and S13). (ii) Despite the lack of broad correspondence between human and mouse aging effects, we could identify individual genes and gene sets with relatively species-convergent or species- divergent aging patterns (see Figures S18 and S19). For instance, points of convergence include negative regulation of epithelial cell proliferation, peptide cross-linking, collagen fibril organization and tumor necrosis factor cytokine production (Figure S19), while divergent points include positive regulation of ab T-cell activation, leukocyte cell-cell adhesion, positive regulation of NF-kappaB transcription factor activity, and autophagy (Figure S19). (iii) Human-mouse similarity of transcriptional aging effects may be strengthened if human aging effects are estimated from a cohort that includes very old individuals. For example, we observed that (central nervous system) transcriptional aging signatures estimated from cohorts that include very old subjects (e.g., above 95 years old) did in fact cluster together with transcriptional aging signatures derived from mouse brain tissues (see Figure 3). On the other hand, in the context of experimental aging research, investigators have argued the counter-point, and have suggested that very old subjects do not, in fact, provide good models for understanding basic aging mechanisms [63]. Many datasets have now been generated for characterizing the aging transcriptomes of humans and mice [11,23,61]. It is important to emphasize, however, that both technical and biological factors limit consistency between studies, such as noise in genome-wide expression assays, idiosyncratic characteristics of mouse strains, disparities in sample processing methods, and stochastic aspects of aging processes. We estimate, for instance, that overlap among results from microarray-based gene expression studies of aging in human and mouse tissues is typically less than 20% at a comparison-wise type I error rate of 0.05 (Figure S20). From this perspective, the necessity of meta-analysis for establish- ing a strong foundation for future work is apparent, and we and others have emphasized this approach [11,23,61,64]. While no individual gene exhibits a completely consistent pattern across datasets, we were able to identify well-supported single-gene patterns associated with aging in multiple tissues from humans and mice [11,23,64] (see Figure S18). Our results show, for instance, that clusterin (Clu) expression is elevated with age in multiple organs of CB6F1 mice (tail skin, heart and kidney) and that expression is also diminished in liver and hippocampus of ‘‘slow aging’’ Snell mice that lack circulating levels of GH and IGF-1 [38–41] (Figure 9). This result underscores a link between a well- supported age-associated expression pattern and an endocrine mutation known to alter aging trajectory in mammalian systems. We anticipate that meta-analyses will, in coming years, further orient our focus towards high-confidence aging effects with connections to basic aging mechanisms, and that this may lead to a more precise operational definition of aging. Mammalian aging has been linked to a broad range of pathological processes at the cellular and histological levels, including inflammation, accumulation of oxidative stress damage, formation of advanced glycation end products, and diminished protein homeostasis [65]. In skin, these basic mechanisms of aging interact with declines in collagen synthesis and collagen degrada- tion, with progressive declines in the integrity of the extracellular matrix [8,9]. The transcriptome shifts that occur with aging can yield new hypotheses for mechanisms that participate in these processes. Here, we have provided evidence for concerted shifts in the expression patterns of genes associated with T-cells, B-cells and dendritic cells with increased age, along with directional shifts in the expression of genes associated with key transcription factors, such as Zeb1 in both humans and mice. Interestingly, these trends had sex- and species-specific characteristics, and in mice, were Mechanisms of Transcriptome Aging coupled to decreased expression of Clec7a, Lyz1 and Lyz2 with increased age. These findings provide data-driven insight into the mechanisms that shape the aging transcriptome, which, in turn, can provide guideposts towards development of more compre- hensive conceptual models of skin aging. Future studies of these mechanisms may facilitate development of therapeutics that improve the likelihood of healthy aging outcomes in mammals. Methods Ethics statement This study was conducted in compliance with good clinical practice and according to the Declaration of Helsinki principles. Informed written consent was obtained from all human subjects, under protocols approved by the institutional review board of the University of Michigan (HUM00037994). All animal protocols were approved by the University of Michigan committee on the use and care of animals (018 ARF 5614). Microarray analysis of sun-protected human skin The population cohort and sample processing methods for human microarray data has been described in a recent report [66]. In brief, 62 healthy individuals were recruited from areas surrounding Detroit, Michigan between October 2001 and November 2002 (n = 31 women 18–75 years of age; n = 31 men 18–60 years of age). Skin biopsies were obtained from the buttock or upper thigh region, and throughout we have assumed that these skin regions are relatively sun-protected and thus predominantly influenced by intrinsic aging rather than the extrinsic ‘‘aging’’ associated with chronic UV light exposure. Processed samples from each subject were hybridized to Affymetrix Human Genome U133 Plus 2.0 arrays, which include probe sets corresponding to 54675 human transcripts. Raw microarray data has been deposited in the NCBI Gene Expression Omnibus database and is accessible through accession number GSE13355. Human Genome U133 Plus 2.0 array annotation was obtained from release 31 of the NetAffx database [67]. Microarray analysis of mouse tail skin (CB6F1 strain) CB6F1 hybrid mice were obtained from NIA (n = 5 males at 5 months of age; n = 5 females at 5 months of age; n = 5 males at 17 months of age; n = 5 females at 17 months of age; n = 6 males at 30 months of age; n = 6 females at 30 months of age). For these treatments, ‘‘young’’ mice correspond to those at 5 months of age, and ‘‘old’’ mice correspond to those at 30 months of age. Median lifespan for this hybrid mouse strain is reported to be 28.5 months [68]. Mice were fasted for 6–8 hours and then sacrificed by CO2 inhalation. Upon sacrifice, ear, back and tail skin was collected along with internal organs, and samples were stored in RNAlater (Qiagen cat. no. 76106) and placed at 4uC for 24 hours before transitioning to 220uC. For microarray analyses, we have focused on mouse tail skin because this region has relatively low density of hair follicles in correspondence with the more interfollicular nature of human skin. Additionally, mouse tail skin compares favorably with human skin due to its relative enrichment in epidermis and attenuation of the panniculus carnosus, a subcutaneous muscle layer prominent in dorsal regions of mice but nearly absent in humans [69]. All samples were disrupted using a rotor-stator homogenizer and RNA extractions were performed using the Qiagen RNeasy Fibrous Tissue kit (Qiagen cat. no. 74704), with on-column DNAase digestion (Qiagen cat. no. 79254). Extracted RNA was quantified using the NanoDrop spectrophotometer and RNA quality was evaluated using the Agilent Bioanalyzer. Processed samples from young and old mice were hybridized to PLoS ONE | www.plosone.org 13 March 2012 | Volume 7 | Issue 3 | e33204