SUPPLEMENTARY APPENDIX

Circulating Extracellular Vesicles as a Noninvasive Biomarker of

Rejection in Heart Transplant

Chiara Castellani, Jacopo Burrello, Marny Fedrigo, et al.

TABLE OF CONTENTS

Extended Methods

Figure S1 – Nanoparticle tracking analysis.

Figure S2 – Evaluation of EV surface markers interactors

Table S1 – Characteristics of patients from the validation cohort

Table S2 – Biochemical characteristics and ejection fraction.

Table S3 – Training cohort vs. validation cohort.

Table S4 – EV quantification (training cohort).

Table S5 – EV-surface markers (training cohort).

Table S6 – Correlations with patient parameters.

Table S7 – Correlations with leukocyte count.

Table S8 – Sub-analysis according to sex differences.

Table S9 – Correlation analysis according to endomyocardial biopsy.

Table S10 – Functional evaluation by DAVID database.

Table S11 – Network topological analysis.

EXTENDED METHODS

Histology and Definition of heart allograft rejectionFormalin-fixed, paraffin-embedded (FFPE) -EMB were stained with H&E and immuno-histochemistry was performed on tissue sections with monoclonal antibodies anti-C4d (Biomedica Groupe, Vienna, Austria), and anti-CD68 (ClonePG-M1; Dako Cytomation, Milano, Italy) by an immunoperoxidase method, as previously described(1). EMB were carefully reviewed by two independent pathologists, with divergences solved by discussion. Antibody-mediated rejection (AMR) was diagnosed according to International Society for Heart and Lung Transplantation (ISHLT) 2013 and 2015 updated guidelines. ISHLT 2005 working formulation was used to diagnose acute cellular rejection (ACR) (2). The rejection score, representing the total damaged tissue in EMB, was calculated for all biopsies(3).

Assessment of donor and non-donor-specific antibodies A Luminex, bead-based screening assay was performed to quantify circulating anti-HLA I/II antibodies in blood samples drawn prior to EMB. Antibodies were considered positive if the median fluorescence intensity was greater than 1,000, as previously described(4).

Nanoparticle Tracking AnalysisEV were quantified by nanoparticle tracking analysis using LM10 Nanosight equipped with a 405 nm laser (Malvern Instruments, United Kingdom) and Nanoparticle Tracking Analysis NTA 2.3 analytic software, as previously described(5). Briefly, 1 uL plasma was diluted 1:2000 in 1999 μL phosphate buffered saline sterile solution and then analyzed; three videos of 60 seconds were recorded for each sample to perform analyses.

Western Blot AnalysisWestern Blot analysis was performed on 100 L of serum samples incubated overnight with MACSPlex capture beads. Next day, the unbounded fraction was discarded, and samples were lysed with RIPA Buffer. Total proteins (15 g) were separated on SDS Page 4-12% gel (BioRad) and transferred onto PVDF membrane. The blot was incubated with the following primary antibody: Rabbit monoclonal anti-TSG101 (ab125011 Abcam); mouse monoclonal anti-CD81 (ab2539321 Invitrogen); and rabbit polyclonal anti-apolipoprotein B48 (ab31992 Abcam).

Flow cytometry EV-surface marker analysisAn aliquot of 60 uL of plasma for each patient was used for bead-based, multiplex EV capture and analysis by flow cytometry, using MACSPlex human Exosome Kit (Miltenyi Biotec; Bergisch Gladbach, Germany), according to the manufacturer’s instructions(6-9). More specifically, plasma was diluted to a final volume of 120 uL with MACSPlex buffer (MPB) and incubated overnight (14-16h) on an orbital shaker (800 rpm at 10°C, protected from light) with MACSPlex Exosome Capture Beads, containing 37 antibody-coated bead subsets. MPB was used as a blank control. After incubation, 1 mL of MPB was added to each tube, which were then centrifuged at 3.000 g for 10 minutes at 10°C to wash beads. After careful aspiration of 1 mL supernatant, 15 uL of MACSPlex Exosome Detection Reagent (5 uL for each allophycocyanin [APC]-conjugated anti-CD9, anti-CD63, and anti-CD81 detection antibody) were added and incubated for 15 minutes on an orbital shaker (450 rpm at 10°C, protected from light). After another washing step, samples were manually mixed and immediately loaded onto and acquired by MACSQuant Analyzer 10

flow cytometer (Miltenyi Biotec; Bergisch Gladbach, Germany). Multiplex platform analyses and gating strategies were previously described(6, 8).

Diagnostic modellingMachine-learning supervised algorithms are exploited in clinical practice to formulate predictions of selected outcomes based on a given set of labeled, paired, input-output training sample data(10, 11). To build the diagnostic model, a random forest (RF) algorithm was created using Python 3.5 (library, scikit-learn). The model was built through a RF classification algorithm; the algorithm created 40 different classification trees with a maximum number of 8 splits for each tree. The predicted diagnosis was based on the outcome of each classification tree; if at least 21 of 40 trees of the RF predict the absence of graft rejection, the patient was classified as R0 (level 1); in case of prediction of graft rejection, a second RF algorithm was created to distinguish ACR from AMR (level 2). A combined model was also built to distinguish R0 vs. ACR vs. AMR, in one single step. Models were validated by a leave-one-out cross-validation algorithm; briefly, the RF algorithm was trained on n-1 patients (where “n” was the total number of patients included in the analysis), and the remaining patient was used to test the model. The test-patient was then changed and accordingly the training subgroup. The process is repeated a total of n-times, with the test-patient rotating at each round and the remaining subgroup used for model training. The accuracy of the leave-one-out validation algorithm resulted from the correct predictions on test-patients.

Protein interactor network analysisProtein interactors with EV-surface markers were retrieved by Cytoscape PESCA plugin(12) and a global Homo sapiens protein-protein interaction (PPI) network of 1,588 nodes and 36,984 edges was reconstructed. For each quantitative comparison (R0 vs. ACR and R0 vs. AMR), a specific PPI sub-network was reconstructed considering the first neighbors of each differentially expressed EV-surface marker protein. Each sub-network was analyzed at topological level by Cytoscape Centiscape plugin(13); to select putative hubs and bottlenecks, nodes with all Betweenness, Bridging and Centroid values above average (calculated using the entire network) were retained as previously reported(14). At the same time, nodes belonging to each sub-network were evaluated at the functional level with the DAVID database(15, 16). The most enriched KEGG pathways and molecular functions were extracted; data were filtered using gene count > 5 and P-value < 0.001.

