Discovery and cross-validation of peripheral blood and renal biopsy gene expression signatures from ethnically diverse kidney transplant populations

Blood Gene Signature as a Biomarker for Subclinical Kidney Allograft Rejection: Where Are We?

The observation decades ago that inflammatory injuries because of an alloimmune response might be present even in the absence of concomitant clinical impairment in allograft function conduced to the later definition of subclinical rejection. Many studies have investigated the different subclinical rejections defined according to the Banff classification (subclinical T cell-mediated rejection and antibody-mediated rejection), overall concluding that these episodes worsened long-term allograft function and survival. These observations led several transplant teams to perform systematic protocolar biopsies to anticipate treatment of rejection episodes and possibly prevent allograft loss. Paradoxically, the invasive characteristics and associated logistics of such procedures paved the way to investigate noninvasive biomarkers (urine and blood) of subclinical rejection. Among them, several research teams proposed a blood gene signature developed from cohort studies, most of which achieved excellent predictive values for the occurrence of subclinical rejection, mainly antibody-mediated rejection. Interestingly, although all identified genes relate to immune subsets and pathways involved in rejection pathophysiology, very few transcripts are shared among these sets of genes, highlighting the heterogenicity of such episodes and the difficult but mandatory need for external validation of such tools. Beyond this, their application and value in clinical practice remain to be definitively demonstrated in both biopsy avoidance and prevention of clinical rejection episodes. Their combination with other biomarkers, either epidemiological or biological, could contribute to a more accurate picture of a patient's risk of rejection and guide clinicians in the follow-up of kidney transplant recipients.

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

Discovery of Biomarkers Related to Interstitial Fibrosis and Tubular Atrophy among Kidney Transplant Recipients by mRNA-Sequencing

Interstitial fibrosis and tubular atrophy (IF/TA) after kidney transplantation causes a chronic deterioration of graft function. IF/TA can be diagnosed by means of a graft biopsy, which is a necessity as non-invasive diagnostic methods are unavailable. In this study, we identified IF/TA-related differentially expressed genes (DEGs) through next-generation sequencing using peripheral blood mononuclear cells. Blood samples from kidney transplant recipients undergoing standard immunosuppressive therapy (tacrolimus/mycophenolate mofetil or mycophenolate sodium/steroid) and diagnosed as IF/TA (n = 41) or normal (controls; n = 41) at their one-year protocol biopsy were recruited between January of 2020 and August of 2020. DEGs were derived through mRNA sequencing and validated by means of a quantitative real-time polymerase chain reaction. We identified 34 DEGs related to IF/TA. ADAMTS2, PLIN5, CLDN9, and KCNJ15 demonstrated a log2(fold change) of >1.5 and an area under the receiver operating characteristic curve (AUC) value of >0.6, with ADAMTS2 showing the largest AUC value and expression levels, which were 3.5-fold higher in the IF/TA group relative to that observed in the control group. We identified and validated DEGs related to IF/TA progression at one-year post-transplantation. Specifically, we identified ADAMTS2 as a potential IF/TA biomarker.

Integrative Analysis of Prognostic Biomarkers for Acute Rejection in Kidney Transplant Recipients

BACKGROUND

Noninvasive biomarkers may predict adverse events such as acute rejection after kidney transplantation and may be preferable to existing methods because of superior accuracy and convenience. It is uncertain how these biomarkers, often derived from a single study, perform across different cohorts of recipients.

METHODS

Using a cross-validation framework that evaluates the performance of biomarkers, the aim of this study was to devise an integrated gene signature set that predicts acute rejection in kidney transplant recipients. Inclusion criteria were publicly available datasets of gene signatures that reported acute rejection episodes after kidney transplantation. We tested the predictive probability for acute rejection using gene signatures within individual datasets and validated the set using other datasets. Eight eligible studies of 1454 participants, with a total of 512 acute rejections episodes were included.

RESULTS

All sets of gene signatures had good positive and negative predictive values (79%-96%) for acute rejection within their own cohorts, but the predictability reduced to <50% when tested in other independent datasets. By integrating signature sets with high specificity scores across all studies, a set of 150 genes (included CXCL6, CXCL11, OLFM4, and PSG9) which are known to be associated with immune responses, had reasonable predictive values (varied between 69% and 90%).

CONCLUSIONS

A set of gene signatures for acute rejection derived from a specific cohort of kidney transplant recipients do not appear to provide adequate prediction in an independent cohort of transplant recipients. However, the integration of gene signature sets with high specificity scores may improve the prediction performance of these markers.

An age-independent gene signature for monitoring acute rejection in kidney transplantation

Acute rejection (AR) remains a significant problem that negatively impacts long-term renal allograft survival. Numerous therapies are used to prevent AR that differ by center and recipient age. This variability confounds diagnostic methods. Methods: To develop an age-independent gene signature for AR effective across a broad array of immunosuppressive regimens, we compiled kidney transplant biopsy (n=1091) and peripheral blood (n=392) gene expression profiles from 12 independent public datasets. After removing genes differentially expressed in pediatric and adult patients, we compared gene expression profiles from biopsy and peripheral blood samples of patients with AR to those who were stable (STA), using Mann-Whitney U Tests with validation in independent testing datasets. We confirmed this signature in pediatric and adult patients (42 AR and 47 STA) from our institutional biorepository. Results: We identified a novel age-independent gene network that identified AR from both kidney and blood samples. We developed a 90-probe set signature targeting 76 genes that differentiated AR from STA and found an 8 gene subset (DIP2C, ENOSF1, FBXO21, KCTD6, PDXDC1, REXO2, HLA-E, and RAB31) that was associated with AR. Conclusion: We used publicly available datasets to create a gene signature of AR that identified AR irrespective of immunosuppression regimen or recipient age. This study highlights a novel model to screen and validate biomarkers across multiple treatment regimens.