Inflammatory Gene Expression in SGBS Adipocytes protocol (Clontech, BD Biosciences, Oxford, UK). The resulting dsDNA product was used in a reaction with ribonucleotides and T7 RNA polymerase and then incubated at 37 for 18 h. Amplified cRNA was labeled with either Cy3 or Cy5 dye, using a Micromax kit (PerkinElmer, London, UK). Hybridization and image processing 10 μg labeled cRNA was dried in a Speedvac and resuspended in 40 μL hybridization buffer (MWG Biotech AG, London, UK). The cRNA was denatured at 95 for 3 min, vortexed, and briefly spun-down to collect the contents. The cRNA was chilled on ice for 30 sec and pipetted onto the MWG human inflammatory array (MWG Biotech AG). A coverslip was carefully placed on the microarray, which was then sealed in the hybridi- zation chamber and placed into an incubator at 42 overnight. Microarrays were subjected to 5 min washes in 2X SSC, 0.1% SDS followed by 1X SSC, and, finally, 0.1X SSC. The microarrays were scanned at a resolution of 10 μm, using an Affymetrix 428 confocal laser scanner. The microarray images were analyzed using Imagene (Biodiscovery, CA, USA). Signal and background means for both Cy3 and Cy5 channels were calculated for each individual spot. These results were then imported into Genesight (Biodiscovery, CA, USA). The microarrays were corrected by local background correction, which involves sub- tracting each spot's background from the signal value of the same spot. All of the genes in our dataset were used to calculate normalization parameters. Using all genes assumes that the majority of the genes measured are not differ- entially regulated. Therefore, when taken as a whole, the population accurately represents the channel bias. Data analysis Biodiscovery Genesights 4 (Biodiscovery, CA, USA) was used for the analysis of the data. The data for each of four signals (mean, four signals) were combined and analyzed. All of the data was corrected for background intensity, using local background correction, and multiple spots with flags were omitted. Normalization was performed, using Lowess normalization, with a smoothing parameter of 0.2 and one degree of fitness. Genes that were two-fold up- or down-regulated were partitioned with 95% confidence intervals. Real-time polymerase chain reaction (PCR) analysis In a LightCycler (Roche Applied Science. India- napolis, IN, USA), 1 μL from 5 μL of each reverse- transcribed reaction was amplified in a 20 μL PCR mix, containing 4 mM MgCl2, 10 pM each primer, and 1x LightCycler DNA Master SYBR Green 1 mix (Roche Applied Science. Indianapolis, IN, USA). Samples were incubated for an initial denaturation at 94 for 30 s, followed by 40 PCR cycles. Each cycle consisted of 95 for 10 s, each annealing temperature for 5 s, and 72 for 12 s. For the analysis of up- and down-regulated genes, PCR primers, scanning exon-intron boundaries were designed using Primer Premier 5 software (Biosoft International, Palo Alto, CA, USA) and were manu- factured by Bioneer (Bioneer, Daejeon, Korea). The oligonucleotide primers for the experiment are listed in Table 1. To confirm the amplification of specific transcripts, melting curve profiles (cooling the sample to 65 for 15 s and heating slowly to 95 with continuous measurement of fluores- cence) were produced at the end of each PCR. RESULTS Inflammatory gene expression profile of TNF-α- treated SGBS cells using inflammatory human array Differentiated SGBS cells were stained with Oil Red O staining 0, 4, 10, and 20 days after the induction of differentiation. Most of the cells were differentiated as adipocytes and filled with lipid droplets at Day 20 after the induction of differen- tiation (Fig. 1). In order to study the effect of a pro-inflam- matory cytokine on the gene expression profile of human SGBS cells, we treated differentiated SGBS adipocytes with TNF-α, which has a well-charac- terized effect in adipocytes. In this study, we Yonsei Med J Vol. 47, No. 5, 2006