REFERENCES1. Fedrigo M, Gambino A, Benazzi E, et al.: Role of morphologic parameters on

endomyocardial biopsy to detect sub-clinical antibody-mediated rejection in heart transplantation. J Heart Lung Transplant 2011;30:1381-8.

2. Berry GJ, Burke MM, Andersen C, et al.: The 2013 International Society for Heart and Lung Transplantation Working Formulation for the standardization of nomenclature in the pathologic diagnosis of antibody-mediated rejection in heart transplantation. J Heart Lung Transplant 2013;32:1147-62.

3. Caforio AL, Belloni Fortina A, Gambino A, et al.: High rejection score in the first year and risk of skin cancer in heart transplantation. Transplant Proc 2001;33:1608-9.

4. Raess M, Frohlich G, Roos M, et al.: Donor-specific anti-HLA antibodies detected by Luminex: predictive for short-term but not long-term survival after heart transplantation. Transpl Int 2013;26:1097-107.

5. Balbi C, Bolis S, Vassalli G, Barile L: Flow Cytometric Analysis of Extracellular Vesicles from Cell-conditioned Media. J Vis Exp 2019.

6. Wiklander OPB, Bostancioglu RB, Welsh JA, et al.: Systematic Methodological Evaluation of a Multiplex Bead-Based Flow Cytometry Assay for Detection of Extracellular Vesicle Surface Signatures. Front Immunol 2018;9:1326.

7. El Andaloussi S, Lakhal S, Mager I, Wood MJ: Exosomes for targeted siRNA delivery across biological barriers. Adv Drug Deliv Rev 2013;65:391-7.

8. Koliha N, Wiencek Y, Heider U, et al.: A novel multiplex bead-based platform highlights the diversity of extracellular vesicles. J Extracell Vesicles 2016;5:29975.

9. Andriolo G, Provasi E, Lo Cicero V, et al.: Exosomes From Human Cardiac Progenitor Cells for Therapeutic Applications: Development of a GMP-Grade Manufacturing Method. Frontiers in Physiology 2018;9.

10. Burrello J, Burrello A, Stowasser M, et al.: The Primary Aldosteronism Surgical Outcome Score for the Prediction of Clinical Outcomes After Adrenalectomy for Unilateral Primary Aldosteronism. Ann Surg 2019.

11. Meyer LS, Wang X, Susnik E, et al.: Immunohistopathology and Steroid Profiles Associated With Biochemical Outcomes After Adrenalectomy for Unilateral Primary Aldosteronism. Hypertension 2018;72:650-7.

12. Scardoni G, Tosadori G, Pratap S, Spoto F, Laudanna C: Finding the shortest path with PesCa: a tool for network reconstruction. F1000Res 2015;4:484.

13. Scardoni G, Tosadori G, Faizan M, Spoto F, Fabbri F, Laudanna C: Biological network analysis with CentiScaPe: centralities and experimental dataset integration. F1000Res 2014;3:139.

14. Sereni L, Castiello MC, Di Silvestre D, et al.: Lentiviral gene therapy corrects platelet phenotype and function in patients with Wiskott-Aldrich syndrome. J Allergy Clin Immunol 2019.

15. Huang da W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57.

16. Huang da W, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009;37:1-13.

Figure S1 – Nanoparticle tracking analysis

Legend to Figure S1 – Characterization by nanoparticle tracking analysis (NTA) and flow cytometry (FC) of circulating plasma-derived extracellular vesicles (EV) from patients of the training cohort (n=53) with cellular-mediated rejection (ACR; orange; n=11), antibody-mediated rejection (AMR blue; n=9), compared to controls without graft rejection (rejection 0, R0; green; n=33). (A) EV concentration (n/mL). Data are reported for all EV and compared with smaller (30–150 nm) and larger (151–500 nm) fractions. (B) Diameter of EV in nanometers (nm).

Figure S2 – Evaluation of EV surface markers interactors

Legend to Figure S2 – Protein-protein interaction network of the first neighbours of each differentially expressed EV surface marker protein for rejection 0 (R0) vs. cellular-mediated rejection (ACR), and R0 vs. antibody-mediated rejection (AMR). KEGG pathways enriched by considering the first neighbours of each differentially expressed EV surface marker protein (gene count > 5 and P value < 0.001) are shown in the lower panel.

Table S1 – Characteristics of patients from the validation cohort

VariableRejection 0 (R0) [n=20]

Cellular-mediated Rejection

(ACR) [n=13]

Antibody-mediated Rejection

(AMR) [n=4]P-value

Sex (ref. Male) 13 (65.0) 8 (61.5) 2 (50.0) 0.851

Age at HT (years) 61 [50; 65] 57 [53; 61] 46 [21; 69] 0.656

Time from HT to biopsy (months) 8 [3; 11] 8 [2; 12] 9 [4; 12] 0.830

EMB characteristicsFibrosis (ref. present)Inflammatory infiltrate (ref. present)Myocytolisis (ref. present)Myocyte necrosis (ref. present)Ischemic damage (ref. present)Edema (ref. present)Vasculitis (ref. present)Micro-vasculopathy (ref. present)Vessel thrombosis (ref. present)CD4d grading (ref. >/= 1)CD68 grading (ref. > 10%)

10 (50.0)3 (15.0)0 (0.0)0 (0.0)0 (0.0)1 (5.0)1 (5.0)

2 (10.0)0 (0.0)0 (0.0)0 (0.0)

8 (61.5)13 (100.0)11 (84.6)12 (92.3)2 (15.4)3 (23.1)6 (46.2)1 (7.7)1 (7.7)

2 (15.4)1 (7.7)

1 (25.0)2 (50.0)0 (0.0)0 (0.0)0 (0.0)

2 (50.0)1 (25.0)0 (0.0)0 (0.0)

2 (50.0)2 (50.0)

