Use of quantitative real time PCR to assess gene transcripts associated with

antibody-mediated rejection of kidney transplants

Dominy, KMa, Roufosse, Cab, de Kort, Ha, Willicombe, Mc, Brookes, Pd, Behmoaras, JVa,

Petretto, EGe, Galliford, Jc, Choi, Pc, Taube, Dc, Cook, HTa,b, Mclean, AGc

a Centre for Complement and Inflammation Research, Division of Immunology and Inflammation, Department of Medicine, Imperial College, London, UKb Dept Cellular Pathology, Hammersmith Hospital, London, UKc Imperial College Kidney and Transplant Institute, Hammersmith Hospital, London, UKd Histocompatibility and Immunogenetics Laboratory, Imperial College Healthcare NHS Trust, London, UK e Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, UK

Corresponding author: Dr Adam Mclean adam.mclean@imperial.nhs.uk

Word Count

Abstract: 205

Text: 3078

Key words:

Antibody-mediated rejection (AbMR); Endothelial associated transcripts (ENDATs), Kidney

transplantation; Natural Killer (NK) cells; quantitative real time PCR (qRT-PCR);

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Statement of author contributions

KD carried out experiments and data analysis. HdK, MW and PB identified suitable patients

and obtained DSA data. JG, PC and AM collected biopsy material. EP performed data

analysis. CR and HdK assessed histology. AM, JB, HTC and CR designed the study and

obtained funding. All authors were involved in writing the paper and had final approval of

the submitted and published versions.

FundingWe are grateful for support from the NIHR Biomedical Research Centre funding scheme. The

authors would like to acknowledge the European Renal Association—European Dialysis and

Transplant Association (ERA/EDTA) for the awarded long-term fellowship.

Conflict of interestNone

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List of abbreviations

Antibody mediated rejection (AbMR), Cadherin 5 (CDH5), complementary DNA (cDNA),

Threshold Cycle Number (Ct), chemokine (C-X3-C motif) receptor 1 (CX3CR1), Duffy Coat

Antigen Receptor (DARC),Donor specific antibody (DSA), Endothelial associated transcripts

(ENDAT), Fibroblast Growth Factor binding Protein 2 (FGFBP2), Glomerulitis (G), Granulysin

(GNLY), diabetic glomerulopathy (GP), Hypoxanthine phosphoribosyltransferase 1 (HPRT1),

Killer Cell Lectin-like receptor subfamily F, Member 1 (KLRF1), Mean Fluorescent Intensity

(MFI), Microcirculation inflammation (MI), v-myb myeloblastosis viral oncogene homolog

(avian) like 1 (MLYB1), Natural killer (NK), Platelet / Endothelial cell Adhesion Molecule 1

(PECAM1), Peritubular Capillaritis (ptc), Peritubular capillary basement membrane

multilayering (PTCBML) ,quantitative real time PCR (qRT-PCR), Receiver operating

characteristic (ROC), SH2 domain containing 1B (SH2D1b (EAT2)), Sex determining region Y

box 7 (SOX7), T-Cell mediated rejection (TCMR), Transplant glomerulopathy (TG), von

Willebrand Factor (vWF)

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Abstract

Introduction

Microarray studies have shown elevated transcript levels of endothelial and natural killer

(NK) cell associated genes during antibody mediated rejection (AbMR) of the renal allograft.

This study aimed to assess the use of quantitative real-time PCR (qRT-PCR) as an alternative

to microarray analysis on a subset of these elevated genes.

Methods

39 renal transplant biopsies from patients with de novo donor-specific antibodies and 18

one year surveillance biopsies with no histological evidence of rejection were analysed for

expression of 11 genes previously identified as elevated in AbMR.

Results

Expression levels of NK markers were correlated to microcirculation inflammation (MI) and

graft outcomes to a greater extent than endothelial markers. Creating a predictive model

reduced the number of gene transcripts to be assessed to 2, SH2D1b and MYBL1, resulting

in 66.7% sensitivity and 89.7% specificity for graft loss.

Discussion

This work demonstrates that elevated gene expression levels, proposed to be associated

with AbMR, can be detected by established qRT-PCR technology, making transition to the

clinical setting feasible. Transcript analysis provides additional diagnostic information to the

classification schema for AbMR diagnosis but it remains to be determined whether

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significant numbers of centres will validate transcript analysis in their labs and put such

analysis into clinical use.

