binding sites is significantly different from that of genes with fewer than t binding sites (P,0.05; two-sample t-test). The dotted horizontal line represents the average 2-year fold-change (old/ young) among all 17, 329 mouse genes included in the analysis. (TIF) Figure S17 Genomic regions with increased Zeb1 motif density are associated with increased gene expression with aging of tail skin from both sexes (CB6F1 strain). The panels (A)–(L) compare Zeb1 motif density between sequences adjacent to or within genes exhibiting increased (or decreased) expression with age (i.e., foreground sequences), relative to sequences adjacent to or within genes that exhibit decreased (or increased) expression with age (i.e., background sequences) (i.e., as described in the AP-2 analysis shown in Figure S6). For each sequence examined, a length-adjusted residual score was calculat- ed, which is proportional to the density of Zeb1binding sites within that sequence. Each panel shows the average residual score among sequences adjacent to or within age-increased genes (see red lines and 95% confidence intervals in A, C, E, G, I and K) or the average residual score among sequences adjacent to or within age- decreased genes (see green lines and 95% confidence intervals in B, D, F, H, J and L). The grey region in each panel outlines a 95% confidence interval for the average residual score among sequences assigned to the background set (which includes all sequences not within the foreground set). The analysis was repeated based upon foreground sets of varying size and selectivity (horizontal axis; see Figure S6 legend). A significant motif- expression association is indicated by non-overlap between 95% confidence intervals associated with foreground and background gene sets (denoted by blue bars located near the top or bottom of each panel). (PDF) Figure S18 Ranked lists of genes altered by aging in both human and mouse tissues. Tables display age- associated expression patterns for ranked lists of the top 50 genes most strongly (A) increased by aging across all human tissues, (B) decreased by aging across all human tissues, (C) increased by aging in human skin (both sexes), (D) decreased by aging in human skin (both sexes), (E) increased by aging across all mouse tissues, (F) decreased by aging across all mouse tissues, (G) increased by aging in mouse skin (both sexes) and (H) decreased by aging in mouse skin (both sexes). In each figure (A)–(H), age-associated gene expression patterns in human tissues are displayed in the left panel, and age-associated gene expression patterns in mouse tissues are displayed in the right panel. Ranked lists include only orthologous genes that are shared between humans and mice (and thus may differ from those presented in Figures S1 and S10). In (A), (B), (E) and (F), genes have been ranked according to the total number of significant results observed for a given gene across human and mouse tissues (i.e., the total number of up-triangles per row or the total number of down-triangles per row). Tables (C), (D), (G) and (H) list genes most strongly regulated by aging in skin specifically, with genes first filtered to include only genes significantly altered by age in both sexes of a given species (P,0.05), and then ranked according to the estimated 40-year or 2-year fold-change (old/ young; averaged between males and females). (PDF) Figure S19 Biological processes associated with concor- dant and discordant age-associated expression patterns in human sun-protected skin and mouse tail skin. Figures (A) and (B) list gene ontology (GO) biological process terms significantly overrepresented (P,0.05) among genes with (A) concordant or (B) discordant age-associated expression patterns in Mechanisms of Transcriptome Aging humans and mice. The analysis was performed based upon (A) 2190 genes concordantly altered by aging in humans and mice (i.e., increased or decreased in the same sex of both species; P,0.05 for each species) and (B) 644 genes altered discordantly altered by aging in humans and mice (i.e., increased in one species and decreased in the other for the same sex; P,0.05 per species). GO terms in both (A) and (B) have been clustered based upon a similarity metric that is proportional to the number of shared ancestral terms in the GO hierarchy. For each term, an exemplar gene is listed in parentheses, which exhibits either a corresponding (in part A) or non-corresponding (in part B) expression pattern in humans and mice. As an alternative strategy (Figures C–F), we scored 5242 GO biological process terms (all GO terms associated with orthologous genes in humans and mice), and identified those for which the direction of aging effects (i.e., increase or decrease) was most similar or discordant in human and mouse skin (i.e., regardless of whether genes were significantly altered by aging in either species; see Figures C–F). For each GO term selected in this fashion, figures (C)–(F) display the proportion of age-increased and age-decreased genes in each species (red and green bars). The right margin of Figures (C)–(F) lists three exemplar genes associated with the selected terms. (PDF) Figure S20 The overlap among sets of age-regulated genes derived from different datasets with young and old tissues is characteristically 10–15%. We calculated the pairwise overlap between sets of age-regulated genes derived from (A) 27 datasets that included young and old human tissue samples and (B) 30 datasets that included young and old mouse tissue samples. All human datasets considered in part (A) were generated from the same Affymetrix Human Genome U133 Plus 2.0 array platform (Table S1), and all mouse datasets considered in part (B) used the same Affymetrix Mouse Genome 430 2.0 Array platform (Table S2). Further details on each dataset included are provided in the Methods section. In both (A) and (B), datasets used to derive sets of age-regulated genes are listed along the horizontal axis. The height of each corresponding grey bar indicates the percentage of genes significantly altered by age (either increased or decreased), based upon a comparison-wise Type I error rate of P,0.05, such that approximately 5% of genes are expected to be age-regulated by chance (see yellow background behind grey bars). For each dataset, red and green dots indicate the percent overlap between the set of age-regulated genes identified from that dataset as compared to the others included in the same figure. Percent overlap is defined as 1006 (x/y), where x is the number of unique genes significantly altered by age (in the same direction) in two separate datasets, and y is the number of unique genes significantly altered by age in the reference dataset listed along the horizontal axis. Red dots indicate overlap with respect to sets of age-increased genes, while green dots indicate overlap with respect to sets of age- decreased genes. In (A), we evaluated gene set overlap among all 351 pairwise combinations among the 27 human datasets, and on average, overlap between sets of age-regulated genes was 11.4%, with a minimum of 0.50% and a maximum of 66.5%. In (B), we evaluated gene set overlap among all 435 pairwise combinations among the 30 mouse datasets, and on average, overlap between sets of age-regulated genes was 12.5%, with a minimum of 0.40% and a maximum of 54.3%. (PDF) Table S1 Human datasets (Affymetrix Human Genome U133 Plus 2.0 array). This table lists datasets used to evaluate the effects of aging on gene expression in skin and other human tissues. The table includes experimental details for each dataset PLoS ONE | www.plosone.org 19 March 2012 | Volume 7 | Issue 3 | e33204