein secretion and gene expression in HGMECs (Figure 7, B and C). Similarly, both α-HLA-I and IFNγ significantly increased TAP1 gene expression (Supplemental Figure 7A). TAP1 expression was increased in the AMR glomeruli (Figure 2A) and in the transcriptomic data set (GSE36059) (Figure 6A). In agreement with our protease prediction, stimulation of HGMECs with α-HLA-I, but not isotype control, significantly increased CTSV gene expression, compared with vehicle-treated cells. IFNγ significantly enhanced CTSL and CTSS gene expression in HGMECs (Supplemental Figure 7B). We also assessed the expression of another predicted protease involved in ECM proteolysis: MMP2.62,63 Whereas α-HLA-I significantly upregulated MMP2 gene expression in HGMECs, IFNγ induced the opposite effect (Supplemental Figure 7B).
Characterization of LGALS1 in primary HGMECs. (A) Protein-protein interaction network of LGALS1 and its interactors among the proteins differentially expressed in the AMR glomeruli, as compared with ACR and ATN. Only protein-protein interactions annotated in the kidney were retained, and the network was built in NAViGaTOR. Each node represents a protein, and each edge represents a predicted or experimentally validated protein-protein interaction. Each node was colored according to a specific pathway category. The pie pieces surrounding the main nodes represent the top, nonredundant, enriched GO terms associated with the protein. The orientation of the node vertex depicts the direction of change in AMR compared with other biopsy specimens. Stimulation with 5 µg/ml of α-HLA-I for 18 hours significantly upregulated LGALS1 (B) protein secretion and (C) gene expression in HGMECs. Data are expressed as mean±SEM. *P<0.05, **P<0.01. CC, cellular compartment; MF, molecular function; BP, biological process; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; RPL31, 60S ribosomal protein L31.
Because metabolic processes were also enriched in the AMR glomeruli, we examined the effects of α-HLA-I on the HGMEC metabolic function. Without affecting glycolysis, α-HLA-I antibodies induced a significant increase in the OCR (Supplemental Figure 7C) and numerically increased cellular levels of superoxide ion (Supplemental Figure 7D) and ATP (Supplemental Figure 7E) in HGMECs. ATP was also numerically increased by IFNγ and TNFα.
TNFα Increased GSTO1 Expression in Human PTECs
We first stimulated PTECs with IFNγ and TNFα, and demonstrated the expected proinflammatory response to these cytokines (Supplemental Figure 8). We next focused on GSTO1, a metabolic protein increased in the AMR tubulointerstitium (Figure 3A) that modifies ECM proteins and increases their susceptibility to proteolytic cleavage,64 representing a potential link between metabolism and ECM-related alterations. TNFα significantly increased GSTO1 protein expression in PTECs (Supplemental Figure 9A). In line with our protease prediction, TNFα significantly increased CTSS expression, compared with vehicle, in PTECs. This increase was more pronounced upon stimulation with IFNγ, which also dramatically increased LGMN (Supplemental Figure 9B).
Because GSTO1 is directly involved in metabolism and the redox balance of the cell, we studied the metabolic function of cytokine-treated PTECs. Both TNFα and IFNγ increased glycolysis in PTECs, especially after metabolic stress (Supplemental Figure 9C). Although TNFα and IFNγ did not significantly alter oxygen consumption or superoxide ion levels (Supplemental Figure 9, C and D), both cytokines induced a significant increase in intracellular ATP (Supplemental Figure 9E). PTECs thus display a higher dependence on glycolysis for energy production upon TNFα and IFNγ stimulation.
Discussion
This is the first proteomics study of laser-captured/microdissected glomeruli and tubulointerstitium in kidney AMR biopsy specimens compared with other forms of graft injury. Our main observation is that AMR is associated with a compartment-specific decrease in ECM protein expression, before histologic signs of BM injury or fibrosis. This ECM remodeling was not observed in transcriptional studies35,65–⇓⇓68; instead, our work suggests it is associated with dysregulated protease expression. LGALS1 and GSTO1 were upregulated in the glomerular and tubulointerstitial AMR proteomes, respectively, and validated in models of anti-HLA- and TNFα-mediated injury. These ECM-modifying proteins may represent novel targets to ameliorate ECM remodeling associated with AMR.
