Figure 2.
Both TN and MP CD8+T cells from uninfected neonatal gBT-I mice express different genes from adults. (A) Principal component analysis on RNA-Seq data. Mean FPKM values from well-expressed genes were used from adult and neonatal bulk (total), TN, and MP cells. Bulk adult, MP adult, and MP neonatal samples consist of 3 pooled biological replicates; bulk neonatal, TN adult, and TN neonatal samples consist of 2 pooled biological replicates. The percentage of the overall variation accounted for by principal components 1 (x-axis) and 2 (y-axis) is indicated for each axis. Gene loadings for principal components 1 and 2 are shown in supplemental Figure 2. (B) Color-coded pairwise Spearman rank correlation coefficients comparing FPKM values for genes that are significantly differentially expressed between adults and neonates in at least 1 sample; P < 1015 for all comparisons. (C) Gene expression values for adults and neonates in MP cells. Gray indicates lowly expressed genes, black indicates nondifferentially expressed genes, orange indicates the 126 genes upregulated in neonatal cells, and blue indicates the 159 genes upregulated in adult cells. (D) Gene expression values for adults and neonates in TN cells, where 204 genes are upregulated in neonatal cells and 195 genes are upregulated in adult cells. (E) Clustering of genes in all samples. Fold-change differences for significantly differentially expressed genes were calculated between adults and neonates. Clustering was performed to identify genes with similar differences in expression in each sample; fold-change for each gene is plotted in each sample, and genes are shown in their clusters. (F) Genes in each cluster were compared with genes that define naïve cells before infection and effector or memory cells after infection. Enrichment was calculated as number of genes in each cluster compared with the number expected. Significance was determined by Fisher exact tests; *P < .05, **P < .005, ***P < .0005. See supplemental Table 1 for gene expression values and clustering.