Intimal arteritis is known to be a negative prognostic factor for kidney allograft survival. Isolated v-lesion (IV) is defined as intimal arteritis with minimal tubulointerstitial inflammation (TI). Although the Banff classification assesses IV as T cell-mediated rejection (TCMR), clinical, and prognostic significance of early IV (early IV, eIV) with negative C4d and donor-specific antibodies (DSA) remains unclear. To help resolve if such eIV truly represents acute rejection, a molecular study was performed. The transcriptome of eIV (n=6), T cell-mediated vascular rejection with rich TI (T cell-mediated vascular rejection, TCMRV, n=4) and non-rejection histologic findings (n=8) was compared using microarrays. A total of 310 genes were identified to be deregulated in TCMRV compared with eIV. Gene enrichment analysis categorized deregulated genes to be associated primarily with T-cells associated biological processes involved in an innate and adaptive immune and inflammatory response. Comparison of deregulated gene lists between the study groups and controls showed only a 1.7% gene overlap. Unsupervised hierarchical cluster analysis revealed clear distinction of eIV from TCMRV and showed similarity with a control group. Up-regulation of immune response genes in TCMRV was validated using RT-qPCR in a different set of eIV (n=12) and TCMRV (n=8) samples. The transcriptome of early IV (< 1 month) with negative C4d and DSA is associated with a weak immune signature compared with TCMRV and shows similarity with normal findings. Such eIV may feature non-rejection origin and reflect an injury distinct from an alloimmune response. The present study supports use of molecular methods when interpreting kidney allograft biopsy findings.

Introduction

Intimal arteritis, also called v-lesion, is known to be a negative prognostic factor for kidney allograft survival [1–3]. According to the Banff classification, intimal arteritis is characterized by infiltration of mononuclear cells beneath the arterial endothelium. It is considered pathognomonic of acute rejection, either T cell- (TCMR) or antibody-mediated rejection (ABMR) [4,5].

Isolated v-lesion (IV), defined as intimal arteritis that occurs with minimal concurrent tubulointerstitial inflammation (TI), poses a challenge of unclear clinical and prognostic significance. According to the Banff classification, arterial involvement is sufficient to classify IV as type II or III acute TCMR regardless of interstitial inflammation (i) and tubulitis (t). IV could also be considered as ABMR in the presence of donor-specific antibodies (DSA) and evidence of antibody interaction with the endothelium (C4d positive staining, moderate microvascular inflammation (MI) or gene expression markers of endothelial injury in the biopsy tissue) [6].

However, the role of the immune response in the pathophysiology of IV remains unclear. Recent studies have reported diverse findings on IV response to antirejection treatment, clinical and prognostic significance [7–12]. While some authors believe in hidden ischemic/reperfusion injury, others are convinced of its rejection origin.

In response to the limitations of conventional histologic assessment, molecular technologies are being explored in the quest for greater precision [13]. In the transplantation field, a few genome-wide transcriptional studies have aimed to elucidate the origin of IV [14–16]. However, inclusion of heterogeneous samples regardless of DSA and time after transplantation, brought no clear findings, particularly on the non-antibody-associated phenotype of IV.

Therefore, this retrospective study was conducted to thoroughly evaluate the transcriptome of early v-lesion within the first month after kidney transplantation with no signs of ABMR (negative DSA, absence of C4d, and MI) and minimal (eIV) or rich (T cell-mediated vascular rejection, TCMRV) tubulointerstitial inflammation.

Materials and methods

Study design and patients

An observational, nested case–control study within a large single-center retrospective cohort study was performed to compare the expression profiles of renal allograft biopsies with histologic findings of eIV and TCMRV. eIV was defined as intimal arteritis (v > 0) with minimal tubulitis (t ≤ 1) and interstitial inflammation (i ≤ 1), no MI [glomerulitis (g) + peritubular capillaritis (ptc) = 0], with C4d and DSA negativity. TCMRV was defined as the presence of intimal arteritis with significant TI (t > 1, i > 1) and negative C4d, MI and DSA and served as a positive control. Only indication kidney allograft biopsies performed within the first month of post-transplant were included. As a negative control group, normal histologic findings from 3-month protocol biopsies without a history of previous rejection were included.

For a training set, 18 patients undergoing kidney transplantation at the Prague-based Institute for Clinical and Experimental Medicine (IKEM) from 2009 to 2014, with eIV (n=6), TCMRV (n=4), or normal histology (n=8) and sufficient biopsy samples for microarray evaluation were selected. A flowchart of the training set is provided in Figure 1, patients’ baseline characteristics are shown in Table 1.

Study flowchart of the training set (cross-sectional screening)

Figure 1
Study flowchart of the training set (cross-sectional screening)

All patients after kidney transplantation at our department in the years 2009–2014 were considered for inclusion in the study. Abbreviations: AVR, acute vascular rejection; DSA, donor specific antibodies; eIV, early isolated v-lesion; IKEM, Institute for Clinical and Experimental Medicine; TCMRV, T cell-mediated vascular rejection; Tx, transplantation.

Figure 1
Study flowchart of the training set (cross-sectional screening)

All patients after kidney transplantation at our department in the years 2009–2014 were considered for inclusion in the study. Abbreviations: AVR, acute vascular rejection; DSA, donor specific antibodies; eIV, early isolated v-lesion; IKEM, Institute for Clinical and Experimental Medicine; TCMRV, T cell-mediated vascular rejection; Tx, transplantation.

