Unknown,Transcriptomics,Genomics,Proteomics

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Innate and adaptive immune responses associate with progressive histological damage of renal allografts

ABSTRACT: CONTEXT Slowly progressive chronic tubulo-interstitial damage jeopardizes long-term renal allograft survival. Both immune and non-immune mechanisms are thought to contribute, but the most promising targets for timely intervention have not been identified. OBJECTIVE In the current study we seek to determine the driving force behind progressive histological damage of renal allografts, without the interference of donor pathology, delayed graft function and acute graft rejection. DESIGN We used microarrays to examine whole genome expression profiles in renal allograft protocol biopsies, and analyzed the correlation between gene expression and the histological appearance over time. The gene expression profiles in these protocol biopsies were then compared with gene expression of biopsies with acute T-cell mediated rejection. PATIENTS Human renal allograft biopsies (N=120) were included: 96 rejection-free protocol biopsies and 24 biopsies with T-cell mediated acute rejection. RESULTS In this highly cross-validated study, we demonstrate the significant association of established, ongoing and future chronic histological damage with regulation of adaptive immune gene expression (T-cell and B-cell transcript sets) and innate immune response gene expression (dendritic cell, NK-cell, mast cell and granulocyte transcripts). We demonstrate the ability of gene expression analysis to perform as a quantitative marker for ongoing inflammation with a wide dynamic range: from subtle subhistological inflammation prior to development of chronic damage, over moderate subclinical inflammation associated with chronic histological damage, to marked inflammation of Banff-grade acute T-cell mediated rejection. CONCLUSION Progressive chronic histological damage after kidney transplantation is associated with significant regulation of both innate and adaptive immune responses, months before the histological lesions appear. This study therefore corroborates the hypothesis that quantitative inflammation below the diagnostic threshold of classic T-cell or antibody-mediated rejection is associated with early subclinical stages of progressive renal allograft damage. We used microarrays to examine whole genome expression profiles in renal allograft protocol biopsies, and analyzed the correlation between gene expression and the histological appearance over time. The gene expression profiles in these protocol biopsies were then compared with gene expression of biopsies with acute T-cell mediated rejection. Human renal allograft biopsies (N=120) were included: 96 rejection-free protocol biopsies and 24 biopsies with T-cell mediated acute rejection.

ORGANISM(S): Homo sapiens

SUBMITTER: Maarten Naesens

PROVIDER: E-GEOD-25902 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:CONTEXT Slowly progressive chronic tubulo-interstitial damage jeopardizes long-term renal allograft survival. Both immune and non-immune mechanisms are thought to contribute, but the most promising targets for timely intervention have not been identified. OBJECTIVE In the current study we seek to determine the driving force behind progressive histological damage of renal allografts, without the interference of donor pathology, delayed graft function and acute graft rejection. DESIGN We used microarrays to examine whole genome expression profiles in renal allograft protocol biopsies, and analyzed the correlation between gene expression and the histological appearance over time. The gene expression profiles in these protocol biopsies were then compared with gene expression of biopsies with acute T-cell mediated rejection. PATIENTS Human renal allograft biopsies (N=120) were included: 96 rejection-free protocol biopsies and 24 biopsies with T-cell mediated acute rejection. RESULTS In this highly cross-validated study, we demonstrate the significant association of established, ongoing and future chronic histological damage with regulation of adaptive immune gene expression (T-cell and B-cell transcript sets) and innate immune response gene expression (dendritic cell, NK-cell, mast cell and granulocyte transcripts). We demonstrate the ability of gene expression analysis to perform as a quantitative marker for ongoing inflammation with a wide dynamic range: from subtle subhistological inflammation prior to development of chronic damage, over moderate subclinical inflammation associated with chronic histological damage, to marked inflammation of Banff-grade acute T-cell mediated rejection. CONCLUSION Progressive chronic histological damage after kidney transplantation is associated with significant regulation of both innate and adaptive immune responses, months before the histological lesions appear. This study therefore corroborates the hypothesis that quantitative inflammation below the diagnostic threshold of classic T-cell or antibody-mediated rejection is associated with early subclinical stages of progressive renal allograft damage.

