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Year : 2019  |  Volume : 13  |  Issue : 1  |  Page : 5-8

World of omics in transplantation – “Transplantomics”

Department of Nephrology, Osmania Medical College and General Hospital, Hyderabad, Telangana, India

Date of Web Publication29-Mar-2019

Correspondence Address:
Prof. Manisha Sahay
Department of Nephrology, Osmania Medical College and General Hospital, Afzal Gunj, Hyderabad - 500 012, Telangana
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijot.ijot_6_19

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Over recent years, the transplantation outcomes have improved dramatically. However, rejections still occur despite potent immunosuppression. Furthermore, long-term transplant complications affect outcomes, for example, new-onset diabetes after transplantation (NODAT) or cancers worsen outcomes. The science of transplantomics is a relatively novel science directed at further improving short- and long-term outcomes after transplantation. Hence, nephrologists need to be aware of potential applications of omics in transplantation.

Keywords: Genomics, metabolomics, outcomes, rejection, renal transplantation, transcriptomics

How to cite this article:
Sahay M. World of omics in transplantation – “Transplantomics”. Indian J Transplant 2019;13:5-8

How to cite this URL:
Sahay M. World of omics in transplantation – “Transplantomics”. Indian J Transplant [serial online] 2019 [cited 2023 Feb 3];13:5-8. Available from: https://www.ijtonline.in/text.asp?2019/13/1/5/255185

  Introduction Top

Transplantomics is the study of genomics, transcriptomics, epigenomics, metabolomics, and proteomics in transplantation. Omics may help in early diagnosis of acute and chronic rejection, delivering personalized immunosuppression and diagnosis of infections and cancers.[1]

  Genomics Top

Genomics deals with analysis of genes.

How it is done?

It can be done by the following methods: (i) candidate gene approach which analyses associations between predefined genes and the given trait of interest. It is less expensive and simpler; however, it can miss rare diseases. (ii) Genome-wide association study (GWAS) can scan millions of common genetic variants across the entire genome to find variants that associate with the trait of interest. (iii) Whole-genome sequencing examines both rare and common variations and scans entire genome. GWAS scans are significantly cheaper than whole-genome sequencing; however, they are limited by their low affinity for capturing rare variants. (iv) Whole-exome sequencing examines specifically the coding regions of all genes present in the given organism.[1]

Clinical utility

Human leukocyte antigen (HLA) system is critical to long-term transplant outcome; however, close matching between donor and recipient for HLAs does not guarantee good allograft outcome. This has led researchers to explore genetic effects outside of the HLA locus. Genomics finds clinical utility in transplantation for these non-HLA loci as shown in the following examples:

  1. APOLI gene is related to chronic kidney disease in Africans. APOLI gene is positively selected in Africans to impart resistance to Trypanosoma brucei rhodesiense infection. It has been identified that if the donor has two APOLI gene variants named G1 and G2, the transplant outcomes are poor[2]
  2. ABCB1 gene polymorphism in the donor is associated with poor outcomes in the graft as it interferes with metabolism of calcineurin inhibitors[3]
  3. Donor variant of the gene encoding caveolin-1 (plasma membrane protein involved in G-protein signaling) is found to have an association with allograft failure[4]
  4. Donor/recipient whole-exome sequencing can be used to estimate all cell surface antigen mismatches[5]
  5. Three single-nucleotide polymorphisms found in TCF7L2, CDKAL1, and KCNQ1 have been implicated in NODAT[6],[7]
  6. Genes such as GSTM1 and MTHFR are linked to posttransplant nonmelanoma skin cancers while CACNA1D and CSMD1 polymorphisms in genes and complete loss of the short arm of chromosome 9 are associated with squamous cell cancers (SCCs) and other cancers. A number of oncogenic pathways that are activated in SCC include the nuclear factor-kappa B (NF-κB) and tumor necrosis factor (TNF) pathways. Furthermore, RAS and MYC, which regulate apoptosis, tumorigenesis, and cellular proliferation, are activated in SCC[8]
  7. Common variants in the CYP3A4, CYP3A5, P450 oxidoreductase, and cytochrome b5 (CYB5A) alleles are responsible for tacrolimus metabolism and influence tacrolimus levels.[9]

Thus, genomics may help in improving outcomes by enabling personalized therapy.