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Introduction

Antibody-mediated rejection (AbMR) is an important cause of graft injury (1) and a major

source of graft failure (2). Diagnosis of AbMR is based on criteria in the Banff classification of

renal allograft pathology (3, 4), requiring presence of donor-specific antibody (DSA) in the

circulation, a renal biopsy with defined histological features, and evidence of current /

recent antibody interaction with the vascular endothelium, such as positive C4d staining (5).

Hyperacute AbMR has been virtually eliminated because of improved cross-matching prior

to transplantation (1). Acute and chronic AbMR can nevertheless develop because of the

appearance of de novo DSA against the graft post-transplantation.

Recent microarray analyses carried out on renal biopsies have identified endothelial

associated gene transcripts (ENDATs) (6) and NK cell associated transcripts (7), which are

elevated in cases of AbMR. The identification of these high transcript levels in some cases

with histological evidence of AbMR and the presence of DSA but no staining for C4d, has

helped confirm the existence of C4d-negative AbMR (8-10). It was demonstrated by Sis et al

(6) that although C4d staining is highly specific for AbMR, it lacks sensitivity. A proposal has

been made that analysis of expression levels of endothelial and/or natural killer (NK) cell

transcripts could complement C4d as a marker of endothelial injury by the antibody in these

cases (11-14).

The most recent Banff update recognises C4d-negative AbMR, which is observed particularly

in presensitised patients, but also in patients with de novo DSA (9). An alternative to the

presence of C4d is a microcirculation inflammation (MI) score ≥2, comprising the sum of

peritubular capillaritis (ptc) and glomerulitis (g) scores, but meaningful cut-off levels and

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reproducibility for ptc and g scores have proved difficult issues to fully resolve. Alternatively,

increased gene expression could be indicative of endothelial injury.

Microarray analysis is advantageous because a small sample amount can be used to analyse

many gene transcripts simultaneously. However, this method is hybridisation based and

previous studies have demonstrated that quantitative real time PCR (qRT-PCR) yields

comparable results but has a greater sensitivity for individual gene expression levels due to

its increased dynamic range (15).

qRT-PCR is emerging as a valuable technique in the clinical diagnostic setting and is currently

used in a variety of different contexts including detection of viral load, therapy monitoring

and for diagnosis and detection of disease-specific prognostic markers in leukaemia patients

(16).

We hypothesised that measurement of gene expression by qRT-PCR, of the most

significantly elevated transcripts defined by microarray analysis, would correlate with the

histological features of AbMR and potentially provide sufficient information to be of

importance in diagnosis of AbMR and prediction of graft loss.

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Results

De novo DSA biopsies for transcript analysis

For the purposes of this study, biopsies collected between February 2010 and August 2012

from patients with de novo DSA (developed at any time post-transplant) were examined.

127 biopsies from 96 patients were included in the study (mean number of biopsies =1.35

(±0.95) per patient).

15 biopsies were excluded because they were either ABO incompatible (n=13) or

simultaneous pancreas kidney transplants (n=2). From the remaining 112, 53 had sufficient

RNA for analysis (>200ng), of which 39 biopsies from 30 patients passed qRT-PCR quality

control checks and were used to assess transcript levels. Average time of biopsy post-

transplant was 2.02 years (±2.04). One year surveillance biopsies (n=18) were also assayed

for comparison.

Table 1 details histological findings. Further patient information was collected from medical

notes (Table S1).

RNA yield and quality

From the 57 biopsies (39 de novo DSA and 18 surveillance), mean RNA yield was

1328.56ng/biopsy (±962.34). Mean weight of biopsy was 1.94mg (±1.14). There was a

significant correlation between the weight of the biopsy and the RNA yield (p=0.00017,

paired T-Test). Mean RNA yield per mg of biopsy was 889.27 ng/mg (±903.8).

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Biopsies were performed by 3 individuals who yielded significantly different amounts of RNA

(p<0.001). However, when ng RNA per mg of biopsy was calculated, there was no significant

difference in RNA yield between different clinicians (p=0.44, Kruskal-Wallis test).

RNA quality was assessed in a selection of 15 samples. RNA integrity number (17) ranged

from 1.0 to 8.8 but all samples amplified in qRT-PCR analysis.

