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Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles.

ABSTRACT: OBJECTIVES:Renal transplantation is the preferred method for most patients with end-stage renal disease, however, acute renal allograft rejection is still a major risk factor for recipients leading to renal injury. To improve the early diagnosis and treatment of acute rejection, study on the molecular mechanism of it is urgent. METHODS:MicroRNA (miRNA) expression profile and mRNA expression profile of acute renal allograft rejection and well-functioning allograft downloaded from ArrayExpress database were applied to identify differentially expressed (DE) miRNAs and DE mRNAs. DE miRNAs targets were predicted by combining five algorithm. By overlapping the DE mRNAs and DE miRNAs targets, common genes were obtained. Differentially co-expressed genes (DCGs) were identified by differential co-expression profile (DCp) and differential co-expression enrichment (DCe) methods in Differentially Co-expressed Genes and Links (DCGL) package. Then, co-expression network of DCGs and the cluster analysis were performed. Functional enrichment analysis for DCGs was undergone. RESULTS:A total of 1270 miRNA targets were predicted and 698 DE mRNAs were obtained. While overlapping miRNA targets and DE mRNAs, 59 common genes were gained. We obtained 103 DCGs and 5 transcription factors (TFs) based on regulatory impact factors (RIF), then built the regulation network of miRNA targets and DE mRNAs. By clustering the co-expression network, 5 modules were obtained. Thereinto, module 1 had the highest degree and module 2 showed the most number of DCGs and common genes. TF CEBPB and several common genes, such as RXRA, BASP1 and AKAP10, were mapped on the co-expression network. C1R showed the highest degree in the network. These genes might be associated with human acute renal allograft rejection. CONCLUSIONS:We conducted biological analysis on integration of DE mRNA and DE miRNA in acute renal allograft rejection, displayed gene expression patterns and screened out genes and TFs that may be related to acute renal allograft rejection.

SUBMITTER: Huang SM

PROVIDER: S-EPMC4307466 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles.

Huang Shi-Ming SM Zhao Xia X Zhao Xue-Mei XM Wang Xiao-Ying XY Li Shan-Shan SS Zhu Yu-Hui YH

International journal of clinical and experimental medicine 20141215 12

<h4>Objectives</h4>Renal transplantation is the preferred method for most patients with end-stage renal disease, however, acute renal allograft rejection is still a major risk factor for recipients leading to renal injury. To improve the early diagnosis and treatment of acute rejection, study on the molecular mechanism of it is urgent.<h4>Methods</h4>MicroRNA (miRNA) expression profile and mRNA expression profile of acute renal allograft rejection and well-functioning allograft downloaded from A ...[more]

PMID: 25664019

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Project description:The data set contains 67 array (lymphochip cDNA array), published in N Engl J Med 2003 Jul 10;349(2):125-38. PMID: 12853585. TITLE: Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. BACKGROUND: The causes and clinical course of acute rejection vary, and it is not possible to predict graft outcome reliably on the basis of available clinical, pathological, and genetic markers. We hypothesized that previously unrecognized molecular heterogeneity might underlie some of the variability in the clinical course of acute renal allograft rejection and in its response to treatment. METHODS: We used DNA microarrays in a systematic study of gene-expression patterns in biopsy samples from normal and dysfunctional renal allografts. A combination of exploratory and supervised bioinformatic methods was used to analyze these profiles. : We found consistent differences among the gene-expression patterns associated with acute rejection, nephrotoxic effects of drugs, chronic allograft nephropathy, and normal kidneys. The gene-expression patterns associated with acute rejection suggested at least three possible distinct subtypes of acute rejection that, although indistinguishable by light microscopy, were marked by differences in immune activation and cellular proliferation. Since the gene-expression patterns pointed to substantial variation in the composition of immune infiltrates, we used immunohistochemical staining to define these subtypes further. This analysis revealed a striking association between dense CD20+ B-cell infiltrates and both clinical glucocorticoid resistance (P=0.01) and graft loss (P<0.001). CONCLUSIONS: Systematic analysis of gene-expression patterns provides a window on the biology and pathogenesis of renal allograft rejection. Biopsy samples from patients with acute rejection that are indistinguishable on conventional histologic analysis reveal extensive differences in gene expression, which are associated with differences in immunologic and cellular features and clinical course. The presence of dense clusters of B cells in a biopsy sample was strongly associated with severe graft rejection, suggesting a pivotal role of infiltrating B cells in acute rejection. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: normal vs disease Keywords: disease_state_design Using regression correlation

Project description:Acute graft rejection is an important clinical problem in renal transplantation and an adverse predictor for long term graft survival. Plasma biomarkers may offer an important option for post-transplant monitoring and permit timely and effective therapeutic intervention to minimize graft damage. This case-control discovery study (n = 32) used isobaric tagging for relative and absolute protein quantification (iTRAQ) technology to quantitate plasma protein relative concentrations in precise cohorts of patients with and without biopsy-confirmed acute rejection (BCAR). Plasma samples were depleted of the 14 most abundant plasma proteins to enhance detection sensitivity. A total of 18 plasma proteins that encompassed processes related to inflammation, complement activation, blood coagulation, and wound repair exhibited significantly different relative concentrations between patient cohorts with and without BCAR (p value <0.05). Twelve proteins with a fold-change >or=1.15 were selected for diagnostic purposes: seven were increased (titin, lipopolysaccharide-binding protein, peptidase inhibitor 16, complement factor D, mannose-binding lectin, protein Z-dependent protease and beta(2)-microglobulin) and five were decreased (kininogen-1, afamin, serine protease inhibitor, phosphatidylcholine-sterol acyltransferase, and sex hormone-binding globulin) in patients with BCAR. The first three principal components of these proteins showed clear separation of cohorts with and without BCAR. Performance improved with the inclusion of sequential proteins, reaching a primary asymptote after the first three (titin, kininogen-1, and lipopolysaccharide-binding protein). Longitudinal monitoring over the first 3 months post-transplant based on ratios of these three proteins showed clear discrimination between the two patient cohorts at time of rejection. The score then declined to baseline following treatment and resolution of the rejection episode and remained comparable between cases and controls throughout the period of quiescent follow-up. Results were validated using ELISA where possible, and initial cross-validation estimated a sensitivity of 80% and specificity of 90% for classification of BCAR based on a four-protein ELISA classifier. This study provides evidence that protein concentrations in plasma may provide a relevant measure for the occurrence of BCAR and offers a potential tool for immunologic monitoring.

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.

Dataset Information

Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles.

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Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles.

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