Pretransplant transcriptomic signature in peripheral blood predicts acute rejection and renal allograft loss
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA449520 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6978430.fastq.gz | Fastqsanger.gz | |||
| SRR6978440.fastq.gz | Fastqsanger.gz | |||
| SRR6978450_1.fastq.gz | Fastqsanger.gz | |||
| SRR6978450_2.fastq.gz | Fastqsanger.gz | |||
| SRR6978460_1.fastq.gz | Fastqsanger.gz |
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Project description:Early development of acute rejection after kidney transplantation is associated with diminished long-term graft survival. Predicting early acute rejection (EAR) at the time of transplant is important to risk-stratify patients and titrate immunosuppression accordingly. We performed whole-blood RNA sequencing at the time of transplant in 235 kidney transplant recipients enrolled in a prospective-cohort study [one discovery set (N=81), two validation sets (N=74 and N=80)] and evaluated the relationship with EAR and graft loss. We identified a blood based 23-gene set in recipients at the time of transplant that predicts the risk of EAR and is associated with late AR and allograft loss. This gene set is an important new tool to risk-stratify recipients before kidney transplantation and help guide immunosuppressive therapy accordingly.
2019-10-01 | GSE112927 | GEO
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
2003-07-01 | E-GEOD-343 | biostudies-arrayexpress
Project description:Compromised renal function after renal allograft transplantation often results in anemia in the recipient. Molecular mechanisms leading to anemia during acute rejection are not fully understood; inadequate erythropoietin production and iron deficiency have been reported to be the main contributors. To increase our understanding of the molecular events underlying anemia in acute rejection, we analyzed the gene expression profiles of peripheral blood lymphocytes (PBL) from four pediatric renal allograft recipients with acute rejection and concurrent anemia, using DNA microarrays containing 9000 human cDNA clones (representing 7469 unique genes). In these anemic rejecting patients, an 'erythropoiesis cluster' of 11 down-regulated genes was identified, involved in hemoglobin transcription and synthesis, iron and folate binding and transport. Additionally, some alloimmune response genes were simultaneously down-regulated. An independent data set of 36 PBL samples, some with acute rejection and some with concurrence of acute rejection and anemia, were analyzed to support a possible association between acute rejection and anemia. In conclusion, analysis using DNA microarrays has identified a cluster of genes related to hemoglobin synthesis and/or erythropoeisis that was altered in kidneys with renal allograft rejection compared with normal kidneys. The possible relationship between alterations in the expression of this cluster, reduced renal function, the alloimmune process itself, and other influences on the renal transplant awaits further analysis. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Keywords: disease_state_design
2006-01-06 | GSE3976 | GEO
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
2003-07-01 | GSE343 | GEO
Project description:Compromised renal function after renal allograft transplantation often results in anemia in the recipient. Molecular mechanisms leading to anemia during acute rejection are not fully understood; inadequate erythropoietin production and iron deficiency have been reported to be the main contributors. To increase our understanding of the molecular events underlying anemia in acute rejection, we analyzed the gene expression profiles of peripheral blood lymphocytes (PBL) from four pediatric renal allograft recipients with acute rejection and concurrent anemia, using DNA microarrays containing 9000 human cDNA clones (representing 7469 unique genes). In these anemic rejecting patients, an 'erythropoiesis cluster' of 11 down-regulated genes was identified, involved in hemoglobin transcription and synthesis, iron and folate binding and transport. Additionally, some alloimmune response genes were simultaneously down-regulated. An independent data set of 36 PBL samples, some with acute rejection and some with concurrence of acute rejection and anemia, were analyzed to support a possible association between acute rejection and anemia. In conclusion, analysis using DNA microarrays has identified a cluster of genes related to hemoglobin synthesis and/or erythropoeisis that was altered in kidneys with renal allograft rejection compared with normal kidneys. The possible relationship between alterations in the expression of this cluster, reduced renal function, the alloimmune process itself, and other influences on the renal transplant awaits further analysis.
2005-11-01 | E-SMDB-2730 | biostudies-arrayexpress
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.
2009-02-24 | E-GEOD-9492 | biostudies-arrayexpress
Project description:Transcriptomic analyses of renal allograft biopsies reveal conserved rejection signatures and molecular pathways. GEO SuperSeries. This dataset is part of the TransQST collection.
2009-02-24 | E-GEOD-9493 | biostudies-arrayexpress
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 Hôpital Tenon, Paris (February 2003 until September 2004) and few respective patients from Hôpital Bicêtre, Paris, Hôpital Pellegrin, Bordeaux, and Hôpital Dupuytren, Limoges, plus control normal kidney samples from Hôpital 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.
2009-02-28 | E-GEOD-9489 | biostudies-arrayexpress
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)
2009-08-27 | E-GEOD-17861 | biostudies-arrayexpress
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
2009-02-24 | GSE9492 | GEO