Transcript expression: correlation with MI score

All 57 biopsies were scored according to the Banff criteria (4), an MI score calculated as

previously described (8, 18) and then grouped into surveillance, MI score of ≤1, and MI

score >1. Expression levels of 11 genes (5 ENDATs and 6 NKs) (Table S2) were determined by

qRT-PCR.

Expression levels were compared between the two MI score groups (≤1 and >1) and the

surveillance samples (Figure 1). Mann-Whitney U tests (Table 2) demonstrated a significant

difference between expression levels of the two MI groups for 4 of the 11 genes (FGFBP1

p=0.007, GNLY p=0.001, MYBL1 p=0.010, SH2D1b p=0.004), all of which were NK cell

associated transcripts.

Transcript analysis has been key to the definition of C4d negative AbMR, therefore a subset

analysis of biopsies that were DSA positive but C4d negative (n=30) was performed to

determine if association between transcript levels and MI remains (Table 2). FGFBP1

(p=0.031), GNLY ( p=0.001), MYBL ( p=0.003), and SH2D1b (p=0.012) remained significantly

associated with MI and DARC (p=0.01) additionally achieved significance.

The presence of donor specific antibodies is a key factor in diagnosis of AbMR. We sought to

compare transcript expression in samples with single or multiple HLA antibodies and across

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different HLA classes (Table S3). In biopsies from patients with more than one DSA, there

was significant elevation of FGFBP2 (p=0.006), GNLY (p=0.003), MYBL (p=0.046) and SH2D1b

(p=0.006). No difference was seen between HLA classes.

Acute and chronic AbMR are known to be clinically different and therefore the 39 de novo

DSA samples were separated according to AbMR status (Figure S1). Formal analysis

comparing acute and chronic AbMR has not been carried out due to the small sample size,

however there is an apparent trend that the ENDATs are elevated in only chronic cases

whereas NK transcripts are elevated in both acute and chronic cases.

In light of the association between MI and NK cells, further genes were selected to

represent T-cell and macrophage mRNA levels. This was to determine whether the elevated

transcript level reflected an association with antibodies or simply inflammation. Six out of 7

T-cell and macrophage transcripts showed no elevation in the MI positive group. CXCL11 did

show association with MI in both the de novo DSA group (p=0.041) and the combined de

novo DSA and surveillance group (p=0.007). However, given that the majority of

inflammation transcripts did not associate with MI, we do not consider elevated NK

transcript levels to indicate just inflammation.

Building a predictive model

To determine if transcript analysis could predict MI group, binary logistic regression analysis

was performed. An initial univariate analysis was performed on each individual transcript

and other pre-existing factors. The outcome measure was MI group. When examining all

samples, GNLY, MYBL1 and SH2D1b showed significant correlation with MI score (p= 0.002,

0.003, 0.001 respectively) (Table 3a). DARC (p=0.05) and time from transplant to biopsy

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(p=0.003) were also significantly correlated with MI score but significance was lost in the

subgroup of de novo DSA samples (p=0.176 and 0.313 respectively)(Table 3Table 3b).

To create a predictive model, parameters with suggestive significance (p<0.10) were

included in a multivariate binary logistic regression. The generated model contained two

parameters – SH2D1b and MYBL1.

Graft Survival

During follow-up (maximum 4 years 2 months), 10/30 grafts were lost from the de novo DSA

group and 0/18 from the surveillance group. The predictive model, based on SH2D1b and

MYBL1, was used to assign each patient to a low or high risk group and Kaplan Meier

survival curves (Figure 2) were generated. When all patients were assessed there was an

association between risk group and graft loss (p=0.008). Within the de novo DSA group the

association increased (p<0.001). The 3 graft losses from the low risk group were in patients

where the biopsy had extensive scarring.

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Discussion

In this study, we sought to assess whether we could focus gene expression analysis in AbMR

on the most elevated transcripts from previous microarray studies. We have used qRT-PCR

to demonstrate increased expression of genes in biopsies with high MI scores, suggesting

that gene expression correlates with AbMR and subsequently graft loss. The association of

NK transcript expression with graft loss in this study is a novel finding.

Genes without elevated expression in high MI score samples could genuinely lack a

correlation or the sample size is too small to detect an effect. The differing technology may

influence results, although factors such as immune suppression regimes and time since

transplant will not be matched to previous studies. We have examined early and late

biopsies whereas previous studies have been focussed on either late or early biopsies (6).

