825 ng of sample and reference cRNA was mixed and fragmented. cRNA was hybridized to whole mouse genome (4 × 44 K) microarrays (Agilent Technologies Inc.) in stainless steel chambers. A block design was used with three samples and one control placed on each slide. Hybridization was carried out in a rotating hybridization oven in the dark at 65 °C and 10 rpm for 17 h. The slides were then washed for 1 min in each of Agilent’s Gene Expression Wash Buffers 1 and 2. Arrays were scanned on an Agilent DNA Microarray Scanner at 5 μ resolution using Agilent Scan Control software and data were extracted using Agilent Feature Extraction 9.5. A reference

design (Kerr, 2003 and Kerr and Churchill, 2001) with arrays as blocks of size 2 (each block containing the corresponding reference: Dabrafenib supplier Cy3 = green and sample: Cy5 = red channels) was used to analyze the median signal intensities of the two-color microarray data. The experiment included main effects of dose (4

levels, including control), time (2 levels) and dose-by-time interaction. Five biological replicates per condition were used for each of the eight conditions, for a total of 80 microarrays. Six MSC and four TSC “outlier” microarrays were removed based on quality control checks (i.e., poor signal intensity, high background, etc.), leaving a minimum of 3 replicates per group. The background signal intensity for each array was estimated using the 153(−)3xSLv1 negative controls present on each array. All pre-processing of the data was conducted 4-Aminobutyrate aminotransferase using R (R Development CoreTeam, 2005 and Yang et al., 2002). The data were normalized using the LOWESS normalization method in the R library “MAANOVA”. Differential Sirolimus cost expression

between the control and exposed samples for each of the three dose levels at each of the two time points was tested using the MAANOVA library (Wu et al., 2003). The ANOVA model was fitted to include the main effects of dose and time, with a dose by time interaction term and the array as a blocking variable. The Fs statistic ( Cui et al., 2005), a shrinkage estimator, was used for the gene-specific variance components, and the associated p-values for all the statistical tests were estimated using the permutation method (30,000 permutations with residual shuffling). These p-values were then adjusted for multiple comparisons using the false discovery rate approach ( Benjamini and Hochberg, 1995). The least squares mean ( Goodnight and Harvey, 1978 and Searle et al., 1980), a function of the model parameters, was used to estimate the fold change for each pairwise comparison of the six pairwise comparisons of interest among the eight treatment-by-time groups. The microarray data for this experiment has been submitted to the Gene Expression Omnibus (GEO) repository and can be accessed under record number GSE44603. Visualization and analysis of significantly changing genes was performed using GeneSpring GX 7.3 (Agilent Technologies).