1. Konvalinka A, Batruch I, Tokar T, et al. Quantification of angiotensin II-regulated proteins in urine of patients with polycystic and other chronic kidney diseases by selected reaction monitoring. Clin Proteomics. 2016;13:16.

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Supplemental Methods Urine Processing

For analysis, samples were thawed in 37 °C bath, urine samples were centrifuged at 2000 g for 10 min at RT to remove debris and supernatants were used. The urinary protein concentration was measured by a clinical benzethonium chloride-based assay (Abbott ARCHITECT). Urine creatinine concentrations were determined by Jaffé colorimetric assay. Our starting material included urine volumes containing 100 μg of total protein were used for each sample. To assess reproducibility and recovery, bovine serum albumin (BSA) (proteomics-grade, >98% pure, Sigma-Aldrich) was first added at a known concentration of 1 μg (1% of total protein). The samples were precipitated overnight with acetonitrile at a ratio of 1:9 (v/v) followed by centrifugation at 3220 g for 30 min at 4 °C. The pellets were washed with acetonitrile twice, then air-dried. Pellets were then resuspended in 100μL of denaturing buffer consisting of 8M urea and 0.1M ammonium bicarbonate (Sigma-Aldrich). Denatured samples were reduced with 20 mM dithiotreitol (Sigma-Aldrich) at 37 °C for 30 min and then alkylated with 80mM iodoacetamide (Sigma-Aldrich) at RT for 30 min in the dark. At this point, purified, heavy isotope-labeled peptides corresponding to TSP1 (TIVTTLQDSIR and GGVNDFQGVLQNVR), BST1 (GFFADYEIPNLQK), GLNA (LVLCEVFK), RHOB (IQAYDYLECSAK), LAMB2 (GSCYPATGDLLVGR) and LYPLA1 (LAGVTALSCWLPLR) (JPT peptide technologies) were spiked in at a concentration of 100 fmol/μl. The samples were then incubated with Lys-C/trypsin (1:50 w/w) (Promega) at 37 °C for 3 h. Trypsin digestion was then facilitated by adding 0.1 M ammonium bicarbonate to reduce to urea concentration to 2 M and incubating the samples at 37 °C overnight. Trypsin digestion was stopped by the addition of 1% (v/v) formic acid. Samples were then vortexed, separated into aliquots containing 20 μg of total protein and frozen at −20

°C until further analysis. After thawing of individual aliquots containing 20 μg of total protein, three crude heavy isotope-labeled proteotypic peptides of BSA were spiked in at a concentration of 100 fmol/μL (LVNELTEFAK, HLVDEPQNLIK and LGEYGFQNALIVR).

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Samples were then desalted and concentrated using OMIX C18 10 μl tips (Agilent Technologies). Buffer A containing 0.1% formic acid and Buffer B containing 65% acetonitrile and 0.1% formic acid were used to concentrate the peptides.

Selected Reaction Monitoring

Concentrated peptides were loaded in a volume of 18 μL onto a 3.3 cm precolumn (C18, 5 μm) and eluted on a 15-cm analytical column (C18, 3 μm). The following parameters were used:

Flow rate = 400 nl/min during gradient, C18 material: Agilent Pursuit 5 μm for precolumn, 3 μm for analytical column; Precolumn ID: 150 μm × 3.3 cm; Analytical column ID:75 μm × 15 cm.

The reversed-phase liquid chromatography (EASY-nLC1000, Thermo-Fisher Scientific, San Jose CA) was coupled to a triple- quadrupole mass spectrometer (TSQ Quantiva, Thermo Fisher Scientific Inc., San Jose, CA) using a nanoelectrospray ionization source. Peptides were separated over 60 min, with a previously optimized 3-step gradient. Q1 was set to 0.4 Thompson FWHM (Th, Full Width Half Maximum), Q3 to 0.7 FWHM, and Q2 pressure was set to 1.5 mTorr. The instrument was run in a positive ion mode, with collision energies predicted by Skyline software, according to the following formula: CE = 0.03 × (precursor m/z) + 2.905.