Table 1
Characteristics of patients in the training set
TCMRV (n=4)eIV (n=6)normal (n=8)P
Donor characteristics 
Age, years 44.5 (40.3; 57) 58.5 (51.3; 68.3) 54 (31; 58) 0.186 
Gender (female) 2 (50) 5 (83.3)2 1 (14.3)2 0.045 
Type of donor (deceased) 3 (75) 6 (100) 8 (100) 0.157 
ECD 1 (15) 5 (83.3) 4 (57) 0.175 
Hypertension 1 (15) 5 (83.3) 3 (43) 0.125 
Recipient characteristics 
Age, years 55.2 (44; 66) 59.5 (53.3; 66.3) 62.5 (56.5; 66.2) 0.792 
Gender (female) 1 (25) 1 (16.7) 2 (25) 0.923 
Dialysis vintage, months 39.7 (24.7; 45) 30 (11; 43) 21.6 (15.6; 30) 0.678 
PRA max, % 3 (0.5; 32.5) 22 (6.5; 34.75) 1 (0; 3.5) 0.062 
HLA mismatch 4.5 (4; 5) 3 (2.75; 4.25) 3 (2; 4.75) 0.22 
HLA at biopsy (neg./pos./not known) 2/0/2 6/0/02 0/0/82 0.001 
DSA at biopsy (neg./pos./not known) 2/0/2 6/0/02 0/0/82 0.001 
Transplantation characteristics 
Retransplantation N/A 
Cold ischemia, hours 15.25 (6.5; 19.8) 15 (14; 16) 18.1 (7.4; 19.5) 0.696 
Delayed graft function 1 (15) 2 (33) 3 (43) 0.908 
Biopsy characteristics 
Postoperative day 6.5 (6; 8)3 7.5 (7; 10)2 97 (93.5; 101)2,3 0.001 
Clinical characteristics 
Induction therapy    0.005 
 None 3 (75)1 01 8 (100)  
 Basiliximab 1 (25) 5 (83.3)2 02  
 Thymoglobulin 1 (16.7)  
Maintenance immunosuppression    0.245 
 TAC/MMF/steroids 3 (75) 5 (62.5)  
 CsA/MMF/steroids 1 (25) 3 (37.5)  
TAC level at the biopsy (µg/l) 14.6 (10.3; 17.2) 10.95 (8; 16.6) 8.7 (8.3; 10.4) 0.646 
CsA level at the biopsy (µg/l) 231 NA 191 (176; 278) 
Anti-rejection treatment     
 Steroids 2 (50) 5 (83) N/A 0.3745 
 Thymoglobulin 1 (25) 1 (17) N/A  
 Steroids + Thymoglobulin 1 (25) N/A  
Serum creatinine (µmol/l)     
 At biopsy 378 (307; 448)3 441 (226; 514)2 115 (97; 154)2,3 0.002 
 12 months 119 (100; 121) 161 (151; 172) 115 (93; 148) 0.171 
 24 months 114 (100; 121) 186 (158; 187)2 106 (99; 144)2 0.017 
Proteinuria (g/24 h)     
 At biopsy 0.21 (0.105; 1.37)1 1.52 (1.25; 2.27)1,2 0.36 (0.27; 0.55)2 0.038 
 12 months 0 (0; 0.11) 0.26 (0.23; 0.26) 0.26 (0.18; 0.40) 0.144 
 24 months 0.12 (0.06; 0.54) 0.29 (0.09; 0.3) 0.19 (0.10; 0.33) 0.913 
Banff scores in diagnostic biopsy (grade)     
 mm 0 (0; 0) 0 (0; 0) 0 (0; 0) 0.535 
 g 0 (0; 0) 0 (0; 0) 0 (0; 0) 
 cg 0 (0; 0) 0 (0; 0) 0 (0; 0) 
 i 2 (2; 2)1,3 0 (0; 0)1 0 (0; 0)3 0.0001 
 t 2.5 (2; 3)1,3 0 (0; 0)1 0 (0; 0)2 0.001 
 v 1(1; 1.75)3 1 (1; 1.25)2 0 (0; 0)2,3 0.0001 
 ptc 0 (0; 0) 0 (0; 0) 0 (0; 0) 
 ti 2 (2; 2)3 0 (0; 1)2 0 (0; 0)2,3 0.001 
 ci 0 (0; 0.75) 1 (0.75; 1)2 0 (0; 0)2 0.027 
 ct 0.5 (0; 1) 0 (0; 1) 1 (0; 1) 0.871 
 ah 1 (1; 1) 1.5 (1; 1.25)2 0 (0; 0.75)2 0.004 
 cv 1 (0.25; 1) 2 (1; 3)2 0 (0; 0)2 0.002 
C4d positivity, n N/A 
Pathologic diagnosis     
  pure TCMR 33 42 02,3 0.0012 
  TCMR + ATN  
  Normal 03 02 82,3  
Number of glomeruli 8.5 (7.25; 9) 12 (7; 15) 8 (7; 12.25) 0.522 
TCMRV (n=4)eIV (n=6)normal (n=8)P
Donor characteristics 
Age, years 44.5 (40.3; 57) 58.5 (51.3; 68.3) 54 (31; 58) 0.186 
Gender (female) 2 (50) 5 (83.3)2 1 (14.3)2 0.045 
Type of donor (deceased) 3 (75) 6 (100) 8 (100) 0.157 
ECD 1 (15) 5 (83.3) 4 (57) 0.175 
Hypertension 1 (15) 5 (83.3) 3 (43) 0.125 
Recipient characteristics 
Age, years 55.2 (44; 66) 59.5 (53.3; 66.3) 62.5 (56.5; 66.2) 0.792 
Gender (female) 1 (25) 1 (16.7) 2 (25) 0.923 
Dialysis vintage, months 39.7 (24.7; 45) 30 (11; 43) 21.6 (15.6; 30) 0.678 
PRA max, % 3 (0.5; 32.5) 22 (6.5; 34.75) 1 (0; 3.5) 0.062 
HLA mismatch 4.5 (4; 5) 3 (2.75; 4.25) 3 (2; 4.75) 0.22 
HLA at biopsy (neg./pos./not known) 2/0/2 6/0/02 0/0/82 0.001 
DSA at biopsy (neg./pos./not known) 2/0/2 6/0/02 0/0/82 0.001 
Transplantation characteristics 
Retransplantation N/A 
Cold ischemia, hours 15.25 (6.5; 19.8) 15 (14; 16) 18.1 (7.4; 19.5) 0.696 
Delayed graft function 1 (15) 2 (33) 3 (43) 0.908 
Biopsy characteristics 
Postoperative day 6.5 (6; 8)3 7.5 (7; 10)2 97 (93.5; 101)2,3 0.001 
Clinical characteristics 
Induction therapy    0.005 
 None 3 (75)1 01 8 (100)  
 Basiliximab 1 (25) 5 (83.3)2 02  
 Thymoglobulin 1 (16.7)  
Maintenance immunosuppression    0.245 
 TAC/MMF/steroids 3 (75) 5 (62.5)  
 CsA/MMF/steroids 1 (25) 3 (37.5)  
TAC level at the biopsy (µg/l) 14.6 (10.3; 17.2) 10.95 (8; 16.6) 8.7 (8.3; 10.4) 0.646 
CsA level at the biopsy (µg/l) 231 NA 191 (176; 278) 
Anti-rejection treatment     
 Steroids 2 (50) 5 (83) N/A 0.3745 
 Thymoglobulin 1 (25) 1 (17) N/A  
 Steroids + Thymoglobulin 1 (25) N/A  
Serum creatinine (µmol/l)     
 At biopsy 378 (307; 448)3 441 (226; 514)2 115 (97; 154)2,3 0.002 
 12 months 119 (100; 121) 161 (151; 172) 115 (93; 148) 0.171 
 24 months 114 (100; 121) 186 (158; 187)2 106 (99; 144)2 0.017 
Proteinuria (g/24 h)     
 At biopsy 0.21 (0.105; 1.37)1 1.52 (1.25; 2.27)1,2 0.36 (0.27; 0.55)2 0.038 
 12 months 0 (0; 0.11) 0.26 (0.23; 0.26) 0.26 (0.18; 0.40) 0.144 
 24 months 0.12 (0.06; 0.54) 0.29 (0.09; 0.3) 0.19 (0.10; 0.33) 0.913 
Banff scores in diagnostic biopsy (grade)     
 mm 0 (0; 0) 0 (0; 0) 0 (0; 0) 0.535 
 g 0 (0; 0) 0 (0; 0) 0 (0; 0) 
 cg 0 (0; 0) 0 (0; 0) 0 (0; 0) 
 i 2 (2; 2)1,3 0 (0; 0)1 0 (0; 0)3 0.0001 
 t 2.5 (2; 3)1,3 0 (0; 0)1 0 (0; 0)2 0.001 
 v 1(1; 1.75)3 1 (1; 1.25)2 0 (0; 0)2,3 0.0001 
 ptc 0 (0; 0) 0 (0; 0) 0 (0; 0) 
 ti 2 (2; 2)3 0 (0; 1)2 0 (0; 0)2,3 0.001 
 ci 0 (0; 0.75) 1 (0.75; 1)2 0 (0; 0)2 0.027 
 ct 0.5 (0; 1) 0 (0; 1) 1 (0; 1) 0.871 
 ah 1 (1; 1) 1.5 (1; 1.25)2 0 (0; 0.75)2 0.004 
 cv 1 (0.25; 1) 2 (1; 3)2 0 (0; 0)2 0.002 
C4d positivity, n N/A 
Pathologic diagnosis     
  pure TCMR 33 42 02,3 0.0012 
  TCMR + ATN  
  Normal 03 02 82,3  
Number of glomeruli 8.5 (7.25; 9) 12 (7; 15) 8 (7; 12.25) 0.522 

