promote an AP-1 and a2b1 integrin-dependent elevation of matrix metalloproteinase 1 (MMP-1) expression, and in turn MMP-1 cleaves extracellular collagen, resulting in loss of mechanical tension in dermal fibroblasts [9]. This loss of mechanical tension leads to greater production of intracellular oxidants within fibroblasts, which further heightens MMP-1 activity and reinforces the cycle by which extracellular matrix is degraded in aging skin [9]. The importance of these processes to aging is not restricted to skin alone. In heart, for instance, heighted TGF-b activity and excess collagen deposition are factors that promote fibrotic disease, which can decrease elasticity of ventricular walls and compromise contractile function [10]. In mice, shifts in the expression of procollagen-encoding genes (Col1a2 and Col3a1) with age are among the most well-supported and widespread of all age-associated gene expression patterns [11]. These alterations in procollagen gene expression may, in part, reflect age-related attenuation of estrogen, growth hormone and/or insulin-like growth factor endocrine pathways [6,12–16]. Genome-wide expression profiling provides broad insights that can assist development of more comprehensive skin aging models [17–20]. Such investigations have identified unexpected and previously unknown effects of intrinsic skin aging, but require extension in several ways. First, expression profiling studies have primarily focused on one sex (males) [17,18], but effects of aging on gene expression in humans can be sex-specific [21,22]. Second, while the influence of aging among mammalian cell types and organ systems is partly overlapping and partly tissue-specific [11,23,24], it is unclear which aspects of skin aging are among the core senescent processes that influence other organ systems, which might be broadly targeted by therapeutic compounds that prevent or delay onset of age-related conditions [25,26]. Third, although large-scale shifts in gene expression occur with intrinsic skin aging, mechanistic underpinnings of these transcriptional changes remain unclear. On the one hand, the aging skin transcriptome is shaped by intracellular events and the activation or inhibition of transcription factors, such as ETS1, ETS2, AP-1, p53, E2F1 and NF-kB [8,19,27,28]. At the same time, extracellular features of skin aging also influence the composition of RNA species within aging skin. These features include degradation of the extracellular matrix [9], decline in the average diameter of blood vessels [29], and inflammatory events with infiltration of aging tissues by, for example, macrophages, T-cells, B-cells, natural killer cells, and neutrophils [30]. A complete mechanistic model must tie together the major intracellular and intercellular features of skin aging, and a key challenge is development of analytical pipelines that can exploit large-scale genomic datasets for this purpose. We used Affymetrix high density oligonucleotide arrays to provide a comprehensive analysis of transcriptome aging in sun- protected skin obtained from female and male human subjects, as well as in tail skin from CB6F1 mice of both sexes. We investigate broad correspondence between effects of natural aging in human and mouse skin. By analyzing transcriptome data within a meta- analysis framework, we highlight intrinsic features of skin aging that are closely tied to basic aging mechanisms, with the aim of establishing skin aging biomarkers that are best supported by existing data. In silico approaches are also used to identify both intercellular (inflammatory) and intracellular (cis-regulatory) mechanisms that underlie expression changes associated with advancing age, and our findings provide support for mechanisms that are sex-specific and/or species-specific. Mechanisms of Transcriptome Aging Results In silico inflammation profiling uncovers female-specific elevation of lymphocyte signatures with intrinsic aging of human skin Genes for which expression is altered by aging in sun-protected skin (buttock and upper thigh) were identified from analysis of 31 female subjects (ages 18–75) and 31 male subjects (ages 18–60) (Table S1). Expression signatures of aging in female and male skin were compared with those generated from 25 other datasets, where each dataset included expression measurements from a combination of young and old subjects (e.g., airway epithelia, trachea, muscle, blood and central nervous system; all data were generated using the same Affymetrix Human Genome U133 Plus 2.0 platform; total of 573 arrays; Figure S1 and Table S1). From this integrative analysis, a partially overlapping set of gene expression responses to aging in skin and other organ systems could be discerned (Figure S1). For instance, among the top 50 genes increased by aging most frequently across tissues, 32 were also increased in female and/or male skin (e.g., CLU, DSE, MT2A; see Figure S1A). Likewise, among the top 50 genes most often decreased by aging, 24 were correspondingly decreased in female and/or male skin (e.g., CA11, CHST2, DCAF11; see Figure S1B). However, based upon analysis of overrepresented Gene Ontology (GO) terms, it was also clear that many effects of aging in human skin were sex-specific. In females, for instance, aging decreased expression of genes associated with diverse metabolic processes (e.g., ATP, acetyl-CoA and alkaloid metabolism), but such genes were not correspondingly decreased in males (Figure S2B). In males, but not females, aging decreased expression of genes associated with immune-related GO terms (e.g., immune response, cellular defense, antigen processing and presentation; Figure S2D). This pattern could be due to sex differences in inflammatory events and localized shifts in lymphocyte abundance with advancing age [30]. To evaluate this possibility, we applied an algorithm for generating ‘‘inflammation profiles’’ based on microarray data, which focuses on ‘‘signature transcripts’’ highly expressed in immunocyte populations (or other cell types) in order to gauge whether there exists significant evidence for shifts in cell type abundance between two groups (e.g., young and old) [31]. This approach detects directional bias in the effects of aging among signature transcripts associated with specific cell types, and uses this information to infer shifts in cell type abundance with increased age (see Methods and Figure 2 from Swindell et al. [31]). For both female and male skin, transcripts with high expression in adipose tissue increased with age, supporting expansion of the subcutaneous adipose tissue compartment in both sexes (Figure 1). However, divergent inflammatory patterns in females relative to males were identified (Figures 1 and 2). In females, aging increased expression of transcripts associated with CD3+ T-cells, CD4+ T- cells, CD8+ T-cells, B-cells, macrophages, dendritic cells, monocytes and neutrophils (Figures 1 and 2). In males, these patterns were absent, and in contrast, there was evidence for decreased expression of transcripts associated with CD3+ T-cells, CD4+ T-cells, CD8+ T-cells and B-cells (Figures 1 and 2). More broadly, the female signature closely paralleled those observed in skeletal muscle and central nervous system regions, while that of males was more similar to airway epithelia, trachea and whole blood (Figure 1). PLoS ONE | www.plosone.org 2 March 2012 | Volume 7 | Issue 3 | e33204