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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Mech Ageing Dev. Author manuscript; available in PMC Jun 29, 2009.
Published in final edited form as:
PMCID: PMC2702675
NIHMSID: NIHMS110401

COMPARATIVE ANALYSIS OF MICROARRAY DATA IDENTIFIES COMMON RESPONSES TO CALORIC RESTRICTION AMONG MOUSE TISSUES

Abstract

Caloric restriction has been extensively investigated as an intervention that both extends lifespan and delays age-related disease in mammals. In mice, much interest has centered on evaluating gene expression changes induced by caloric restriction (CR) in particular tissue types, but the overall systemic effect of CR among multiple tissues has been examined less extensively. This study presents a comparative analysis of microarray datasets that have collectively examined the effects of CR in ten different tissue types (liver, heart, muscle, hypothalamus, hippocampus, white adipose tissue, colon, kidney, lung, cochlea). Using novel methods for comparative analysis of microarray data, detailed comparisons of the effects of CR among tissues are provided, and 28 genes for which expression response to CR is most shared among tissues are identified. These genes characterize common responses to CR, which consist of both activation and inhibition of stress-response pathways. With respect to liver tissue, transcriptional effects of CR exhibited surprisingly little overlap with those of aging, and a variable degree of overlap with the potential CR-mimetic drug resveratrol. These analyses shed light on the systemic transcriptional activity associated with CR diets, and also illustrate new approaches for comparative analysis of microarray datasets in the context of aging biology.

Keywords: aging, diet restriction, gene expression, longevity, microarray

1. Introduction

Caloric restriction is an effective intervention for delaying age-related disease and extending good health into later ages of the mammalian lifespan. Much interest in caloric restriction (CR) has centered on its unique life-extending effects, but increasingly, its impact on age-related disease and biomarkers of the aging process have received attention. In mammals, CR has been found to combat inflammation (Kalani et al., 2006), reduce endogenous levels of oxidative stress (Yu, 2006), increase insulin sensitivity (Anderson and Weindruch, 2006), decrease core body temperature (Mattison et al., 2003) and promote elevated resistance to stress treatments (Bruce-Keller et al., 1999; Apte et al., 2003). Taken together, these effects appear to combat many diseases of aging. Mammals that consume 30–40% fewer calories exhibit a lower incidence of Type 2 diabetes (Astrup, 2001), neurodegenerative disorders (Logroscino et al., 1996; Patel et al., 2005), hearing loss (Someya et al., 2006) and cardiovascular disease (Fontana et al., 2004). An especially important effect of CR is tumor suppression and reduced mortality due to cancer (Kritchesky, 2001; Hursting et al., 2003; Klebanov, 2007), which in mice, may be the key factor underlying lifespan extension (Spindler, 2005; Spindler and Dhabi, 2007). These results have been supported by ongoing experimental studies with rhesus monkeys (Ingram et al., 2006), and several observational analyses have revealed health advantages of CR diets in humans as well (Holloszy and Fontana, 2007).

Gene expression patterns are a fundamental level of investigation that may reveal key mechanisms underlying the health benefits of CR diets (Spindler, 2006; Spindler and Mote, 2007). DNA microarrays are especially valuable in this regard, since microarrays allow the genome-wide transcriptional effects of CR to be quantified. This is useful for evaluating the expression patterns of genes already known to mediate the effects of CR, but more importantly, can identify new candidate genes that were previously unlinked to CR mechanisms. In mice, previous studies have used microarrays to investigate the effects of CR in a wide range of tissue types (Lee et al., 2002; Higami et al., 2003; Massaro et al., 2004; Tsuchiya et al., 2004; Dhabi et al., 2005; Dhabi et al., 2006; Fu et al., 2006; Selman et al., 2006; Someya et al., 2006; Edwards et al., 2007; Wu et al., 2007). Results from these studies have identified genes that may be important mediators of health benefits that stem from CR diets. Genes for which expression is induced by CR in one study, however, often differ from those identified in other studies, even when the same tissue has been examined in both cases. Such discrepancy may result from Type I errors, alternative statistical analysis methods or experimental differences between two studies (Rosati et al., 2004). Results obtained from a single microarray study may therefore be of limited generality. Additionally, since previous studies have examined the effects of CR in one or only a small number of tissue types, only limited conclusions regarding expression patterns among tissues have been obtained. Few studies, for example, have been sufficiently comprehensive to identify genes induced by CR in the same direction across multiple tissue types (Fu et al., 2006; Selman et al., 2006; Spindler and Dhabi, 2007).