Reproducibility of SRM signal was ensured by running a QC solution of 1 fmol/μL (10 fmol on column) BSA every five runs. We utilized previously determined¹ retention times of our peptides of interest. Table S1 shows optimized scheduled SRM methods for heavy-labeled and light peptides. Raw files recorded for each sample were analyzed using Skyline software, and CSV files with peptide areas were extracted. All peptides were manually inspected.

Renal Pathology assessment

Fibrosis severity was assessed by a renal pathologist. A score of 0 = no fibrosis (ci0), 1 = mild (ci1), 2 = moderate (ci2), and 3 = severe fibrosis (ci3) (according to Banff 2015 criteria). Bright field microscopy at 20X option was used on the Aperio Image Scope software to scan the whole stained tissue on the slide. Pathology image analysis was performed on Definiens Tissue Studio 4.0 software, using the Marker Area Analysis module. Pathology images were grouped in

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batches by the marker being assessed (GLNA or TSP1), and loaded into the software. A machine-learning algorithm was trained to detect tissue from background and artifact, and the training was reviewed and manually corrected to exclude artifacts. In a blinded fashion, tissue sections that were too small in size, contained luminal staining or only comprised of medulla with no cortex were eliminated. Thresholds for positive staining were set based on review of positive and negative controls and applied to all images. Three thresholds for the staining are set to detect low, medium and high intensity staining. Analysis results included the total area assessed, and the percentage of area that contained low, medium and high intensity staining was calculated. The calculation used to assess area stained for TSP1 or GLNA included: Total stained marker area (%) = Area of low (%)+ Area of medium (%) + Area of high (%) intensity staining.

Immunohistochemical Staining

TSP1 monoclonal antibody (A6.1) (ThermoFisher Scientific MA5-13398) was used at a dilution of 1:100 for 1 h at RT. For GLNA staining, slides were incubated for 1 hour at RT in primary antibody, anti-Glutamine Synthetase (Abcam Cat. ab64613), diluted 1:60 in dako diluent (Dako Cat. S0809). Secondary antibody was biotinylated Anti-mouse Ig (Vector Labs Cat. BA-2000).

The slides were treated with Avidin biotin complex (ABC) system (Vector Labs Cat. PK-6100) for 30 minutes at RT to detect positive staining and colour development was performed using DAB (Abcam). All stained sections were counterstained with Mayer’s Hematoxylin, dehydrated in alcohols, cleared in xylene and mounted with Permount mounting medium (Fisher cat. SP15- 500).

Analysis of GEO datasets

To investigate whether our AngII signature proteins were differentially expressed in kidney biopsies from patients with IFTA, we first performed an Advanced GEO database search for keywords “kidney fibrosis” AND “transplant” and then selected “homo sapiens” as top organism.

Our search yielded 34 results out of which 27 were series and 1 was a dataset. Another search

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for “chronic renal allograft nephropathy” in “homo sapiens” yielded 53 results out of which 1 was a dataset and 9 were series. Series include original submitter-supplied records that are reassembled by GEO staff into curated GEO datasets. Upon inspection of the results from the two searches, we found 5 that studies contained IFTA vs. control samples where biopsies showed comparable scoring to our samples, these included O’Connell et al, Rodder et al,³ Park et al,⁴ Maluf et al,⁵ and Modena et al.⁶ When comparing IFTA vs. control using GEO2R, only 3^3,5,6 out 5 of studies contained differentially regulated genes when using adjusted p-values. The remaining two studies had no significantly differentially expressed genes between IFTA and control biopsies. Differentially expressed genes (adjusted P value < 0.05) from these 3 studies were then intersected with our previously established AngII signature proteins⁷ using Venny 2.1.0. Chi-square test (R’s chisq.test function) was used to test whether the expression of AngII signature in these datasets was significantly higher than would be predicted from chance alone when comparing it with their entire microarrays gene-set.