Importantly, ECM proteins downregulated in AMR glomeruli (NID1, collagen-IV chains, and laminin subunits) belong to the glomerular BM 69, which is the pillar of the filtration barrier.9 Double contouring of BMs is a hallmark of TG.70,71 Our AMR cases did not show such injury histologically. Protein changes in AMR glomeruli could thus reflect an ECM remodeling starting early in AMR. Glomerular BM is secreted by endothelial cells and podocytes, representing an interface between them.72 Glomerular BM proteins anchor podocytes via adhesion receptors connected to the cytoskeleton.69 Several adhesion proteins were downregulated in AMR. Thus, the interactions between endothelium, BM, and podocytes may be compromised in early AMR. We observed a significant downregulation of slit diaphragm proteins, NPHS1 and PTPRO, in AMR.73,74 PTPRO-deficient mice developed abnormal podocytes, but also remodeled glomerular BM, suggesting that PTPRO is important for podocyte and glomerular BM integrity.75,76 Reduced PTPRO expression correlated with podocyte loss in TG.77 Although we did not detect podocyte effacement, decreased BM proteins occurred in association with altered expression of podocyte proteins in AMR. ECM homeostasis in the glomerular microvasculature is critical for the stability of the BM and the structural integrity of the filtration barrier.78,79 Early molecular alterations of the glomerular ECM in AMR may set into motion processes leading to TG, and may represent a novel therapeutic target.
Graft endothelium is central to antibody-mediated injury, and we next focused on linking endothelial cell injury to ECM remodeling. LGALS1 was particularly interesting, because it was increased in our glomerular AMR proteome and the AMR transcriptome,35 it is secreted by endothelial cells,58 and linked to ECM remodeling.57 LGALS1 recognizes β-galactose moieties of ECM proteins,57 and is involved in immunomodulation, angiogenesis, survival, and proliferation.80,81 We demonstrated that α-HLA-I increased LGALS1 in HGMECs. Similar to previous studies, this was related to increased cell proliferation, signaling, and inflammation.21,22,23 A protective role for LGALS1 was previously proposed. LGALS1−/− animals displayed enhanced inflammation and oxidative stress,82 and transfer of B cells from LGALS1−/− mice failed to prolong skin allograft survival in mice.82 Conversely, recombinant LGALS1 decreased inflammation in renal ischemia-reperfusion injury,83 and prolonged graft survival in a model of MHC-mismatched kidney transplantation.84 LGALS1 may thus participate in the graft endothelial response to DSA.
This is the first study that investigates the tubulointerstitial proteome in AMR. We observed significantly decreased collagens and BM proteoglycans in the AMR tubulointerstitium. Tubular BM disruption may cause abnormal tubular cell function and tissue destruction.85 We thus studied ECM-modifying enzymes in PTECs exposed to TNFα and IFNγ. GSTO1 was upregulated in the AMR tubulointerstitium and in TNFα-treated PTECs. GSTO1 is an enzyme that mediates S-deglutathionylation of ECM and cytoskeletal proteins, increasing the pool of cytosolic glutathione capable of neutralizing reactive oxygen species.86 However, S-deglutathionylation also affects the ECM-cytoskeleton network, altering protein susceptibility to proteolytic cleavage.64 TNFα-induced GSTO1 upregulation in PTECs was linked to augmented glycolysis, ATP levels, oxidative stress, and inflammation. Thus, as other studies support,87–⇓⇓90 increased GSTO1 in AMR may reflect a maladaptive response to TNFα-induced metabolic stress.
