Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA125335 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Full title: Expression data from whole blood gene expression analysis of stable and acute rejection pediatric kidney transplant patients Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression studies of blood cells and great difficulty in identifying the cellular source of any perturbations. Ideally, one would like to perform differential expression analysis between patient groups for each of the cell-types within a tissue but this is impractical and prohibitively expensive. This dataset is the validation dataset used to test the csSAM gene expression deconvolution algorithm as reported in the accompanying paper. Whole blood gene expression measurements for 24 pediatric renal transplant patients were analyzed on human specific HGU133V2.0 (+) whole genome expression arrays. Blood drawn using PaxGene Blood RNA Tubes (PreAnalytiX, Qiagen).
2010-04-06 | E-GEOD-20300 | biostudies-arrayexpress
Project description:In the present work, we have used whole genome expression profiling of peripheral blood samples from 51 patients with biopsy-proven acute kidney transplant rejection and 24 patients with excellent function and biopsy-proven normal transplant histology. The results demonstrate that there are 1738 probesets on the Affymetrix HG-U133 Plus 2.0 GeneChip representing 1472 unique genes which are differentially expressed in the peripheral blood during an acute kidney transplant rejection. By ranking these results we have identified minimal sets of 50 to 150 probesets with predictive classification accuracies for AR of greater than 90% established with several different prediction tools including DLDA and PAM. We have demonstrated that a subset of peripheral blood gene expression signatures can also diagnose four different subtypes of AR (Banff Borderline, IA, IB and IIA) and the top 100 ranked classifiers have greater than 89% predictive accuracy. Finally, we have demonstrated that there are gene signatures for early and late AR defined as less than or greater than one year post-transplant with greater than 86% predictive accuracies. We also confirmed that there are 439 time-independent gene classifiers for AR. Based on these results, we conclude that peripheral blood gene expression profiling can be used to diagnose AR at any time in the first 5 years post-transplant in the setting of acute kidney transplant dysfunction not caused by BK nephropathy, other infections, drug-induced nephrotoxicity or ureteral obstruction. Keywords: kidney transplantation, peripheral blood, DNA microarrays, acute kidney rejection, biomarkers Microarray profiles of peripheral blood from 51 biopsy-proven acute kidney rejection (AR) and 24 well-functioning kidney transplants were randomized and compared using class comparisons, network and biological function analyses.
2014-03-01 | E-GEOD-15296 | biostudies-arrayexpress
Project description:Kidney transplantation is the treatment of choice for patients with end-stage chronic kidney disease (ESKD). Despite the usefulness of transplantation as replacement therapy, long-term graft survival represents a major challenge for transplant immunology. Although nowadays there has been an advance in understanding immunological mechanisms mediating rejection, and the improvement of immunomodulation therapies, there are still underlying molecular processes marking an important variability among patients, and presumably influencing allograft rejection. With our analysis we explored differences in gene expression by Next Generation Sequencing implementing RNA-Seq in biopsies, blood and urine from kidney transplant patients with acute and chronic rejection. For this, we performed an intra-outcome analysis simultaneously in acute and chronic rejection, with which we sought: 1. To identify differences in gene expression between peripheral blood vs renal tissue and peripheral blood vs urine in acute rejection and chronic rejection; 2. To identify the level of agreement in gene expression between renal tissue and urine in acute rejection and chronic rejection and 3. To identify genes and biological processes associated with acute rejection and chronic rejection that could be potentially detected in blood, and simultaneously in urine and biopsy in acute rejection and in chronic rejection.
2023-12-31 | GSE242302 | GEO
Project description:In the present work, we have used whole genome expression profiling of peripheral blood samples from 51 patients with biopsy-proven acute kidney transplant rejection and 24 patients with excellent function and biopsy-proven normal transplant histology. The results demonstrate that there are 1738 probesets on the Affymetrix HG-U133 Plus 2.0 GeneChip representing 1472 unique genes which are differentially expressed in the peripheral blood during an acute kidney transplant rejection. By ranking these results we have identified minimal sets of 50 to 150 probesets with predictive classification accuracies for AR of greater than 90% established with several different prediction tools including DLDA and PAM. We have demonstrated that a subset of peripheral blood gene expression signatures can also diagnose four different subtypes of AR (Banff Borderline, IA, IB and IIA) and the top 100 ranked classifiers have greater than 89% predictive accuracy. Finally, we have demonstrated that there are gene signatures for early and late AR defined as less than or greater than one year post-transplant with greater than 86% predictive accuracies. We also confirmed that there are 439 time-independent gene classifiers for AR. Based on these results, we conclude that peripheral blood gene expression profiling can be used to diagnose AR at any time in the first 5 years post-transplant in the setting of acute kidney transplant dysfunction not caused by BK nephropathy, other infections, drug-induced nephrotoxicity or ureteral obstruction. Keywords: kidney transplantation, peripheral blood, DNA microarrays, acute kidney rejection, biomarkers
2014-03-01 | GSE15296 | GEO
Project description:We used DNA microarrays (HG-U95Av2 GeneChips) to determine gene expression profiles for kidney biopsies and peripheral blood lymphocytes (PBLs) in transplant patients. Sample classes include kidney biopsies and PBLs from patients with 1) healthy normal donor kidneys, 2) well-functioning transplants with no clinical evidence of rejection, 3) kidneys undergoing acute rejection, and 4) transplants with renal dysfunction without rejection. Nomenclature for samples is as follows: 1) all sample names include either BX or PBL to indicate that they were derived from biopsies or PBLs respectively, 2) C indicates samples from healthy normal donors, 3) TX indicates samples from patients with well-functioning transplants with no clinical evidence of rejection, 3) AR indicates samples from transplant patients with kidneys undergoing acute rejection, 4) NR indicates samples from transplant patients with renal dysfunction without rejection. Abbreviations used to describe patient samples include the following: BX - Biopsy; PBL- Peripheral Blood Lymphocytes; CsA -Cyclosporine; MMF - Mycophenolate Mofetil; P - Prednisone; FK - Tacrolimus; SRL - Sirolimus; CAD -Cadaveric; LD - Live Donor; Scr - Serum Creatinine; ATN - Acute Tubular Necrosis CNI - Calcineurin Inhibitor; FSGS - Focal Segmental Glomerulosclerosis several array data sets did not pass quality control and were not analyzed. These include AR1PBL, NR4BX, and NR6PBL Keywords = DNA microarrays, gene expression, kidney, rejection, transplant Keywords: other. This dataset is part of the TransQST collection.