Data Analysis

Skyline was used to manually inspect the chromatograms, and to calculate peptide areas as done previously.¹ Light/heavy peak area intensity ratios were used to calculate concentrations of peptides monitored, by using the equation: light/heavy peptide ratio × heavy peptide concentration (fmol/μL) × volume of heavy peptides (μL)/(urine creatinine (μmol) × dilution factor). Protein concentrations adjusted by urine creatinine (Cr) were normalized by log2

transformation. For each of the proteins, an unpaired two-tailed t-test was conducted to assess the differences in the protein excretion in urine and IHC slides between IFTA and control (R’s function t.test). To generate the receiver-operating characteristics curves (ROC) and to calculate the area under ROC (AUC) we used R package plotROC. We first generated ROC and calculated AUC for each peptide separately, taking peptide excretion levels as a predictor and IFTA status as a predicted binary response. We then used sum of the z-score transformed peptide excretion levels ("SUM of ALL") as a predictor to generate additional ROC and

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calculated the corresponding AUC. Finally, we applied lasso-regularized logistic regression (lambda parameter equal to 0.1) using excretion levels of all seven peptides as a feature to generate IFTA prediction probabilities (logistic regression probabilities). This was done in leave- one-out fashion, i.e. IFTA probability of each of the patients was predicted by regression model fitted on all the remaining patient excretion levels. Unsupervised hierarchical clustering (R’s function pheatmap) was used to assess the separation of samples based on urine AngII- regulated protein excretion. Spearman’s rank correlation (R’s function cor) was used to evaluate whether urine excretion of AngII-regulated proteins correlated with each other and/or other measures of renal function including total protein (mg/mmol of Cr), serum creatinine, graft age, recipient age and donor age. Spearman rank correlations between the individual proteins’

excretion and individual Banff scores were also calculated to assess the univariate associations between the proteins’ excretion and Banff scores. In the RAS inhibitor treated group, a paired t- test was used to evaluate the effect of RAS inhibitor use on urine excretion of AngII-regulated proteins with RAS inhibitor use. All steps were performed in R version 3.2.2. Two-tailed p-value

≤0.05 was considered significant. R and GraphPad Prism software version 5 (GraphPad Software, Inc. La Jolla, CA) were used to display graphs.

Network and enrichment analysis

Using PathDIP⁸ ver. 2.5 (http://ophid.utoronto.ca/pathDIP), we performed core pathway enrichment analysis of the six AngII signature proteins (gene names): BST1, GLUL, LAMB2, LYPLA1, RHOB and THBS1. This search yielded 50 significantly enriched pathways (q value <

0.05) shown in Figure S5A. Using the Integrated Interactions Database⁹ ver. 04-2017 (http://ophid.utoronto.ca/iid), we determined the proteins that interact with the AngII signature proteins. Our search only included protein interaction partners in human kidneys (tissue specificity predicted using RNA and protein evidence as described in reference⁹) that were supported by experimental evidence or orthologous interaction evidence. This search yielded 222 protein interaction partners. The protein interaction partners as well as the AngII signature

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proteins were analyzed for pathway enrichment using pathDIP as described above. This search yielded 656 significantly enriched pathways (q value<0.05). Pathways that were highly significant (Bonferroni q.value<0.01) are depicted in Figure S5B. Graphs were generated using R version 3.2.2.

Supplemental Tables

Table S1. Optimized scheduled SRM methods for heavy-labelled and light peptides.

Compound

Start Time (min)

End Time

(min) Polarity Precursor (m/z)

Product (m/z)

Collision Energy (V)

