Background
Kidney transplantation remains the treatment of choice for patients with end stage renal disease (ESRD) and advanced chronic kidney disease (CKD). In spite of the improvement in short term allograft survival due to the newer potent immunosuppression [
1] in the recent decades, long-term survival has not improved comparatively [
2,
3]. Cumulative data have emphasized the importance of late clinical and sub-clinical rejections as major causes for shorter allograft survival and ultimately allograft loss. These types of rejections are, in most part, very difficult to treat and are often diagnosed very late after the onset of the rejection.
The diagnosis of acute kidney rejection still relies on obtaining kidney biopsy. In addition to the risks of this procedure, such as bleeding, obtaining kidney biopsy is costly and time consuming for the physicians, staff and patients. Therefore, research efforts have focused on identifying biomarkers that can be easily obtained from patient’s fluid (blood or urine), and can serve as markers for acute rejection [
4‐
10]. However, the complexity of the immune system involved in rejection and the lack of specificity and sensitivity of these biomarkers limited their use in clinical settings [
11].
T cell-mediated rejection (TCMR) is the most common cause of acute rejection [
12], and is a major cause of allograft dysfunction and failure [
13]. It is crucial to detect this type of rejection as early as possible, so it can be treated promptly before it leads to irreversible interstitial fibrosis and tubular atrophy (IFTA) [
14]. In transplant patients, an allorecognition process occurs when T-cells, via their receptors (TCRs), recognize donor HLA molecules. This recognition is either direct when T-cells recognize intact donor HLA-peptide complex molecules presented by donor cells, or indirect when peptides derived from donor major histocompatibility complex (MHC) or minor Histocompatibility (miH) antigen are degraded and presented by recipient antigen presenting cells (APCs). Both direct and indirect allorecognition processes are thought to contribute to acute and chronic allograft injury [
15]. Traditional methods for measurements of donor-reactive memory T-cells by flow cytometry and cytokine ELISPOT have shown to predict post transplant outcome [
16‐
18]. While these seem to be promising biomarkers in transplantation, such measurement of T-cells requires an intensive labor and high cost, in addition to the complexity of the assays.
With the recent feasibility of high throughput sequencing, investigations have been directed toward a comprehensive analysis of TCR repertoire in the transplant setting. In this study, we utilized an mRNA-based, 5′RACE (rapid amplification of cDNA ends) PCR method followed by next generation sequencing (NGS) technology approach [
19,
20] to identify combination of TCR beta (TCRB) in blood and graft samples obtained before and after kidney transplantation. We comprehensively characterized the detailed TCR repertoire changes in patients with kidney transplant in an attempt to correlate expansion of certain T-cell populations with graft rejection. We also measured the RNA expression levels of FOXP3, Perforin, Granzyme, CD4 and CD8 in blood samples to correlate these biomarkers with TCMR.
Methods
Study cohort
This is a prospective multi-center study approved by the Institutional Review Boards of Johns Hopkins Hospital, University of Chicago, and Osaka University. Patients were recruited after written consents at Johns Hopkins Hospital and Osaka University Hospital. We enrolled 50 patients who receive kidney transplantation between September 2013 and June 2014. Table
1 shows the clinical characteristics of our study cohort.