The differing transcript levels in the chronic and acute AbMR groups warrants further

investigation and could be masking association of ENDAT levels and AbMR. The conclusion

of this study should therefore be seen as preliminary and will require confirmation with a

larger sample size of patients and longer follow-up.

Increasing the number of transcripts assessed does not necessarily provide additional

information, as indicated by our predictive model which contained just 2 genes. With an

ever changing list of top hits from microarray analysis, we would hypothesise that a small

selection of genes should be sufficient to represent expression differences in AbMR patients,

without the need for complete microarray analysis. However, we have not assessed the full

range of possible diagnoses and inclusion of a wider range may bring up a different set of

top transcripts.

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In some circumstances, biopsy material can be insufficient for diagnostic purposes. The

three graft losses in the low risk group were from patients with extensive scarring and we

hypothesise that transcript levels might not have any diagnostic value in these

circumstances. A disadvantage of transcript analysis is the destruction of the structural

integrity of the specimen; histological assessment can be made on very small non-scarred

areas in an otherwise scarred biopsy, but this scarring may mask any elevated mRNA

transcripts. Future use of laser-microdissection of non-scarred areas from formalin-fixed and

paraffin embedded tissues may provide a solution to this problem.

The study also assessed the feasibility of obtaining adequate samples for mRNA analysis in

the routine transplant biopsy clinic. It appears that in order to obtain consistently good

yields of RNA, sufficient for qRT-PCR analysis, a whole additional biopsy core would be

needed. The diagnostic benefit of transcript analysis must be weighed up against the

potential risk to the patient of taking an additional core.

In contrast to other work (6), this study has found little evidence that ENDATs are associated

with AbMR. Other, larger cohorts have included ENDATs in microarray analysis, to improve

stratification for graft loss within the first year post transplant (19). This could be explained

by the larger sample size in these studies or the use of mainly chronic AbMR samples rather

than both active and chronic active AbMR cases.

The diagnostic criteria of AbMR have recently been reassessed to recognise subclinical

AbMR (8, 20, 21), and C4d-negative AbMR (5), both of which can lead to late allograft loss.

Features predicting risk of progression to chronic rejection and graft loss in patients with

DSA are still being defined. Potential candidates include DSA levels, type and ability to fix

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complement, histological features, C4d staining and molecular findings (22). NK cells may

participate in the progression to chronic rejection independent of complement activation

through antibody-dependent cell-mediated cytotoxicity, and NK cell related transcripts

could therefore identify cases at risk (7).

NK transcript levels have the potential to enhance recognition of cases of AbMR by joining

the diagnostic criteria, and also to enhance accurate prediction of outcome. In particular

because no reliable immunohistochemical method has yet been found to identify NK cells in

the graft or to document endothelial activation, mRNA analysis may have an important role

to play.

New therapies are being developed for the treatment of AbMR and demonstrate evidence

of improved outcome (23). Early detection and treatment of subclinical rejection has

demonstrated beneficial effects (24), although prospective studies are required to

determine if current early treatment methods result in improved kidney function and graft

survival over time.

Biopsies are required in order to diagnose AbMR, but if rejection is sub-clinical, this requires

a protocol biopsy or biopsy for another clinical indication. Material which is less invasive to

collect, such as blood or urine, could be a target for the future. Hayde et al (25) have

analysed whole blood gene expression profiles of AbMR patients and found increased gene

transcripts associated with cytotoxic T cells and macrophages, indicating that it is possible to

detect transcript level changes in alternative material.. Another area of interest may be

microRNA expression patterns, both in blood and biopsy material (26).

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The “gold standard” for diagnosis of AbMR has not been established and there is variability

in histological scoring, making validation of new methods more difficult. Work such as that

presented here provides additional support for the role of transcript analysis in AbMR

diagnostics, by illustrating the feasibility of sample collection for qRT-PCR and by confirming

elevated NK transcript levels in AbMR. It remains to be determined if this approach, along

with DSA levels, type and ability to fix complement, histological features and C4d staining,

will provide more accurate and timely information compared to that which is already

included in the Banff schema.

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Materials and methods

Patients and sample collection

All patients gave written, signed consent for the use of biopsy material surplus to clinical

diagnostic requirements to be used for research, and the study was undertaken with

approval from the local institutional review board (Local Research Ethics Committee

reference LREC 08/H0707/14).

