JBR Journal of Translational Biomarkers & Diagnosis (JBR-TBD)  /  JBR-TBD-02-001e

Biomarkers in Renal Transplantation: Evolution of Strategies



Shrestha BM*

Division of Renal Transplantation, Sheffield Kidney Institute, Northern General Hospital, Herries Road, Sheffield, UK.

*Corresponding Author

Badri Man Shrestha MD FRCS FACS FEBS,
Division of Renal Transplantation, Sheffield Kidney Institute,
Herries Road, Sheffield, S5 7AU, UK.
Tel : +44 114 2434343
Fax : +44 114 2714604
E-mail: shresthabm@doctors.net.uk

Received: August 29, 2016; Published: September 07, 2016;

Citation: Shrestha BM (2016) Biomarkers in Renal Transplantation: Evolution of Strategies. J Translational Biomarkers Diagn. 2(1e), 1-3.

Copyright: Shrestha BM© 2016. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.


The benefits conferred by renal transplantation (RT), such as the improved quality of life, prolonged survival and cost-effectiveness, are compromised by premature allograft losses from the deleterious effects of ischaemia-reperfusion injury (IRI) leading to delayed graft function (DGF), acute rejection (AR), infections, calcineurin-inhibitor toxicity, chronic allograft dysfunction (CAD), recurrent disease and cardiovascular deaths with functioning graft [1]. Current management of RT is largely reliant on monitoring the late manifestation of graft injury, such as serum creatinine level. Protocol surveillance biopsy is an invasive method of monitoring graft status, which has several limitations [2]. Non-invasive approach of RT management by measurement of biomarkers has been studied to assess their efficacy in prevention, prediction, early diagnosis and treatment of above conditions in both experimental and clinical settings. Biomarkers are measurable indicators of the presence or severity of pathological conditions affecting renal allografts. They indicate a change in expression or state of a protein that correlates with the risk, progression of a disease, or with the susceptibility of the disease to a given treatment, which can be detected and measured in blood, urine, perfusate and tissues [3, 4].

Evaluation of biomarkers in RT began in 1980 by measurement of urinary enzymes such as alanine aminopeptidase, microprotein and beta-glucuronidase in reversible and irreversible rejection episodes [5]. Since then, over 15000 biomarkers have been examined with respect to solid organ transplantation based on functional immune assay and non-invasive test based on blood gene expression [6]. Development of laboratory techniques such as real-time polymerase chain reaction, microarray profiling, mass spectrometry, in-situ hybridisation, gene and protein expression analysis, immunohistochemistry, enzyme-linked immunosorbent spot (ELISPOT) assay, flow cytometry and immune cell functional assays have been employed in wide range of RT scenarios [7, 8].

Despite development of newer immunosuppressive agents, tissue typing, sensitive cross-match techniques and regular monitoring of antibodies, both cell-mediated (CM) and antibody-mediated rejection (AMR) remain prevalent following living donor (LD) and deceased donor (DD) RT, which impact long-term allograft survival. Sarwal et al., have examined the predictive capacity of transcriptional kidney Solid Organ Response Test (kSORT) and interferon-ELISPOT assay in the Evaluation of sub-clinical Acute Rejection (sc-AR) PrEdiction (ESCAPE) Study in 75 consecutive RT recipients, who received 6-months protocol biopsies. The kSORT assay showed high accuracy predicting sc-AR (specificity, 98%; positive predictive value 93%) whereas the ELISPOT showed high precision ruling out sc-T-CMAR (specificity = 70%, negative predictive value = 92.5%), but could not predict sc- AMR, unlike kSORT [9].

In a cohort of RT recipients from Europe, genotype of single nucleotide polymorphisms (SNPs) was done and genetic variants in donor/recipient was associated with risk and severity of AR and allograft survival. Acute rejection was associated with presence of loci encompassing PTRO, coding for a receptor-type tyrosine kinase essential for B cell receptor signalling and ciliary gene CCDC67 [10]. Analysis of transcriptome of pre-transplantation biopsy specimens, showed significant difference in C3 gene expression between LD and DD, which was related to the length of cold ischaemia time, which correlated well with 2-year graft function [11].

