Gene expression Analysis in Renal Allograft Biopsies from Deceased and Living Donors
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PROVIDER: PRJNA740015 | ENA |
REPOSITORIES: ENA
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Project description:The survival of transplant kidneys using deceased donors (DD) is inferior to living donors (LD). In this study, we conducted a whole-transcriptome expression analysis of 24 human kidney biopsies paired 30 minutes and 3 months post-transplantation using DD and LD. The transcriptome profile was found significantly different between two time points regardless of donor types. There were 446 differentially expressed genes (DEGs) between DD and LD at 30 minutes and 146 DEGs at 3 months, with 25 genes common to both time points. These DEGs reflected donor injury and acute immune responses associated with inflammation and cell death as early as at 30 minutes, which could be a precious window of potential intervention. DEGs at 3 months mainly represented the changes of adaptive immunity, immunosuppressive treatment, remodeling or fibrosis via different networks and signaling pathways. The expression level of 20 DEGs involved in kidney diseases, and 10 genes found dysregulated at 30 minutes were validated by quantitative PCR (qPCR) in the 24 samples analyzed by microarray as well as in a validation cohort of 33 unpaired allograft biopsies. Their expression levels were found correlated with renal function and histology at 12 months, suggesting they could be potential biomarkers to predict kidney function. This analysis revealed that SERPINA3, SLPI and CBF is upregulated at 30 minutes in DD compared to LD, whereas FTCD and TASPN7 are up-regulated at both time points; at 3 months VCAN and TIMP1 are up-regulated, whereas FOS is decreased in both donors. Taken together, divergent transcriptomic signatures between DD and LD, and changed by the time post-transplantation, might contribute to different allograft survival of two type kidney donors. Some DEGs such as FTCD and TASPN7 could be novel biomarkers not only for timely diagnosis, but also early for precise genetic intervention at donor preservation, implantation and post-transplantation, in particular to effectively improve the quality and survival of DD.
2021-06-23 | GSE178689 | GEO
Project description:Profiling of 0-hour wedge biopsies from deceased kidney donors (without signs of AKI) to recognize differences in age groups.
2016-04-25 | GSE60807 | GEO
Project description:Background: Strategies to improve long term renal allograft survival have been directed to recipient dependent mechanisms of renal allograft injury. In contrast, no such efforts have been made to optimize organ quality in the donor. In order to get insight into the deleterious gene pathways expressed at different time points during deceased kidney transplantation, transcriptomics was performed on kidney biopsies from a large cohort of deceased kidney transplants. Methods: A total of 554 kidney biopsies were taken from living and deceased donor kidneys at donation, after cold ischemia and after reperfusion. Transcriptomics by means of whole genome micro-array analyses followed by functional pathway analyses was performed. Results: Oxidative stress and complement- and coagulation pathways were uncovered as potential pathways for intervention in deceased donors. No genes were found to be differentially expressed between donation and cold ischemia. After reperfusion, pathways related to oxidative stress, NOD-like signalling, MAPK, cytokine-cytokine receptor, complement- and-coagulation and chemokines were enriched in kidneys from deceased organ donors. Pathways related to prolonged and worsening deprivation of oxygen were associated with delayed graft function of DCD grafts. Conclusions: The present study reveals oxidative stress and enrichment of complement and coagulation pathways in deceased donor kidneys. Future intervention therapies to optimize donor organ quality and prolong allograft survival should target oxidative stress and innate immune activation in the donor.
2015-04-02 | GSE43974 | GEO
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
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 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. Keywords: disease state analysis
2009-02-24 | GSE9489 | GEO
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: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:The survival of transplant kidneys using deceased donors (DD) is inferior to living donors (LD). In this study, we conducted a whole-transcriptome expression analysis of 24 human kidney biopsies paired at 30 minutes and 3 months post-transplantation using DD and LD. The transcriptome profile was found significantly different between two time points regardless of donor types. There were 446 differentially expressed genes (DEGs) between DD and LD at 30 minutes and 146 DEGs at 3 months, with 25 genes common to both time points. These DEGs reflected donor injury and acute immune responses associated with inflammation and cell death as early as at 30 minutes, which could be a precious window of potential intervention. DEGs at 3 months mainly represented the changes of adaptive immunity, immunosuppressive treatment, remodeling or fibrosis via different networks and signaling pathways. The expression levels of 20 highly DEGs involved in kidney diseases and 10 genes dysregulated at 30 minutes were found correlated with renal function and histology at 12 months, suggesting they could be potential biomarkers. These genes were further validated by quantitative polymerase chain reaction (qPCR) in 24 samples analysed by microarray, as well as in a validation cohort of 33 time point unpaired allograft biopsies. This analysis revealed that SERPINA3, SLPI and CBF were up-regulated at 30 minutes in DD compared to LD, while FTCD and TASPN7 were up-regulated at both time points. At 3 months, SERPINA3 was up-regulated in LD, but down-regulated in DD, with increased VCAN and TIMP1, and decreased FOS, in both donors. Taken together, divergent transcriptomic signatures between DD and LD, and changed by the time post-transplantation, might contribute to different allograft survival of two type kidney donors. Some DEGs including FTCD and TASPN7 could be novel biomarkers not only for timely diagnosis, but also for early precise genetic intervention at donor preservation, implantation and post-transplantation, in particular to effectively improve the quality and survival of DD.
| S-EPMC8282197 | biostudies-literature
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 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. Keywords: disease state analysis Keywords: Expression profiling by array
2009-08-28 | GSE17861 | GEO
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