Data are presented as medians (interquartile (IQ) range) or n (%). Differences were calculated by the Kruskal–Wallis test or χ2 Fisher exact test and significant results of post hoc comparisons were adjusted by the Bonferroni correction for multiple tests (1TCMRV compared with eIV, 2eIV compared with control, 3TCMRV compared with control). Abbreviations: ah, arteriolar hyaline thickening; ATN, acute tubular necrosis; cg, transplant glomerulopathy; ci, interstitial fibrosis; ct, tubular atrophy; cv, vascular intimal fibrosis; CsA, cyclosporine A; ECD, expanded criteria donor; g, glomerulitis; HLA, human leukocyte antigen; i, interstitial inflammation; mm, mesangial matrix expansion; MMF, mycophenolate mofetil; ptc, peritubular capillaritis; PRA, panel reactive antibody; t, tubulitis; TAC, tacrolimus; ti, total interstitial inflammation; v, intimal arteritis.

For a validation set, 12 eIV and 8 TCMRV biopsy samples from patients transplanted between 2010 and 2016 were retrospectively identified and validated by RT-qPCR (Table 2). The Institutional Review Board (IRB) of IKEM approved the study protocol (G09-12-20), and all patients provided informed consent to participate in the study.

Table 2
Characteristics of patients in the validation set
TCMRV (n=8)eIV (n=12)P
Donor characteristics 
Age, years 46 (41; 62) 57 (50; 66) 0.082 
Gender (female) 4 (50) 7 (51.3) 0.535 
Type of donor (deceased) 5 (62.5) 10 (83.2) 0.296 
ECD 3 (37.5) 6 (50) 0.465 
Recipient characteristics 
Age, years 52 (43; 62) 58 (54; 60) 0.3841 
Gender (female) 1 (12.5) 2 (16.7) 0.656 
Dialysis vintage, months 41 (17.5; 51) 20.4 (7.1; 25) 0.698 
PRA max, % 3 (0; 10) 2 (0.5; 32.3) 0.973 
HLA mismatch 5 (4; 5) 3 (3; 4.75) 0.39 
HLA at biopsy (neg./pos./not known) 1/1/6 4/1/7 0.571 
DSA at biopsy (neg./pos./not known) 2/0/6 5/0/7 0.392 
CKD diagnosis, n (%)   0.339 
 Diabetes 1 (12.5) 5 (41.7)  
 Glomerulonephritis 2 (25) 2 (16.7)  
 Polycystosis 1 (12.5) 2 (16.7)  
 TIN 2 (25)  
 Hypertension 1 (12.5) 2 (16.7)  
 Ischemic nephropathy 1 (12.5)  
 Other 1 (8.3)  
Transplantation characteristics 
Retransplantation 1 (12.5) 1 (8.3) 0.653 
Cold ischemia, hours 14 (1.25; 14) 15 (11.7; 18.2) 
Delayed graft function 4 (50) 6 (50) 0.675 
Biopsy characteristics 
Postoperative day 6.5 (6; 18) 7 (6; 13) 
Clinical characteristics 
Induction therapy   0.966 
 None 1 (12.5) 2 (16.7)  
 Basiliximab 5 (62.5) 7 (58.3)  
 Thymoglobulin 2 (25) 3 (25)  
Maintenance immunosuppression   
 TAC/MMF/steroids 8 (100) 11 (100)  
TAC level at the biopsy (µg/l) 11.6 (8.3; 15.1) 10.3 (7.55;14.7) 0.629 
Rejection treatment   0.4177 
 Steroids 4(50) 8 (67)  
 Thymoglobulin 3 (37.5) 4 (33)  
 Steroids + Thymoglobulin 1 (12.5)  
Serum creatinine (µmol/l)    
 At biopsy 378 (166; 470) 464 (231; 609) 0.305 
 12 months 154 (112; 167) 154 (135; 188) 0.571 
 24 months 142 (118; 172) 144 (113; 184) 1.0 
Banff scores in diagnostic biopsy (grade)    
 mm 0 (0; 0) 0 (0; 0) 
 g 0 (0; 0) 0 (0; 0) 
 cg 0 (0; 0) 0 (0; 0) 
 i 2 (2; 2) 0 (0; 0.75) 0.000016 
 t 2 (1.25; 3) 0 (0; 1) 0.0002 
 v 1 (1; 1.75) 1 (1; 2) 0.734 
 ptc 0 (0; 0) 0 (0; 0) 
 ti 2 (2; 2) 0 (0; 1) 0.000016 
 ci 0 (0; 0.75) 0.5 (0; 1) 0.384 
 ct 0.5 (0; 1) 1 (0.25; 1) 0.384 
 ah 1 (1; 1) 1 (1; 2) 0.181 
 cv 1 (1; 1) 2 (1; 2) 0.02 
C4d positivity, n 
Pathologic diagnosis   0.2421 
  Pure TCMR  
  TCMR + ATN  
Number of glomeruli 9 (7–19) 12 (7.5–14) 0.851 
TCMRV (n=8)eIV (n=12)P
Donor characteristics 
Age, years 46 (41; 62) 57 (50; 66) 0.082 
Gender (female) 4 (50) 7 (51.3) 0.535 
Type of donor (deceased) 5 (62.5) 10 (83.2) 0.296 
ECD 3 (37.5) 6 (50) 0.465 
Recipient characteristics 
Age, years 52 (43; 62) 58 (54; 60) 0.3841 
Gender (female) 1 (12.5) 2 (16.7) 0.656 
Dialysis vintage, months 41 (17.5; 51) 20.4 (7.1; 25) 0.698 
PRA max, % 3 (0; 10) 2 (0.5; 32.3) 0.973 
HLA mismatch 5 (4; 5) 3 (3; 4.75) 0.39 
HLA at biopsy (neg./pos./not known) 1/1/6 4/1/7 0.571 
DSA at biopsy (neg./pos./not known) 2/0/6 5/0/7 0.392 
CKD diagnosis, n (%)   0.339 
 Diabetes 1 (12.5) 5 (41.7)  
 Glomerulonephritis 2 (25) 2 (16.7)  
 Polycystosis 1 (12.5) 2 (16.7)  
 TIN 2 (25)  
 Hypertension 1 (12.5) 2 (16.7)  
 Ischemic nephropathy 1 (12.5)  
 Other 1 (8.3)  
Transplantation characteristics 
Retransplantation 1 (12.5) 1 (8.3) 0.653 
Cold ischemia, hours 14 (1.25; 14) 15 (11.7; 18.2) 
Delayed graft function 4 (50) 6 (50) 0.675 
Biopsy characteristics 
Postoperative day 6.5 (6; 18) 7 (6; 13) 
Clinical characteristics 
Induction therapy   0.966 
 None 1 (12.5) 2 (16.7)  
 Basiliximab 5 (62.5) 7 (58.3)  
 Thymoglobulin 2 (25) 3 (25)  
Maintenance immunosuppression   
 TAC/MMF/steroids 8 (100) 11 (100)  
TAC level at the biopsy (µg/l) 11.6 (8.3; 15.1) 10.3 (7.55;14.7) 0.629 
Rejection treatment   0.4177 
 Steroids 4(50) 8 (67)  
 Thymoglobulin 3 (37.5) 4 (33)  
 Steroids + Thymoglobulin 1 (12.5)  
Serum creatinine (µmol/l)    
 At biopsy 378 (166; 470) 464 (231; 609) 0.305 
 12 months 154 (112; 167) 154 (135; 188) 0.571 
 24 months 142 (118; 172) 144 (113; 184) 1.0 
Banff scores in diagnostic biopsy (grade)    
 mm 0 (0; 0) 0 (0; 0) 
 g 0 (0; 0) 0 (0; 0) 
 cg 0 (0; 0) 0 (0; 0) 
 i 2 (2; 2) 0 (0; 0.75) 0.000016 
 t 2 (1.25; 3) 0 (0; 1) 0.0002 
 v 1 (1; 1.75) 1 (1; 2) 0.734 
 ptc 0 (0; 0) 0 (0; 0) 
 ti 2 (2; 2) 0 (0; 1) 0.000016 
 ci 0 (0; 0.75) 0.5 (0; 1) 0.384 
 ct 0.5 (0; 1) 1 (0.25; 1) 0.384 
 ah 1 (1; 1) 1 (1; 2) 0.181 
 cv 1 (1; 1) 2 (1; 2) 0.02 
C4d positivity, n 
Pathologic diagnosis   0.2421 
  Pure TCMR  
  TCMR + ATN  
Number of glomeruli 9 (7–19) 12 (7.5–14) 0.851 