0.435< 0.001< 0.001< 0.0010.1420.0590.0190.7980.3870.0110.004

Cellular Rejection Score (RS)0.2800

[0.0000; 0.5000]1.3000

[0.8975; 1.6600]0.0000

[0.0000; 0.2475]< 0.001

Anti-HLA-I DSA (ref. positive) Mean Fluorescence Intensity

0 (0.0)0 0.0

0 (0.0)0 0.0

1 (25.0)720 1,247.1

0.0070.008

Anti-HLA-I non-DSA (ref. positive)Mean Fluorescence Intensity

0 (0.0)0 0.0

1 (7.7)112 386.5

1 (25.0)1,930 2,729.4

0.0830.021

Anti-HLA-II DSA (ref. positive)Mean Fluorescence Intensity

0 (0.0)0 0.0

3 (23.1)3,647 6,597.7

1 (25.0)271 468.8

0.0690.081

Anti-HLA-II non-DSA (ref. positive)Mean Fluorescence Intensity

3 (15.0)284 631.5

1 (7.7)208 721.4

1 (25.0)3,658 6,335.3

0.5350.482

WBC (*10e9/L) 6.9 3.14 6.0 2.5 10.6 6.1 0.067

Neutrophils (*10e9/L) 4.7 2.83 4.7 1.54 8.8 5.64 0.035

Lymphocytes (*10e9/L) 1.1 0.49 1.2 0.81 0.9 0.81 0.704

Monocytes (*10e9/L) 0.7 0.41 0.6 0.28 0.8 0.61 0.794

Eosinophils (*10e9/L) 0.09 0.059 0.08 0.041 0.13 0.118 0.461

Basophils (*10e9/L) 0.02 0.010 0.03 0.015 0.02 0.015 0.082

AST (U/L) 26 9.4 26 13.4 37 23.2 0.306

ALT (U/L) 19 6.7 26 8.3 37 36.1 0.058

GGT (U/L) 55 25.9 48 51.1 152 110.5 0.001

CPK (U/L) 131 157.8 56 21.6 53 28.1 0.268

Creatinine (umol/L) 144 50.1 126 33.5 117 39.5 0.361

eGFR (mL/min) 53 20.9 55 19.4 60 35.9 0.867

Cyclosporinemia (ug/L) 180 29.9 178 34.2 138 46.2 0.076

EF at echo (%) 59 4.2 58 8.9 60 5.7 0.856

Legend to Table S1 – Sex, age at heart transplantation (HT), endomyocardial biopsy (EMB) characteristics, cellular rejection score (RS), HLA-I/II donor- specific and nonspecific antibodies (DSA), biochemical characteristics, and ejection fraction (EF; %) at echocardiography, in patients from the validation cohort, without rejection (R0; n=20), with cellular-mediated (ACR; n=13) or with antibody-mediated rejection (AMR; n=4). P-values of less than 0.05 were considered significant (in bold).

Table S2 – Biochemical characteristics and ejection fraction (training cohort)

VariableRejection 0 (R0) [n=33]

Cellular-mediated Rejection

(ACR) [n=11]

Antibody-mediated Rejection

(AMR) [n=9]

Overall P-value

Pairwise Comparisons

R0 vs. ACR

R0 vs. AMR

ACR vs. AMR

WBC (*10e9/L) 5.8 2.33 4.8 1.35 7.2 3.80 0.106 - - -

Neutrophils (*10e9/L) 4.4 2.20 3.1 0.88 5.1 3.25 0.128 - - -

Lymphocytes (*10e9/L) 0.7 0.31 1.1 0.53 1.0 0.57 0.025 0.048 0.144 1.000

Monocytes (*10e9/L) 0.6 0.27 0.4 0.18 0.8 0.27 0.007 0.125 0.147 0.005Eosinophils (*10e9/L) 0.06 0.036 0.08 0.052 0.15 0.116 < 0.001 1.000 < 0.001 0.012

Basophils (*10e9/L) 0.02 0.015 0.03 0.014 0.02 0.011 0.476 - - -

AST (U/L) 26 21.1 30 29.7 22 5.9 0.717 - - -

ALT (U/L) 20 8.6 17 7.5 23 18.0 0.483 - - -

GGT (U/L) 40 38.1 38 31.3 55 52.3 0.574 - - -

CPK (U/L) 79 29.7 84 73.4 57 14.0 0.280 - - -

Creatinine (umol/L) 141 42.9 93 35.1 89 34.3 < 0.001 0.004 0.004 1.000

eGFR (mL/min) 45 14.8 73 27.2 77 19.2 < 0.001 < 0.001 < 0.001 1.000

Cyclosporinemia (ug/L) 145 42.2 163 44.5 198 56.7 0.012 0.749 0.010 0.292

EF at echo (%) 58 8.6 61 4.6 58 3.6 0.624 - - -

Legend to Table S2 – Biochemical parameters and ejection fraction (EF; %) at echocardiography in patients from the training cohort (n=53), without rejection (R0; n=33), or with cellular-mediated (ACR; n=11) or antibody-mediated rejection (AMR; n=9). WBC, white blood cells; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, gamma-glutamyl transferase; CPK, creatinine phosphokinase; eGFR, estimate glomerular filtration rate. P-values less than 0.05 were considered significant (in bold).

Table S3 – Training cohort vs. validation cohort

Variable Training Cohort [n=53]

Validation Cohort[n=37]

P-value

Sex (ref. Male) 29 (54.7) 23 (62.2) 0.482

Age at HT (years) 63 [55; 65] 57 [49; 65] 0.283

Time from HT to biopsy (months) 8.0 [4.0; 11.0] 8.0 [2.5; 11.0] 0.385

EMB characteristicsFibrosis (ref. present)Inflammatory infiltrate (ref. present)Myocytolisis (ref. present)Myocyte necrosis (ref. present)Ischemic damage (ref. present)Edema (ref. present)Vasculitis (ref. present)Micro-vasculopathy (ref. present)Vessel thrombosis (ref. present)CD4d grading (ref. >/= 1)CD68 grading (ref. > 10%)

23 (43.4)19 (35.8)7 (13.2)12 (22.6)1 (1.9)4 (7.5)5 (9.4)6 (11.3)1 (1.9)7 (13.2)3 (5.7)

19 (51.4)18 (48.6)11 (29.7)12 (32.4)2 (5.4)6 (16.2)8 (21.6)3 (8.1)1 (2.7)4 (10.8)3 (8.1)

0.4570.2250.0650.3010.3600.1980.1060.6170.7960.7330.647

Cellular Rejection Score (RS)0.3636

[0.0334; 0.8452]0.5032

[0.0000; 1.0300]0.679

WBC (*10e9/L) 5.8 2.55 6.9 3.51 0.093

Neutrophils (*10e9/L) 4.3 2.27 5.1 3.06 0.146

Lymphocytes (*10e9/L) 0.9 0.43 0.9 0.54 0.059

Monocytes (*10e9/L) 0.6 0.27 0.7 0.41 0.438

Eosinophils (*10e9/L) 0.08 0.068 0.09 0.061 0.258

Basophils (*10e9/L) 0.02 0.015 0.02 0.013 0.469

AST (U/L) 26 21.3 28 12.8 0.682

ALT (U/L) 20 10.5 21 11.5 0.140

GGT (U/L) 42 39.2 63 55.9 0.061

CPK (U/L) 76 40.4 101 128.8 0.293

Creatinine (umol/L) 122 46.1 134 43.9 0.216

eGFR (mL/min) 56 23.4 54 21.2 0.773

Cyclosporinemia (ug/L) 158 48.7 163 44.9 0.116

EF at echo (%) 59 7.2 59 6.3 0.799

Diagnosis Rejection 0 (R0) Acute Cellular Rejection (ACR) Antibody-mediated Rejection (AMR)

