Dataset Information

Pretransplant transcriptomic signature in peripheral blood predicts early acute rejection.

ABSTRACT: Commonly available clinical parameters fail to predict early acute cellular rejection (EAR, occurring within 6 months after transplant), a major risk factor for graft loss after kidney transplantation. We performed whole-blood RNA sequencing at the time of transplant in 235 kidney transplant recipients enrolled in a prospective cohort study (Genomics of Chronic Allograft Rejection [GoCAR]) and evaluated the relationship of pretransplant transcriptomic profiles with EAR. EAR was associated with downregulation of NK and CD8+ T cell gene signatures in pretransplant blood. We identified a 23-gene set that predicted EAR in the discovery (n = 81, and AUC = 0.80) and validation (n = 74, and AUC = 0.74) sets. Exclusion of recipients with 5 or 6 HLA donor mismatches increased the AUC to 0.89. The risk score derived from the gene set was also significantly associated with acute cellular rejection after 6 months, antibody-mediated rejection and/or de novo donor-specific antibodies, and graft loss in a cohort of 154 patients, combining the validation set and additional GoCAR patients with surveillance biopsies between 6 and 24 months (n = 80) posttransplant. This 23-gene set is a potentially important new tool for determination of the recipient's immunological risk before kidney transplantation, and facilitation of an individualized approach to immunosuppressive therapy.

SUBMITTER: Zhang W

PROVIDER: S-EPMC6629121 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Pretransplant transcriptomic signature in peripheral blood predicts early acute rejection.

Zhang Weijia W Yi Zhengzi Z Wei Chengguo C Keung Karen L KL Sun Zeguo Z Xi Caixia C Woytovich Christopher C Farouk Samira S Gallon Lorenzo L Menon Madhav C MC Magee Ciara C Najafian Nader N Samaniego Milagros D MD Djamali Arjang A Alexander Stephen I SI Rosales Ivy A IA Smith Rex Neal RN O'Connell Philip J PJ Colvin Robert R Cravedi Paolo P Murphy Barbara B

JCI insight 20190606 11

Commonly available clinical parameters fail to predict early acute cellular rejection (EAR, occurring within 6 months after transplant), a major risk factor for graft loss after kidney transplantation. We performed whole-blood RNA sequencing at the time of transplant in 235 kidney transplant recipients enrolled in a prospective cohort study (Genomics of Chronic Allograft Rejection [GoCAR]) and evaluated the relationship of pretransplant transcriptomic profiles with EAR. EAR was associated with d ...[more]

PMID: 31167967

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Project description:Acute rejection (AR) is closely associated with renal allograft dysfunction. Here, we utilised RNA sequencing (RNA-Seq) and bioinformatic methods to characterise the peripheral blood mononuclear cells (PBMCs) of patients with acute renal allograft rejection. Pretransplant blood samples were collected from 32 kidney allograft donors and 42 corresponding recipients with biopsies classified as T cell-mediated rejection (TCMR, n = 18), antibody-mediated rejection (ABMR, n = 5), and normal/non-specific changes (non-AR, n = 19). The patients with TCMR and ABMR were assigned to the AR group, and the patients with normal/non-specific changes (n = 19) were assigned to the non-AR group. We analysed RNA-Seq data for identifying differentially expressed genes (DEGs), and then gene ontology (GO) analysis, Reactome, and ingenuity pathway analysis (IPA), protein-protein interaction (PPI) network, and cell-type enrichment analysis were utilised for bioinformatics analysis. We identified DEGs in the PBMCs of the non-AR group when compared with the AR, ABMR, and TCMR groups. Pathway and GO analysis showed significant inflammatory responses, complement activation, interleukin-10 (IL-10) signalling pathways, classical antibody-mediated complement activation pathways, etc., which were significantly enriched in the DEGs. PPI analysis showed that IL-10, VEGFA, CXCL8, MMP9, and several histone-related genes were the hub genes with the highest degree scores. Moreover, IPA analysis showed that several proinflammatory pathways were upregulated, whereas antiinflammatory pathways were downregulated. The combination of NFSF14+TANK+ANKRD 33 B +HSPA1B was able to discriminate between AR and non-AR with an AUC of 92.3% (95% CI 82.8-100). Characterisation of PBMCs by RNA-Seq and bioinformatics analysis demonstrated gene signatures and biological pathways associated with AR. Our study may provide the foundation for the discovery of biomarkers and an in-depth understanding of acute renal allograft rejection.

Project description:BackgroundSubclinical acute rejection (subAR) can only be diagnosed by protocol biopsy and is correlated with worse graft outcomes. However, noninvasive biomarkers of subAR are lacked for kidney transplantation recipients in clinic. This study aims to utilize to construct a peripheral blood-based gene signature for subAR diagnosis after kidney transplantation.MethodsAfter systematically screening databases, two cohorts of high quality with 3-month blood profiles and biopsy-proven graft status from the Gene Expression Omnibus databases were employed as training and validation cohorts. Then, the support vector machine recursive feature elimination (SVM-RFE) and the least absolute shrinkage and selection operator (LASSO) logistic regression were used to identify key biomarkers for subAR. Subsequently, the stepwise logistic regression method was applied to construct a gene signature for subAR in the training cohort. Patients were divided into high-risk and low-risk groups based on the cutoff point identified by the receiver operating characteristic (ROC) curve. Then, the signature was validated in a validation cohort with fixed formula. The single-sample gene set enrichment analysis was used to estimate immune cells in the blood.ResultsFifty key biomarkers were filtered out with the machine learning algorithms. Then, a novel six-gene signature was constructed using the LASSO and stepwise logistic regression method. The signature had high accuracy in both training [area under the curve (AUC) =0.923] and validation cohort (AUC =0.855). Additionally, these six genes were found to have significant and consistent relationships with blood immune cells in both cohorts, especially for T cells subtypes.ConclusionsWe developed and validated a novel noninvasive six-gene signature based on peripheral blood to diagnose subAR, which offered a potential tool for clinical practice. The six-gene signature offered a potential method to monitor patients following transplantation and make a timely intervention.

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Pretransplant transcriptomic signature in peripheral blood predicts early acute rejection.

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Pretransplant transcriptomic signature in peripheral blood predicts early acute rejection.

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