Transcriptome Analysis in Renal Transplant Biopsies Not Fulfilling Rejection Criteria
Abstract
:1. Introduction
2. Transcriptomes in Biopsies Categorized as Borderline Changes
3. Transcriptome Analysis in Biopsies Categorized as Interstitial Fibrosis/Tubular Atrophy
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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---|---|---|---|
Mueller TF et al. | Clinical indication | Affymetrix GeneChip Human Genome U133 Plus 2.0 Arrays | ABMR and TCMR manifested similar PBT disturbances. Biopsies with minimal PBT disturbances had a very low incidence of rejection. |
Am J Transplant 2007 [35] | N = 143. | ↑ CAT1, CAT2, GRIT1, GRIT2; | |
↓ KT1-KT2 in TCMR GRIT1 associated with C4d staining (ABMR) | |||
De Freitas DG et al. | Clinical indication | Affymetrix GeneChip Human Genome U133 Plus 2.0 Arrays | Most cases designated borderline by histopathology are found to be non-rejection by molecular phenotyping. |
Am J Transplant 2011 [42] | TCMR(n = 35), BL (n = 45), non-rejection (n = 116) | Molecular changes measured according to T-cell burden; a rejection classifier; a canonical TCMR classifier; and the risk score. Reassigned borderline biopsies as TCMR like 13/40 (33%) or non-rejection-like 27/40 (67%). | |
Decision tree analysis showed that i-total >27% and tubulitis extent > 3% match the molecular diagnosis of TCMR in 85% of cases. | |||
Halloran PF et al. | Clinical indication | Affymetrix microarrays. | The molecular TCMR score has potential to add new insight, particularly in situations where histology is ambiguous or potentially misleading. |
Am J Transplant 2013 [43] | International Collaborative Microarray Study (n = 300). | Microarray expression files for BFC403 (GSE36059) and INT300 (GSE48581) cohorts. TCMR scores divided into high or low using the same cut off of 0.1. | |
TCMR (n = 32), BL (n = 46) | |||
Hrubá P et al. | BL early clinical biopsies (n = 13) and 3-month protocol biopsies (n = 15) | Illumina microarray analysis. | Variations in gene expression between clinical and subclinical borderline changes despite similar histological findings. |
Kidney Int 2017 [45] | ↑ C19orf59, CXCL2, IL6, S100A8, S100A9, FGA in early clinical biopsies as compared to protocol biopsies | ||
↑ SAA1, CLEC5A, FGA in borderline biopsies with IFTA progression | |||
Halloran PF et al. | Clinical indication | Affymetrix hgu219 PrimeView microarray chips. | MMDx would add valuable support for clinical decisions beyond current standard-of care. |
Am J Transplant 2017 [44] | International Collaborative Microarray Study (n = 519). | Molecular classifier scores (ABMRpm [positive ≥0.20], TCMRt [positive ≥0.10], Rejection [positive ≥0.30]) | |
ABMR (n = 88), ABMR suspected (n = 10), TCMR (n = 29), AKI (n = 43), BL (n = 31), atrophy/fibrosis (n = 84), “no abnormalities” (n = 141). | |||
Reeve J et al. | Clinical indication. 13 centres (n = 1208) | Affymetrix hgu219 PrimeView microarray chips. | Borderline changes are classified as no rejection (72%), TCMR (6%), ABMR (20%) and mixed ABMR/TCMR (1%). |
JCI insights 2017 [46] | BL (n = 109), TCMR (n = 87) | Archetypal analysis of molecular phenotypes. |
Reference | Time & Type of Biopsy Sample Size | Methods & Results | Main Conclusion |
---|---|---|---|
Sherer A et al. Nephrol Dial Transplant 2009 [49] | Paired 3- and 6-month protocol biopsies. | Affymetrix GeneChip Human Genome U133 Plus 2.0 Arrays | Gene expression profiling of early protocol biopsies identified changes in the transcriptome of grafts, which may be important for development of IFTA. |
Non IFTA progression (n = 12) | IFTA progression is associated with overexpression of T-, B-cell activation, immune response and profibrotic genes. | ||
IFTA progression (n = 8) | Under expression of genes related with transporter and metabolic functions in IFTA progression. | ||
Vitalone MJ et al. | Paired 0-, 1-, 3- and 12-month protocol biopsies (59 biopsies from 18 patients) | Human 8K cDNA microarrays, Australian Genome Research | Allografts display immune and fibrotic gene expression profiles with patterns of expression gradually varying by time after transplantation. |
Transplantation 2010 [48] | Subclinical rejection = 14% | Immune pathway activity peaked at 1-month, fibrotic expression at 3 months, wound healing-remodelling and cell proliferation-repair processes were activated between 3 and 12 months, whereas macrophage-related gene expression occurred late by 12 months | Gene expression predated histologic damage. |
Naesens M et al. | Paediatric transplants. | Affymetrix Gene Chip Human Genome U133 Plus 2.0 Arrays | Progressive chronic histological damage is associated with regulation of both innate and adaptive immune responses that cannot be evaluated by histology. |
Kidney Int 2011 [50] | 24 patients with paired 0-, 6- and 24 months protocol biopsies. 24 patients with TCMR. | Upregulation of adaptive (T- and B-cell signatures) and innate immune cell transcripts (dendritic cell and NK cell transcripts) is already present in biopsies of kidneys several months before chronic histological damage occurs. | |
Mengel M et al. | 6-weeks protocol biopsies (n = 107). TCMR (n = 9), Borderline (n = 20) | Affymetrix Gene Chip Human Genome U133 Plus 2.0 Arrays | The molecular phenotype reflects the injury–repair response to implantation stresses, and has little relationship to future events. |
Am J Transplant 2011 [51] | ↑ QCAT, QCMAT, GRIT1, AMAT; ↓ KT1-KT2 in TCMR and borderline. | ||
PBTs correlated with DGF but not with ΔeGFR at 2 years, ΔIF/TA at 6 months or i-Banff at 6 months. | |||
O’Conell PJ et al. | Discovery set: 3-month (n = 159) and 12-month paired protocol biopsies (n = 101). | Affymetrix human exon 1.0 ST array in the discovery set and qPCR in the validation set. 13 genes related with active repair and regeneration pathways predicts the development and progression of chronic allograft damage and subsequent allograft loss | Kidney transplant recipients at risk of allograft loss can be identified before the development of irreversible damage. |
The Lancet 2016 [52] | Validation set (n = 45) |
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Moreso, F.; Sellarès, J.; Soler, M.J.; Serón, D. Transcriptome Analysis in Renal Transplant Biopsies Not Fulfilling Rejection Criteria. Int. J. Mol. Sci. 2020, 21, 2245. https://doi.org/10.3390/ijms21062245
Moreso F, Sellarès J, Soler MJ, Serón D. Transcriptome Analysis in Renal Transplant Biopsies Not Fulfilling Rejection Criteria. International Journal of Molecular Sciences. 2020; 21(6):2245. https://doi.org/10.3390/ijms21062245
Chicago/Turabian StyleMoreso, Francesc, Joana Sellarès, María José Soler, and Daniel Serón. 2020. "Transcriptome Analysis in Renal Transplant Biopsies Not Fulfilling Rejection Criteria" International Journal of Molecular Sciences 21, no. 6: 2245. https://doi.org/10.3390/ijms21062245
APA StyleMoreso, F., Sellarès, J., Soler, M. J., & Serón, D. (2020). Transcriptome Analysis in Renal Transplant Biopsies Not Fulfilling Rejection Criteria. International Journal of Molecular Sciences, 21(6), 2245. https://doi.org/10.3390/ijms21062245