Figure 1
Figure 1. Type 1 IFN gene targets in CD8 T cells. CD8 T cells were purified from uninfected WT, STAT1-deficient, and STAT4-deficient mice and either control-treated or treated with 10 000 U/mL IFNα for 90 minutes. The RNA was then extracted and analyzed on Affymetrix Arrays, displaying 10 000 known genes, using dChip software for data mining (A-B). The hierarchical clustering algorithm used is based on the Pearson correlation coefficient. Red areas indicate high gene expression, and green areas indicate low expression (A). The cutoff for gene induction was considered 2.35, and the samples were compared by permuting them 50 times to assess the false discovery rate (B). Real-time PCR analysis of STAT1, OASl1, Mx2, c-myc, MAP3K8, and IFNγ mRNA was performed. Black bars show results with control-treated and gray bars show results with IFNα-treated CD8 T-cell samples (C). Where shown, bars represent SEMs. Results are based on accumulated data from 3 replicates.

Type 1 IFN gene targets in CD8 T cells. CD8 T cells were purified from uninfected WT, STAT1-deficient, and STAT4-deficient mice and either control-treated or treated with 10 000 U/mL IFNα for 90 minutes. The RNA was then extracted and analyzed on Affymetrix Arrays, displaying 10 000 known genes, using dChip software for data mining (A-B). The hierarchical clustering algorithm used is based on the Pearson correlation coefficient. Red areas indicate high gene expression, and green areas indicate low expression (A). The cutoff for gene induction was considered 2.35, and the samples were compared by permuting them 50 times to assess the false discovery rate (B). Real-time PCR analysis of STAT1, OASl1, Mx2, c-myc, MAP3K8, and IFNγ mRNA was performed. Black bars show results with control-treated and gray bars show results with IFNα-treated CD8 T-cell samples (C). Where shown, bars represent SEMs. Results are based on accumulated data from 3 replicates.

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