Altered ECM protein expression in AMR could be attributed to increased proteolytic cleavage. Cathepsins degrade ECM proteins, including collagens, laminins, and proteoglycans.91–⇓⇓94 Additionally, NID1 binding to the BM is impaired after cleavage by CTSS,95 which was transcriptionally enhanced in kidney AMR35 and in response to IFNγ.96–⇓98 Cathepsins also interact with LGALS1.99 Accordingly, increased IL-6, IL-8, and LGALS1 in α-HLA-I-treated HGMECs coincided with increased CTSV expression. CTSV mediates inflammation in human umbilical vein endothelial cells by upregulating these cytokines.100 LGALS1 binds to membrane glycoproteins, inhibiting their endocytosis and cathepsin-mediated cleavage,99 whereas cathepsins induce LGALS1 in endothelial cells during angiogenesis.101 Whether decreased expression of cathepsin targets in AMR is due to higher cleavage and higher HLA presentation requires further investigation.
This work has some limitations. The number of cases was small, because we focused on extreme phenotypes, and we were constrained by cases with predominantly mixed lesions or limited available tissue. We focused on injury mechanisms in kidney parenchymal cells, but acknowledge that immune cells also produce ECM-modulating proteins.102,103 Cell models cannot recapitulate the complexity of in vivo systems. Future studies will determine the relationship between DSA and BM remodeling ex vivo and in vivo.
We have identified compartment-specific ECM remodeling in the absence of BM lesions, in early AMR. We propose a model of antibody- and cytokine-mediated injury in AMR (Figure 8). Decreased BM and ECM proteins in the AMR glomeruli were associated with α-HLA-I-induced upregulation of antigen-presenting (TAP1) and ECM-modifying proteins (LGALS1 and CTSV), and with IFNγ-induced upregulation of key proteases (CTSL, CTSS). In the AMR tubulointerstitium, decreased ECM-related proteins were associated with TNFα- and IFNγ-induced upregulation of ECM-modifying enzymes (GSTO1, CTSS, CTSL and LGMN). Our findings thus point to early, potentially targetable, alterations in AMR.
Proposed model of cell-specific ECM injury in AMR. Simplified scheme depicting the key proteins and processes differentially expressed in the AMR (A) glomeruli and (B) tubulointerstitium (B). ECM components are outlined in orange, whereas immune-related proteins are outlined in green. The direction of change is indicated for the proteins verified by immunohistochemistry or validated in vitro. Relevant protein-protein interactions between LGALS1, LAMC1, cathepsins, and AMR-enriched processes are represented by dotted lines. Proposed molecular alterations associated with AMR are summarized in red boxes. CXCL10, C-X-C motif chemokine 10; ER, endoplasmic reticulum; GBM, glomerular BM.
Disclosures
All authors have nothing to disclose.
Funding
A. Konvalinka is supported by Kidney Foundation of Canada Predictive Biomarker grant KFOC160010, the Canadian Institutes of Health Research (CIHR), Canada Foundation for Innovation (CFI) grant 37205, and Kidney Research Scientist Core Education and National Training (KRESCENT) program grants CIHR148204, KRES160004, and KRES160005. A. Konvalinka has also received funding from the Toronto General and Western Hospital Research Foundation (TGTWF 1617-464; TGTWF MKFTR 1718-1268). S. Clotet-Freixas is supported by the KRESCENT program (2019KP-PDF637713). I. Jurisica, C. Pastrello, and M. Kotlyar were supported in part by Ontario Research Fund grant 34876, Natural Sciences and Engineering Research Council of Canada grant 203475, and CFI grants 29272, 225404, and 30865.
Acknowledgments
Special thanks to Marc Angeli, Sharon Selvanayagam, Terrance Ku, Dr. Victor Ferreira, Dr. Bedra Sharif, Matthew Ierullo, Beata Majchrzak-Kita, and Dr. Elisa Pasini.