Dwell Time (ms) sp|P02769|ALBU_BOVIN

15 24 Positive 653.361701 712.435201 22.5 10

HLVDEPQNLIK sp|P02769|ALBU_BOVIN

15 24 Positive 653.361701 956.504737 22.5 10

15 24 Positive 653.361701 1055.573151 22.5 10

15 24 Positive 657.368801 720.4494 22.5 10

15 14 Positive 657.368801 964.518936 22.5 10

15 24 Positive 657.368801 1063.58735 22.5 10

HLVDEPQNLIK sp|P62745|RHOB_HUMAN

23 29 Positive 730.839942 985.429523 24.8 30

IQAYDYLEC[+57.0]SAK sp|P62745|RHOB_HUMAN

23 29 Positive 730.839942 1148.492852 24.8 30

23 29 Positive 730.839942 1219.529966 24.8 30

23 29 Positive 731.33195 707.339252 24.8 30

IQ[+1.0]AYDYLEC[+57.0]SAK sp|P62745|RHOB_HUMAN

23 29 Positive 731.33195 985.429523 24.8 30

23 29 Positive 731.33195 1219.529966 24.8 30

23 29 Positive 734.847041 993.443722 24.8 30

23 29 Positive 734.847041 1156.507051 24.8 30

IQAYDYLEC[+57.0]SAK

sp|P62745|RHOB_HUMAN 23 29 Positive 734.847041 1227.544165 24.8 30

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23 29 Positive 735.339049 715.353451 24.8 30

23 29 Positive 735.339049 993.443722 24.8 30

23 29 Positive 735.339049 1227.544165 24.8 30

IQ[+1.0]AYDYLEC[+57.0]SAK sp|P07996|TSP1_HUMAN

28 33 Positive 808.911 629.373 27 20

GGVNDNFQGVLQNVR sp|P07996|TSP1_HUMAN

28 33 Positive 808.911 785.463 27 20

28 33 Positive 808.911 913.521 27 20

28 33 Positive 809.403 629.373 27 20

GGVNDNFQ[+1.0]GVLQNVR sp|P07996|TSP1_HUMAN

28 33 Positive 809.403 785.463 27 20

28 33 Positive 809.403 914.505 27 20

28 33 Positive 813.915 639.381 27 20

28 33 Positive 813.915 795.471 27 20

28 33 Positive 813.915 923.53 27 20

28 33 Positive 814.407 639.381 27 20

28 33 Positive 814.407 795.471 27 20

28 33 Positive 814.407 924.514 27 20

GGVNDNFQ[+1.0]GVLQNVR sp|P15104|GLNA_HUMAN

29 36 Positive 504.283346 682.322873 18 30

LVLC[+57.0]EVFK sp|P15104|GLNA_HUMAN

29 36 Positive 504.283346 795.406937 18 30

29 36 Positive 504.283346 894.475351 18 30

29 36 Positive 508.290445 690.337072 18 30

LVLC[+57.0]EVFK

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sp|P15104|GLNA_HUMAN

29 36 Positive 508.290445 803.421136 18 30

29 36 Positive 508.290445 902.48955 18 30

LVLC[+57.0]EVFK sp|P02769|ALBU_BOVIN

32 37 Positive 740.401358 1017.58399 25.1 10

LGEYGFQNALIVR sp|P02769|ALBU_BOVIN

32 37 Positive 740.401358 813.494113 25.1 10

32 37 Positive 740.401358 685.435535 25.1 10

32 37 Positive 745.405492 1027.592259 25.1 10

32 37 Positive 745.405492 823.502382 25.1 10

32 37 Positive 745.405492 695.443804 25.1 10

LGEYGFQNALIVR sp|O75608|LYPA1_HUMAN

37 50 Positive 778.934511 385.25578 26.3 30

LAGVTALSC[+57.0]WLPLR sp|O75608|LYPA1_HUMAN

37 50 Positive 778.934511 931.481834 26.3 30

LAGVTALSC[+57.0]WLPLR sp|O75608|LYPA1_HUMAN

37 50 Positive 778.934511 1044.565898 26.3 30

LAGVTALSC[+57.0]WLPLR

sp|O75608|LYPA1_HUMANLAGVTALSC[+57.0]WLPLR 37 50 Positive 783.938645 395.264049 26.3 30 sp|O75608|LYPA1_HUMANLAGVTALSC[+57.0]WLPLR 37 50 Positive 783.938645 941.490103 26.3 30 sp|O75608|LYPA1_HUMANLAGVTALSC[+57.0]WLPLR 37 50 Positive 783.938645 1054.574167 26.3 30

