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Expression data from uremic patients and 20 healthy controls (normals)

ABSTRACT: 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. The study was conducted at the University of British Columbia and approved by the human ethics research board. A 3:1 case-control design was employed to compare gene expression in patients with chronic renal failure and healthy controls. Patients with stage 5 renal disease aged 18 to 75 years, who were clinically stable awaiting renal transplantation, were not receiving immunosuppressive medications, and who provided written informed consent were enrolled into the study. Patients were treated according to Canadian Guidelines for Chronic Kidney Disease (13). Normal controls of comparable age and gender to the patients who were screened to ensure freedom from known illness and medical therapy served as comparators.

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PROVIDER: S-DIXA-D-1126 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:Vitiligo Blood Transcriptomics Provides New Insights into Disease Mechanisms and Identifies Potential Novel Therapeutic Targets Abstract Background: Significant gaps remain regarding the pathomechanisms underlying the autoimmune response in vitiligo (VL), where the loss of self-tolerance leads to the targeted killing of melanocytes. Specifically, there is incomplete information regarding alterations in the systemic environment that are relevant to the disease state. Methods: We undertook a genome-wide profiling approach to examine gene expression in the peripheral blood of VL patients and healthy controls in the context of our previously published VL-skin gene expression profile. We used several in silico bioinformatics-based analyses to provide new insights into disease mechanisms and suggest novel targets for future therapy. Results: Unsupervised clustering methods of the VL-blood dataset demonstrate a “disease-state”-specific set of co-expressed genes. Ontology enrichment analysis of 99 differentially expressed genes (DEGs) uncovers a down-regulated immune/inflammatory response, B-Cell antigen receptor (BCR) pathways, apoptosis and catabolic processes in VL-blood. There is evidence for both type I and II interferon (IFN) playing a role in VL pathogenesis. We used interactome analysis to identify several key blood associated transcriptional factors (TFs) from within (STAT1, STAT6 and NF-kB), as well as “hidden” (CREB1, MYC, IRF4, IRF1, and TP53) from the dataset that potentially affect disease pathogenesis. The TFs overlap with our reported lesional-skin transcriptional circuitry, underscoring their potential importance to the disease. We also identify a shared VL-blood and -skin transcriptional “hot spot” that maps to chromosome 6, and includes three VL-blood dysregulated genes (PSMB8, PSMB9 and TAP1) described as potential VL-associated genetic susceptibility loci. Finally, we provide bioinformatics-based support for prioritizing dysregulated genes in VL-blood or skin as potential therapeutic targets. Conclusions: We examined the VL-blood transcriptome in context with our (previously published) VL-skin transcriptional profile to address a major gap in knowledge regarding the systemic changes underlying skin-specific manifestation of vitiligo. Several transcriptional “hot spots” observed in both environments offer prioritized targets for identifying disease risk genes. Finally, within the transcriptional framework of VL, we identify five novel molecules (STAT1, PRKCD, PTPN6, MYC and FGFR2) that lend themselves to being targeted by drugs for future potential VL-therapy. We used microarrays to detail the global programme of gene expression underlying the disease state in vitiligo and established differentially expressed genes by comparing gene expression values from diseased patients to healthy controls.

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Expression data from uremic patients and 20 healthy controls (normals)

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