Dr. Ana Konvalinka conceived the study; Dr. Ana Konvalinka and Dr. Rohan John participated in study design; Dr. Sergi Clotet-Freixas, Dr. Caitriona M. McEvoy, Dr. Ihor Batruch, Dr. Sofia Farkona, Dr. Julie Anh Dung Van, Dr. Sajad Moshkelgosha, Dr. Madhurangi Arambewela, Dr. Alex Boshart, Dr. Andrzej Chruscinski, Dr. Andrea Bozovic, Dr. Vathany Kulasingam, Dr. Rohan John, and Dr. Ana Konvalinka carried out experiments; Dr. Sergi Clotet-Freixas, Dr. Caitriona M. McEvoy, Dr. Ihor Batruch, Dr. Max Kotlyar, Dr. Chiara Pastrello, Dr. Yun Niu, Dr. Iigor Jurisica, Dr. Sajad Moshkelgosha, Dr. Stephen Juvet, Dr. Madhurangi Arambewela, Dr. Tereza Martinu, Dr. Syed Ashiqur Rahman, Dr. Jishnu Das, Dr. Rohan John, and Dr. Ana Konvalinka analyzed the data; Dr. Sergi Clotet-Freixas, Dr. Caitriona M. McEvoy, Dr. Chiara Pastrello, and Dr. Max Kotlyar made the figures; Dr. Caitriona M. McEvoy, Dr. Yanhong Li, Dr. Peixuen Chen, and Dr. Emilie Chan retrieved and curated clinical data; Dr. Yanhong Li, Dr. Olusegun Famure, and Dr. S. Joseph Kim selected the cases from the CoReTRIS registry and performed case-control matching; Dr. Sergi Clotet-Freixas, Dr. Caitriona M. McEvoy, and Dr. Ana Konvalinka drafted and revised the paper; and all authors approved the final version of the manuscript. Dr. Iigor Jurisica reports receiving personal fees and other from Canadian Rheumatology Association, grants and nonfinancial support from IBM, and personal fees and other from Novartis, outside the submitted work. Dr. Stephen Juvet reports receiving grants from Sanofi, outside the submitted work. Dr. Tereza Martinu reports receiving nonfinancial support from APCBio and grants from Sanofi, outside the submitted work.
Supplemental Material
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2020030286/-/DCSupplemental.
Supplemental Table 1. Primer sequences used for real-time quantitative PCR in our gene expression studies.
Supplemental Table 2. Quantified and differentially expressed proteins in the AMR glomeruli and tubulointerstitium, compared with ACR and ATN.
Supplemental Table 3. Most representative gene ontology terms and pathways significantly enriched among the proteins differentially expressed in the AMR glomeruli, compared with ACR and ATN.
Supplemental Table 4. Most representative gene ontology terms and pathways significantly enriched among the proteins differentially expressed in the AMR tubulointerstitium, compared with ACR and ATN.
Supplemental Table 5. Mapping between pathways significantly enriched in AMR compared with ACR and ATN and their ancestors.
Supplemental Figure 1. Study workflow.
Supplemental Figure 2. Histograms depicting the distribution of the original and imputed protein intensity values in our study samples.
Supplemental Figure 3. Representative light and electron microscopic images and injury scores of ultrastructural alterations in AMR, ACR and ATN cases.
Supplemental Figure 4. Collagen chains quantified and differentially expressed in the glomerular compartment.
Supplemental Figure 5. Distribution of the Q values of the pathways significantly enriched in the AMR glomeruli and tubulointerstitium.
Supplemental Figure 6. Characterization of human glomerular microvascular endothelial cells.
Supplemental Figure 7. Characterization of regulated proteins and predicted proteases in the AMR glomeruli in human glomerular microvascular endothelial cells.
Supplemental Figure 8. Characterization of the inflammatory response of PTECs upon exposure to key cytokines.
Supplemental Figure 9. Characterization of regulated proteins and predicted proteases in the AMR tubulointerstitium in primary human proximal tubular epithelial cells.
Footnotes
S.C.-F. and C.M.M. contributed equally to this work.
Published online ahead of print. Publication date available at www.jasn.org.
- Copyright © 2020 by the American Society of Nephrology
References
In this issue