Ischaemia-reperfusion injury occurs following restoration of blood flow and oxygenation of implanted kidney due to releases of free radicals which can lead to DGF and primary non-function (PNF). The adverse effects of DGF on long-term graft survival is well established, which occurs more frequently in donation after circulatory death (DCD) RTs compared to donation after brain death or LD RTs. Neutrophil gelatinase-associated lipocalin (NGAL) is released by activated neutrophils, which accumulates in the proximal convoluted tubules (PCT) after acute tubular injury. The urinary level of NGAL and expression of NGAL in the PCT were significantly increased in the episodes AR reflecting acute tubular injury [12]. In a study including 49 LD RT recipients, the serum NGAL correlated with graft function recovery and long-term graft function [13]. Urinary NGAL was evaluated in 124 RT recipients, which showed 10% increased risk of DGF and 15% risk of CAD progression in association with NGAL [14].

Hypothermic machine perfusion (HMP) is used in DCD RT for preservation purpose. In a prospective study, perfusate biomarkers such as NGAL, kidney injury molecule-1 (KIM-1), interleukin-18 (ILR-18) and liver-type fatty acid-binding protein (L-FABP) were found to correlate with reduced e-GFR at 6 months, but not with the incidence of DGF. However, most patients with “undesirable” biomarkers levels experienced acceptable 6-month allograft function, suggesting these biomarker characteristics should not be used in isolation for discard decisions [15].

Smith et al., performed proteomic analysis of serum of 54 RT recipients and observed correlation between the serum aminocyclase- 1 (ACY-1) levels at day 1 and 3 post-transplant and DGF, slow graft function. immediate graft function and long-term graft function. ACY-1 is expressed predominantly in the PCT in pig and human kidneys and DGF results from acute tubular injury causing significant increase of ACY-1 level [16]. The level of lactic dehydrogenase and ILR-18 were found to be elevated in the perfusate of the HMP kidneys, which correlated with the incidence of DGF and PNF, but there was no significant association with 1-year graft survival. Glutathione S-transferase and aspartate transaminase were found to be significantly associated with DGF [17, 18].

Proteomics and metabolomics biomarkers studies in RT have been explored to generate diagnostic fingerprints. Chemokines CXCl-9 and CXCL-10 have been discovered in proteomics studies, which may help in guiding and individualising immunosuppressive regimens and predict acute and chronic T-CMAR and AMR [19]. Critical genes (CXCR4, CCL5 and ITGB2) were identified in 112 specimens when examined by microarray profiling technique, which are useful in assessing organ quality and predict kidney graft function [20].

Detection of Fas-ligand, granzyme B (GZMB) and perforin (PRF) in the blood and urine was significantly elevated in AR following RT. However, a recent meta-analysis concluded neither GZMB nor PRF, if evaluated alone, could be a convincing non-invasive diagnostic biomarker of AR in clinical practice. Combined GZMB and PRF post-RT may be a better choice in AR evaluation to direct allograft biopsy and earlier therapeutic intervention [21].

Micro-ribonucleic acid (miRNA) is a noncoding small molecule, discovered in 1993, plays an important role in the regulation of immune and adaptive immune response, which can be measured in blood, urine, urine cell pellets and tissues [22]. The miRNA has been extensively studied in RT recipients who developed acute kidney injury, AMR, CAMR and CAD and their detection before appearance of histological changes has significant implication in clinical transplantation [23, 24]. De novo donor-specific antibodies (DSA) do not always contribute to CAMR in RT. Investigation of miRNAs by microarray profiling revealed significant underexpression of miR-142-5p in patients with DSA. After DSA production, miR-486-5p and its target PTEN/foxO3 mRNA were significantly over-expressed (p < 0.01) and under-expressed (p < 0.01), respectively, in patients with biopsy-proven CAMR, compared with non-CAMR. Thus miRNA expression patterns may serve as non-invasive diagnostic biomarkers to evaluate immune response and RT status [25].

Urine miRNAs were compared between RT recipients diagnosed with CAD and those with normal function, which showed differential expression of miRNAs in recipients of CAD, which was identified as potential biomarker for monitoring graft function and anticipating progression to CAD [26]. In another study comprising of 47 RT recipients, two aberrant urinary miR-21 and miR0-200b expression levels were accompanied by renal allograft dysfunction [24].