From February 2010, renal transplant biopsies in our institute (whether indication or

surveillance) included a portion preserved in RNAlater (Life Technologies, Paisley, UK), when

sufficient material was available. Renal biopsy cores were obtained under ultrasound

guidance by 18 gauge spring-loaded needle. The sample in RNALater was retained for

diagnostic purposes, and if necessary, retrieved and formalin-fixed for diagnosis. Otherwise,

the sample was used directly for RNA extraction.

Histopathology

All biopsies were graded using Banff 2007 criteria (4). C4d staining was carried out by

immunoperoxidase on paraffin sections, using polyclonal rabbit anti-C4d antibody at 1/40

(Oxford Biosystems, BI-RC4D). The slides were subjected to microwave antigen retrieval (in

citrate buffer pH6), then placed on the Biogenex i6000 autostainer. The Biogenex Non-Biotin

detection kit was employed. C4d staining in peritubular capillaries was classified as

negative/minimal (C4d0/C4d1 <1% and 1-10% of peritubular capillaries respectively), focal

(C4d2, 11-50% of peritubular capillaries) or diffuse (C4d3, >50% of peritubular capillaries)(4).

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Patients of this study were selected for DSA positivity, so acute AbMR was defined by the

presence of MI (glomerulitis and/or peritubular capillaritis and/or thrombotic

microangiopathy) with C4d (focal or diffuse). C4d-negatice acute AbMR was defined as MI,

and negative or minimal C4d staining. Chronic AbMR was defined as the presence of

transplant glomerulopathy (TG) and/or significant multilayering of peritubular capillary

basement membranes (PTCBML), and/or chronic allograft arteriopathy, with C4d (focal or

diffuse). C4d-negative chronic AbMR was defined as the same features but no C4d staining.

TG was defined by the presence of double contours on light microscopy, with only small

amounts of electron dense deposits on electron microscopy, and no clinical features of

thrombotic microangiopathy or hepatitis C infection. Significant PTCBML was defined as ≥1

ptc with ≥7 layers or ≥3 ptc with ≥5 layers of basement membrane (27).

Anti-HLA antibody screening

DSA were assessed using LABScreen® mixed beads (One lambda, Inc., Canoga Park, CA, USA)

and if positive, the anti-HLA antibody specificity was identified using LABScreen® single

antigen beads. Before transplantation all donor-recipient pairs had a negative T- and B cell

complement-dependent cytotoxicity crossmatch and a negative T cell flow cytometric

crossmatch, defined as mean fluorescence intensity (MFI) <300 pre-transplantation. Post-

transplantation an MFI >500 was considered DSA positive or MFI 300-500, in two

independent serum samples. Patients were typed for HLA –A, –B, –Cw, –DR and –DQ

antigens.

RNA extraction and reverse transcription

RNA extraction was performed using the RNAqueous micro kit (Ambion, Paisley, UK) and

quantified with a NanoDrop 1000 Spectrophotometer (LabTech, East Sussex, UK). Up to 1µg

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RNA was converted to complementary DNA (cDNA) using an iScript select kit (Bio-Rad,

Hemel Hempstead, UK) with random hexamer priming. Sample quality was assessed using a

2100 Bioanalyzer and Agilent RNA 6000 Nano chip (Agilent Technologies, Berkshire, UK).

Quantitative real time PCR

Quantitative real time PCR was carried out using an Applied Biosystems 7500 real time qPCR

machine. 10ng cDNA was used in a SYBR green assay (Agilent Technologies, Berkshire, UK)

with gene specific primers spanning an intron (Table S2). Top gene hits from microarray

studies (6, 7, 9) were selected for analysis. Reactions were performed in triplicate at 95°C

for 10 min, followed by 40 cycles of 95°C for 15 seconds, and 60°C for 1 min. A threshold

cycle (CT) was recorded in the exponential phase of amplification and melt curves were

created to confirm primer specificity (15 seconds 95°C, 1 minute 60°C increasing at

0.05°C/second to 95°C for 15 seconds). HPRT1 was the reference gene and results were

measured relative to Stratagene QPCR Reference RNA (Agilent Technologies, Berkshire, UK)

using the ∆∆CT method (28).

Graft Survival

Graft failure was defined as resuming dialysis and censored for patient death and short

follow up times. Graft survival was calculated by the Kaplan-Meier method and the log rank

test was applied. Survival time was measured from time of biopsy to prevent bias from older

transplants. For multiple biopsies from the same patient, the first biopsy after DSA detection

was used for survival analysis. Graft survival was assessed as of April 2014, resulting in a

follow up time between 20 and 50 months.

