human tissues (e.g., cerebellum, skin, colon, pancreas, see GEO accession number GSE7307). Likewise, to generate mouse profiles (Figure 7), a database of 871 Affymetrix Mouse Genome 430 2.0 array hybridizations was manually assembled using annotated CEL files downloaded from Gene Expression Omnibus. In total, the 871 CEL files belonged to 317 groups, where each group contained two or more replicate CEL files associated with a given cell population (average of 871/317 = 2.75 replicates per group). Signature transcripts associated with each treatment were identified by comparison to an independent reference set of 47 samples (representing treatment A in Figure 2 from [31]), consisting of array hybridizations that collectively involved a heterogeneous collection of mouse tissues (e.g., hippocampus, heart, kidney; see GEO accession number GSE10246). Given these procedures, signature transcripts associated with a given cell population treatment (either mouse or human) represent tran- scripts with high expression in that treatment, as compared to a diverse panel of other tissue types. Signature transcripts were identified using two-sample comparisons (cell population versus reference) and linear modeling methods implemented in the Limma package [31,72]. For each cell population treatment, the ratio n1/n2 was calculated, where n1 represents the number of signature transcripts increased by age, and n2 represents the number of signature transcripts decreased by age. Additionally, an adjusted ratio n1*/n2* was also calculated, which is identical to n1/ n2, except only a subset of signature transcripts are utilized in the calculation [31]. This subset included only those signature transcripts that were not signature transcripts for any other cell population with a larger n1/n2 ratio (see Figure 2 and Methods from [31]). Significant evidence for infiltration or expansion of a cell population with age was present if both n1/n2 and n1*/n2* were significantly large (FDR-adjusted P,0.05; Fisher’s Exact test). P- values generated among all cell populations were adjusted using the Benjamini-Hochberg procedure [73]. Motif analyses Human and mouse genome coordinates were obtained from refGene files downloaded from the UCSC database (human version hg19, GRCh37, mouse version mm9). Genomic sequences were derived from Bioconductor BSgenome packages (BSgeno- me.Hsapiens.UCSC.hg19 and BSgenome.Mmusculu- s.UCSC.mm9). Position weight matrices (PWMs) were obtained from the UniPROBE and Jaspar databases [32–34]. For each nucleotide position within a PWM, ratios of posterior to prior probabilities were calculated using empirically estimated nucleo- tide background frequencies (see Table S3). Input sequences were scored for matches to a given PWM by summing log2-transformed (posterior/prior) probability ratios across nucleotide positions within the PWM, and a match was called for sequence regions with scores exceeding 90% of the maximum score for the PWM [82]. If overlapping sequence matches to a given motif were identified, such overlapping matches were not double-counted but were instead counted as a single match in all genome scans. Initial screens considered PWM matches only within genomic regions 2000 bp upstream and 200 bp downstream of annotated transcription start sites (Figures S3, S4, 7 and S15). Based on results of these initial screens, a selected subset of top-ranking motifs were further investigated with respect to all intergenic, all intronic and conserved sequence regions 2000 bp upstream of the TSS for each gene (Zeb1, AP-2 and YY1 motifs in human; Zeb1 and Prrx2 motifs in mouse; Figures S6 and S17). For human, we screened a total of 19,494 intergenic regions and 191,912 intronic regions for matches to the selected PWMs (irrespective of sequence conservation). Likewise, for mouse we screened a total of 20,079 Mechanisms of Transcriptome Aging intergenic regions and 179,146 intronic regions (irrespective of sequence conservation). Human conserved sequences (#2000 bp) upstream of transcription start sites were obtained from UCSC multiple alignments of 45 vertebrate genomes (hg19, GRCh37). Mouse conserved sequences (#2000 bp) upstream of transcription start sites were obtained from UCSC multiple alignments of 29 vertebrate genomes (mm9). For analyses of intergenic, intronic and conserved sequences (Figures S6 and S17), sequence lengths were variable and we noted a correlation between PWM match counts and sequence length. To remove this effect, we calculated a length-adjusted count score for each sequence, which was proportional to the density of PWM matches within a given sequence. Length-adjusted scores were calculated as the residual values in a Poisson regression model, with PWM counts as the response variable and sequence length (ln-transformed) as a single predictor variable. Average length-adjusted scores were then compared between foreground and background sequence sets as described in Figure S6 and S17 legends. Supporting Information Figure S1 Ranked lists of genes altered by aging in skin and other human tissues. Tables display age-associated expression patterns for ranked lists of the top 50 genes (A) increased by aging across all human tissues, (B) decreased by aging across all human tissues, (C) increased by aging in human skin, or (D) decreased by aging in human skin. For a given gene (row) and tissue (column), colors denote the estimated fold-change expression ratio (old/young) between an individual t+40 years of age and an individual t years of age (a linear rate of change with age is assumed). Filled triangles indicate whether genes were significantly increased or decreased by aging (P,0.05). Tables (A) and (B) list genes most frequently regulated by aging across all human tissues, with genes ranked according to the total number of significant results obtained across all columns in each table (i.e., the total number of up or down triangles per row). Genes for which expression is similarly altered in human skin are indicated by an asterisk symbol. Tables (C) and (D) 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 (P,0.05 in each sex), and then ranked according to the estimated 40-year fold-change (i.e., old/young; averaged between males and females). Genes for which expression is similarly altered in three or more other human aging profiles are indicated by an asterisk symbol. (PDF) Figure S2 Gene ontology biological process terms associated with human skin aging and their association with aging across human tissues. Genes regulated by aging in human skin were analyzed to identify significantly over- represented gene ontology (GO) biological process terms (P,0.05; Fisher’s exact test). The analysis was performed based upon (A) a set of 2469 human genes increased by aging in female skin (P,0.05), (B) a set of 2578 human genes decreased by aging in female skin (P,0.05), (C) a set of 2199 human genes increased by aging in male skin and (D) a set of 1113 human genes decreased by aging in male skin (P,0.05). The font color of each GO term in the left margin corresponds to the degree to which that term was overrepresented (P,0.05, black font; P,0.01, blue font; P,0.001, red font). For each GO term, colors within the chart denote the average 40-year fold change (old/young) among genes associated with that GO term. The number of genes associated with each GO term is indicated in brackets (this total only includes genes that are among the 2469, 2578, 2199 or 1113 genes analyzed in parts PLoS ONE | www.plosone.org 15 March 2012 | Volume 7 | Issue 3 | e33204