The comparative analysis of DNA microarray datasets can identify highly robust gene expression patterns that occur most consistently among multiple experimental investigations (Larrson et al., 2006). In the context of studies investigating genes activated by CR, comparative analysis yields two specific points of insight. First, with respect to any one tissue type, identifying genes correspondingly induced across multiple studies provides a way of filtering out false positive identifications, as well as genes whose identification may be dependent upon the strain of mouse used, nutritional composition of diet, tissue processing prior to array hybridization and other methodological practices that may vary among laboratories. This yields a list of highly supported candidate genes, for which the effects of CR on expression patterns is most likely repeatable in other contexts. Secondly, with respect to multiple tissue types, comparative analysis can identify genes for which the effect of CR is most common among tissues (Spindler and Dhabi, 2007). Given that some mechanisms of CR may be conserved between invertebrates and mammals (Motta et al., 2004), and that some mechanisms could be mediated by a common set of genes independently of tissue type (e.g., combating oxidative stress), it is plausible that such shared CR response genes exist. If so, their identification would shed light on systemic processes underlying the health benefits of CR, and also provide targets whose expression patterns can be mimicked through interventions not localized to individual tissues.

This study presents a comparative analysis of microarray datasets that have examined the effects of CR in a wide range of mouse tissues. The analysis is based on data generated from studies that have collectively examined the effect of CR in ten different tissue types (liver, heart, muscle, hypothalamus, hippocampus, white adipose tissue, colon, kidney, lung, cochlea). A detailed comparison of the effects of CR among tissues is provided, and genes for which response to CR is most shared among tissue types are identified. Further analyses were aimed at evaluating the effects of both CR and aging on gene expression in liver, since this tissue type has been a focus in many studies (Amador-Noguez et al., 2004; Tsuchiya et al., 2004; Dhabi et al., 2005; Boyleston et al., 2006; Niedernhofer et al., 2006; Someya et al., 2006; Fu et al., 2006; Edwards et al., 2007; Wu et al., 2007). With regard to liver, it was possible to identify candidate CR genes based on consistency among CR studies, as well as expression changes due to aging. The effects of CR in liver were also compared to those of resveratrol, a promising CR mimetic compound that has received much interest (Baur et al., 2006). Results from these analyses characterize robust gene expression patterns associated with CR, and also demonstrate new analytical approaches for the comparative analysis of microarray datasets.

2. Materials and Methods

Microarray datasets were obtained from Gene Expression Omnibus (Barrett et al., 2007), ArrayExpress (Parkinson et al., 2007) or directly from the contact author of published studies. All datasets were generated from experiments that evaluated the effects of CR on gene expression patterns in one tissue, or in some cases, multiple types of tissue. While most datasets were generated using Affymetrix microarray platforms, data generated from commercial non-Affymetrix platforms were also incorporated into the analysis. Since the Affymetrix 430 2.0 array is the most recent Affymetrix microarray platform and provides the most comprehensive coverage of the murine genome, data from all experiments were joined through linkage with this platform. Mappings between the 430 2.0 array and other platforms were not strictly one-to-one, since probesets from other arrays sometimes matched more than one probeset on the more comprehensive 430 2.0 array. Probesets from different generations of Affymetrix microarrays were linked using best match tables published by Affymetrix. Probesets from non-Affymetrix commercial arrays were linked to the 430 2.0 array using maps generated by the Resourcerer online database (Tsai et al., 2001).

All experiments featured treatments with mice provided a CR diet, along with a corresponding control treatment in which mice were provided a normal diet (usually about 90 kcal per week). Each CR and control treatment pair was used to formulate a statistical contrast evaluating the effect of CR within a particular tissue type and under a particular set of conditions. A total of 23 such contrasts were included in the analysis (see Table 1). Six contrasts evaluated the effects of CR in liver, while five contrasts evaluated the effects of CR in heart tissue. For other tissues, the effects of CR were evaluated by either two (muscle, hypothalamus, white adipose tissue) or one contrast (hippocampus, colon, kidney, lung, cochlea). Each contrast corresponded to a series of differential expression tests, in which probesets significantly upregulated were assigned a score of 1, probesets significantly downregulated were assigned a score of −1, and probesets exhibiting no significant differential expression were assigned a score of 0. The pattern of 1, −1 and 0 values among all probesets defined the differential expression signature associated with a given contrast (Swindell, 2007a). Nearly all differential expression signatures analyzed in this study were generated from raw data using the same statistical methodology. This methodology consisted of normalization by Robust Multichip Average (Irizarry et al., 2003), and differential expression analysis using the Limma linear modeling package (Smyth, 2004), with P-value adjustment using the Benjamini-Hochberg method (Benjamini and Hochberg, 1995). In two cases where raw data was not available, differential expression signatures were generated using results provided as supplemental data in original research reports (Corton et al., 2004; Fu et al., 2006). For all signatures, functional analysis of differentially expressed genes was based on gene ontology terms, and overrepresentation analysis of differentially expressed genes was carried out using GOstats (Falcon and Gentleman, 2007). Overrepresentation analysis was carried out by first identifying ontology terms significantly overrepresented with respect to at least one signature for each of the ten tissues examined. Terms overrepresented with respect to five or more tissues were then identified. Following p-value adjustments for multiple testing, a significance level of 0.05 was used to identify differentially expressed genes as well as significantly overrepresented gene ontology terms.

