Transcriptomic analyzes of bone biopsies from patients with renal osteodystrophy (ROD)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA798962 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17677200.fastq.gz | Fastqsanger.gz | |||
| SRR17677201.fastq.gz | Fastqsanger.gz | |||
| SRR17677202.fastq.gz | Fastqsanger.gz | |||
| SRR17677203.fastq.gz | Fastqsanger.gz | |||
| SRR17677204.fastq.gz | Fastqsanger.gz |
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Project description:Kidney transplants that develop dysfunction or proteinuria after one year post transplant are at considerable risk for progression to renal failure. Identifying the molecules associated with graft failure could potentially lead to interventions that would slow the progression of organ failure. We analyzed the relationship between gene expression in late biopsies for cause in human kidney transplants and subsequent graft loss, and assessed the predictive value of gene expression. We evaluated the performance of these genes in an independent , and in a population of early biopsies that have a very low risk of subsequent graft failure. All consenting renal transplant patients undergoing biopsies for cause as standard of care between 09/2004 and 10/2007 at the university of Alberta or between 11/2006 and 02/2007 at the University of Illinois were included in the analysis. In addition, biopsies obtained from Minnesota between 09/2006 and 09/2007 were used as an independent . In addition to the cores required for standard histopathology, we collected one core for gene expression studies. the relationship between gene expression in the biopsy and subsequent graft loss was analyzed. This dataset is part of the TransQST collection.
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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.
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