Shrestha et al., observed significant elevation of cross-linking enzyme tissue transglutaminase and urinary epsilon(gammaglutamyl)- lysine levels in the Fisher-to-Lewis rat model of chronic allograft nephropathy, which correlated with degree of tubulointerstitial fibrosis [27]. After RT, donor-derived cell-free DNA (ddcfDNA) can be detected in the recipient’s blood and urine. Introduction of digital droplet PCR and massive parallel sequencing have been the major breakthrough in the investigation technique. Increased levels of ddcfDNA during AR even weeks to months before histological features of AR points to a possible role of ddcfDNA as an early non-invasive rejection biomarker [28].

The ultimate goal of biomarker studies is to find non-invasive biomarkers of transplant pathologies by using recipient’s urine and serum, those indicate changes at the molecular level before the development of phenotype, that would predict allograft outcome, response to therapy and possibly reveal novel targets for therapeutic interventions [29]. Future strategies should be targeted to develop biomarkers in relation to the non-HLA antibodies, C4Dnegative AMR, the role of the innate immune system in AR and complement-system-associated molecules.

Due to multifactorial aetiology of RT pathologies, biomarker studies need to be standardised and validated prospectively in large cohort of patients to eliminate the effects of confounding variables. The US “Clinical Trials in Organ Transplantation” (CTOT), the Canadian “Biomarkers in Transplantation” (BIT) project and the European study of “Reprogramming the Immune System for Establishment of Tolerance” (RISET) have been established for evaluations of biomarkers in transplantation in multicentre studies. The successful transfer of biomarkers to clinic will lead to personalised transplantation medicine, including improved donor-recipient matching, individual immunosuppressive regimens and individual risk assessment for DGF, AR, CAD and graft tolerance. These improvements will translate into improved graft and patient survivals and reduced cost to the health care providers [30].