Table 1
Contrasts evaluating the effects of CR on gene expression. The contrast ID indicates the type of tissue examined and ends with an identification number that differentiates multiple contrasts associated with the same tissue type. The % CR column indicates ...

Differential expression signatures generated by each statistical contrast were clustered based upon their overlap (Swindell, 2007a). Equation (1) describes the similarity measure used to carry out an average linkage hierarchical clustering of differential expression signatures. Notations associated with Equation (1) are provided in Table 2.

Table 2
Notations associated with Equation (2). Consider two contrasts A and B. Contrast A evaluates whether gene expression levels differ significantly between CR mice and mice provided a normal diet. Contrast B evaluates this same hypothesis for another experiment ...
s=n+,++n,n+,++n,+n+,+n,++Min[(n+,0+n,0),(n0,++n0,)]
(1)

The value of s ranges between 0 and 1, such that the distance between two differential expression signatures is defined as d = 1 − s. For a given pair of signatures, the value of s is a proportion representing the number of genes with corresponding differential expression patterns ( n+.+ +.n− −), divided by the number of genes differentially expressed within the signature associated with fewer differentially expressed genes. This similarity measure is configured to ensure that signatures with many genes differentially expressed can be similar to signatures with few genes differentially expressed, provided that most genes differentially expressed in the latter signature have the same pattern in the former signature. This criterion is sensible because the total number of genes differentially expressed within any one signature is a function of statistical power. It should not be concluded that signatures with many differentially expressed genes are dissimilar from those with fewer, since the difference may be attributable to statistical power and sample size. The similarity measure represented by Equation (1) circumvents this potential difficulty. It should also be noted that Equation (1) does not assign any similarity based upon the n0.0 genes that are not differentially expressed with respect to either of two signatures being compared. This is appropriate, since n0.0 is typically large and would otherwise mask similarity based upon genes that are differentially expressed.

Statistical significance of the overlap between two differential expression signatures was evaluated by simulation analysis. For any one pair of differential expression signatures, the test statistic is T as defined by Equation (2) (see Table 2 notations).

T=n+,++n,
(2)

The null hypothesis of interest is that the probability of a gene being differentially expressed within any one signature is independent of its probability of differential expression within another signature. Under this condition, the null distribution of T can be described analytically based upon approximations to the binomial distribution (Smid et al., 2003; Swindell, 2007a). In the present study, however, probeset maps between the Affymetrix 430 2.0 array and other platforms were not strictly one-to-one. In some cases, the same probeset from one platform was assigned to multiple probesets on the Affymetrix 430 2.0 array. It is therefore unrealistic to assume that the probability of up or downregulation is independent among probesets within the same signature. The Bernoulli trial condition is therefore untenable and the distribution of T cannot be modeled using a Binomial model. It was possible, however, to generate a null distribution of T for each pair of differential expression signatures by simulation. With marginal totals in Table 2 fixed for a given pair of signatures being compared, values of 0, 1 and −1 were randomly permuted among probesets within signatures, and the value of T was calculated after each of 10,000 permutations. For each pair of signatures being compared, this procedure generated a null distribution for T against which an observed value of T was compared. The analysis was carried out for all 23(23-1)/2 = 253 pairwise combinations of signatures included in the anlaysis, and resulting P-values were adjusted for multiple comparisons using the Benjamini-Hochberg method (Benjamini and Hochberg, 1995).

3. Results

3.1 Overview

Fewer than 23% of examined genes were significantly induced by CR in any one experiment, and in most cases, CR had a significant effect on less than 5% of examined genes (Table 3). The overall effects of CR varied among studies that examined the same type of tissue. With regard to liver, for instance, 22.1% of genes were differentially expressed with respect to the lvr1 contrast, while less than 1% of genes were differentially expressed with respect to the lvr4b and lvr10 contrasts. Among all tissue types examined, CR most commonly led to upregulation of genes involved in lipid metabolism and metal ion response, and downregulation of genes associated with immunity and protein folding. For each contrast, gene ontology terms significantly overrepresented among significantly up and downregulated genes were identified, pooled within tissue types, and then compared across tissue types. For CR-upregulated genes, this analysis revealed that some individual biological process terms were overrepresented with respect to as many as five tissue types, including metabolic process (GO:0008152), response to metal ion (GO:0010038), detoxification of copper ion (GO:0010273) and cellular lipid metabolic process (GO: 0044255). For CR-downregulated genes, the terms protein folding (GO: 0006457), phosphate transport (GO:0006817), humoral immune response (GO:0006959), immunoglobulin mediated immune response (GO:0016064) and antigen processing and presentation of exogenous peptide antigen via MHC class II (GO:0019886) were overrepresented with respect to seven tissue types.