33 (62.2)11 (20.8)9 (17.0)

20 (54.1)13 (35.1)4 (10.8)

0.285

Legend to Table S3 – Comparison of patient characteristics of the training cohort (n=53) vs. validation cohort (n=37). The table reports sex, age at heart transplantation (HT), endomyocardial biopsy (EMB) characteristics, cellular rejection score (RS), biochemical parameters, and ejection fraction (EF; %) at echocardiography.

Table S4 – Nanoparticle Tracking Analysis (training cohort)

VariableRejection 0 (R0) [n=33]

Cellular-mediated Rejection

(ACR) [n=11]

Antibody-mediated Rejection

(AMR) [n=9]

Overall P-value

Pairwise Comparisons

R0 vs.

ACR

R0vs.

AMR

ACR vs.

AMR

Diameter(nm)

164 [155; 188]

136[109; 149]

134[119; 162]

< 0.001 0.001 0.016 1.000

EV concentration(all vesicles)

1.34e12 [1.14e12; 2.12e12]

3.14e12 [2.14e12; 4.43e12]

3.58e12 [2.86e12; 5.43e12]

< 0.001 0.001 < 0.001 1.000

EV concentration (30-150 nm)

7.43e11 [5.83e11; 1.16e12]

2.01e12 [1.53e12; 3.40e12]

2.61e12 [1.77e12; 4.03e12]

< 0.001 < 0.001 < 0.001 1.000

EV concentration(151-500 nm)

6.70e11 [4.85e11; 1.08e12]

1.03e12 [5.21e11; 1.41e12]

1.21e12 [8.23e11; 1.74e12]

0.059 - - -

Area under the curve

0.101[0.079; 0.188]

0.302[0.190; 0.445]

0.351[0.271; 0.557]

< 0.001 0.001 < 0.001 1.000

FC (CD9 MFI)8.55

[5.06; 14.95]23.74

[14.54; 31.89]47.87

[31.14; 67.77]< 0.001 0.018 < 0.001 0.225

FC (CD63 MFI)3.00

[1.84; 4.72]6.35

[3.57; 7.77]16.03

[11.02; 27.66]< 0.001 0.041 < 0.001 0.341

FC (CD81 MFI)24.06

[11.1; 33.08]42.26

[25.6; 63.57]75.60

[62.84; 105.78]< 0.001 0.032 < 0.001 0.414

FC (CD9-CD63-CD81 MFI)13.06

[7.78; 18.11]24.30

[15.73; 33.60]46.50

[38.55; 54.06]< 0.001 0.007 < 0.001 0.211

Legend to Table S4 – Nanoparticle tracking analysis (NTA) of plasma extracellular vesicles (EV) in patients from the training cohort (n=53), without rejection (R0; n=33), or with cellular-mediated (ACR; n=11) or antibody-mediated rejection (AMR; n=9). Median fluorescence intensity (MFI) was reported for CD9, CD63, CD81, and the mean of CD9-CD63-CD81 measured with flow cytometry (FC). Data are expressed as median and interquartile range; P-values less than 0.05 were considered significant (in bold).

Table S5 – EV-surface markers (training cohort)

Variable[%, MFI]

Rejection 0 (R0) [n=33]

Cellular-mediated Rejection

(ACR) [n=11]

Antibody-mediated Rejection

(AMR) [n=9]

Overall P-value

Pairwise Comparisons

R0 vs.

ACR

R0vs.

AMR

ACR vs.