Data are presented as medians (interquartile (IQ) range) or n (%). Differences were calculated by the Mann–Whitney test or χ2 Fisher exact test. Abbreviations: ah, arteriolar hyaline thickening; ATN, acute tubular necrosis; cg, transplant glomerulopathy; ci, interstitial fibrosis; CKD, chronic kidney disease; ct, tubular atrophy; cv, vascular intimal fibrosis; ECD, expanded criteria donor; g, glomerulitis; HLA, human leukocyte antigen; i, interstitial inflammation; mm, mesangial matrix expansion; MMF, mycophenolate mofetil; ptc, peritubular capillaritis; PRA, panel reactive antibody; t, tubulitis; TAC, tacrolimus; TCMR, T cell- mediated rejection; ti, total interstitial inflammation; TIN, tubulointerstitial nephritis; v, intimal arteritis.

Induction immunosuppression was based on the immunological risk of recipients according to panel reactive antibodies, DSA, dialysis vintage, and transplantation history. High-risk patients received depletive induction treatment with Thymoglobulin, medium-risk patients received basiliximab, and low-risk patients received no induction treatment. Maintenance immunosuppression was based on standard triple immunosuppression regimen with tacrolimus in the majority of patients. T cell-mediated rejection was treated by steroid pulses. Thymoglobulin was administered in the case of type 2B TCMR or in steroid-resistant rejections.

Biopsy of kidney allografts

All the kidney allograft biopsies were obtained under ultrasound guidance using a 16-gauge Tru-Cut needle (Somatex®; Medical Technologies GmbH, Germany). A small portion (2 mm) of the cortical zone of the biopsy specimen was immediately placed in the RNA later Stabilization Reagent (Qiagen) and stored at −20 or −80°C until RNA extraction. The remaining part of each particular biopsy was sent for histological examination and found representative.

Microarray analysis

RNA preparation and hybridization

Total RNA was isolated from 16-gauge renal biopsies using the RNeasy Micro Kit (Qiagen, Hilden, Germany) according to manufacturer’s instructions. Samples with RNA integrity number < 6 (measured using the Agilent RNA 6000 Nano Kit on the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, U.S.A.) were excluded from analysis. A total of 200 ng of purified RNA served as a template for the amplification and biotinylation of cRNA using the Illumina® TotalPrep™ RNA Amplification Kit (Thermo Fisher Scientific, Waltham, MA, U.S.A.) according to manufacturer’s instructions. Yields of labeled cRNA were determined using the Agilent RNA 6000 Nano Kit on the Agilent 2100 Bioanalyzer (Agilent Technologies). Labeled cRNA (750 ng) was hybridized at 58°C for 16 h to Illumina HumanHT-12 v4.0 Expression BeadChips (Illumina, Inc., San Diego, CA, U.S.A.), and then washed and Cy3-stained according to manufacturer’s instructions.

Scanning and microarray data analysis

After hybridization, the chip was scanned using the iScan System (Illumina), and raw data were extracted with the BeadStudio Data Analysis Software (Illumina). The lumi package in R software was used to process the raw data. The quantile method was used for normalization [17]. The given workflow represents one of the standard preprocessing pipelines for Illumina microarrays with verified applicability under similar conditions [18].

Only probes with detectable signal intensity in at least five samples were included in the analysis. After normalization, probes with log of average expression intensity less than 3.5 were removed from further analysis. Differentially expressed genes were chosen as those with a fold change > 2 and adjusted P<0.05 (Benjamini–Hochberg method). The affected genes were functionally annotated, and the deregulated pathways were identified using the David database (http://david.abcc.ncifcrf.gov). For comparing the lists of deregulated genes, an interactive online tool for Venn diagrams http://bioinformatics.psb.ugent.be/webtools/Venn/ was used. Complete raw and normalized data were deposited in the NCBI Gene Expression Omnibus (GEO) database [19] and are accessible through the GEO Series accession number GSE114712 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114712).

Validation of microarray data

In order to validate the microarray results, internal validation and validation by RT-qPCR analysis was performed.

Internal validation

To identify the most relevant genes that distinguish between the TCMRV and eIV groups, the well-known support vector machine (SVM) classification algorithm [20], specifically e1071 R library implementation was applied [21]. The negative effect of overfitting caused by the small number of samples and the large number of evaluated genes was minimized by the recursive feature elimination (RFE) algorithm [22]. We first trained the classifiers on microarray data. Performance of SVM–RFE was evaluated by leave-one-out cross validation (LOOCV) [23]. Since the group sizes were not perfectly balanced in their size, the usual measure of classification accuracy (ACC) was complemented by another measure robust to skewed groups, the area under the ROC curve (AUC) [24]. The LOOCV demonstrated that TCMRV and eIV groups can be perfectly differentiated by a large scale of gene sets of various sizes, i.e., the groups can be separated by a small number of genes as well as by much larger gene sets. The gene set size ranges from 5 to 500 genes and showed absolute 100% ACC and maximum AUC (Supplementary Figure S1). However, the single small set of genes that separate the study groups was not easy to determine due to the large number of differentially expressed microarray genes. SVM–RFE perfectly split the groups with a large scale of potentially different gene sets of various sizes. For this reason, the final selection of genes for external validation was not induced directly from SVM-RFE as described below.