Table 3
Overview of differential expression results. For each signature, the Total column indicates the number of unique genes for which expression data was available. The Upregulated column indicates the number of unique genes for which expression was upregulated ...

3.2. Effects of CR among tissues

Tissue type was the strongest factor determining gene expression changes induced by a CR diet. This was demonstrated by cluster analysis of differential expression signatures (see Figs. 1 and and2).2). Signatures associated with the same type of tissue clustered together in nearly every case, even when data were generated by separate laboratories in different studies. In Figure 2, for example, the seven signatures associated with liver clustered together in the same branch, which was also the case for signatures associated with the heart and hypothalamus. Signatures associated with muscle (msl1 and msl3), in contrast, did not cluster together, which was probably due to a large difference between msl1 and msl3 in terms of the duration of CR (Table 1).

Figure 1
Differential expression signatures. Each row corresponds to a contrast that evaluates the effects of CR on the expression of a large number of genes (see Table 1). Information listed in Table 1 has been incorporated into the labels shown in the figure. ...
Figure 2
Differential expression signature cluster analysis. The dendrogram provides results of a cluster analysis performed on differential expression signatures using the similarity metric described by Equation (1). Each leaf corresponds to a contrast involving ...

Despite the strong effect of tissue type, the laboratory or experimental study in which data were generated also had strong influence on clustering patterns. Signatures joined at very low levels of dissimilarity (d < 0.70), for instance, were usually generated by the same laboratory as part of the same study (Figure 2). Within tissue-specific branches (see Figure 2), signatures clustered according to the study from which data were obtained, rather than duration of CR or age at which CR was initiated. Additionally, there were several cases in which signatures generated as part of the same study clustered together, even though each signature was associated with a different type of tissue (e.g., see cln1 and msl1 in Figure 2). These results indicate that the laboratory in which experiments are performed, which encompasses numerous technical and experimental factors, has strong influence on results obtained from microarray investigations of CR.

Genes differentially expressed in any one experiment were often not differentially expressed in other comparable experiments. An illustrative case is the comparison between hrt7 and hrt8b (Figure 3). Both signatures were obtained from studies that examined the effects of long-term CR in heart tissue (Lee et al., 2002; Dhahbi et al., 2006). While the overlap between hrt7 and hrt8b signatures is statistically significant (P < 0.001, see Figure 3), fewer than 20% of differentially expressed genes were common to both studies. This level of overlap (or less) was typical of the signatures examined in this analysis. In Figure 2, for example, most branches are joined at high levels of dissimilarity (d > 0.70), indicating that intersections between differentially expressed gene sets are on the order of 30% or less. At the same time, however, among the many possible signature comparisons, signature overlap was commonly greater than expected on the basis of chance. Supplemental Data File 1 provides an informative similarity matrix that displays the similarity level and statistical significance of all 253 possible pairwise comparisons among the 23 signatures analyzed in this study.

Figure 3
Overlap of differential expression signatures. (A) A total of 719 probesets were induced by CR with respect to contrast hrt7, while 120 probesets were induced by CR with respect to contrast hrt8b (see Table 1). The Venn diagram shows the overlap between ...

3.3. Common CR Responses among Tissues

A small number of genes exhibited shared expression responses to CR across five or more tissues (Figs. 4 and and5).5). Such genes were a rare extreme with respect to the murine genome, since most genes did not exhibit expression response to CR in any tissue, or only responded to CR within one tissue type (Table 4). Of the ten tissues examined, 16 genes were upregulated with respect to five or more tissues (Figure 4), while 12 genes were downregulated with respect to five or more tissues (Figure 5). The biological significance of identified genes is supported by the observation that gene pairs with similar functional properties are included in both gene sets. For example, the 16 upregulated genes include two metallothioneins (Mt1 and Mt2) as well as two period homologues (Per1 and Per2) (Figure 4). Likewise, among the 12 downregulated genes, two procollagen alpha 1 genes (Col1a1 and Col3a1) and two heat shock proteins were included (Hsp110 and Serpinh1/Hsp47) (Figure 5). Among the 16 upregulated genes, the three most overrepresented biological process gene ontology terms were nitric oxide mediated signal transduction (GO:0007263), zinc ion homeostasis (GO:0006882) and circadian rhythm (GO:0007623) (P < 0.05). Among the 12 downregulated genes, overrepresented gene ontologies included those associated with response to heat (GO:0009408), unfolded protein (GO:0006986), biotic stimuli (GO:0009607), chemical stimuli (GO:00042221) and response to pest, pathogen and parasite (GO:0009613) (P < 0.05).