References


  1. Shrestha BM, Haylor JL (2007) Factors influencing long-term outcomes following renal transplantation: a review. JNMA J Nepal Med Assoc. 46(167): 136-42.
  2. Tsuji T, Yanai M, Itami H, et al., (2015) Microvascular inflammation in early protocol biopsies of renal allografts in cases of chronic active antibodymediated rejection. Nephrology (Carlton). 2: 26-30.
  3. Anglicheau D, Naesens M, Essig M, Gwinner W, Marquet P (2016) Establishing biomarkers in transplant medicine: a critical review of current approaches. Transplantation.
  4. Townamchai N, Eiam-Ong S (2015) Biomarkers in kidney transplantation: From bench to bedside. World J Nephrol. 4(54): 487-91.
  5. Pantschewa-Haschen R, Schulze R, Schneider G, Schabel J (1980) Urinary enzymes in monitoring kidney transplant patients. Z Urol Nephrol. 73(10): 719-24.
  6. Sigdel TK, Vitalone MJ, Tran TQ, et al., (2013) A rapid noninvasive assay for the detection of renal transplant injury. Transplantation. 96(1): 97-101.
  7. Moreso F, Torres IB, Martinez-Gallo M, et al., (2014) Gene expression signature of tolerance and lymphocyte subsets in stable renal transplants: results of a cross-sectional study. Transpl Immunol. 31(1): 11-6.
  8. Sigdel TK, Gao Y, He J, et al., (2016) Mining the human urine proteome for monitoring renal transplant injury. Kidney Int. 89: 1244-52.
  9. Crespo E, Roedder S, Sigdel T, et al., (2016) Molecular and Functional Noninvasive Immune Monitoring in the ESCAPE Study for Prediction of Subclinical Renal Allograft Rejection. Transplantation.
  10. Ghisdal L, Baron C, Lebranchu Y, et al., (2016) Genome-Wide Association Study of Acute Renal Graft Rejection. Am J Transplant.
  11. Naesens M, Li L, Ying L, et al., (2009) Expression of complement components differs between kidney allografts from living and deceased donors. J Am Soc Nephrol. 20(8): 1839-51.
  12. Kohei J, Ishida H, Tanabe K, Tsuchiya K, Nitta K (2013) Neutrophil gelatinase- associated lipocalin is a sensitive biomarker for the early diagnosis of acute rejection after living-donor kidney transplantation. Int Urol Nephrol.45(4): 1159-67.
  13. Liu Y, Li HX, Ying ZW, et al., (2016) Serum Neutrophil Gelatinase-Associated Lipocalin and Cystatin C for Assessing Recovery of Graft Function in Patients Undergoing Living-Donor Kidney Transplantation. Clin Lab.62(1-2): 155-63.
  14. Lacquaniti A, Caccamo C, Salis P, et al., (2016) Delayed graft function and chronic allograft nephropathy: diagnostic and prognostic role of neutrophil gelatinase-associated lipocalin. Biomarkers. 21(4): 371-8.
  15. Parikh CR, Hall IE, Bhangoo RS, et al., (2016) Associations of Perfusate Biomarkers and Pump Parameters With Delayed Graft Function and Deceased Donor Kidney Allograft Function. Am J Transplant. 16(5): 1526-39.
  16. Welberry Smith MP, Zougman A, Cairns DA, et al., (2013) Serum aminoacylase- 1 is a novel biomarker with potential prognostic utility for long-term outcome in patients with delayed graft function following renal transplantation. Kidney Int. 84: 1214-25.
  17. Hoogland ER, de Vries EE, Christiaans MH, Winkens B, Snoeijs MG, van Heurn LW (2013) The value of machine perfusion biomarker concentration in DCD kidney transplantations. Transplantation. 95(4): 603-10.
  18. Bhangoo RS, Hall IE, Reese PP, Parikh CR (2012) Deceased-donor kidney perfusate and urine biomarkers for kidney allograft outcomes: a systematic review. Nephrol Dialysis Transplant. 27(8): 3305-14.
  19. Christians U, Klawitter J, Klawitter J (2016) Biomarkers in Transplantation- -Proteomics and Metabolomics. Ther Drug Monit. 38(1): S70-4.
  20. Scian MJ, Maluf DG, Archer KJ, et al., (2012) Identification of biomarkers to assess organ quality and predict posttransplantation outcomes. Transplantation. 94(8): 851-8.
  21. Heng B, Li Y, Shi L, et al., (2015) A Meta-analysis of the Significance of Granzyme B and Perforin in Noninvasive Diagnosis of Acute Rejection After Kidney Transplantation. Transplantation. 99(7): 1477-86.
  22. Lee RC, Feinbaum RL, Ambros V (1993) The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell.75(5): 843-54.
  23. Wilflingseder J, Sunzenauer J, Toronyi E, et al., (2014) Molecular pathogenesis of post-transplant acute kidney injury: assessment of whole-genome mRNA and miRNA profiles. PLoS One. 9(8): e104164.
  24. Zununi Vahed S, Omidi Y, Ardalan M, Samadi N (2016) Dysregulation of urinary miR-21 and miR-200b associated with interstitial fibrosis and tubular atrophy (IFTA) in renal transplant recipients. Clin Biochem.
  25. Iwasaki K, Yamamoto T, Inanaga Y, et al., (2016) MiR-142-5p and miR- 486-5p as biomarkers for early detection of chronic antibody-mediated rejection in kidney transplantation. Biomarkers. 1-10.
  26. Maluf DG, Dumur CI, Suh JL, et al., (2014) The urine microRNA profile may help monitor post-transplant renal graft function. Kidney Int. 85(2): 439-49.
  27. Shrestha B, Butt I, Da Silva M, et al., (2014) Upregulation of transglutaminase and epsilon (gamma-glutamyl)-lysine in the Fisher-Lewis rat model of chronic allograft nephropathy. Biomed Res Int. 2014: 651608.
  28. Gielis EM, Ledeganck KJ, De Winter BY, et al., (2015) Cell-Free DNA: An Upcoming Biomarker in Transplantation. Am J Transplant. 15(10): 2541-51.
  29. Sawitzki B, Reinke P, Pascher A, Volk HD (2010) State of the art on the research for biomarkers allowing individual, tailor-made minimization of immunosuppression. Curr Opin Organ Transplant. 15(6): 691-6.
  30. Ho J, Rush DN, Nickerson PW (2015) Urinary biomarkers of renal transplant outcome. Curr Opin Organ Transplant. 20(4): 476-81.

         Indexed in

pubhub  CGS  indexcoop  
j-gate  DOAJ  Google_Scholar_logo

       Total Visitors

SciDoc Counter

Get in Touch

SciDoc Publishers
16192 Coastal Highway
Lewes, Delaware 19958
Tel :+1-(302)-703-1005
Fax :+1-(302)-351-7355
Email: contact.scidoc@scidoc.org


porn