AMR

CD32.25

[0.85; 9.28]15.27

[10.20; 22.92]7.31

[5.10; 10.81]0.001 0.001 0.169 0.643

CD40.65

[0.10; 7.18]3.56

[1.58; 4.78]4.24

[1.09; 4.90]0.191 - - -

CD192.19

[1.25; 12.79]3.60

[1.99; 12.36]16.51

[13.55; 19.02]0.024 1.000 0.019 0.183

CD824.27

[12.44; 31.20]25.03

[12.83; 70.41]25.41

[23.74; 35.88]0.417 - - -

HLA-II27.12

[17.04; 58.03]52.79

[23.76; 85.53]57.92

[38.43; 81.64]0.019 0.299 0.029 1.000

CD560.00

[0.00; 2.03]5.00

[0.00; 10.96]0.41

[0.00; 7.44]0.170 - - -

CD1054.12

[0.90; 10.94]5.08

[1.29; 36.00]5.27

[3.78; 37.35]0.309 - - -

CD20.00

[0.00; 2.07]11.20

[4.32; 15.29]2.07

[1.01; 5.13]< 0.001 < 0.001 0.179 0.488

CD1c0.54

[0.02; 7.19]2.99

[0.45; 6.15]2.08

[1.63; 8.51]0.234 - - -

CD250.33

[0.00; 5.49]1.36

[0.34; 2.69]7.66

[4.87; 11.44]0.034 1.000 0.028 0.270

CD49e8.32

[1.92; 18.79]10.30

[0.73; 17.68]9.94

[1.71; 17.94]0.994 - - -

ROR14.53

[1.72; 9.51]8.61

[7.12; 21.00]18.39

[13.76; 33.13]< 0.001 0.038 0.001 0.767

CD2091.90

[0.00; 18.87]3.00

[1.00; 4.66]6.52

[4.23; 9.80]0.230 - - -

CD970.39

[57.60; 100.64]93.73

[51.68; 121.74]102.95

[61.23; 131.84]0.408 - - -

SSEA-48.76

[4.22; 19.43]35.23

[15.90; 92.08]31.73

[16.59; 46.90]< 0.001 0.002 0.014 1.000

HLA-I10.43

[3.85; 17.20]40.03

[29.38; 56.46]25.03

[17.12; 68.33]< 0.001 < 0.001 0.002 1.000

CD6323.03

[16.87; 39.90]26.28

[14.40; 37.68]33.69

[27.09; 39.02]0.292 - - -

CD4011.82

[4.31; 29.31]7.24

[1.96; 25.13]14.56

[6.39; 20.77]0.814 - - -

CD62P65.91

[24.54; 112.98]78.65

[40.35; 217.13]56.66

[31.78; 129.80]0.577 - - -

CD11c0.76

[0.18; 7.52]2.46

[1.31; 7.86]3.17

[1.21; 7.97]0.384 - - -

CD81197.93

[158.44; 211.71]189.22

[140.58; 227.63]162.58

[134.60; 194.31]0.288 - - -

MCSP2.21

[0.32; 8.19]2.86

[1.58; 5.30]4.66

[0.46; 7.99]0.820 - - -

CD1460.32

[0.00; 2.37]1.74

[1.10; 4.78]0.34

[0.16; 4.85]0.090 - - -

CD41b23.51

[12.43; 47.54]67.75

[35.80; 109.56]48.39

[30.69; 85.56]0.001 0.004 0.048 1.000

CD42a103.39

[36.58; 284.89]110.62

[39.77; 376.54]128.00

[31.89; 250.75]0.933 - - -

CD242.55

[1.11; 14.36]2.77

[1.90; 7.46]5.01

[3.06; 11.91]0.488 - - -

CD860.73

[0.10; 14.17]1.81

[1.00; 11.55]7.32

[1.81; 9.92]0.220 - - -

CD445.04

[1.67; 13.34]4.59

[2.77; 16.62]10.13

[4.45; 14.46]0.495 - - -

CD3266.11

[1.15; 18.73]18.49

[6.25; 25.35]28.25

[14.51; 50.39]0.011 0.219 0.017 1.000

CD133/17.80

[1.93; 13.79]4.96

[2.58; 18.06]12.71

[4.18; 16.40]0.666 - - -

CD2939.97

[17.43; 67.35]47.06

[20.91; 136.65]33.22

[10.92; 116.02]0.641 - - -

CD6914.61

[7.55; 34.11]23.69

[15.65; 46.12]22.51

[8.30; 31.16]0.512 - - -

CD1420.43

[0.00; 10.98]2.31

[2.00; 5.20]3.37

[2.25; 8.69]0.268 - - -

CD453.64

[1.27; 8.68]6.30

[4.13; 10.47]7.88

[3.78; 14.09]0.126 - - -

CD3125.57

[11.20; 86.25]34.03

[5.62; 162.97]28.80

[8.44; 47.17]0.801 - - -

CD200.82

[0.00; 5.09]1.40

[0.45; 6.70]10.01

[8.38; 19.03]0.014 0.737 0.012 0.390

CD142.21

[0.31; 8.67]8.81

[1.43; 13.28]3.52

[1.78; 8.99]0.302 - - -

Legend to Table S5 – Median fluorescence intensity (MFI, expressed as percentage [%] after normalization by the mean of CD9, CD63, and CD81) of the 37 EV-surface markers evaluated by flow cytometry (FC). Patients from the training cohort (n=53) with cellular-mediated (ACR; n=11), or antibody-mediated rejection (AMR; n=9) were compared with patients without rejection (R0; n=33). Data are expressed as median and interquartile range; P-values less than 0.05 were considered significant (in bold).

Table S6 – Correlations with patient parameters

Variable Pearson's R(P-value)

All Patients [n=90]

R0 and ACR [n=77]

R0 and AMR[n=66]

Age at HT(years)

Time from HT to biopsy (months)

Cellular Rejection Score (RS)

Anti-HLA-I DSA

Anti-HLA-I non-DSA

Anti-HLA-II DSA

Anti-HLA-II non-DSA

EV concentration -0.132 0.044 0.581 0.193 0.465 0.200 0.253(0.182) (0.677) (< 0.001) (0.133) (< 0.001) (0.119) (0.048)

CD9-CD63-CD81 MFI -0.032 0.124 0.057 -0.007 -0.059 0.008 0.009(0.764) (0.245) (0.627) (0.959) (0.654) (0.954) (0.946)

CD3 -0.052 -0.115 0.154 - - - -(0.629) (0.141) (0.180)

CD19 -0.138 0.154 - -0.078 -0.091 -0.042 -0.035(0.193) (0.146) (0.547) (0.487) (0.748) (0.785)

HLA-II 0.076 0.098 - 0.015 -0.140 0.037 0.031(0.476) (0.357) (0.907) (0.281) (0.775) (0.812)

CD2 0.073 -0.109 0.060 - - - -(0.497) (0.305) (0.604)

CD25 0.146 0.164 - -0.065 -0.128 -0.049 -0.049(0.170) (0.123) (0.617) (0.325) (0.708) (0.706)

ROR1 -0.164 -0.034 0.067 0.257 0.074 0.267 0.266(0.122) (0.749) (0.561) (0.045) (0.569) (0.039) (0.040)

SSEA-4 -0.102 0.073 0.409 0.011 -0.055 0.016 0.022(0.341) (0.493) (< 0.001) (0.935) (0.671) (0.899) (0.866)

HLA-I 0.011 0.011 0.332 0.265 0.073 0.287 0.270(0.918) (0.915) (0.003) (0.040) (0.577) (0.032) (0.036)

CD41b -0.037 -0.003 0.323 -0.060 -0.149 -0.023 0.010(0.732) (0.981) (0.004) (0.643) (0.252) (0.861) (0.937)

CD326 -0.059 0.006 - -0.111 -0.105 -0.065 -0.056(0.578) (0.956) (0.392) (0.420) (0.617) (0.663)

CD20 -0.125 -0.053 - -0.082 -0.122 -0.015 0.010(0.239) (0.622) (0.524) (0.348) (0.906) (0.936)

Legend to Table S6 – Correlations between age at heart transplantation (HT), time from HT to biopsy, cellular rejection score (RS) and HLA-I/II donor- specific and nonspecific antibodies (DSA) with EV parameters evaluated by NTA (EV concentration) and FC (mean MFI for CD9-CD63-CD81, CD3, CD19, HLA-II, CD2, CD25, ROR1, SSEA-4, HLA-I, CD41b, CD326, and CD20). Analyses were performed in all patients from the training and the validation cohort (n=90), in patients without rejection (R0) combined with cellular-mediated rejection (ACR; n=77), or in R0 patients combined with antibody-mediated rejection (AMR; n=66). Pearson’s R coefficient (above) and p-value (below) are reported for each comparison. P-values less than 0.05 were considered significant and reported in bold.

Table S7 – Correlations with leukocyte count.