Table 1
Patients clinical characteristics
Mean age (SD), years | 51 (15) | 45 (13) | 52 (15) | 0.25 |
Gender, male (%) | 28 (56) | 2 (28) | 26 (60) | NS |
Race (%) |
White | 17 (34) | 3 (43) | 14 (33) | NS |
Black | 21 (42) | 1 (14) | 20 (46) | NS |
Asian | 7 (14) | 3 (43) | 4 (9) | NS |
Others | 5 (10) | 0 | 5 (12) | NS |
Preemptive (%) | 8 (16) | 1 (14) | 7 (16) | NS |
Mean CIT (SD) hours | 19.7 (12.9) | 12.5 (16.8) | 20 (12.3) | 0.22 |
Cause of kidney disease (%) |
HTN | 8 (16) | 1 (14) | 7 (16) | NS |
DM and HTN | 16 (32) | 2 (29) | 14 (32) | NS |
Glomerular diseases | 17 (34) | 3 (43) | 14 (32) | NS |
Others | 10 (20) | 1 (14) | 9 (20) | NS |
DGF | 18 (36) | 1 (14) | 17 (39) | NS |
Induction therapy (%) |
Thymoglobulin | 36 (72) | 4 (57) | 32 (74.4) | NS |
Alemtuzumab | 7(14) | 0 | 7 (16.2) | NS |
Basiliximab | 7 (14) | 3 (43) | 4 (9.3) | NS |
Type of Donor (%) | | | | |
Living | 16 (32) | 4 (57) | 12 (28) | NS |
Deceased | 34 (68) | 3 (43) | 31 (72) | NS |
Samples’ collection
We collected blood samples before kidney transplantation, at 24–48 h, one week, one month and three months after transplantation, and at time of confirmed TCMR. We also obtained kidney graft biopsy samples from the patients who had TCMR.
We collected 15 mL of blood sample from each patient at each time point in two Cell Preparation Tubes (CPT). These samples were processed in our laboratory within one hour of collection. After collecting the blood samples, we processed the samples using this centrifuging setting: temperature of 21 °C; speed of 3000 revolutions per minute (RPM), for 20 min with breaks on. After the first round of centrifugation we collected the cells carefully and wash them twice with PBS. We added PBS to fill the tube up to 15 mL and centrifuged it (temperature: 21 °C; speed: 1500 RPM; time: 10 min; Breaks on). We did a second wash by discarded the supernatant PBS and disturbed the cell pellet at the bottom by gentle tapping; we added 10 mL of PBS and centrifuged at the same setting. Finally, we collected the cell pellet in the small tube and centrifuged it for 2 min in the cold room to remove supernatant fluid, and preserved it at −80 °C until used for sequencing.
We used frozen section of the kidney biopsies and saved them in −80 °C. We extracted the RNA from these tissue samples.
RNA isolation and PCR amplification
Total RNAs were isolated from peripheral blood mononuclear cells (PBMCs) and from graft biopsies using RNeasy mini kit (Qiagen, Valencia, CA) and treated with DNase to remove genomic DNA contamination. cDNA was then synthesized using the SMART cDNA library construction kit (Clontech Laboratories, Mountain View, CA), according to the manufacturer’s instructions. A common adaptor (SMART IV oligonucleotide) was ligated to the 5′ end of cDNA. PCR was then performed to amplify all the possible combination of TCR beta from cDNA, using one common forward primer, which is designed based on the sequence of SMART IV adaptor and a reverse primer specific to the constant region of TCR beta [
21,
22].
Template preparation and sequencing
Six patients had complete sets of blood and tissue samples at the intended time points; these samples were sequenced. Out of these six patients, two had TCMR. Each sample was barcoded at both ends of the library with different combination of index 1 and index 2 using Nextera XT kit (Illumina, San Diego, CA, US). Several samples were pooled together into single sequencing run on the Illumina MiSeq using Miseq Reagents Kits v3 600 cycles (Illumina).
Sequences analysis were performed using Tcrip algorithm [
21,
22]. Sequencing reads in FASTQ files were mapped to the reference sequences derived from IMGT/GENE-DB (
http://www.imgt.org), using Bowtie2 aligner (Version 2.1.0) [
21,
22]. The V, D, J genes were designated according to the nomenclature provided by the international ImMunoGeneTics information system (IMGT). A CDR3 region was defined by identifying the second conserved cysteine encoded in the 3′ portion of the V segment and the conserved phenylalanine encoded in the 5′ portion of the J segment that form the boundaries of the CDR3. The nucleotide sequences between both conserved TCR V cysteine and TCR J phenylalanine were extracted to determine the amino acid sequence of CDR3 region.
Analysis of TCMR biomarkers
To analyze markers of activated T-cells in kidney transplant patients and correlate these markers with graft rejection, we measured mRNA levels of the following genes: FOXP3, Perforin, Granzyme, CD4 and CD8 by RT-PCR in all blood samples of the whole cohort.