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Data Analysis

Correlation between biopsy weight and RNA yield was tested with a paired T-Test and

expression data was normalised onto the same scale for each gene by calculating a Z-score

(Z= (X-mean )/Standard deviation) using Microsoft Excel. Further statistical calculations were

carried out using IBM SPSS statistics 19 software package. Kruskal-Wallis tests were applied

to comparisons of RNA yield from different clinicians. Mann-Whitney U tests were applied

to the data to detect differences in gene expression between two MI groups. Univariate

logistic regression was applied to individual factors. P-values <0.05 were considered to be

statistically significant. Multivariate logistic regression with a forward stepwise method was

applied to obtain a predictive model.

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23. Raghavaiah S, Stegall MD. New therapeutic approaches to antibody-mediated rejection in renal transplantation. Clin Pharmacol Ther 2011; 90 (2): 310.

24. Rush D, Nickerson P, Gough J, et al. Beneficial effects of treatment of early subclinical rejection: a randomized study. J Am Soc Nephrol 1998; 9 (11): 2129.

25. Hayde N, Broin P, Bao Y, et al. Increased intragraft rejection-associated gene transcripts in patients with donor-specific antibodies and normal biopsies. Kidney Int 2014; 86 (3): 600.

26. Danger R, Paul C, Giral M, et al. Expression of miR-142-5p in peripheral blood mononuclear cells from renal transplant patients with chronic antibody-mediated rejection. PLoS One 2013; 8 (4): e60702.

27. Iványi B, Fahmy H, Brown H, Szenohradszky P, Halloran PF, Solez K. Peritubular capillaries in chronic renal allograft rejection: a quantitative

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ultrastructural study. Hum Pathol 2000; 31 (9): 1129.28. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using

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378

379

Table 1 Histological Findings

A) Biopsy diagnosis for de novo DSA patients (n=39 biopsies in n=30 patients)

Acute AbMR, C4d +, MI + (n=9) N=alone 2with features of chronic AbMR 5with features of chronic AbMR and TCMR 1with borderline TCMR 0with TCMR 1

Acute AbMR, C4d -, MI + (n=15)alone 4with features of chronic AbMR 4with features of chronic AbMR and TCMR 2with borderline TCMR 4with TCMR 1

C4d positive, no histological features of AbMR (n=2)Acute tubular injury 1BK, borderline TCMR 1

No AbMR (n=13)TCMR 1Acute tubular injury 5BK 2CNI toxicity 1Pyelonephritis 1Previously C4d- AbMR 1Diabetic GP 1Chronic vascular changes 1

Surveillance biopsies (n=18) were either within normal limits or had tubular injury/tubular

atrophy without histological features of rejection. All patients with biopsies in the de novo

DSA group had de novo DSA detected prior to the time of biopsy.

MI microcirculation inflammation; AbMR Antibody mediated rejection; TCMR T-cell mediated rejection; CNI calcineurin

inhibitor; Diabetic GP diabetic glomerulopathy; BK nephropathy

23

380

381

382

383384

385

386

387

388

389

b) Microcirculation inflammation

De novo DSA (n=39)

Surveillance (n=18)

PTC 0123

188

121

17100

G 0123

23664

18000

MI <11>1

156

18

1710

C4d 0123

161463

15300

Numbers of biopsies with each histological parameter

24

390391

392393

394

Table 2 Analysis of gene expression in different MI score groups

The de novo DSA only column shows the difference of transcript levels between MI0/1 and MI2+ only in group of patients with de novo DSA; de novo DSA and surveillance column shows difference of transcript levels between MI0/1 and MI2+ in the full group of biopsies; de novo DSA, C4d negative show only biopsies which have DSA but are C4d negative. Mann-Whitney U test MI ≤1v >1 has been used to generate significance p values.