Figure 4
Genes upregulated by CR in five or more tissue types. A total of 16 genes met this criterion and are included in the figure. Each row corresponds to an individual gene, while each column corresponds to a contrast (see Table 1). Within the grid, up-triangles ...
Figure 5
Genes downregulated by CR in five or more tissue types. A total of 12 genes met this criterion and are included in the figure. Each row corresponds to an individual gene, while each column corresponds to a contrast (see Table 1). Grid symbols are described ...
Table 4
Common responses to CR among tissues. Results from experimental studies were linked to probesets associated with the Affymetrix 430 2.0 microarray. A total of 45,037 probesets (approximately 20,000 unique genes) were considered in the analysis and the ...

Per2 (period homologue 2) and Serpinh1 (serine peptidase inhibitor/Hsp47) exhibited the most shared CR response patterns among tissues. Per2 was upregulated by CR in seven tissues (liver, heart, muscle, hypothalamus, hippocampus, colon, cochlea), while Serpinh1 was downregulated by CR in seven tissues (liver, heart, muscle, white adipose tissue, hypothalamus, colon, lung) (Figs. 4 and and5).5). It is noteworthy that the effects of CR on Per2 and Serpinh1 expression, as well as other genes listed in Figures 4 and and5,5, is much more common among tissues than genes commonly cited as playing key roles in the mammalian CR response, such as Sirt1, Igf-I and mTOR (Dilova et al., 2007; Yamaza et al., 2007). For instance, Igf-I was significantly downregulated with respect to only liver and muscle (lvr4b, msl1, msl3), while mTOR was upregulated in just the liver and heart (lvr1, lvr4a, hrt7). Surprisingly, the effect of CR on Sirt1 expression was non-significant in every tissue for which data was available, and in each case, the effect of CR on Per2 expression was larger than the effects of CR on Sirt1 expression (Figure 6).

Figure 6
Effects of CR on Per2 versus Sirt1 expression. Solid circles connected by solid lines represent log-transformed fold-changes associated with Per2, and open circles connected by dotted lines represent log-transformed fold-changes associated with Sirt1. ...

3.4. Effects of CR and Aging in Liver

Five differential expression signatures associated with aging in liver were generated using data downloaded from the Gene Expression Omnibus and ArrayExpress databases (GSE3129, GSE3150, EMEXP153, EMEXP839) (Amador-Noguez et al., 2004; Boyleston et al., 2006; Niedernhofer et al., 2006). The signatures are identified as age1 through age5, where each quantified gene expression differences between young (< 6 months) versus old mice (> 22 months). Each signature was generated using data collected from standard laboratory strains (C57B1/6J), or from mice with normal phenotypes that are heterozygous for recessive endocrine mutations (Prop1df/+, Pit1dw/+, Ghrhrlit/+). There was only slight evidence to indicate that, in liver, the effects of CR strongly overlapped with those of aging. The signatures associated with CR and aging in liver generally clustered in two separate branches (see Figure 7 and Supplemental Data File 2). The one exception was age5, which clustered in the same branch as signatures associated with CR, although it should be noted that age5 was joined to the CR cluster at a high level of dissimilarity (d ≈ 0.90). A more striking observation was that signatures associated with CR rarely overlapped significantly with those associated with aging. Supplemental data file 2 provides a similarity matrix of the CR and aging signatures shown in Figure 7, and reveals that of the 35 pairwise comparisons between CR and aging signatures, significant overlap was present in just 6 cases.

Figure 7
Effects of CR and aging in liver: Differential expression signature cluster analysis. Differential expression signatures associated with the effects of CR in liver tissue were clustered together with those corresponding to the effects of aging in liver. ...

To investigate specific anti-aging effects of CR, genes with opposite induction patterns under CR and aging in liver were identified. Ten genes were identified as upregulated by CR in three of more lvr signatures, as well as downregulated by aging in at least one age signature (Figure 8). Likewise, ten genes were downregulated by CR in three or more lvr signatures and upregulated by aging in at least one age signature (Figure 9). These genes correspond to cases in which gene expression changes induced by CR are in opposition to those that result from aging. Genes listed in Figure 8 were disproportionately associated with electron transport (GO:0006118) and cellular metabolism (GO:0044237) (P < 0.05). Likewise, genes listed in Figure 9 were associated with positive regulation of organismal physiological process and metabolism (GO:0057240, GO:0009893), response to stimulus (GO:0050896) and defense response (GO:0006952) (P < 0.05). Williams et al. (2006) has recently evaluated baseline hepatic gene expression levels of BxD mouse strains, for which lifespan data had previously been collected by Gelman et al. (1988). Combining these two data sources revealed that hepatic expression levels of Serpina12 (serine peptidase inhibitor; see Figure 9) was negatively related to lifespan among BxD mouse strains (rs = −0.366, P = 0.034), although this relationship was non-significant after adjusting for multiple testing of all genes included in Figures 8 and and99 (P = 0.238).