  Transcriptomics Top

DNA is transcribed into RNA. Measurement of this messenger RNA (mRNA) is very useful to determine the processes happening at cellular levels. A quantitative assessment of the complete set of these mRNAs is termed transcriptomics.

How is it done?

There are two main methodologies applied in this field – probe-based microarrays and RNA sequencing (RNA-seq). Microarrays are used to analyze predefined targets, whereas RNA-seq uses deep-sequencing technologies to examine all sequences present in the given sample. Transcriptomics can be done on allograft or on peripheral blood cells.

Clinical utility

Allograft transcriptomics

Halloran has performed molecular analyses of allograft biopsies to enrich the standard pathology approach by this molecular microscope strategy.[10]

  1. It can detect allograft upregulation of T-cell-related/immune transcripts (e.g., chemokines, CD3, forkhead box P3 (FOXP3), interferon-gamma (IFN-γ), Fas/FasL, perforin, and granzyme B) in rejection. These changes can be recognized before histological changes in biopsy. Seventeen gene panels in kidney solid organ response testpredict rejection (both antibody mediated and cell mediated) 3 months before clinical event.[11]
  2. Genomics of Chronic Allograft Rejection consortium panel includes a set of 13 gene transcripts, which is predictive for the development of allograft fibrosis between 3 and 12 months, as well as early allograft loss at 2 or 3 years.[12] (iii) Twenty-one genes involved in tolerance were identified at the University of California, San Francisco, in the program kidney spontaneous operational tolerance test. A three-gene assay (KLF6, BNC2, and CYP1B1) correlates with operational tolerance and a significant shift toward dendritic cells as well as B-lymphocytes and NK cells.[13]

Peripheral blood cell transcriptomics

(i) AlloMap test can identify mRNA of 11 genes in peripheral blood to identify rejection.[14] Furthermore, a peripheral blood five-gene test – DUSP, PBEF1, PSEN1, MAPK9, and NKTR[15] – was developed to identify AR. (ii) A 33 gene peripheral blood gene expression panel can predict a tolerant state, i.e. reduced costimulatory signaling, apoptosis, and immune quiescence with memory T-cell responses.[16] A greater number of regulatory T-cells expressing the transcription factor FOXP3 (FOXP3+Tregs) are observed in peripheral blood of tolerant patients versus those with chronic rejection.


MicroRNAs (miRNAs) are short endogenous noncoding RNA molecules (~22 nucleotides in length) that bind complementary sequences of target mRNAs and inhibit their translation and thus inhibit gene expression. They are key posttranscriptional gene expression regulators. A number of studies have investigated associations with miRNA and gene expression profiles in nonmelanoma skin cancers. A role in the pathogenesis of posttransplant delayed graft function (DGF) was found for two miRNAs: MiR-182-5p and miR-21-3p.[17]

Thus, transcriptomics and miRNAs can aid in early diagnosis of rejection, tolerance, DGF, and even cancers.

  Epigenomics Top

Epigenomics is the study of epigenetic modifications, such as histone modifications and DNA methylation, across the entire genome. There is an alteration of chromosome without alteration of DNA sequence and this change is heritable. Epigenetic modifications can suppress the expression of target genes. Methods for analyzing epigenetics include quantification of global methylation through high-performance liquid chromatography and DNA methylation analysis through whole-genome bisulfite sequencing.

Clinical utility

(i) Hypomethylated genes in transplant recipients are associated with allograft rejection[18] and with a large number of cancers in transplant populations, including SCC. Cold ischemic time of 4 h induces aberrant demethylation of the promoter of complement factor gene (C3), a key regulator of innate immunity, increasing its expression in the donor kidney.[19]

  Metabolomics Top

What is it?