Validation by RT-qPCR analysis

Using a custom-made Taqman low-density array (Applied Biosystems, Foster City, CA, U.S.A.) on a validation set of patients (n=20), RT-qPCR was employed to analyze 38 genes identified by microarray to be deregulated between TCMRV and eIV and found to be of biological interest in the rejection process (Supplementary Table S1). Quantitative RT-qPCR based on TLDA technology was carried out as described elsewhere [25]. Real-time RT-qPCR data were quantified using the SDS 2.4 software package (Applied Biosystems) and relative gene expression values were determined using the comparative 2−ΔΔCt method of the relative quantification (RQ) Manager Software v 1.2.1 (Applied Biosystems) with normalization to an endogenous control (HPRT1). The endogenous control was chosen from three candidate housekeeping genes (GAPDH-Hs99999905_m1, PGK1-Hs99999906_m1, HPRT1- Hs01003267_m1) using NormFinder (www.mdL.dk) as the gene with the most stable expression (HPRT1 with a stability value of 0.003). As a calibrator, one of the samples with a good expression profile on all of the target genes was used. All investigated mRNAs were measured in triplicate for each sample.

Risk of overfitting

In our study, we deal with the well-known n<<P problem (the large number of variables and the small number of samples) that represents a specific case of ill-posed problem and may result in overfitting [26,27]. This risk is minimized by careful handling with the train, test and validation datasets. First, we employ LOOCV to split between train and test sets. Both gene selection and classifier construction are performed solely on train sets, while the corresponding test sets serve for their evaluation. In particular, the SVM–RFE procedure for gene selection was re-performed with each iteration of the LOOCV procedure, so that the features are selected from each train set and applied independently to each test set. In general, this train-test split allows us to detect overfitting and avoid complex biomarkers that heavily overfit the data used for model construction. It enables to propose simple biomarkers and to smoothly distinguish between them in terms of their performance. Second, we work with the independent RT-qPCR data set that serves to validate the selected biomarkers, remove the selection bias and get an unbiased estimate of their classification accuracy (expressed in terms of AUC to compensate for unbalanced classes) [27,28].

Statistical methods

Normality of the data was tested using the Kolmogorov–Smirnov test. Nonparametric values are presented as median and interquartile range. Two groups were compared by the two-tailed Mann–Whitney U-test and three groups by the Kruskal–Wallis test with post hoc adjustment by the Bonferroni correction for multiple tests. For comparison of categorical data, the χ2 Fisher exact test was used. Two-sided P-values considered statistically significant for a P<0.05.

In order to visualize different transcriptomes of studied samples, principal component analysis (PCA) and unsupervised hierarchical clustering (HC) were performed using R software. Both visualization tools were constructed using all genes evaluated by microarray to provide independent evidence of potential phenotype separation and to exclude circular misleading conclusion by choosing only genes deregulated between the study groups. In PCA analysis, a 3D scatter plot was produced based on the whole microarray expression profile of each sample. Calculation was done by the singular value decomposition of the centered expression matrix; no further scaling of the normalized expressions was performed.

In unsupervised HC, the Euclidean distance was selected as a similarity measure between the normalized expression profiles. The agglomerative complete-linkage HC was performed. The function hclust from R stats package was used for HC construction.

Results

Training set (microarray analysis)

Distinct microarray profiles of TCMRV and eIV

Using microarray-based gene expression profiling in the training set of patients, we identified 310 differentially regulated genes in early TCMRV compared with eIV (Supplementary Table S2). The volcano plot demonstrates deregulated genes between both groups after correction for multiple testing and shows most genes being up-regulated in TCMRV (n=288, 92.9%) compared with eIV (Figure 2A).

Genes with different mRNA expression in studied groups.
Figure 2
Genes with different mRNA expression in studied groups.

The volcano plot analysis showing differences in mRNA expression values between TCMRV and eIV (A) and between eIV and normal (B) considering an adjusted P-value cut-off = 0.05 and a fold-change cut-off = 2. The data for all genes are plotted as log2-fold change compared with the -log10 of the p-value. Thresholds are shown as dashed lines. (C) Venn diagram of shared deregulated genes between TCMRV compared with normal and eIV compared with normal. Comparison of deregulated gene lists between the study groups and controls shows a 1.7% (n=33) gene overlap with no association with any gene ontology (GO) term.

Figure 2
Genes with different mRNA expression in studied groups.

The volcano plot analysis showing differences in mRNA expression values between TCMRV and eIV (A) and between eIV and normal (B) considering an adjusted P-value cut-off = 0.05 and a fold-change cut-off = 2. The data for all genes are plotted as log2-fold change compared with the -log10 of the p-value. Thresholds are shown as dashed lines. (C) Venn diagram of shared deregulated genes between TCMRV compared with normal and eIV compared with normal. Comparison of deregulated gene lists between the study groups and controls shows a 1.7% (n=33) gene overlap with no association with any gene ontology (GO) term.

To get an insight into relevant biological processes, we used the DAVID database that allows gene annotation to gene ontology (GO) terms. We found that the genes regulated differentially between TCMRV and eIV were primarily associated with the innate and adaptive immune and inflammatory responses. In detail, TCMRV samples showed up-regulation of T-cell activation, costimulation, differentiation, receptor signaling pathway, positive regulation of T-cell proliferation, antigen processing, and presentation as well as B-cell activation and receptor signaling pathway, interferon-γ- and tumor necrosis factor-mediated signaling pathway and its positive regulation etc. Details of top 25 out of 65 enriched GO terms for biological processes are shown in Table 3. Activated biological processes indicate higher activation of immunity and inflammation in early TCMRV biopsies compared with eIV. The Circos plot was created based on 15 most significantly up-regulated genes between TCMRV and eIV to visualize gene association with significantly enriched pathways and GO terms that play a role in the immune response (Figure 3).

The Circos plot represents significantly enriched pathways and GO terms for biological process associated with the 15 most significant up-regulated genes between TCMRV compared with eIV samples, detected using the DAVID database
Figure 3
The Circos plot represents significantly enriched pathways and GO terms for biological process associated with the 15 most significant up-regulated genes between TCMRV compared with eIV samples, detected using the DAVID database

Outside the circle, dysregulated genes and significantly enriched pathways together with GO terms are indicated.

Figure 3
The Circos plot represents significantly enriched pathways and GO terms for biological process associated with the 15 most significant up-regulated genes between TCMRV compared with eIV samples, detected using the DAVID database

Outside the circle, dysregulated genes and significantly enriched pathways together with GO terms are indicated.

