Ji et al. BMC Genomics 2012, 13:441 http://www.biomedcentral.com/1471-2164/13/441 PCR detection system (Bio-Rad.com). All samples were analyzed in triplicate and normalized to ß-tubulin. Relative differences in gene expression were determined using the 2-ΔΔCT method and statistical differences were tested by analysis of variance (ANOVA) [58]. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) Abdominal adipose tissue samples from five birds in each treatment group (the same five birds used for ex- pression profiling) were extracted by placing tissue in a mortar containing liquid nitrogen and then powdering with a pestle. Portions (8–40 mg) of the powered tissue were weighed into 1.5 mL centrifuge tubes. Chilled methanol (0.5 mL at −80°C) and internal standard (5 μL of 1.7 mM benzoic acid in negative mode or 4.25 mM tris(hydroxymethyl)aminomethane in positive mode) were added to each tube. Each tube was mixed thor- oughly by vortexing for two minutes, and the metabo- lites were extracted from the tissue for 30 min at 4°C. The tubes were then centrifuged (5 min, 4°C, 16.1 rcf ) and supernatant (210 μL) was split into two autosampler vials. One of these samples was immediately placed on the LC-MS/MS for analysis, while the other was stored at −80°C for analysis in the opposite polarity ion mode on the following day. Samples were placed in an autosampler tray chilled to 4°C, and 10 μL of each was injected onto an LC column for analysis. The chromatography method for positive ion mode was reported previously by Bajad and cowor- kers [59], with one exception that the column was cooled to 10°C. The chromatography method for nega- tive ion mode was performed as reported by Waters and coworkers [60], except the gradient was allowed to run 50 min instead of 45 min to allow more thorough equili- bration of the column. The eluent was introduced dir- ectly into the MS via an electrospray ionization (ESI) source fitted to a Finnigan TSQ Quantum Discovery Max triple quadrupole MS (Thermo Electron, Waltham, MA) through a 0.1 mm internal diameter fused silica ca- pillary. The spray voltage was 4500 V in positive mode or 3000 V in negative mode. The sheath gas (nitrogen) was set to 40 psi, and the capillary temperature was set to 290°C. The collision cell gas (argon) was set to a pres- sure of 1.5 mTorr. Samples were analyzed using selected reaction monitoring (SRM) mode with a scan width of 1 m/z and a scan time of 0.05 s. The SRM parameters for most metabolites have been published previously [59]. This method was used to scan for almost 300 meta- bolites. Xcalibur software (Thermo Scientific, Waltham, MA) was used to manually assess the elution time of the correct LC spectral peak for each metabolite-specific SRM. The Quan Browser utility in Xcalibur was then used to integrate the LC spectral peak area (in ion Page 14 of 16 counts) for each detected compound, and these data were exported to a Microsoft Excel spreadsheet for fur- ther processing. Statistical analysis Statistical analysis of the microarray data was performed using R 2.9.0 and routines contained in Bioconductor (bio- conductor.org). GC robust multi-array average (GCRMA) was used to normalize and scale the raw data from CEL files. The normalized data were filtered for low expression by removing any probes with normalized expression less than 3 in at least 5 arrays. Statistical significance of gene expression differences were analyzed by one-way ANOVA and empirical bayes using the limma package. Differential expression was defined based on false discovery rate (FDR) adjusted p-value <0.05. False discovery rate for differential expression and for GO and KEGG enrichment testing was controlled using the Benjamini-Hochberg method [61]. Venn diagrams of differentially expressed genes were plot- ted to visualize the number of differentially expressed genes for each treatment comparison and their intersec- tions. Hierarchical clustering of significant genes was per- formed using the hclust function and a hierarchical clustering heatmap was created using heatmap.2 in the gplots package. Hierarchical clustering also was used to identify correlated patterns of gene expression and meta- bolites. The Database for Annotation, Visualization and Integrated Discovery (DAVID, version 6.7) and ClueGO, a Cytoscape plug-in, were used for Gene Ontology (GO) at level 6 and 7 and KEGG analysis of differentially expressed genes [62,63]. Statistical analysis of metabolomic data was performed using an analysis tool that we developed specif- ically for metabolomic data analyses (Ernest et al., manu- script in review). The script (metabR), written in the language R, uses linear mixed-effect modeling to normalize metabolomics data containing both fixed- and random- effect confounding variables. The script averages any replicate measurements (statistical sampling) made on ex- perimental units and performs ANOVA to test for statis- tical differences between experimental groups. Additional files Additional file 1: Differentially expressed genes for fasted vs. fed chickens with fold change > |1.5|. Probe ID, gene symbol, gene title and fold change are provided. Additional file 2: Differentially expressed genes for insulin neutralized vs. fed chickens. Probe ID, gene symbol, gene title and fold change are provided. Additional file 3: Metabolite levels across the three pairwise comparisons of treatments. Metabolite, fold-change and p-value are provided. Additional file 4: Metabolite-gene clusters. Cluster, metabolite,gene Ontology and KEGG annotation and benjamini corrected pvalue are provided for clusters that contained both metabolites and genes.