sp|P55268|LAMB2_HUMANGSC[+57.0]YPATGDLLVGR 23 29 Positive 733.359 729.425 25 20

sp|P02769|ALBU_BOVIN

26 30 Positive 582.319 595.309 20 10

LVNELTEFAK sp|P02769|ALBU_BOVIN

26 30 Positive 582.319 708.393 20 10

26 30 Positive 582.319 951.478 20 10

26 30 Positive 586.326 603.323 20 10

LVNELTEFAK

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sp|P02769|ALBU_BOVIN

26 30 Positive 586.326 716.407 20 10

26 30 Positive 586.326 959.492 20 10

LVNELTEFAK sp|P07996|TSP1_HUMAN

28 35 Positive 623.853709 832.452307 23.4 30

TIVTTLQDSIR sp|P07996|TSP1_HUMAN

28 35 Positive 623.853709 933.499986 23.4 30

28 35 Positive 623.853709 1032.5684 23.4 30

28 35 Positive 624.345717 833.436323 23.4 30

TIVTTLQ[+1.0]DSIR sp|P07996|TSP1_HUMAN

28 35 Positive 624.345717 934.484002 23.4 30

28 35 Positive 624.345717 1033.552416 23.4 30

28 35 Positive 628.857844 842.460576 23.4 30

28 35 Positive 628.857844 943.508255 23.4 30

28 35 Positive 628.857844 1042.576669 23.4 30

28 35 Positive 629.349852 843.444592 23.4 30

28 35 Positive 629.349852 944.492271 23.4 30

28 35 Positive 629.349852 1043.560685 23.4 30

TIVTTLQ[+1.0]DSIR sp|Q10588|BST1_HUMAN

32 40 Positive 771.385373 841.477794 26 20

GFFADYEIPNLQK sp|Q10588|BST1_HUMAN

32 40 Positive 771.385373 712.435201 26 20

32 40 Positive 771.385373 599.351137 26 20

32 40 Positive 775.392473 849.491993 26 20

32 40 Positive 775.392473 720.4494 26 20

GFFADYEIPNLQK

sp|Q10588|BST1_HUMAN 32 40 Positive 775.392473 607.365336 26 20

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32 40 Positive 772.369389 1121.536097 28.8 20

GFFADYEIPN[+1.0]LQ[+1.0]K sp|Q10588|BST1_HUMAN

32 40 Positive 772.369389 714.403233 28.8 20

32 40 Positive 772.369389 601.319169 28.8 20

32 40 Positive 776.376489 1129.550296 28.8 20

32 40 Positive 776.376489 722.417432 28.8 20

32 40 Positive 776.376489 609.333368 28.8 20

32 40 Positive 771.877381 713.419217 26.1 20

GFFADYEIPN[+1.0]LQK sp|Q10588|BST1_HUMAN

32 40 Positive 771.877381 600.335153 26.1 20

32 40 Positive 771.877381 503.282389 26.1 20

32 40 Positive 775.884481 721.433416 26.1 20

32 40 Positive 775.884481 608.349352 26.1 20

32 40 Positive 775.884481 511.296588 26.1 20

32 40 Positive 771.877381 1120.552081 26.1 20

GFFADYEIPNLQ[+1.0]K sp|Q10588|BST1_HUMAN

32 40 Positive 771.877381 713.419217 26.1 20

32 40 Positive 771.877381 600.335153 26.1 20

32 40 Positive 775.884481 1128.56628 26.1 20

32 40 Positive 775.884481 721.433416 26.1 20

32 40 Positive 775.884481 608.349352 26.1 20

GFFADYEIPNLQ[+1.0]K

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Table S2. Correlation values for Spearman’s rank correlation in Figure 5.

Donor.Age

Recipient.

Age Graft.Age

Serum.Cre atinine

TP.(mg/m mol.of.cre

atinine) BST1 RHOB LAMB2 LYPLA1 GLNA

TSP1_GG

V TSP1_TIV

Donor.Age 1 0.198797 0.385344 0.400583 0.177724 0.349163 0.272394 0.17293 0.404063 0.295253 0.111072 0.352831

Recipient.