Variable Pearson's R (P-value)

All Patients [n=90] R0 and ACR [n=77] R0 and AMR [n=66]

WBC(*10e9/L)

Lymphocytes(*10e9/L)

Monocytes(*10e9/L)

WBC(*10e9/L)

Lymphocytes(*10e9/L)

Monocytes(*10e9/L)

WBC(*10e9/L)

Lymphocytes(*10e9/L)

Monocytes(*10e9/L)

NP concentration

0.025 0.360 0.008 -0.081 0.449 -0.052 0.006 0.239 0.159(0.818) (0.001) (0.938) (0.486) (< 0.001) (0.658) (0.964) (0.045) (0.205)

CD9-CD63-CD81 MFI

-0.001 0.301 -0.002 0.036 0.263 0.022 -0.007 0.264 -0.059(0.990) (0.005) (0.988) (0.759) (0.025) (0.850) (0.953) (0.038) (0.639)

CD3 0.126 -0.122 0.030 0.205 -0.083 0.055 - - -(0.239) (0.261) (0.779) (0.045) (0.486) (0.636)

CD19 0.017 0.044 0.058 - - - 0.051 0.185 0.092(0.874) (0.688) (0.592) (0.685) (0.150) (0.468)

HLA-II 0.067 0.212 0.194 - - - 0.124 0.490 0.248(0.533) (0.045) (0.069) (0.325) (< 0.001) (0.041)

CD2 0.230 -0.092 0.227 0.290 -0.116 0.286 - - -(0.030) (0.397) (0.032) (0.011) (0.327) (0.012)

CD25 0.139 -0.023 0.208 - - - 0.129 0.036 0.237(0.195) (0.835) (0.047) (0.307) (0.782) (0.046)

ROR1 0.014 0.047 -0.036 0.001 0.001 -0.076 0.027 0.081 0.053(0.898) (0.668) (0.737) (0.993) (0.998) (0.512) (0.829) (0.533) (0.677)

SSEA-4 0.086 0.415 0.137 0.037 0.345 0.118 0.200 0.550 0.164(0.425) (<0.001) (0.199) (0.750) (0.003) (0.310) (0.110) (< 0.001) (0.192)

HLA-I 0.060 0.229 0.097 0.014 0.130 0.053 0.154 0.467 0.151(0.578) (0.034) (0.364) (0.904) (0.273) (0.652) (0.221) (< 0.001) (0.229)

CD41b 0.214 0.353 0.186 0.099 0.244 0.111 0.269 0.487 0.164(0.044) (0.001) (0.081) (0.394) (0.038) (0.339) (0.030) (< 0.001) (0.191)

CD326 0.016 -0.055 -0.085 - - - 0.051 0.065 0.049(0.884) (0.614) (0.430) (0.687) (0.618) (0.701)

CD20 -0.027 -0.017 -0.091 - - - 0.008 0.158 0.024(0.798) (0.873) (0.395) (0.949) (0.221) (0.846)

Legend to Table S7 - Correlations between white blood cells (WBC), lymphocytes, and monocytes with the different EV parameters evaluated by NTA (EV concentration) and FC (mean MFI for CD9-CD63-CD81, CD3, CD19, HLA-II, CD2, CD25, ROR1, SSEA-4, HLA-I, CD41b, CD326, and CD20). Analyses were performed in the training and validation cohorts of all patients (n=90), of patients without rejection (R0) combined with cellular-mediated rejection (ACR; n=77), and of R0 combined with patients with antibody-mediated rejection (AMR; n=66), as indicated. Pearson’s R coefficient (above) and p-value (below) are reported for each comparison. P-values less than 0.05 were considered significant and reported in bold.

Table S8 – Sub-analysis according to sex differences

Variable

Males [n=52]

P-value

Females [n=38]

P-valueRejection 0 (R0) [n=31]

Cellular-mediated Rejection

(ACR) [n=13]

Antibody-mediated Rejection

(AMR) [n=8]

Rejection 0 (R0) [n=22]

Cellular-mediated Rejection

(ACR) [n=11]

Antibody-mediated Rejection

(AMR) [n=5]

Diameter (nm) 157 [138; 192] 138 [117; 169] 135* [123; 184] 0.048 165 [157; 208] 137* [109; 149] 169 [125; 195] 0.005

EV concentration1.27e12

[1.06e12; 1.68e12]

3.69e12*[2.52e12; 7,61e12]

4.37e12*[3.14e12; 5.81e12]

< 0.0011.20e12

[8.70e11; 2.15e12]

3.21e12*[2.51e12; 5.34e12]

2.64e12*[2.44e12; 4.25e12]

< 0.001

CD9-CD63-CD81 MFI

13.41[6.78; 19.52]

25.48*[15.51; 90.17]

41.48*[25.77; 56.31]

0.01613.88

[9.14; 23.11]41.90*

[30.09; 170.51]42.39*

[23.32; 133.83]0.002

CD34.75

[1.06; 19.06]11.70

[7.69; 27.14]7.41

[4.22; 26.35]0.164

1.62[0.61; 8.19]

10.42*[2.06; 21.59]

6.79[3.92; 10.00]

0.012

CD198.14

[1.14; 15.73]5.31

[2.36; 11.63]14.87

[1.16; 17.78]0.849

0.85[0.13; 2.09]

3.45*[2.42; 11.81]

13.87*[2.82; 21.33]

0.001

HLA-II35.83

[16.60; 94.11]61.44

[27.22; 137.59]45.94

[21.96; 88.15]0.339

58.04[25.67; 82.25]

85.53[49.77; 152.65]

57.92[19.54; 88.58]

0.150

CD20.54

[0.00; 15.58]10.0

[1.13; 14.15]3.82

[2.02; 15.13]0.205

0.02[0.00; 0.75]

4.32*[1.35; 11.88]

1.13[0.08; 3.03]

< 0.001

CD251.63

[0.26; 16.24]2.46

[0.52; 7.69]7.33

[2.07; 12.44]0.536

0.21[0.00; 0.92]

1.21[0.58; 1.93]

3.93*[1.38; 12.60]

0.010

ROR15.42

[0.90; 17.63]8.48

[1.25; 14.00]17.05*

[13.41; 38.36]0.041

2.95[0.30; 4.74]

7.99[1.44; 21.00]

14.04*[5.07; 16.63]

0.032

SSEA-417.55

[7.15; 58.15]97.40*

[24.27; 141.13]22.21

[2.90; 43.18]0.043

8.93[3.99; 38.77]

122.37*[38.36; 164.77]

48.62*[22.62; 88.06]