Figure 8
Effects of CR and aging in liver: Genes upregulated by CR and downregulated by aging. A total of 10 genes were upregulated by CR with respect to three or more liver contrasts, and downregulated with respect to at least one aging contrast. Each row corresponds ...
Figure 9
Effects of CR and aging in liver: Genes downregulated by CR and upregulated by aging. A total of 10 genes were downregulated by CR with respect to three or more lvr contrasts, and upregulated with respect to at least one age contrast. Each row corresponds ...

The transcriptional effects of CR in liver were compared to those of resveratrol, a postulated CR mimetic compound that has recently received much interest (Baur et al., 2006). The similarity between the effects of CR and resveratrol on gene expression depends on the experiment to which a comparison is made (Figure 10). In three cases, the effects of resveratrol on gene expression in liver significantly overlapped with those of CR (lvr1, lvr5a and lvr5b), while in four cases no significant overlap was found (lvr4a, lvr4b, lvr10 and lvr13).

Figure 10
Effects of CR and resveratrol in liver. The effects of resveratrol in liver were evaluated by Baur et al. (2006) (Gene Expression Omnibus series GSE6089). Differential expression signatures ...

4. Discussion

Microarrays are powerful tools for screening large numbers of genes and making unbiased determinations regarding which genes are best candidates for detailed experimental study. The comparative analysis of microarray data integrates results from many independent investigations, filters out experimental noise and false positive identifications, and thus maximizes the insight generated from whole-genome expression analysis. The present study has implemented new approaches for the comparative analysis of microarray data, and has shed light on mechanisms by which caloric restriction (CR) acts among mammalian tissues and how these mechanisms relate to the aging process. Independent experiments involving CR generally identified disparate sets of genes with limited overlap (< 30%). Nevertheless, a total of 28 genes that exhibit shared transcriptional responses to CR across five or more mammalian tissues were identified. Based on gene expression patterns alone, the systemic involvement of these 28 genes in the mammalian CR response is better supported than the involvement of Sirt1, Igf-I and mTOR (Dilova et al., 2007; Yamaza et al., 2007). With regard to liver tissue, surprisingly little overlap between the genome-wide effects of CR and aging were found, suggesting that the effects of CR on gene expression are not necessarily a “reversal” of the gene expression changes that occur during aging. Interestingly, the correspondence between gene expression changes induced by CR and resveratrol in liver depended upon which experimental study comparison was made to. This result indicates that additional data is necessary to determine whether, in liver tissue, the genome-wide transcriptional effects of resveratrol serve to mimic those associated with a CR diet.

Common CR responses among tissues were characterized by upregulation of tumor suppression genes as well as genes that combat oxidative stress. It is especially striking that two period homologues (Per1) and two metallothionein (Mt1 and Mt2) genes were included among the 16 genes upregulated across five or more tissue types under CR. Period homologues are widely recognized for their role in manipulating the biological clock, but also exhibit tumor suppression activity (Lee, 2006). Per1 and Per2 null mice are more susceptible to tumor development, and also exhibit features of premature aging (Fu et al., 2002; Lee, 2005; Lee, 2006). Since decreased tumor incidence is perhaps the major factor underlying extended lifespan in CR mice (Spindler and Dhabi 2007), upregulation of Per1 and Per2 in many tissue types may be a key factor reducing mortality rates under CR. Elevated expression of Cdkn1a (cyclin-dependent kinase inhibitor 1A) and Sult1a1 (sulfotransferase family member 1A) across several tissues could also inhibit tumorigenesis in CR mice. Increased Cdkn1a (also p21) expression inhibits cellular proliferation and induces growth arrest (Xiong et al., 1993; Franklin et al., 2000; Gartel and Tyner 2002) and mice lacking this gene have severely decreased lifespan due to spontaneous tumor development (Martin-Caballero et al., 2001). Sult1a1 polymorphisms have often been linked with cancer risk (Peng et al., 2003; Wang et al., 2002; Wu et al., 2003; Zheng et al., 2003; Bardakci et al., 2007), which may be due to the role of sult1a1 in the bioactivation and inactivation of carcinogens (Miller, 1994; Fan et al., 2007). The metallothionein genes Mt1 and Mt2, also upregulated as a shared CR response, may have beneficial health effects through different mechanisms. Metallothioneins are stress-response genes with potent cytoprotective effects, most notably against oxidative stress damage (Thirumoorthy et al., 2007). Metallothioneins scavenge reactive oxygen species and have thus been found to prevent ROS-induced apoptosis in a wide range of contexts (Haidara et al., 1999; Kawai et al., 2000; Giralt et al., 2002; Penkowa, 2006; Kang, 2007). Since the accumulation of oxidative stress over the lifespan is thought to play a key role in the physiological decline associated with aging (Sohal and Weindruch, 1996), the upregulation of Mt1 and Mt2 by CR may contribute to the anti-aging effects of CR. Taken together, these findings support the hypothesis that, by increasing the expression of tumor suppressor (Per1, Per2 and Cdkn1a) and stress-response genes (Mt1 and Mt2) in multiple tissue types, CR increases lifespan in mice by preventing the onset of cancer and by limiting the accrual of oxidative stress damage during aging.