Age 0.198797 1 -0.146987 -0.054544 -0.354024 -0.244719 -0.323518 -0.115134 -0.147281 -0.166696 -0.071209 -0.033408

Graft.Age 0.385344 -0.146987 1 0.612065 0.531496 0.257071 0.504152 0.268801 0.30485 0.260163 0.10548 0.299691

Serum.Cre

atinine 0.400583 -0.054544 0.612065 1 0.427064 0.144407 0.436962 0.210968 0.335674 0.144649 0.146257 0.488553

TP.(mg/

mmol.of.cr

eatinine) 0.177724 -0.354024 0.531496 0.427064 1 0.327273 0.765601 0.193491 0.367609 0.115143 0.31902 0.313336

BST1 0.349163 -0.244719 0.257071 0.144407 0.327273 1 0.658293 0.732554 0.780151 0.724807 0.362197 0.695696

RHOB 0.272394 -0.323518 0.504152 0.436962 0.765601 0.658293 1 0.488585 0.744016 0.523693 0.522759 0.622596

LAMB2 0.17293 -0.115134 0.268801 0.210968 0.193491 0.732554 0.488585 1 0.661972 0.723636 0.253358 0.737534

LYPLA1 0.404063 -0.147281 0.30485 0.335674 0.367609 0.780151 0.744016 0.661972 1 0.715382 0.50906 0.862282

GLNA 0.295253 -0.166696 0.260163 0.144649 0.115143 0.724807 0.523693 0.723636 0.715382 1 0.414698 0.675787

TSP1_GG

V 0.111072 -0.071209 0.10548 0.146257 0.31902 0.362197 0.522759 0.253358 0.50906 0.414698 1 0.507857

TSP1_TIV 0.352831 -0.033408 0.299691 0.488553 0.313336 0.695696 0.622596 0.737534 0.862282 0.675787 0.507857 1

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Supplemental Figures Figure S1.

Urine volumes containing 100 μg of total protein were obtained from spot urine samples. Protein was denatured using 8 M urea. Samples were then reduced with 20 mM dithiotreitol and alkylated with 80 mM iodoacetamide. Purified, heavy isotope-labeled peptides corresponding to AngII signature proteins were then spiked in (100 fmol/μl). The samples were then incubated with Lys-C/trypsin overnight, then desalted and concentrated using OMIX C18 10 μl tips and loaded on the triple quadrupole instrument (Quantiva).

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Figure S2. Ang II signature genes are significantly increased in kidney biopsies from transplant recipients with IFTA.

A.

B.

(A–B) Differentially expressed genes in kidney tissue with IFTA diagnosis compared to normal^5,6 or nonrejecting³ kidney allografts were obtained from three GEO datasets, Maluf et al,⁵ Modena et al⁶ and Rödder et al.³ These genes were intersected with our 83 AngII-regulated genes.⁷ Venn diagram shows that 47/63, 32/77 and 35/75 AngII-regulated genes were significantly differentially expressed in IFTA kidney biopsies in the Maluf et al (𝝌𝝌² =16.2, p.value

< 0.001), Modena et al (𝝌𝝌² =0.02, p.value=0.89) and Rödder et al (𝝌𝝌² =39.7, p.value < 0.001) data sets, respectively. These include the six genes in red (BST1, LAMB2, LYPLA1, GLUL, RHOB and THBS1) that we monitored in urine (B).¹