0.001

HLA-I12.75

[3.55; 29.19]41.34*

[25.13; 85.02]35.03

[13.46; 71.20]0.007

16.19[8.80; 26.73]

56.46*[34.55; 104.96]

16.92[6.14; 46.20]

0.002

CD41b42.44

[12.51; 58.24]84.75*

[45.56; 136.75]40.42

[28.67; 89.74]0.039

45.30[18.55; 61.50]

67.75[39.45; 111.20]

71.22[38.21; 103.42]

0.061

CD3268.96

[1.00; 28.34]22.63

[15.21; 32.07]20.90

[7.35; 46.02]0.232

3.72[0.88; 11.57]

4.76[1.64; 6.86]

15.46[2.08; 43.32]

0.244

CD20 2.24 12.34 11.42 0.230 0.66 2.71* 8.18* 0.007

[0.38; 16.24] [3.72; 20.83] [2.37; 16.20] [0.00; 2.93] [0.93; 10.32] [3.23; 16.72]

Legend to Table S8 – Sub-analysis by sex (male, n=52; vs. female, n=38) of diameter and number of extracellular vesicles (EV) and median fluorescence intensity (MFI, expressed as percentage [%] after normalization by the mean of CD9, CD63, and CD81) of the EV-surface markers differentially expressed between group diagnosis. Patients from the training and validation cohort (n=90) with cellular-mediated (ACR; n=24), or antibody-mediated rejection (AMR; n=13) were compared with patients without rejection (R0; n=53); *pairwise comparisons: significant differences (P < 0.005) between rejecting and non-rejecting patients. Data are expressed as median and interquartile range; P-values less than 0.05 were considered significant (in bold).

Table S9 – Correlation analysis according to endomyocardial biopsy

Variable Pearson's R (P-value)

R0 and ACR [n=77] R0 and AMR [n=66]

Inflammatory infiltrate Myocytolisis Myocyte

necrosis Vasculitis CD4d >/=1

CD68 >10%

Inflammatory infiltrate Myocytolisis Myocyte

necrosis Vasculitis CD4d >/=1

CD68 >10%

CD30.183 0.079 0.254 0.041 0.067 0.001

- - - - - -(0.110) (0.495) (0.032) (0.721) (0.563)

(0.995)

CD19 - - - - - --0.005 -0.096 -0.064 -0.120 0.013 -0.073

(0.968) (0.444) (0.608) (0.337) (0.920) (0.559)

HLA-II - - - - - -0.242 -0.072 0.019 0.251 -0.095 -0.022

(0.047) (0.563) (0.882) (0.041) (0.447) (0.860)

CD20.075 0.103 0.120 0.116 -0.051 0.025

- - - - - -(0.518) (0.372) (0.297) (0.314) (0.662)

(0.832)

CD25 - - - - - --0.068 -0.080 -0.057 -0.088 -0.072 -0.023

(0.590) (0.523) (0.651) (0.481) (0.567) (0.854)

ROR10.080 -0.079 0.249 0.127 0.077 0.005 0.109 0.099 0.207 0.089 0.077 0.107

(0.490) (0.493) (0.035) (0.271) (0.507)

(0.957) (0.382) (0.430) (0.095) (0.477) (0.541

) (0.390)

SSEA-40.454 0.344 0.451 0.376 0.099 -0.003 0.238 -0.044 0.010 0.280 -0.087 0.031

(< 0.001) (0.002) (<0.001) (0.001) (0.392)

(0.978) (0.048) (0.723) (0.933) (0.023) (0.485

) (0.805)

HLA-I0.513 0.435 0.474 0.313 -0.046 -0.022 0.410 0.074 0.202 0.462 0.007 0.118

(< 0.001) (< 0.001) (<0.001) (0.006) (0.628)

(0.849) (0.001) (0.556) (0.103) (<0.001) (0.956

) (0.344)

CD41b 0.371 0.239 0.365 0.123 0.025 0.065 0.160 -0.124 -0.052 0.079 -0.007 -0.006

(0.001) (0.037) (0.001) (0.287) (0.831)

(0.574) (0.198) (0.321) (0.679) (0.527) (0.954

) (0.959)

CD326 - - - - - -0.128 -0.139 -0.100 -0.170 0.013 0.082

(0.305) (0.266) (0.426) (0.172) (0.919) (0.510)

CD20 - - - - - -0.111 -0.098 -0.071 -0.056 0.041 -0.004

(0.376) (0.435) (0.571) (0.657) (0.747) (0.972)

Legend to Table S9 – Correlations between differentially expressed EV surface markers (CD3, CD19, HLA-II, CD2, CD25, ROR1, SSEA-4, HLA-I, CD41b, CD326, and CD20) and parameters of endomyocardial biopsy (presence of inflammatory infiltrate, myocytolisis, myocyte necrosis, or vasculitis) in patients with rejection 0 (R0), acute cellular rejection (ACR), and antibody-mediated rejection (AMR) from training and validation cohorts (n=90). Analyses were performed in patients without rejection (R0) combined with cellular-mediated rejection (ACR; n=77), or in R0 combined with patients with antibody-mediated rejection (AMR; n=66), as indicated. Pearson’s R coefficient (above) and p-value (below) were reported for each comparison. P-values of less than 0.05 were considered significant (in bold).

Table S10 – Functional evaluation by DAVID database.

ID Term SystemR0 vs. ACR R0 vs. AMR

Count P-value Bonferroni Benjamini FDR Count P-value Bonferroni Benjamini FDR

 IMMUNE SYSTEM

hsa04664 Fc epsilon RI signaling pathway  - - -  -  -  18 4.5E-06 1.2E-03 3.2E-05 5.9E-03

hsa04662 B cell receptor signaling pathway  - - -  -  -  25 2.8E-11 7.5E-09 7.5E-10 3.7E-08

hsa04062 Chemokine signaling pathway  - - -  -  -  31 2.4E-05 6.5E-03 1.5E-04 3.2E-02

hsa04666 Fc gamma R-mediated phagocytosis  - - -  -  -  19 2.4E-05 6.4E-03 1.5E-04 3.2E-02

hsa04670 Leukocyte transendothelial migration  - - -  -  -  21 2.0E-04 5.1E-02 9.9E-04 2.6E-01

hsa04640 Hematopoietic cell lineage 26 4.1E-12 1.0E-09 2.6E-10 5.3E-09 27 2.0E-10 5.3E-08 4.4E-09 2.6E-07

hsa04611 Platelet activation 22 9.1E-06 2.3E-03 1.2E-04 1.2E-02 29 1.2E-07 3.2E-05 1.2E-06 1.6E-04

hsa04650 Natural killer cell mediated cytotoxicity 26 1.0E-08 2.6E-06 2.9E-07 1.3E-05 30 6.7E-09 1.8E-06 9.5E-08 8.9E-06

hsa04672 Intestinal immune network for IgA production 11 2.2E-04 5.3E-02 2.0E-03 2.8E-01 18 1.2E-08 3.1E-06 1.6E-07 1.5E-05

hsa04612 Antigen processing and presentation 29 1.3E-16 2.8E-14 2.8E-14 1.4E-13 41 3.0E-26 8.1E-24 8.1E-24 4.0E-23

hsa04660 T cell receptor signaling pathway 31 7.3E-15 1.8E-12 9.2E-13 9.4E-12 31 6.9E-12 1.9E-09 2.3E-10 9.0E-09