It is the study of chemical processes involving small metabolites (defined as <1500 Da) in a specific matrix, i.e., urine or blood. Metabolomics is affected by environment and the microbiome and is constantly changing and hence more complex than the genome and proteome.[20]

Utility in transplantation

Some of the examples of the use of metabolomics in transplantation are as follows:

  1. It is known that IFN-γ ELISPOT positivity before transplantation correlates with risk of posttransplant acute cellular rejection (ACR) and/or poor graft function.[21] T-cell reactivity index (panel of reactive T-cell [PRT]) is performed using a pool of donor cells; pretransplant PRT results correlate with an elevated risk of posttransplant allograft injury.[22] Metabolomics provides an added platform for the diagnosis of acute rejection (AR) by measuring plasma levels of NF-κB, STAT1 and STAT3, mannose-binding lectin (MBL), interleukins or interferons, etc
  2. Quantification of donor-origin cell-free DNA in recipient blood or urine using metabolomics has potential diagnostic utility for detecting rejection (AlloSure)[23]
  3. sCD30 is a glycoprotein expressed on human CD4+CD8+T-cells that secretes Th2-type cytokines. SCD30 can be measured by metabolomics and is a predictor of AR[24]
  4. ATP generation by mitogen-stimulated CD4 lymphocytes (ImmuKnow assay)[25] measured by metabolomics is an Food and Drug Administration-approved biomarker that is potentially informative in transplant recipients. Results are reported as “within normal limits,” high (suggesting underimmunosuppression), or low (suggesting overimmunosuppression).

Thus, metabolomics provides a reliable indication of kidney function, kidney injury, and immunosuppressive drug toxicity.

How it is done?

The methodology used in metabolomics includes gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis, gel electrophoresis, and matrix-assisted laser desorption/ionization–time-of-flight (MALDI-TOF) mass spectrometry. Nuclear magnetic resonance spectroscopy is another metabolomics technique which does not rely on separation of the analytes, and the sample can thus be recovered for further analyses.

  Proteomics Top

What is it?

Proteomics can assess the complete set of proteins (proteome) in a specific matrix, i.e., urine or plasma.

Plasma proteomics

Freue et al. found that seven proteins were upregulated in the plasma of patients with AR, including connectin (TTN), lipopolysaccharide-binding protein, peptidase inhibitor 16, complement factor D, MBL2, recombinant SERPINA10 protein, and beta-2-microglobulin.[26]

Urinary proteomics

It is like a liquid renal biopsy. Seventy percent of proteins in urine are from the kidney while 30% are from the plasma.

Utility in transplantation

Conventional renal biopsy may miss focal changes in the kidney and is invasive. Urine proteomics does not have this limitation. Some examples of clinical utility include the following:

  1. AR shows TNF, matrix metalloproteinase, uromodulin, and beta-2-microglobulin, etc., in urine. Suthanthiran described Rejectostix in urine, i.e., three-gene signature (CD3ε, IP-10, and 18S rRNA) which distinguishes ACR from non-ACR with high accuracy. This biomarker is detectable before the clinical recognition of the rejection episode.[27] Rabant et al. showed that urinary CXCL9 was a strong predictor of T-cell-mediated rejection, while CXCL10 showed a better performance to diagnose antibody-mediated rejection.[28] FOXP3 mRNA in urinary cells is higher in patients with biopsy-confirmed AR[29]
  2. Kidney injury molecule, neutrophil gelatinase associated lipocalyn, and fatty acid binding protein in donor urine predict delayed graft function in deceased donor transplants.[30] (iii) Urinary N-acetyl-β-D-glucosaminidase was found to be specific for CyA-related toxicity.

Thus, proteomics, especially urine proteomics, provides a noninvasive tool for the detection of transplant complications.