Project description:Chronic injury in kidney transplants remains a major cause of graft loss. The aim of this study was to identify a predictive gene set capable of classifying renal grafts at risk for progressive injury due to fibrosis.The Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicenter study. Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by microarray. Genes were sought which correlated with subsequent 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters. We identified a set of 13 genes that was independently predictive for the development of fi brosis at 1 year (ie, CADI-12 >=2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fi brosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by Affymetrix exon arrays. Genes that were specifically correlated with 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters were identified by correlation analysis. The minimal geneset was further identified to predict the progressors/non progressors and validated by qPCR in an independent cohort and two public datasets. This dataset is part of the TransQST collection.

Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining “molecular Banff” signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set here comprises a small validation batch of renal allograft biopsies for clinical indications plus control normal kidney samples from patients at Hôpital Tenon, Paris (second batch) that complements the first batch of 60 samples. We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Keywords: disease state analysis 4 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to Banff'97 criteria and 2 normal kidney samples.

Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining âmolecular Banffâ signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at HÃ´pital Tenon, Paris (February 2003 until September 2004) and few respective patients from HÃ´pital BicÃªtre, Paris, HÃ´pital Pellegrin, Bordeaux, and HÃ´pital Dupuytren, Limoges, plus control normal kidney samples from HÃ´pital Tenon, Paris, France (first batch). We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Experiment Overall Design: 47 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to BANFF'97 criteria, and 13 normal kidney samples as controls.

Project description:Steroid-refractory acute rejection is a risk factor for inferior renal allograft outcome. We aimed to gain insight into the mechanisms underlying steroid resistance by identifying novel molecular markers of steroid-refractory acute rejection. Eighty-three kidney transplant recipients (1995-2005), who were treated with methylprednisolone during a first acute rejection episode, were included in this study. Gene expression patterns were investigated in a discovery cohort of 36 acute rejection biopsies, and verified in a validation cohort of 47 acute rejection biopsies. In the discovery set, expression of metallothioneins (MT) was significantly (P<0.000001) associated with decreased response to steroid treatment. Multivariate analysis resulted in a predictive model containing MT-1 as an independent covariate (AUC=0.88, P<0.0000001). In the validation set, MT-1 expression was also significantly associated with steroid resistance (P=0.029). Metallothionein expression was detected in macrophages and tubular epithelial cells. Parallel to the findings in patients, in vitro experiments of peripheral blood mononuclear cells from 11 donors showed that non-response to methylprednisolone treatment is related to highly elevated MT levels. High expression of metallothioneins in renal allografts is associated with resistance to steroid treatment. Metallothioneins regulate intracellular concentrations of zinc, through which they may diminish the zinc-requiring anti-inflammatory effect of the glucocorticoid receptor. Transcriptional profiles of 40 renal allograft biopsy samples were analyzed using Illumina HumanRef-8 v3.0 BeadChips (Illumina, San Diego, CA). Expression profiles were investigated in total RNA isolated from biopsy samples of 18 patients with steroid responsive acute rejection and 18 patients with steroid resistant acute rejection. Four biopsies, taken during acute decrease of graft function but with no histomorphologic indication of rejection, were included as controls.

Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining M-bM-^@M-^\molecular BanffM-bM-^@M-^] signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at HM-CM-4pital Tenon, Paris (February 2003 until September 2004) and few respective patients from HM-CM-4pital BicM-CM-*tre, Paris, HM-CM-4pital Pellegrin, Bordeaux, and HM-CM-4pital Dupuytren, Limoges, plus control normal kidney samples from HM-CM-4pital Tenon, Paris, France (first batch). We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Keywords: disease state analysis Keywords: Expression profiling by array 16 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to BANFF'97 criteria (additional samples associated with GSE9489)

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Innate and adaptive immune responses associate with progressive histological damage of renal allografts

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