Common responses to CR include both activation and inhibition of stress-response pathways. Stress-response genes upregulated by CR include metallothioneins (Mt1 and Mt2), cyclin-dependent kinase inhibitor 1A (cdkn1a) and RNA binding motif protein 3 (Rbm3), while stress-response genes downregulated by CR include two heat shock proteins (Serpinh1/Hsp47, Hsp110) and guanine nucleotide binding protein (Gng11). At a systemic level, therefore, response to CR may be viewed as a partial hormesis effect, involving activation of certain stress-response pathways and inhibition of others. The downregulation of heat shock proteins under CR seems contrary to findings from invertebrate model systems, which have suggested that heat shock protein upregulation has positive impacts on longevity (Hsu et al., 2003; Walker and Lithgow, 2003). Evidence regarding similar effects in mammals, however, is currently lacking, and to the contrary, elevated heat shock protein expression has been associated with carcinogenesis (Kim et al., 2007). The expression of Hsp110, in particular, is elevated in tumors and appears to promote resistance of cancer cells to apoptosis (Hosaka et al., 2006). Serpinh1/Hsp47 downregulation may also influence tumorigenesis (Shirakami et al., 1995; Morino et al., 1997), although a more important effect may be to prevent accumulation of excess fibrous tissue during aging. Hsp47 is a collagen-specific chaperone that is essential for construction and maintenance of the extracellular matrix (Nagai et al., 2000), and its overexpression has been found to promote fibrosis and glomerulosclerosis in the aging rat kidney (Razzaque et al., 1998), which is attenuated by a CR diet through Hsp47 downregulation (Razzaque et al., 1999). The inhibition of collagen-building pathways appears to be an important systemic effect of CR, since two procollagen genes (Col1a1 and Col3a1) were also downregulated as shared CR responses.

It was recently found that mice lacking 5 adenylyl cyclase (AC5-KO mice) exhibit a 30% lifespan increase (Yan et al., 2007). AC5 is a downstream signaling component of both β1-adrenergic receptor (Adrb1) and β3-adrenergic receptor (Adrb3), and mediates the synthesis of cAMP from ATP (Susulic et al., 1995). This study found that hepatic expression of β3-adrenergic receptor (Adrb3) increases with age and decreases due to CR (Figure 9), which suggests that Adrb3 signaling through AC5 might be inhibited by CR. If so, this would represent a shared mechanism underlying lifespan extension due to CR and in AC5-KO mice. A complicating factor, however, is that since AC5-KO mice also exhibit significantly reduced growth hormone (GH) levels (Yan et al., 2007), such that inhibition of the GH/IGF-I axis might also explain the observed lifespan increase (Brown-Borg, 2007), perhaps independently of adrenergic receptor signaling. Interestingly, inhibition of the GH/IGF-I axis in the long-lived Ames mouse leads to upregulation of Adrb3 in liver at early ages but not at late ages (Swindell, 2007b), which may reflect either increased Adrb3/AC5 signaling capacity or a compensatory mechanism to account for inhibited Adrb3/AC5 signaling.