Statistical analysis
The inverse Simpson’s index was calculated based on the following equation:
$$ {D}_S={\left[\frac{{\displaystyle {\sum}_{i=1}^K}{n}_i\left({n}_i-1\right)}{N\left(N-1\right)}\right]}^{-1} $$
Where
K is the total number of clonotypes,
n is the number of sequences belonging to the
i-th type, and
N is the total number of sequences for which clonotypes are determined [
21,
22]. Paired Student’s
t-test (two-tailed) was performed for comparison of total proportion of the most abundant ten CDR3 sequences or the diversity index between groups, using GraphPad Prism version 6.0. A
p value of less than 0.05 was considered statistically significant.
Discussion
T-cell immune response is a major cause of post kidney transplant complications. Although the introduction of newer potent immunosuppressive drugs in the last two decades resulted in significant decrease in early acute TCMR, late TCMR and chronic rejections remain common causes of allograft failure [
23]. To prevent TCMR, patients require the right amount of immunosuppressions that keep the immune response adequately suppressed; yet it has the capacity to respond to pathological insults such as infections and malignant turnovers. Finding such balance has been a major goal and working progress by the transplant healthcare providers and investigators.
Our data herein, constitutes a very promising step toward identifying T-cell markers that can be utilized in the diagnosis of TCMR to enable clinicians to treat it promptly; and also to potentially provide the tools to individualize immunosuppressive therapy. We have previously applied NGS techniques to comprehensively analyze T-cell repertoire in blood samples from patients who underwent hematopoietic stem cell transplant [
21]. We found that patients with graft versus host disease (GVHD) had a significant expansion of certain TCR clones and decrease in TCRB diversity in comparison with that in non-GVHD patients [
21].
Our study demonstrated a large turnover of the T-cell populations over time consistent with conditioning immunosuppressive treatments that the patients received. Importantly, high correlation of the TCR repertoire was observed between blood and graft at the time of rejection. Although they cannot precisely point out clones responsible for the graft rejection, our data suggest the feasibility of tracking such clones once identified in the blood. We identified top ten clones profile or recurrent CDR3 profile, which included all the top clones that appeared at each time point in blood and graft samples. We found an expansion of the recurrent CDR3 profile from blood samples obtained at the time of rejection compared to earlier time points in patients with TCMR. Since this phenomenon was not observed in non-TCMR patients, it suggests that TCR clones that cause TCMR could be tracked to earlier time points; therefore identification of such clones can possibly provide a diagnostic value.
The utility of T-cell sequencing in the transplant field has been increasingly investigated. A recent study assessed kidney allograft dysfunction and T cell response to different viral infections using NGS-DNA-based platform demonstrated the feasibility of tracking single antigen-specific T-cell clones in tissues to facilitate differential diagnosis [
24]. Morris et al. applied Immuno-SEQ approach to characterize and track TCR repertoire in association with tolerance in patients with combined kidney and non-myeloablative bone marrow transplantation (CKBMT) [
25]. The authors noted a post transplant reduction in donor-reactive T-cell clones in three CKBMT patients with tolerance; but not in non-tolerant patients or in two with kidney transplant alone on standard immunosuppressive regimens. Additionally, the study showed lower TCRB diversity in non-tolerant subjects compared with that of tolerant subjects [
25].
Other techniques such as microarrays [
26,
27] were also investigated to identify a molecular signature in kidney biopsy samples with pure TCMR. However, the molecular signature for TCMR had low sensitivity (50%) and low positive predictive value (62%) [
12].
Yap et al. [
28] showed that a Polychromatic Flow Cytometry and Quantification of sjTRECs could predict long-term allograft dysfunction. The investigators detected a restriction of TCR Vβ diversity associated with the expansion of terminally differentiated effector memory (TEMRA; CD45RA + CCR7 − CD27 − CD28−) CD8+ T-cells. These cells showed an increased expression of perforin, granzyme B, and T-bet, and correlated with the level of CD57 and the ability of CD8+ T-cells to secrete TNF-α and IFN-γ. This TEMRA was detected in patients who later on experienced kidney dysfunction.