Table 3
Top 25 GO terms for biological process enriched in TCMRV compared with eIV
GO termCountP
GO:0006955 Immune response 54 4.28E-31 
GO:0031295 T-cell costimulation 21 7.03E-17 
GO:0050853 T-cell receptor signaling pathway 25 8.95E-16 
GO:0006954 Inflammatory response 36 9.93E-16 
GO:0002250 Adaptive immune response 21 1.61E-11 
GO:0042110 T-cell activation 14 2.44E-11 
GO:0045087 Innate immune response 31 3.24E-10 
GO:0007165 Signal transduction 50 3.01E-09 
GO:0060333 Interferon-γ-mediated signaling pathway 14 4.92E-09 
GO:0002504 Antigen processing and presentation of peptide or polysaccharide antigen via MHC class II 3.39E-07 
GO:0050776 Regulation of the immune response 17 1.17E-06 
GO:0032729 Positive regulation of interferon-γ production 10 2.31E-06 
GO:0007166 Cell surface receptor signaling pathway 20 2.84E-06 
GO:0033209 Tumor necrosis factor-mediated signaling pathway 13 1.77E-05 
GO:0042102 Positive regulation of T-cell proliferation 10 1.94E-05 
GO:0030217 T-cell differentiation 2.01E-05 
GO:0006935 Chemotaxis 13 2.10E-05 
GO:0006915 Apoptotic process 27 2.53E-05 
GO:0030168 Platelet activation 12 7.54E-05 
GO:0050853 B-cell receptor signaling pathway 7.81E-05 
GO:0043547 Positive regulation of GTPase activity 26 7.88E-05 
GO:0001816 Cytokine production 8.71E-05 
GO:0050900 Leukocyte migration 12 1.18E-04 
GO:0042113 B-cell activation 1.96E-04 
GO:0006968 Cellular defense response 2.01E-04 
GO termCountP
GO:0006955 Immune response 54 4.28E-31 
GO:0031295 T-cell costimulation 21 7.03E-17 
GO:0050853 T-cell receptor signaling pathway 25 8.95E-16 
GO:0006954 Inflammatory response 36 9.93E-16 
GO:0002250 Adaptive immune response 21 1.61E-11 
GO:0042110 T-cell activation 14 2.44E-11 
GO:0045087 Innate immune response 31 3.24E-10 
GO:0007165 Signal transduction 50 3.01E-09 
GO:0060333 Interferon-γ-mediated signaling pathway 14 4.92E-09 
GO:0002504 Antigen processing and presentation of peptide or polysaccharide antigen via MHC class II 3.39E-07 
GO:0050776 Regulation of the immune response 17 1.17E-06 
GO:0032729 Positive regulation of interferon-γ production 10 2.31E-06 
GO:0007166 Cell surface receptor signaling pathway 20 2.84E-06 
GO:0033209 Tumor necrosis factor-mediated signaling pathway 13 1.77E-05 
GO:0042102 Positive regulation of T-cell proliferation 10 1.94E-05 
GO:0030217 T-cell differentiation 2.01E-05 
GO:0006935 Chemotaxis 13 2.10E-05 
GO:0006915 Apoptotic process 27 2.53E-05 
GO:0030168 Platelet activation 12 7.54E-05 
GO:0050853 B-cell receptor signaling pathway 7.81E-05 
GO:0043547 Positive regulation of GTPase activity 26 7.88E-05 
GO:0001816 Cytokine production 8.71E-05 
GO:0050900 Leukocyte migration 12 1.18E-04 
GO:0042113 B-cell activation 1.96E-04 
GO:0006968 Cellular defense response 2.01E-04 

The genes with different expression in TCMRV and eIV after correction for multiple comparisons (n=310) were entered into the DAVID gene ontology database. Top 25 out of 65 GO terms are shown.

Analysis of the molecular relationships between differentially expressed genes (TCMRV compared with eIV) revealed involvement of GO terms for molecular functions highly associated with the rejection process (GO:0032395 MHC class II receptor activity, adjusted P=2.63E-07; GO:0004872 receptor activity, P=1.19E-04, GO:0005164 tumor necrosis factor receptor binding, P=2.87E-04; GO:0042605 peptide antigen binding, P=3.08E-04; GO:0045028 G-protein coupled purinergic nucleotide receptor activity, P=2.83E-03; GO:0004896 cytokine receptor activity, P=9.79E-03; GO:0019976 interleukin-2 binding, P=3.24E-02; GO:0004911 interleukin-2 receptor activity, 3.24E-02).

Comparison of the study groups with normal histologic findings

By comparing eIV with normal histologic findings, 28 up- and 22 down-regulated genes were identified (Figure 2B, Supplementary Table S3). Comparison of eIV with normal indicates involvement of down-regulated genes in GO terms for molecular functions highly associated with ion binding (GO:0046872, metal ion binding, P=6.0E-3; GO:0043169, cation binding, P=6.4E-3; GO:0043167, ion binding, P=7.2E-3; GO:0046914, transition metal ion binding, P=26.1E-3; GO:0008270, zinc ion binding, P=36.7E-3, unadjusted P-values shown). Genes up-regulated in eIV compared with normal histology showed association with translation (GO:0006412, unadjusted P=8.39E-03) and RNA binding (GO:0003723, unadjusted P=3.95E-02) reflecting the early post-transplant time period of eIV findings (median 8 days in eIV compared with 97 days in control group).

On the other hand, comparison of TCMRV to the control group revealed a large number of significantly deregulated genes (n=1966). Gene enrichment analysis found 239 GO terms for the biological process, predominantly from an immune response, to be significantly associated with deregulated genes (Supplementary Table S4) and confirmed true rejection origin of TCMRV. Comparison of deregulated gene lists between the study groups and controls using a Venn diagram showed only a 1.7% (n=33) gene overlap (Figure 2C, Supplementary Table S5). The genes shared between those two comparisons (TCMRV compared with normal and eIV compared with normal) were not significantly associated with any GO term. Taken together, the transcriptome of eIV showed relative similarity with the control group but was dramatically different from that of TCMRV.

Visualization of microarray data

The 3D PCA analysis applied to the whole transcriptome data demonstrated clear separation of TCMRV from eIV kidney allografts (Figure 4). Control samples were spread among eIVs demonstrating similarity of both groups’ transcriptome. In addition, unsupervised HC analysis was applied to the whole microarray data and a similar pattern was obtained (Figure 5). Three main subclusters were formed. The first cluster (left) was formed by an individual eIV patient (eIV_5) undergoing successful steroid treatment of early IV but a low level of DSA class I (MFI 1100) was detected retrospectively with no impact on graft function or later development of ABMR. The second cluster included exclusively TCMRV samples. In the third cluster, we were not able to distinguish between eIV and control samples mirroring the groups’ transcriptome similarity. Taken together, unsupervised HC analysis distinguished TCMRV from eIV samples but failed to distinguish between eIV and controls.

3D PCA applied to the whole transcriptome of TCMRV, eIV, and control samples (training set)
Figure 4
3D PCA applied to the whole transcriptome of TCMRV, eIV, and control samples (training set)
Figure 4
3D PCA applied to the whole transcriptome of TCMRV, eIV, and control samples (training set)
Unsupervised hierarchical cluster analysis applied to the whole transcriptome of TCMRV, eIV and control samples (training set)
Figure 5
Unsupervised hierarchical cluster analysis applied to the whole transcriptome of TCMRV, eIV and control samples (training set)
Figure 5
Unsupervised hierarchical cluster analysis applied to the whole transcriptome of TCMRV, eIV and control samples (training set)

Validation set (RT-qPCR analysis)

RT-qPCR analysis of the validation set confirmed up-regulation of the immune response in TCMRV compared with eIV

We verified these findings in a validation set of patients (n=20) using a different technique. The clinical characteristics of the validation cohort are similar to that of the training set (Table 2). A total of 38 genes were selected for RT-qPCR validation (Supplementary Table S1). These genes exhibited at least two-fold change and adjusted P-value <0.05 in microarray expression and were of biological interest for TCMRV and eIV comparison.

RT-qPCR analysis confirmed that early indication biopsy tissues of TCMRV had statistically significantly higher expression of BCL11B, BTLA, CCL17, CCR7, CD2, CTLA4, CXCL13, GIMAP5, IL21R, KLRG1, LAX1, LCK, LTA, LTB, SLA2, SLAMF1, TNFRSF4 and ZAP70 compared with eIV (Figure 6). The validated genes are significantly involved in regulation of immune system process, T-cell differentiation, activation, proliferation, B-cell activation, overall lymphocyte and leukocyte activation, immune response-regulating cell signal transduction, and apoptosis.

Validation of microarray analysis by RT-qPCR of early indication biopsy samples
Figure 6
Validation of microarray analysis by RT-qPCR of early indication biopsy samples

Scatter plots show top 10 deregulated genes between TCMRV and eIV.