Common in all studies

Ang II signature and Rödder

et al

Ang II signature, Maluf et al and

Rödder et al

Ang II signature, Modena et al

and Rödder et al

Ang II signature

and Modena et

al

Ang II signature, Maluf et al and Modena

et al

Ang II signature and Maluf et

al

BAZ1A TXNDC9 AAGAB LYPLA1 GET4 ADA MAT1A

ARL4C CWC27 CDC27 UTP4 DBNL APIP BAG1

ASPHD1 YIPF5 SPTLC3 EXT2 CPNE2 RHOB CTNNAL1

TMEM41B GNB4 TNFRSF10B RPL22L1 DNAJB4 ARHGEF2 EFR3A

KPNA2 PDCD4 EIF1 TRA2A LAMB2 BST1 EGFR

NUP50 RSF1 GLNA TXNIP TRUB1 DCTN6 EIF4B

RBM3 TOR1AIP2 JMJD6 SSH3 STK17A FKBP10

LEPR NIN LYN HMGCS1

RDH10 LARP4 NDE1 HMOX1

RPS6 SPARC PHLDA1 TNIK

RCC1L TSP1 TGFBR2 KIF1C

EMC3 VCPIP1 NME7 PPP2R1B TMEM184C

SMYD5 SLC6A6

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Figure S3. AngII signature proteins, TSP1 and GLNA, are expressed predominantly in kidney allografts with IFTA.

Given that the urine excretion of AngII signature proteins was increased in patients with IFTA, we wondered whether AngII signature proteins were expressed in concomitant kidney allograft biopsies. Immunohistochemistry (IHC) staining was used to quantify two AngII signature proteins, TSP1 and GLNA, in kidney biopsy sections from IFTA (n = 9) and control (n = 13) patients (these patients were selected from the group described in Table 1 and biopsies were matched to their respective urine samples analyzed above). (A) and (B) show representative TSP1 and GLNA staining at low magnification (upper panel) and high magnification of the inset (bottom). As indicated by arrows, there was an increase in TSP1 and GLNA staining predominantly in the proximal tubules in IFTA biopsies. To quantify IHC staining, we calculated the percentage of tissue area covered by the protein staining (C–D). This quantification showed a non-significant increase in the percentage of TSP1 staining area (%) in IFTA versus control kidneys (17.2 ± 13.3 versus 15.5 ± 11.7, P = 0.8). There was also a non-significant increase in GLNA staining area (%) in biopsies with IFTA compared to controls (24.4 ± 11.5 versus 16.6 ± 9.9, P = 0.1).

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Figure S4. Correlating the change in Total urine protein vs. change in urine AngII signature protein excretion post-RAS.

A.

B.

C.

-6 -4 -2 2

-10 -5 5

∆BST1 Log2 [fmol/µmol Cr]

∆ Log2 [urine total protein mg/mmol Cr] R²= 0.62 p.value<0.0001

-8 -6 -4 -2 2

-10 -5 5

∆LYPLA1 Log2 [fmol/µmol Cr]

∆Log2 [urine total protein mg/mmol Cr]

R²= 0.48 p.value= 0.01

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D. E.

F. G.

-6 -4 -2 2

-8 -6 -4 -2 2 4

∆LAMB2 Log2 [fmol/µmol Cr]

∆Log2 [urine total protein mg/mmol Cr] R²= 0.40 p.value= 0.0037

-6 -4 -2 2 4

-8 -6 -4 -2 2 4

∆GLNA Log₂ [fmol/µmol Cr]

∆Log2 [urine total protein mg/mmol Cr]

R²= 0.44 p. value= 0.0019

-8 -6 -4 -2 2

-10 -5 5

∆TSP1_TIV Log2 [fmol/µmol Cr]

-6 -4 -2 2

-8 -6 -4 -2 2 4

∆RHOB Log₂ [fmol/µmol Cr]

-6 -4 -2 2

-10 -5 5

∆ TSP1_GGV Log₂ [fmol/µmol Cr]

∆ Log2 [urine total protein mg/mmol Cr]

R²= 0.62 p.value<0.0001

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(A–G) shows significant correlation between the change in urine total protein (y-axis) and change in urine AngII signature protein excretion (x-axis). Dotted lines show 95% CI around the line of best fit.

Figure S5. AngII signature proteins may contribute to fibrosis development in the kidney.

A.

B.

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(A) AngII signature proteins are enriched in fibrosis pathways including ECM organization. (B) AngII signature proteins interact with proteins involved in key fibrosis signaling such as TGF-b, WNT, EGFR and PDGFR.

References