Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA84825 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Molecular signatures for acute renal allograft rejection
| PRJNA854340 | ENA
Project description:Transcriptomic analyses of renal allograft biopsies reveal conserved rejection signatures and molecular pathways
2025-01-24 | E-GEOD-9493 | ExpressionAtlas
Project description:Expression of the IL-17/Th17 axis in the development of acute renal allograft rejection
2025-01-24 | E-GEOD-34437 | ExpressionAtlas
Project description:Molecular profiling of anemia in acute renal allograft rejection
| PRJNA94239 | ENA
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:Expression data from human renal allograft biopsies to investigate renal transplant rejection and failure
2025-01-24 | E-GEOD-21374 | ExpressionAtlas
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:A peripheral blood diagnostic test for acute rejection in renal transplantation.
2025-01-24 | E-GEOD-14346 | ExpressionAtlas
Project description:Acute renal allograft rejection is an important complication in kidney transplantation. Accurate diagnosis of rejection events is necessary for timely response and treatment. We illustrate the usefulness and biological relevance of selected multivariate approaches to detect rejection from genomic and proteomic signals. The data was used to study gene expression changes using whole genome microarray analysis of peripheral blood from subjects with acute rejection (n=20) and non-rejecting controls (n=20) to obtain insight into the molecular and biological causation of acute renal allograft rejection when combined with proteomics (iTRAQ) data for the same patients/time-points.
2014-04-26 | GSE46474 | 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