 SIGNAL TRANSDUCTON

hsa04630 Jak-STAT signaling pathway  - - -  -  -  23 7.0E-04 1.7E-01 3.2E-03 9.1E-01

hsa04064 NF-kappa B signaling pathway  - - -  -  -  17 4.4E-04 1.1E-01 2.2E-03 5.8E-01

hsa04014 Ras signaling pathway  - - -  -  -  34 7.6E-05 2.0E-02 4.1E-04 1.0E-01

hsa04151 PI3K-Akt signaling pathway 35 8.2E-04 1.9E-01 6.9E-03 1.1E+00 49 6.8E-06 1.8E-03 4.6E-05 9.0E-03

hsa04066 HIF-1 signaling pathway 16 2.6E-04 6.3E-02 2.3E-03 3.3E-01 20 4.7E-05 1.2E-02 2.6E-04 6.1E-02

hsa04015 Rap1 signaling pathway 29 1.4E-05 3.5E-03 1.7E-04 1.8E-02 34 1.7E-05 4.5E-03 1.1E-04 2.2E-02

hsa04012 ErbB signaling pathway 20 2.2E-07 5.5E-05 4.2E-06 2.8E-04 24 3.0E-08 8.1E-06 3.7E-07 4.0E-05

 ENDOCRINE SYSTEM

hsa04910 Insulin signaling pathway  - - -  -  -  22 8.8E-04 2.1E-01 4.0E-03 1.2E+00

hsa04915 Estrogen signaling pathway  - - -  -  -  21 2.2E-05 5.8E-03 1.3E-04 2.8E-02

hsa04917 Prolactin signaling pathway  - - -  -  -  21 7.7E-08 2.1E-05 8.3E-07 1.0E-04

 TRANSPORT AND CATABOLISM

hsa04144 Endocytosis 30 6.9E-05 1.7E-02 7.3E-04 8.9E-02 36 5.2E-05 1.4E-02 2.8E-04 6.8E-02

hsa04145 Phagosome 25 2.5E-06 6.3E-04 3.5E-05 3.2E-03 37 7.1E-11 1.9E-08 1.7E-09 9.3E-08

 SIGNALING MOLECULES AND INTERACTION CELLULAR COMMUNITY

hsa04510 Focal adhesion 32 3.4E-07 8.5E-05 5.7E-06 4.4E-04 35 4.1E-06 1.1E-03 3.0E-05 5.4E-03

hsa04514 Cell adhesion molecules (CAMs) 25 9.0E-07 2.3E-04 1.3E-05 1.2E-03 31 6.7E-08 1.8E-05 7.5E-07 8.8E-05

 FOLDING. SORTING AND DEGRADATION 

hsa03050 Proteasome 17 6.7E-10 1.7E-07 3.4E-08 8.7E-07 18 3.6E-09 9.6E-07 6.0E-08 4.7E-06

hsa04141 Protein processing in endoplasmic reticulum 37 1.4E-12 3.7E-10 1.2E-10 1.9E-09 38 6.5E-10 1.7E-07 1.2E-08 8.5E-07

 OTHER KEGG CATEGORIES

hsa04810 Regulation of actin cytoskeleton 26 2.6E-04 6.4E-02 2.3E-03 3.4E-01          

hsa04380 Osteoclast differentiation  - - -  -  -  28 5.2E-07 1.4E-04 4.5E-06 6.8E-04

hsa04722 Neurotrophin signaling pathway  - - -  -  -  23 4.1E-05 1.1E-02 2.4E-04 5.4E-02

Legend to Table S10 – KEGG pathways enriched by considering the first neighbors of each differentially expressed EV-surface marker protein per comparison (R0 vs. ACR and R0 vs. AMR); data filtered using gene count > 5 and P-value < 0.001.

Table S11 – Network topological analysis.

Gene ProteinR0 vs. ACR R0 vs. AMR

Betweenness Bridging Centroid Betweenness Bridging Centroid

B2M Beta-2-microglobulin 3,596 10.7 -428  - -  - 

CD247 T-cell surface glycoprotein CD3 zeta chain 1,178 10.4 -438  - -  - 

ERBB3 Receptor tyrosine-protein kinase erbB-3 927 9.6 -452  - -  - 

JUN Transcription factor AP-1 1,303 12.2 -458  - -  - 

ABI1 Abl interactor 1 968 11.1 -498  - -  - 

COPB1 Coatomer subunit beta  - -  -  1,386 29 -656

CD74 HLA class II histocompatibility antigen gamma chain  - -  -  996 75 -659

HLA-E HLA class I histocompatibility antigen, alpha chain E 1,025 15.5 -470 3,017 34 -671

ITGA6 Integrin alpha-6  - -  -  1,072 39 -685

VAPA Vesicle-associated membrane protein-associated protein A  - -  -  1,948 26 -689

RANBP9 Ran-binding protein 9  - -  -  1,154 44 -698

SSR4 Translocon-associated protein subunit delta  - -  -  1,409 32 -699

PTCH1 Protein patched homolog 1  - -  -  1,948 51 -718

DYNLL1 Dynein light chain 1, cytoplasmic  - -  -  1,383 31 -719

SGTA Small glutamine-rich tetratricopeptide repeat-containing protein alpha  - -  -  1,277 28 -720

Average network values 649 9 -518 985 23 -721

Legend to Table S11 – Hubs and bottlenecks selected by the topological evaluation of PPI sub-networks reconstructed considering the first neighbors of each differentially expressed EV-surface marker protein, per comparison (R0 vs. ACR and R0 vs. AMR). Betweenness, Bridging and Centroid were calculated and only nodes with all values above the average calculated on whole network were retained; EV-surface marker proteins were removed from this list.