Our data confirmed that patients with TCMR had significant increase in blood FOXP3, perforin, granzyme and the T-cell sub-types CD4 and CD8 comparing to those without rejection. Previously, one-step real-time PCR method was utilized as a non-invasive tool to detect the expression of the T-cell activation markers, granzyme B, perforin, and HLA-DRA in PMC [
29]. Heng et al. showed that the probability of developing acute rejection in kidney transplant recipients increased from 15% to 73% when both granzyme B and perforin tests were positive, and was reduced to 2% if both were negative [
5]. Others also showed that granzyme A mRNA was significantly higher in subclinical and clinical TCMR compared to patients with stable grafts or those with tubular necrosis with 80% sensitivity and up to 100% specificity. Granzyme B and perforin mRNA levels could significantly discriminate acute rejection from stable or tubular necrosis [
30,
31].
Unlike previous studies that utilized genomic DNA-based NGS to characterize the TCR repertoire, our study is the first to utilize NGS techniques in detecting TCMR by analyzing mRNA obtained from blood and kidney biopsy samples in a prospective study of kidney transplant recipients. Furthermore, examination of this repertoire in the graft shed a light on the function of the infiltrated T-cells involved in TCMR. Our data confirmed the correlation of upregulation of T-cell markers in the blood with the pathological changes of TCMR. We acknowledge that our findings are limited by the small number of patients who developed TCMR during the study period. Therefore, a follow-up study would be essential to assess delayed TCMR and long term outcome. Nevertheless, our findings constitute a novel tool in utilizing non-invasive next generation T-cell sequencing in the diagnosis of TCMR and in further understanding the T cells characterization as an immune response in the renal transplant in comparison to conventional tools.
Acknowledgement
We like to acknowledge the super-computing resource was provided by Human Genome Center, the Institute of Medical Science, University of Tokyo.
HA: Dr. Alachkar is an assistant professor at USC School of Pharmacy. Her research is focused on areas of pharmacogenomics, immunogenomics and cancer therapeutics, applying genomic information in the identification and functional characterization of cancer targets, and studying the T cell receptor repertoire in the preclinical and clinical developments of potential targeted therapeutic approaches such as transplantation.
MM: Mr. Mutonga was a research assistant, now a medical student.
TK: Dr. Kato is an assistant professor of medicine. His research is focused on the role of T-cell immunity in the mechanism of different medical disorders.
SK: Dr. Kalluri is an internal medicine resident. Her work currently is clinical work.
YK: Dr. Kakuta is a professor of urology and transplant. His expertise and research are focused on kidney transplantation, T-cell mediated rejection and complication of immunosuppressions and transplant associated cancers.
MU: Dr. Uemura is a professor of urology and transplant. His expertise and research are transplantation and complication of immunosuppressions and transplant associated cancers.
RI: Dr. Imamura is a professor of urology and transplant. His expertise and research are focused on kidney transplantation and complication of immunosuppressions and transplant associated cancers.
NN: Dr. Nonomura is a professor of urology. His expertise is in kidney transplantation and complication of immunosuppressions and transplant associated cancers.
VV: Dr. Vujjini is a research fellow at Johns Hopkins. His research focuses on post kidney transplant complications.
SA: Dr. Alasfar is an instructor of medicine. His expertise includes kidney transplantation. His research interest includes recurrence of TMA and glomerular diseases after transplantation.
HR: Dr. Rabb is a professor of Medicine. His expertise includes kidney transplantation. His research focuses on acute kidney injury.
YN: Dr. Nakamura is a professor of medicine. His research focuses on T-cell sequencing and T-cell role in cancer and finding novel cancer targeted therapies.
NA: Dr. Alachkar is an assistant professor of medicine. Her expertise includes incompatible Kidney transplant, kidney transplant rejections, FSGS and other glomerular diseases after kidney transplant. Her research interests include biomarkers in kidney transplant; Outcome of deceased donor kidney transplant recipients with positive crossmatch; highly sensitized patients, response to desensitization treatments and post-transplant outcome, late AMR and T cell mediated rejection, recurrence of atypical HUS and the role of Eculizumab, recurrence of focal segmental glomerulosclerosis post kidney transplantation.