  Conclusion Top

A number of genomic, transcriptomic, metabolomic, and proteomic studies are being carried out in recent years. These studies are vital for the development of biomarkers to signal the onset of posttransplant morbidities as well as early predictors of graft failure. Currently used biomarkers such as serum creatinine and albuminuria are insensitive, nonspecific, and signal graft dysfunction after the event. However, omics markers need to be further validated to make them suitable for clinical use in transplant patients.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Stapleton CP, Conlon PJ, Phelan PJ. Using omics to explore complications of kidney transplantation. Transpl Int 2018;31:251-62.  Back to cited text no. 1
Newell KA, Formica RN, Gill JS, Schold JD, Allan JS, Covington SH, et al. Integrating APOL1 gene variants into renal transplantation: Considerations arising from the American Society of Transplantation Expert Conference. Am J Transplant 2017;17:901-11.  Back to cited text no. 2
Moore J, McKnight AJ, Döhler B, Simmonds MJ, Courtney AE, Brand OJ, et al. Donor ABCB1 variant associates with increased risk for kidney allograft failure. J Am Soc Nephrol 2012;23:1891-9.  Back to cited text no. 3
Moore J, McKnight AJ, Simmonds MJ, Courtney AE, Hanvesakul R, Brand OJ, et al. Association of caveolin-1 gene polymorphism with kidney transplant fibrosis and allograft failure. JAMA 2010;303:1282-7.  Back to cited text no. 4
Mesnard L, Muthukumar T, Burbach M, Li C, Shang H, Dadhania D, et al. Exome sequencing and prediction of long-term kidney allograft function. PLoS Comput Biol 2016;12:e1005088.  Back to cited text no. 5
Glowacki F, Lionet A, Buob D, Labalette M, Allorge D, Provôt F, et al. CYP3A5 and ABCB1 polymorphisms in donor and recipient: Impact on tacrolimus dose requirements and clinical outcome after renal transplantation. Nephrol Dial Transplant 2011;26:3046-50.  Back to cited text no. 6
Giri A, Sanders M, Velez D, Ikizler T, Roden D, Birdwell K. A genome wide association study of new onset diabetes after transplant in kidney transplantation. Am J Transplant 2016;16 Suppl 3:B235.  Back to cited text no. 7
Sanders ML, Karnes JH, Denny JC, Roden DM, Ikizler TA, Birdwell KA. Clinical and genetic factors associated with cutaneous squamous cell carcinoma in kidney and heart transplant recipients. Transplant Direct 2015;1:e3.  Back to cited text no. 8
Rojas L, Neumann I, Herrero MJ, Bosó V, Reig J, Poveda JL, et al. Effect of CYP3A5*3 on kidney transplant recipients treated with tacrolimus: A systematic review and meta-analysis of observational studies. Pharmacogenomics J 2015;15:38-48.  Back to cited text no. 9
Halloran PF, Reeve J, Akalin E, Aubert O, Bohmig GA, Brennan D, et al. Real time central assessment of kidney transplant indication biopsies by microarrays: The INTERCOMEX study. Am J Transplant 2017;17:2851-62.  Back to cited text no. 10
Roedder S, Sigdel T, Salomonis N, Hsieh S, Dai H, Bestard O, et al. The kSORT assay to detect renal transplant patients at high risk for acute rejection: Results of the multicenter AART study. PLoS Med 2014;11:e1001759.  Back to cited text no. 11
O'Connell PJ, Zhang W, Menon MC, Yi Z, Schröppel B, Gallon L, et al. Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: A multicentre, prospective study. Lancet 2016;388:983-93.  Back to cited text no. 12
Roedder S, Li L, Alonso MN, Hsieh SC, Vu MT, Dai H, et al. Athree-gene assay for monitoring immune quiescence in kidney transplantation. J Am Soc Nephrol 2015;26:2042-53.  Back to cited text no. 13