Figure 6
Validation of microarray analysis by RT-qPCR of early indication biopsy samples

Scatter plots show top 10 deregulated genes between TCMRV and eIV.

Agreement between microarray and RT-qPCR data

Validation of reference genes in the validation set was defined as both qualitative (direction) and quantitative agreement between microarray and RT-qPCR measurements. The direction of RT-qPCR gene expressions agreed with the microarray technique in 100% of validated genes. Quantitative agreement between microarray and RT-qPCR was confirmed by a significant correlation of normalized data (Pearson r = 0.663, P=0.00006) (Supplementary Figure S2). To further validate differences in the transcriptome of the study groups, the SVM–RFE classifiers were trained on RT-qPCR data. LOOCV confirmed that the genes selected for validation from microarray data showed around 80% accuracy (ACC) and a 0.75 area under the curve (AUC) (Supplementary Figure S3) thus confirming reasonable gene selection for external RT-qPCR validation.

Discussion

In the present study, we investigated the transcriptome of eIV with paucity of TI and TCMRV with rich TI. Our main results are that the transcriptome of eIV revealed a weak immunologic signature compared with TCMRV and showed similarity with non-rejection 3-month protocol biopsy. Based on our results, eIV may feature a non-rejection phenotype and reflect peritransplant injury. As the current Banff histopathological criteria consider intimal arteritis (after exclusion of ABMR) to be at least type II of TCMR irrespective of TI, our results agree with calls for reassessment of the current approach in histology interpretation. Furthermore, difference in non-rejection phenotype of DSA- and C4d-negative eIV described in our study and rejection phenotype of late and often DSA positive IV [16] supports use of a molecular microscope in the case of an IV finding as recommended by the last Banff Kidney Meeting report [6].

Our first goal was to find out whether eIV represents a true TCMR. To do so, we included early intimal arteritis with minimal TI (eIV) and rich TI (TCMRV) and excluded cases with any MI, and C4d or DSA positivity. With these inclusion/exclusion criteria, only not anti-HLA-antibody-mediated intimal arteritis was evaluated. We used microarray technology to allow for investigation at the molecular level. Because the gene expression patterns change over time after transplantation and may vary widely, even among tissues with similar morphological changes [29], we decided to include only early indication kidney allograft biopsies into the study groups.

We found that the transcriptome of eIV was significantly different from that of TCMRV. In general, the genes deregulated between TCMRV and eIV were annotated to GO terms associated with the innate and adaptive immune and inflammatory responses. This is in accordance with other studies which identified relevant biological processes for acute rejection to be up-regulated in T cell-mediated rejection and led to development of molecular test for T cell-mediated rejection (TCMR score) [30,31]. In detail, up-regulated genes in our TCMRV samples were associated with T-cell activation, differentiation, costimulation, T-cell receptor signaling pathway, signal transduction, interferon-γ- and tumor necrosis factor-mediated signaling pathway, antigen processing and presentation, apoptosis, and others as listed in Table 3. Vice versa, eIV revealed significant down-regulation of T cell-mediated processes similarly to recent study showing negative molecular TCMR score in majority of IV in contrast to positive TCMR score in 95% of v-lesions with rich TI and proposing that IV in absence of DSA should not be used to diagnose TCMR [16].

Another interesting finding of our study is that PCA and HC indicate a rather clear distinction of TCMRV from eIV and controls at the molecular level. The separation is not perfect as all microarray probes, not only the deregulated ones, were used. The standalone eIV_5 patient had retrospectively detected DSA and his separation might reflect the fact that molecular changes precede the histologic ones.

The Venn diagram comparing deregulated genes in TCMRV and eIV compared with controls demonstrated that the shared genes were not annotated to GO terms associated with rejection or the immune response. Moreover, eIV samples revealed relative similarity to normal histologic findings. So, if TCMRV represents true tubulointerstitial rejection, then eIV with a distinct transcriptome profile is probably of non-rejection origin.

Interestingly, by comparison of eIV and controls only 50 genes were found to be differentially regulated and found to be associated with down-regulation of zinc ion binding in early IV. It is well known that zinc plays an important role in ischemia/reperfusion injury and has potential antioxidant properties [32]. Recent work shows that oral zinc chloride supplementation mitigates renal ischemia/reperfusion injury in rats by modulating oxidative stress, endoplasmic reticulum stress, and autophagy [33]. Therefore, we speculate that down-regulation of zinc ion binding genes in eIV might reflect exhaustion of zinc metabolism in early post-transplant biopsies with potential ongoing ischemia/reperfusion injury compared with quiescent 3-month protocol biopsies with normal histology and no signs of acute kidney injury.

Another important finding in the search of IV origin was the identification of several potential target molecules that could have important roles in understanding of IV process. Therefore, we evaluated set of deregulated genes from microarray study with potential biological interest in a validation set of samples using different molecular diagnostic technique. RT-qPCR analysis revealed significant up-regulation of several genes with an important role in rejection process in TCMRV compared with IV, such as regulation of immune system process, T-cell differentiation, activation, proliferation, B-cell activation, overall lymphocyte and leukocyte activation, immune response-regulating cell signal transduction and apoptosis and confirmed stronger activation of an innate and adaptive immune responses in TCMRV compared with eIV.

Our findings agree with other microarray studies which showed a discrepancy between conventional histologic and molecular assessment of TCMR [14–16,31,34]. The discrepancy was particularly marked in IV, where most cases exhibited low TCMR scores [31]. Intimal arteritis has been shown to have less importance in the diagnostics of TCMR than tubulitis and interstitial infiltrate [15]. Salazar reported that only a minority of IV exhibited the molecular signature of TCMR. Particularly early biopsies performed in the first year post-transplant in DSA-negative patients, usually did not reflect rejection, while late IV often associated with DSA was indicative of ABMR. Taken together, current diagnostic conventions for v-lesion tend to overdiagnose rejections as many biopsies with no molecular TCMR score are traditionally assessed as being TCMR [16].

Our and others’ results suggest that early post-transplant IV might reflect mechanisms different from rejection, i.e., injury-repair response to implantation stresses, endothelial injury related to ischemia-reperfusion [7]. The presence of intimal arteritis in early non-rejection biopsies might be explainable by increased vascular permeability and facilitated extravasation of leukocytes by interrupting the integrity of the renal vascular endothelium triggered by renal injury [35,36].

However, our conclusions differ from those of the few clinical studies analyzing IV. Wu et al. [37] reported intimal arteritis to be an independent histologic risk factor of long-term graft loss regardless of the timing of finding and the extent of TI and considered IV to be an acute rejection of kidney allograft with a poor long-term outcome. Sis provided clinical evidence that IV behaves as true acute rejection because it is associated with functional improvement after anti-rejection treatment and represents an independent risk factor for allograft failure [38]. Such a putative discrepancy might be explainable by few facts. First, these studies analyzed the clinical course of IV at different time points after transplantation ignoring the fact that it is the time of biopsy that is critical for understanding IV. Nevertheless, most early IV specimens have no signature of molecular rejection and are DSA negative, whereas late IV ones are often DSA positive and show molecular ABMR [16]. Second, all discrepant studies were based on the clinical presentation of intimal arteritis, not on transcriptome analysis and evaluated the response to anti-rejection treatment in a very heterogeneous cohort. Third, clinical studies reporting the severity of IV analyzed biopsies were from earlier eras when aggressive TCMR was common and ABMR poorly recognized. Historically, IV was believed to actually imply rejection and had a negative effect on kidney graft survival. However, the nature and significance of IV have changed over time and v-lesions from a decade ago must be reinterpreted in the current era [39]. Nowadays, IV represents a rare finding in indication biopsies. At our center, 2.5% of kidney allografts show IV, mostly during the first 3 months post-transplant, with a mild clinical course and a favorable outcome [40].