2004-07-14 | E-GEOD-1563 | biostudies-arrayexpress
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:Histological features of acute rejection can be detected in surveillance biopsies despite stable graft function and can negatively impact graft outcomes. However, routine surveillance biopsies for detection of subclinical rejection are not generally performed due to potential risks and costs associated with repeated biopsies. Noninvasive biomarkers are required to facilitate early detection of acute rejection and borderline changes. We examined the impact of histological abnormalities at 3-month in surveillance biopsies on graft outcomes in kidney transplant recipients from the prospective Genomics of Chronic Allograft Rejection (GoCAR) study. We then performed RNA sequencing on whole blood collected at the time of biopsy in 88 patients (22 vs. 66) to identify transcripts associated with histological abnormalities. Subjects with subclinical ACR or borderline ACR at 3 month (ACR-3) had higher risk of subsequent clinical acute rejection at 12 and 24 month (P < 0.05), more rapid functional decline (P < 0.05) and a decreased graft survival in adjusted cox analysis (P < 0.01) than patients with no abnormalities on 3-month biopsy. We then identified a 17-gene signature in peripheral blood that accurately diagnosed ACR-3.
2019-09-11 | GSE120396 | GEO
Project description:We used DNA microarrays (HG-U95Av2 GeneChips) to determine gene expression profiles for kidney biopsies and peripheral blood lymphocytes (PBLs) in transplant patients. Sample classes include kidney biopsies and PBLs from patients with 1) healthy normal donor kidneys, 2) well-functioning transplants with no clinical evidence of rejection, 3) kidneys undergoing acute rejection, and 4) transplants with renal dysfunction without rejection. Nomenclature for samples is as follows: 1) all sample names include either BX or PBL to indicate that they were derived from biopsies or PBLs respectively, 2) C indicates samples from healthy normal donors, 3) TX indicates samples from patients with well-functioning transplants with no clinical evidence of rejection, 3) AR indicates samples from transplant patients with kidneys undergoing acute rejection, 4) NR indicates samples from transplant patients with renal dysfunction without rejection. Abbreviations used to describe patient samples include the following: BX - Biopsy; PBL- Peripheral Blood Lymphocytes; CsA -Cyclosporine; MMF - Mycophenolate Mofetil; P - Prednisone; FK - Tacrolimus; SRL - Sirolimus; CAD -Cadaveric; LD - Live Donor; Scr - Serum Creatinine; ATN - Acute Tubular Necrosis CNI - Calcineurin Inhibitor; FSGS - Focal Segmental Glomerulosclerosis several array data sets did not pass quality control and were not analyzed. These include AR1PBL, NR4BX, and NR6PBL Keywords = DNA microarrays, gene expression, kidney, rejection, transplant Keywords: other
2004-07-14 | GSE1563 | GEO
Project description:Full title: Expression data from whole blood gene expression analysis of stable and acute rejection pediatric kidney transplant patients Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression studies of blood cells and great difficulty in identifying the cellular source of any perturbations. Ideally, one would like to perform differential expression analysis between patient groups for each of the cell-types within a tissue but this is impractical and prohibitively expensive. This dataset is the validation dataset used to test the csSAM gene expression deconvolution algorithm as reported in the accompanying paper.
2010-03-08 | GSE20300 | GEO
Project description:Histological features of acute rejection can be detected in surveillance biopsies despite stable graft function and can negatively impact graft outcomes. However, routine surveillance biopsies for detection of subclinical rejection are not generally performed due to potential risks and costs associated with repeated biopsies. Noninvasive biomarkers are required to facilitate early detection of acute rejection and borderline changes. We examined the impact of histological abnormalities at 3-month in surveillance biopsies on graft outcomes in kidney transplant recipients from the prospective Genomics of Chronic Allograft Rejection (GoCAR) study. We then performed RNA sequencing on whole blood collected at the time of biopsy in 88 patients (22 vs. 66) to identify transcripts associated with histological abnormalities. Subjects with subclinical ACR or borderline ACR at 3 month (ACR-3) had higher risk of subsequent clinical acute rejection at 12 and 24 month (P < 0.05), more rapid functional decline (P < 0.05) and a decreased graft survival in adjusted cox analysis (P < 0.01) than patients with no abnormalities on 3-month biopsy. We then identified a 17-gene signature in peripheral blood that accurately diagnosed ACR-3. The gene set was then validated for diagnosis of ACR-3 using microarray data (N=65; 12 vs. 53; 26 overlapping with the RNAseq cohort).
2019-09-11 | GSE120397 | GEO