Gene P-value (de novo DSA only, n=39)

P-value ( de novo DSA and Surveillance, n=57)

P-value (de novo DSA, C4d negative samples, n=30)

ENDATsCDH5 0.646 0.520 0.415DARC 0.606 0.012* 0.010*PECAM1 0.561 0.247 0.215SOX7 0.590 0.224 1.000vWF 0.281 0.071 0.112 NK Cell transcriptsCX3CR1 0.367 0.565 0.650FGFBP1 0.007* <0.001* 0.031*GNLY 0.001* <0.001* 0.001*KLRF1 0.073 0.002* 0.436MYBL1 0.010* 0.001* 0.003*SH2D1b 0.004* <0.001* 0.012*Inflammation transcripts

n=30+ n=44+

RPS6 0.126 0.262MALL 0.385 0.348TRIB1 0.486 0.274CXCL11 0.041* 0.007*RPS6KB1 0.395 0.147TNF 0.864 0.828PSMB8 0.673 0.940

*p≤0.05, +reduced numbers due to insufficient remaining RNA in some cases.

25

395

396397398399400

401

402

403

Table 3

a) Univariate binary logistic regression on all samples (n=57)

Parameter P- value Exp(B) 95% CI of Exp(B)

Lower Upper

ENDATsCDH5 0.369 2.165 0.401 11.689

DARC 0.05* 2.190 1.000 4.793

PECAM1 0.163 1.642 0.818 3.298

SOX7 0.193 0.505 0.181 1.414

vWF 0.086 1.965 0.910 4.244

NK Cell TranscriptsCX3CR1 0.156 1.644 0.828 3.265

FGFBP1 0.925 0.970 0.514 1.830

GNLY 0.002* 39.814 3.781 419.231

KLRF1 0.694 0.840 0.353 2.001

MYBL1 0.003* 7.323 2.006 26.737

SH2D1b 0.001* 6.083 2.021 18.313

Other parametersGender

(n=48)

0.547 1.571 0.361 6.842

Age at transplant

(n=48)

0.052 0.947 0.896 1.000

HLA mismatches

(n=48)

0.496 1.206 0.703 2.069

Donor age (n=48) 0.838 0.995 0.947 1.045

Live or deceased

donor (n=48)

0.404 1.789 0.456 7.021

Time from transplant

to biopsy

0.003* 1.749 1.209 2.532

26

404405

406

407

Expression of the 11 genes was assessed for correlation to MI score (≤1 or >1) in all 57

biopsies. Gender and age were also assessed but only in the first biopsy for each patient

(n=48).*p≤0.05

27

408

409

410

411

412

b) Univariate binary logistic regression on de novo DSA samples only (n=39)

Parameter P-value Exp(B) 95% CI of Exp(B)

Lower Upper

ENDATsCDH5 0.458 1.840 0.368 9.207

DARC 0.176 1.713 0.785 3.737

PECAM1 0.377 1.372 0.680 2.771

SOX7 0.212 0.628 0.303 1.302

vWF 0.272 1.518 0.721 3.199

NK Cell TranscriptsCX3CR1 0.162 1.915 0.771 4.756

FGFBP1 0.615 0.844 0.435 1.636

GNLY 0.012* 16.551 1.861 147.179

KLRF1 0.508 0.751 0.321 1.755

MYBL1 0.019* 4.586 1.278 16.456

SH2D1b 0.022* 3.927 1.219 12.658

Other parametersGender

(n=30)

0.706 1.333 0.298 5.957

Age at transplant

(n=30)

0.139 0.987 0.971 1.004

Time from transplant

to biopsy

0.313 1.105 0.910 1.342

Each of the 11 genes were assessed for correlation to MI score (≤1 or >1) in the de novo DSA

biopsies (n=39). Gender and age were assessed only in the first biopsy for each patient

(n=30).*p≤0.05

28

413

414

415

416

417

418419

Figure 1 Gene expression

Average Z-score for each gene is displayed according to MI group. Error bars represent the standard error of the mean (SEM). Surveillance n=18, de novo DSA MI 0-1 n= 21, de novo DSA MI 2-6 n= 18

Figure 2 Outcome Analysis

Patients were assigned to low or high risk groups depending on the transcript analysis. Survival from time of biopsy was plotted for all patients (a) or only patients with de novo DSA (b). Data was censored for patient death with functioning graft and short follow-up times.

Figure S1 Subgroup gene expressionGene expression for 11 genes from 39 de novo DSA and 18 surveillance samples divided according to AbMR status. The surveillance group (n=18) has reduced expression as previously shown. The majority of genes have increased expression in patients with active chronic AbMR. Patients with Acute AbMR appear to have a trend for reduced ENDAT expression but increased NK transcript expression.

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