Potential limitations of our study include its retrospective design and small sample size. However, the study limitations reflect IV in the real-life setting where IV is a rare finding, with heterogeneous interpretation and treatment. We realize that most of the samples in our study had mild to moderate arteritis (v1) and were found in early indication biopsies within the first month post-transplant. Therefore, our study results indicating a potential non-rejection origin of eIV are applicable exclusively to this specific cohort. We are also aware that all findings of intimal arteritis were treated by steroids or Thymoglobulin according to our historical standards of care for TCMR, which might have influenced graft prognosis. However, the renal allograft transcriptome was analyzed before initiating any anti-rejection treatment. Moreover, the question whether eIV patients should be given anti-rejection treatment, augmentation of immunosuppression or whether they should be followed by periodic surveillance biopsies, cannot be answered by the present study and calls for appropriate randomized controlled clinical trials. We are aware of fact that 2 eIV and 1 TCMRV kidney allografts exhibited intimal arteritis in addition to acute tubular necrosis which may have affected mRNA expression and graft function development. Additionally, the eIV group showed higher rates of anti-IL2 blockade (basiliximab) which may have had a potential impact on milder histologic finding and the molecular phenotype of eIV. While, admittedly, mRNA expression evaluation using laser capture microdissection would be of relevance in molecular analysis of highly focal changes, this approach is limited by the tendency of laser capture to damage the RNA, high costs and complexity, making it technically challenging [41]. Moreover, molecular assessments are able to assess an ongoing pathological process as rejection or injury even in the absence of glomeruli and arteries, are independent of cortex proportion and, although histology cannot assess the medulla, rejection does occur in the medulla as well as cortex [42]. Therefore, in our opinion, it still seems reasonable to evaluate vascular rejection from non-dissected tissue.

The strength of the present study is careful selection of AVR samples with the histology of the T cell-mediated phenotype and exclusion of ABMR according to the updated Banff classification [4]. Our study is further strengthened by the uniform approach of a high-volume transplant center and well-selected study groups, where only patients early after kidney transplantation within the first month post-transplant, with no rejection history, no surgical complications, similar immunological risk, and incidence of delayed graft function were enrolled into study groups. We included only indication biopsies and excluded subclinical, late, and DSA positive findings. Furthermore, microarray analysis was performed based on recent recommendations to avoid common errors in the implementation and interpretation of microarray studies [43]. Only genes that passed through correction for multiple testing were judged as significantly deregulated. To avoid circular logic, PCA and cluster analyses were based on the whole microarray data, not only on deregulated genes between the two groups, which allow to show the real separation of the distinct transcriptomes of eIV and TCMRV.

Our microarray study of DSA- and C4d-negative eIV revealed a weaker immune signature compared with TCMRV and showed similarity with normal histologic findings. Our results suggest that mild eIV occurring early after kidney transplantation in a not highly sensitized population may feature non-rejection origin. It seems more likely that inflammation seen in eIV might reflect endothelial injury from the transplantation process distinct from an alloimmune response. The evaluation of problematic biopsies with IV should combine histology and serologic HLA antibody assessment with use of molecular techniques as microarray to ensure correct diagnosis assignment. This is important particularly in case of IV which often show a discrepancy between conventional histology and microarrays. Using current conventions, many cases of IV are interpreted by pathologists as T-cell mediated rejection but exhibit low molecular TCMR score by molecular assessment, especially if DSA negative and observed early after kidney transplantation. Incorporation of molecular microscope into clinical praxis of IV on top of conventional histology and HLA antibody assessment might lower the rate of false positive rejections and help with therapeutic approach. Further prospective studies are warranted to delineate the pathogenesis and prognosis of this rare and heterogeneous entity before any reassessment of guidelines for diagnostic interpretation and treatment of IV.

Clinical perspectives

  • Background as to why the study was undertaken: IV with minimal TI represents a challenging clinical situation after kidney transplantation, especially when occurring early and in the absence of DSA and C4d. To find out if early IV (eIV) represents a true rejection, the present study compared the transcriptome of eIV and T-cell mediated vascular rejection (TCMRV).

  • A brief summary of the results: The transcriptome of early IV (<1 month) with negative C4d and DSA is associated with a weak immune signature compared with TCMRV and shows similarity with normal findings. Such eIV may feature non-rejection origin and reflect an injury distinct from an alloimmune response.

  • The potential significance of the results to human health and disease: Although intimal arteritis has been known to have a deleterious impact on kidney graft survival regardless of TI, our results agree with recent studies impeaching the rejection origin and negative prognosis of eIV. The present study supports reassessment of the current approach in interpretation of kidney allograft biopsy and supports use of molecular methods.

We thank study co-ordinator Marie Kolarova for her participation in research performance and the transplant laboratory team for their contribution to biopsy samples processing.

Competing interests

The author declares that there are no competing interests associated with the manuscript.

Funding

This work was supported by the Ministry of Health of the Czech Republic [grant numbers 15-26519A and 17-29992A]; and the OP VVV MEYS funded project ‘Research Center for Informatics’ [grant number CZ.02.1.01/0.0/0.0/16_019/0000765].

Author contribution

M.W. participated in design and performance of the research, data analysis, writing of the paper. P.H. participated in processing of tissue samples. J.K. and V.S. contributed to statistical analysis. M.N. participated in performance of the research. E.H. participated in histologic assessment. Z.K. participated in microarray evaluation. P.V. participated in immunogenetic examination. O.V. contributed to the research design and writing of the paper

Abbreviations

     
  • ABMR

    antibody-mediated rejection

  •  
  • ACC

    classification accuracy

  •  
  • ah

    arteriolar hyaline thickening

  •  
  • ATN

    acute tubular necrosis

  •  
  • AUC

    area under the ROC curve

  •  
  • AVR

    acute vascular rejection

  •  
  • DSA

    donor-specific antibody

  •  
  • ECD

    expanded criteria donor

  •  
  • eIV

    early isolated v-lesion

  •  
  • GEO

    Gene Expression Omnibus

  •  
  • GO

    gene ontology

  •  
  • HC

    hierarchical clustering

  •  
  • HPRT

    hypoxanthine-guanine phosphoribosyltransferaze

  •  
  • IKEM

    Institute for Clinical and Experimental Medicine

  •  
  • IV

    isolated v-lesion

  •  
  • LOOCV

    leave-one-out cross validation

  •  
  • MI

    microvascular inflammation

  •  
  • PCA

    principal component analysis

  •  
  • PRA

    panel reactive antibodies

  •  
  • RFE

    recursive feature elimination

  •  
  • ROC

    receiver operating characteristic

  •  
  • RT-qPCR

    reverse transcription quantitative polymerase chain reaction

  •  
  • SVM

    support vector machine

  •  
  • TCMR

    T cell-mediated rejection

  •  
  • TCMRV

    T cell-mediated vascular rejection

  •  
  • TI

    tubulointerstitial inflammation

  •  
  • TLDA

    TaqMan Low Density Array

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This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND).

Supplementary data