Dataset Information


Gene Expression Profiling of the Retina after Transcorneal Electrical Stimulation in Wildtype Brown Norway Rats

ABSTRACT: Purpose: To investigate the effect of transcorneal electrical stimulation (TES) on the retina of wildtype Brown Norway (BN) rats by gene expression profiling. Methods: TES was applied to BN adult wildtype rat retina in vivo for 1h (1ms biphasic pulses at 20Hz; current: 200 µA). RNA was isolated, processed and used for microarray-based expression profiling 4hrs after initial TES. An expression profile was generated for genes differentially expressed at 4hrs after TES vs. sham stimulated animals using a fold change cutoff of 1.2. We validated the profile by real-time quantitative reverse transcription-polymerase chain reaction (qPCR). In addition, the application of TES was verified at the structural and functional level. Results: Transcriptome changes associated with TES vs. sham stimulated BN wildtype retina were identified. 490 genes were differentially expressed in TES and included well-known genes as well as a large number of novel genes. Electrophysiological recordings showed physiological retinal function after TES and structural in vivo and ex vivo studies revealed intact retinal layers. Conclusion: Our results demonstrate that TES applied to the retina of wildtype BN rats induce a variety of transcriptome level changes and may help to understand the mechanisms underlying TES. In addition TES has no negative effect on structure and function of wildtype BN retina 24hrs after application. Overall design: RNA isolation and Microarray studies 4 animals were euthanized 4h after TES or respective sham treatment. Extraction of total RNA was performed using the RNeasy Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. QIAshredder mini-spin columns (Qiagen) as well as needle and syringe homogenization were applied for tissue homogenization. To ensure proper integrity all RNA used in our experiments showed single peaks for the 18S and 28S bands as determined by the Agilent 2100 Bioanalyzer using the RNA 6000 Nano LabChip Kit (both Agilent Technologies Inc., CA, USA) following the manufacturer's instructions. The purity and concentration of total RNA was determined by absorbance measurements at 260 and 280nm using a Nanodrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). All RNA that did not have a 260/280 ratio between 1.9 and 2.1 was discarded to ensure high quality of samples. Respective RNAs from stimulated and sham samples were used to generate double-stranded cDNA with the T7-oligo (dT) primer according to GeneChip® (Affymetrix Inc., Santa Clara, CA, USA) two cycle target labeling protocol as described by the manufacturer. The processed cDNA was hybridized to Affymetrix GeneChip® Rat Gene 1.0 ST arrays for 18–24h (Affymetrix Inc.). Four TES and four sham replicates were used for hybridization to Microarray GeneChips®. Each microarray was washed and stained with streptavidin–phycoerythrin and scanned at a 6μm resolution with Agilent Technologies model G2500A GeneArray scanner. A visual quality control measurement was performed to ensure proper hybridization after each chip was scanned. Additional quality control parameters such as scaling factors used to normalize the chips, average background, and noise were also evaluated. Microarray analysis For statistical analysis of microarray data all cell files were imported to GeneSpring GX software (Agilent Technologies Inc.) where GC-RMA algorithm was applied to yield log signal values on each probe set. All transcripts with a minimum change of 1.2 fold together with a p value less than 0.05 were selected using Significance Analysis of Microarray (SAM) (Tusher VG, 2001). The visualization of the relationships between the samples by principal component analysis (PCA) based on the GC-RMA signal and hierarchical clustering using the Pearson correlation was obtained using PARTEK Genomics Suite version 6.4. All differentially regulated genes were imported into Ingenuity Pathway Analysis (Ingenuity Systems, CA, USA) for generation of networks and canonical pathway analysis. The data set containing all significant gene identifiers along with corresponding expression and significance values was uploaded into the application. Each identifier was mapped to its corresponding object in Ingenuity's Knowledge Base. The set fold change cutoff identified all molecules whose expression was significantly differentially regulated. These network eligible molecules were overlaid onto a global molecular network developed from information contained in Ingenuity’s Knowledge Base. Networks were then algorithmically generated based on their connectivity. The functional analysis of a network identified the biological functions using a right-tailed Fisher’s exact test that were most significant to the molecules in the network. Canonical pathways analysis identified the pathways from the Ingenuity Pathways Analysis library of canonical pathways that were most significant to the data set. The significance of the association between the data set and the canonical pathway was measured in 2 ways: 1) A ratio of the number of molecules from the data set that map to the pathway divided by the total number of molecules that map to the canonical pathway is displayed. 2) Fisher’s exact test was used to calculate a p-value determining the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone.

INSTRUMENT(S): [RaGene-1_0-st] Affymetrix Rat Gene 1.0 ST Array [probe set (exon) version]

ORGANISM(S): Rattus norvegicus  

SUBMITTER: Gabriel Willmann   

PROVIDER: GSE26174 | GEO | 2011-11-21



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