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SRP143514
Homo sapiens
12 Downloadable Samples
NextSeq 500
Description
Discarded human aortic valve samples from the second trimester, six from early (14, 15, 17 weeks), and six from late timepoints (20, 21, 22 weeks) were collected. RNA was isolated and cDNA libraries were sequenced. Network analysis of RNASeq data identified subnetworks of significantly increasing and decreasing transcripts; subsequent cluster analysis identified patterns of transcription through the time course. Pathway enrichment analysis determined the predominant biological processes at each interval.
Publication Title
No associated publication
Sample Metadata Fields
Sex, Specimen part
View Samples- Rattus norvegicus
- 2 Downloadable Samples
Affymetrix Rat Genome 230 2.0 Array (rat2302)
Description
Fine needle stimulation also known as acupuncture is a traditional Chinese medical practice which causes relief of pain. We demonstrated that it caused neovascularization and enhanced recovery of blood perfusion in a ischemic portion of skeletal muscle in rats with hindlimb ischemia. Therefore we evaluated the effect of fine needle stimulation on skeletal muscle at gene expression level
Publication Title
No associated publication
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 529 Downloadable Samples
- Affymetrix Human Gene 1.1 ST Array (hugene11st)
Description
Background: COPD is currently the fourth leading cause of death worldwide and predicted to rank third by 2020. Statins are commonly used lipid lowering agents with documented benefits on cardiovascular morbidity and mortality, and have also been shown to have pleiotropic effects including anti-inflammatory and anti-oxidant activity. Objective: Identify a gene signature associated with statin use in the blood of COPD patients, and identify molecular mechanisms and pathways underpinning this signature that could explain any potential benefits in COPD. Methods: Whole blood gene expression was measured on 168 statin users and 452 non-users from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study. Gene expression was measured using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data and to filter out non-informative probe sets. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and employing a surrogate variable analysis. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results: 18 genes were differentially expressed between statin users and non-users at a false discovery rate of 10%. Top genes included LDLR, ABCA1, ABCG1, MYLIP, SC4MOL, and DHCR24. The 18 genes were significantly enriched in pathways and biological processes related to cholesterol homeostasis and metabolism, and were enriched for transcription factor binding sites for sterol regulatory element binding protein 2 (SREBP-2). The resulting gene signature showed correlation with Huntington disease, Parkinsons disease and acute myeloid leukemia. Conclusion: Statins gene signature was not enriched in any pathways related to respiratory diseases, beyond the drugs effect on cholesterol homeostasis.
Publication Title
The Effect of Statins on Blood Gene Expression in COPD.
Sample Metadata Fields
Sex, Age, Disease
View Samples- Homo sapiens
- 196 Downloadable Samples
- Affymetrix Human Gene 1.1 ST Array (hugene11st)
Description
Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood cells is a useful way to study disease pathobiology and may help elucidate biomarkers and molecular mechanisms of disease. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, some cell types and some indication. Accurate enumeration of the many component cell types that make up peripheral whole blood can be costly, however, and may further complicate the sample collection process. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. We present a freely-available, and open-source, multiresponse Gaussian model capable of accurately inferring the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles. The model was developed on a cohort of patients with chronic obtructive pulmonary disease and tested in chronic heart failure patients.
Publication Title
No associated publication
Sample Metadata Fields
Sex, Age, Disease
View Samples- Homo sapiens
- 171 Downloadable Samples
- Affymetrix Human Gene 1.1 ST Array (hugene11st)
Description
Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood cells is a useful way to study disease pathobiology and may help elucidate biomarkers and molecular mechanisms of disease. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, some cell types and some indication. Accurate enumeration of the many component cell types that make up peripheral whole blood can be costly, however, and may further complicate the sample collection process. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. We present a freely-available, and open-source, multiresponse Gaussian model capable of accurately inferring the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles. The model was developed on a cohort of patients with chronic obstructive pulmonary disease (COPD) and tested in chronic heart failure patients.
Publication Title
No associated publication
Sample Metadata Fields
Sex, Age, Disease
View Samples- Homo sapiens
- 115 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Renal failure is characterized by important biological changes resulting in profound pleomorphic physiological effects termed uremia, whose molecular causation is not well understood. The data was used to study gene expression changes in uremia using whole genome microarray analysis of peripheral blood from subjects with end-stage renal failure (n=63) and healthy controls (n=20) to obtain insight into the molecular and biological causation of this syndrome.
Publication Title
Alteration of human blood cell transcriptome in uremia.
Sample Metadata Fields
Sex, Specimen part, Disease, Disease stage, Race
View Samples- Homo sapiens
- 37 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
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.
Publication Title
Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study.
Sample Metadata Fields
Sex, Specimen part, Race
View Samples- Homo sapiens
- 35 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Acute rejection in cardiac transplant patients is still a contributing factor to limited survival of the implanted heart. Currently there are no biomarkers in clinical use that can predict, at the time of transplantation, the likelihood of post-transplantation acute rejection, which would be of great importance for personalizing immunosuppressive treatment. Within the Biomarkers in Transplantation initiative, the predictive biomarker discovery focused on data and samples collected before or during transplantation such as: clinical variables, genes and proteins from the recipient, and genes from the donor. Based on this study, the best predictive biomarker panel contains genes from the recipient whole blood and from donor endomyocardial tissue and has an estimated area under the curve of 0.90. This biomarker panel provides clinically relevant prediction power and may help personalize immunosuppressive treatment and frequency of rejection monitoring.
Publication Title
Predicting acute cardiac rejection from donor heart and pre-transplant recipient blood gene expression.
Sample Metadata Fields
Sex, Age, Specimen part, Race
View Samples- Homo sapiens
- 23 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Acute cardiac allograft rejection is a serious complication of heart transplantation. Investigating molecular processes in whole blood via microarrays is a promising avenue of research in transplantation, particularly due to the non-invasive nature of blood sampling. However, whole blood is a complex tissue and the consequent heterogeneity in composition amongst samples is ignored in traditional microarray analysis. This complicates the biological interpretation of microarray data. Here we have applied a statistical deconvolution approach, cell-specific significance analysis of microarrays (csSAM), to whole blood samples from subjects either undergoing acute heart allograft rejection (AR) or not (NR). We identified eight differentially expressed probe-sets significantly correlated to monocytes (mapping to 6 genes, all down-regulated in ARs versus NRs) at a false discovery rate (FDR) <= 15%. None of the genes identified are present in a biomarker panel of acute heart rejection previously published by our group and discovered in the same data.
Publication Title
White blood cell differentials enrich whole blood expression data in the context of acute cardiac allograft rejection.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 6 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
The adipose tissue is an endocrine regulator and a risk factor for atherosclerosis and cardiovascular disease when by excessive accumulation induces obesity. Although the adipose tissue is also a reservoir for stem cells (ASC) their function and stemcellness has been questioned. Our aim was to investigate the mechanisms by which obesity affects subcutaneous white adipose tissue (WAT) stem cells.
Publication Title
Stem cells isolated from adipose tissue of obese patients show changes in their transcriptomic profile that indicate loss in stemcellness and increased commitment to an adipocyte-like phenotype.
Sample Metadata Fields
No sample metadata fields
View Samples