Pham MX, Teuteberg JJ, Kfoury AG, Starling RC, Deng MC, Cappola TP, et al. Gene-expression profiling for rejection surveillance after cardiac transplantation. N Engl J Med 2010;362:1890-900.  Back to cited text no. 14
Sigdel TK, Li L, Tran TQ, Khatri P, Naesens M, Sansanwal P, et al. Non-HLA antibodies to immunogenic epitopes predict the evolution of chronic renal allograft injury. J Am Soc Nephrol 2012;23:750-63.  Back to cited text no. 15
Brouard S, Mansfield E, Braud C, Li L, Giral M, Hsieh SC, et al. Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance. Proc Natl Acad Sci U S A 2007;104:15448-53.  Back to cited text no. 16
Wilflingseder J, Sunzenauer J, Toronyi E, Heinzel A, Kainz A, Mayer B, et al. Molecular pathogenesis of post-transplant acute kidney injury: Assessment of whole-genome mRNA and miRNA profiles. PLoS One 2014;9:e104164.  Back to cited text no. 17
Bontha SV, Maluf DG, Archer KJ, Dumur CI, Dozmorov MG, King AL, et al. Effects of DNA methylation on progression to interstitial fibrosis and tubular atrophy in renal allograft biopsies: A multi-omics approach. Am J Transplant 2017;17:3060-75.  Back to cited text no. 18
Parker MD, Chambers PA, Lodge JP, Pratt JR. Ischemia-reperfusion injury and its influence on the epigenetic modification of the donor kidney genome. Transplantation 2008;86:1818-23.  Back to cited text no. 19
Bohra R, Klepacki J, Klawitter J, Klawitter J, Thurman JM, Christians U. Proteomics and metabolomics in renal transplantation-quo vadis? Transpl Int 2013;26:225-41.  Back to cited text no. 20
Augustine JJ, Hricik DE. T-cell immune monitoring by the ELISPOT assay for interferon gamma. Clin Chim Acta 2012;413:1359-63.  Back to cited text no. 21
Poggio ED, Clemente M, Hricik DE, Heeger PS. Panel of reactive T cells as a measurement of primed cellular alloimmunity in kidney transplant candidates. J Am Soc Nephrol 2006;17:564-72.  Back to cited text no. 22
Beck J, Oellerich M, Schulz U, Schauerte V, Reinhard L, Fuchs U, et al. Donor-derived cell-free DNA is a novel universal biomarker for allograft rejection in solid organ transplantation. Transplant Proc 2015;47:2400-3.  Back to cited text no. 23
Weimer R, Zipperle S, Daniel V, Carl S, Staehler G, Opelz G. Pretransplant CD4 helper function and interleukin 10 response predict risk of acute kidney graft rejection. Transplantation 1996;62:1606-14.  Back to cited text no. 24
He J, Li Y, Zhang H, Wei X, Zheng H, Xu C, et al. Immune function assay (ImmuKnow) as a predictor of allograft rejection and infection in kidney transplantation. Clin Transplant 2013;27:E351-8.  Back to cited text no. 25
Freue GV, Sasaki M, Meredith A, Günther OP, Bergman A, Takhar M, et al. Proteomic signatures in plasma during early acute renal allograft rejection. Mol Cell Proteomics 2010;9:1954-67.  Back to cited text no. 26
Suthanthiran M, Schwartz JE, Ding R, Abecassis M, Dadhania D, Samstein B, et al. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N Engl J Med 2013;369:20-31.  Back to cited text no. 27
Rabant M, Amrouche L, Lebreton X, Aulagnon F, Benon A, Sauvaget V, et al. Urinary C-X-C motif chemokine 10 independently improves the noninvasive diagnosis of antibody-mediated kidney allograft rejection. J Am Soc Nephrol 2015;26:2840-51.  Back to cited text no. 28
Muthukumar T, Dadhania D, Ding R, Snopkowski C, Naqvi R, Lee JB, et al. Messenger RNA for FOXP3 in the urine of renal-allograft recipients. N Engl J Med 2005;353:2342-51.  Back to cited text no. 29
Koo TY, Jeong JC, Lee Y, Ko KP, Lee KB, Lee S, et al. Pre-transplant evaluation of donor urinary biomarkers can predict reduced graft function after deceased donor kidney transplantation. Medicine (Baltimore) 2016;95:e3076.  Back to cited text no. 30


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