E-GEOD-48944 - Gene expression profiles of Chronic kidney disease

Status
Released on 18 July 2013, last updated on 3 June 2014
Organism
Homo sapiens
Samples (25)
Array (1)
Protocols (5)
Description
Epidemiological studies indicate that adverse intrauterine and postnatal environment has a long-lasting role in chronic kidney disease (CKD) development. Epigenetic information can represent a plausible carrier for mediating this programming effect. Here we demonstrate that genome-wide cytosine methylation patterns of healthy and CKD tubule samples obtained from patients show significant differences. Cytosine methylation changes showed high concordance (98%) with a large (n=87) replication dataset. We rarely observed differentially methylated regions (DMR) on promoters. Histone modification-based kidney specific genome-wide gene regulatory region annotation maps (promoters, enhancers, transcribed and repressed regions) were generated. DMRs mostly overlapped with putative enhancer regions and were enriched in consensus binding sequences for important renal transcription factors, indicating their importance in gene expression regulation. A core set of genes, including transforming growth factors and collagens, showed cytosine methylation changes correlating with downstream transcript levels. Our report raises the possibility that epigenetic dysregulation plays a role in CKD development via influencing core profibrotic pathways. We used microarrays to detail the differences of gene expression of human tubule epithelial cells between chronic kidney disease and normal. We sought to decrease the cell type heterogeneity of kidney tissues to increase the resolution of expression profiles. To that end, microdissected human kidney tissue from both chronic kidney disease patient and normal are used for RNA extraction and hybridization on Affymetrix microarrays.
Experiment type
transcription profiling by array 
Contacts
Yi-An Ko <yian.kia.ko@gmail.com>, Ae S Park, Amit Verma, Davoud Mohtat, Deyou Zheng, Han Si, Hyun M Kang, James Pullman, John M Greally, Katalin Susztak, Maria C Izquierdo, Masako Suzuki, Melissa Fazzari, Sang Y Han, Thomas Hostetter
MIAME
PlatformsProtocolsFactorsProcessedRaw
Files
Investigation descriptionE-GEOD-48944.idf.txt
Sample and data relationshipE-GEOD-48944.sdrf.txt
Raw data (1)E-GEOD-48944.raw.1.zip
Processed data (1)E-GEOD-48944.processed.1.zip
Array designA-AFFY-37.adf.txt
Links