The labeled cRNAs were

purified (QIAquick spin columns, QIAGEN, Venlo, The Netherlands) and 1 μg of each sample was hybridized to 4 × 44 K whole genome mouse oligo microarrays (G4122F, Agilent) according to manufacturer’s instructions (two-color microarray-based gene expression analysis, Agilent). After a 17-h incubation CHIR99021 period, slides were washed using various dilutions of SSPE (sodium chloride, sodium phosphate, EDTA) buffer according to the protocol provided by Agilent. Arrays were scanned using an Agilent microarray scanner (G2565B). The fluorescent readings from the scanner were converted to quantitative files using Feature Extraction 9.1 software (Agilent Technologies). Quality check of the arrays was done using software package LimmaGUI

in R version 2.3.1. Four samples were removed from the analysis due to technical failure. Data were imported in GeneMaths XT 1.5 (Applied Maths, St. MartensLatem, Belgium), and spots with signal intensities below two times PARP inhibitor background were excluded from subsequent analysis. Corrected data were normalized and adjusted for random and systematic error (Pellis et al., 2003). Significance analysis of microarrays (SAM) analysis was applied to detect significantly affected genes for each treatment using the two-class unpaired comparison (Tusher et al., 2001). The False Discovery Rate was set to < 0.5%. No additional filtering on a threshold for up- or downregulation was applied. Evaluation of the outcome of the SAM results showed that the minimal ratio for up- or downregulation was 1.5. Hierarchical clustering was done oxyclozanide with the programs Cluster (uncentered correlation; average linkage

clustering) and Treeview (Eisen et al., 1998). Metacore (GeneGo, St. Joseph, MI) is an online software program that provides, among other options, pathway analysis of microarray data. Groups of co-clustering genes were analyzed for overrepresentation of genes from signaling and metabolic pathways based on hypergeometric distribution (Ekins et al., 2006). Pathways with a p value < 10−5 were considered significant. Gene set enrichment analysis (GSEA) was performed to discover the differential expression of biologically relevant sets of genes that share common biological function or regulation (Subramanian et al., 2005). GSEA has the advantage that no initial filtering is applied to the data set to select for significantly differentially expressed genes. GSEA first ranks all probe sets based on fold changes (algorithm signal to noise) in expression between a treatment and the control. Subsequently, by using pre-defined sets of associated genes based on prior biological knowledge, GSEA calculates whether sets as a whole are enriched at the top or bottom of the fold change-based ranking list, or randomly distributed (Subramanian et al., 2005).