The downregulation of immune-response genes is a common CR response among tissues that should not be overlooked. Positive effects of CR on immune response indicators have been documented in a number of studies (Grossmann et al., 1990; Effros et al., 1991; Pahlavani, 2004; Messaoudi et al., 2006), but survival rates of CR and control-fed mice in response to pathogen infection have been examined less extensively. When survival of CR and control-fed mice has been compared, discouraging results have been obtained (Gardner, 2005; Kristan et al., 2007). For instance, mortality of old mice treated with influenza virus was significantly increased by CR, resulting in complete mortality of CR mice compared to just 40–60% mortality of control mice (Gardner, 2005). Furthermore, as pointed out by Spindler and Mote (2007), a large scale study of humans aged 50 – 75 found that low body mass index is associated with increased mortality, which was largely attributable to elevated rates of infection (Sunder, 2005). In most tissues, genes downregulated by CR were disproportionately associated with humoral and immunoglobulin immune response, as well as antigen processing and presentation. The 12 genes downregulated across five or more tissue types included antigens (Cd74 and H2-Ea), T-Cell specific GTPase (Tgtp), complement component 1 (C1qb) and an interferon alpha stimulated gene (Ifi27). It is unclear how the downregulation of these genes by CR could affect health and survival of mammals within natural environments where pathogen exposure is unregulated. Downregulation of interferon alpha inducible protein 27 (Ifi27) may decrease the ability of mice to cope with viral infection, especially in aged animals (Labrada et al., 2002). Likewise, downregulation of T-Cell specific GTPase (Tgtp) by CR may increase susceptibility to infection, since Tgtp transfection was found to increase resistance of L cells to an RNA virus (Carlow et al., 1998). Additionally, upregulation of the metallothioneins Mt1 and Mt2, while protective against oxidative stress, may be yet another factor inhibiting immune response under CR, since elevated Mt1 and Mt2 lowers zinc bioavailability and thus weakens immunity (Cipriano et al., 2003; Mocchegiani et al., 2004).

Guanine nucleotide binding protein (Gng11) was downregulated in five tissue types under CR (liver, muscle, hypothalamus, colon and lung) and represents a possible link between cellular and organismal senescence. Hossain et al. (2006) found that downregulation of Gng11 in human fibroblasts led to an extension of replicative lifespan, while its overexpression rapidly induced cellular senescence. Gng11 expression is also induced by oxidative stress (Hossain et al., 2006), suggesting that CR may inhibit cellular senescence by preventing the accumulation of oxidative stress and consequently inhibiting Gng11 expression.

CR is commonly viewed as a treatment that not only improves health and lifespan, but actually slows the process of aging. In support of this notion, previous microarray studies have reported that gene expression changes resulting from CR are largely a reversal of expression changes that result from aging. In a recent study, for example, Edwards et al. (2007) reported that 87% of age-related gene expression changes in skeletal muscle were countered by CR. This result was in qualitative agreement with findings from an earlier study indicating that CR reversed 19% of age-related gene expression changes in the heart (Lee et al., 2002). Results from the present analysis, however, reveal that in liver tissue, gene expression changes due to CR seldom have a statistically significant relationship with those that result from aging. This result is based on 35 comparisons among seven contrasts evaluating the effects of CR and five contrasts evaluating the effects of aging in liver, in which significant overlap between differential expression signatures was present in just 6 of 35 comparisons (see Supplemental Data File 3). A possible explanation of this result is that, in earlier studies, the effects of CR and aging were not a priori independent due to experimental design. Both Edwards et al. (2007) and Lee et al. (2002) examined three treatment comparisons among young mice, old mice, and old-CR mice. Given this experimental setup, the aging effect on gene expression (young vs. old mice) cannot be independent of the CR effect on gene expression (old mice vs. old-CR mice), since association between the two effects will be generated via the common comparator (the old mice treatment). This design could inflate associations between the effects of CR and aging, which may explain the difference between results of the present analysis and results from Edwards et al. (2007) and Lee et al. (2002). The ideal approach for evaluating the effects of CR and aging is to employ a two-factor factorial design with age and diet as fixed effects. Using this design, Dhabi et al. (2006) compared the effects of aging and CR on gene expression in the heart and found, in agreement with the present analysis, weak association between the two effects, with only 79 of 1075 (7.3%) age-responsive genes affected by CR.

Common responses to CR provide benchmarks for evaluating potential CR mimetic compounds. The degree to which the beneficial effects of CR arise from tissue-specific versus common mechanisms is not fully established (Spindler and Dhabi, 2007). It is unlikely, however, that CR-mediated lifespan extension depends overwhelmingly on gene expression changes within any one tissue type alone. The extended lifespan phenotype more likely depends on continued systemic functioning during the course of aging, involving all major organ systems and tissues. Thus, while DNA microarrays have been valuable for identifying important gene expression changes associated with CR (Spindler and Mote, 2007), this technology may be less critical beyond the current discovery phase. A more cost-effective and biologically meaningful strategy for screening potential CR mimetics may be to evaluate expression patterns of a small number of genes among a variety of tissues. The present study provides some support for the notion that the beneficial effects of CR diets are at least partly mediated by a set of shared gene expression changes that lead to tumor suppression and reduction of oxidative stress in multiple tissues. Such shared gene expression changes warrant special consideration when evaluating candidate CR mimetic compounds. A useful direction for future studies, therefore, is to evaluate whether the 28 common gene expression patterns identified in this study are also associated with CR mimetic compounds currently under investigation (e.g., Baur et al., 2006), or alternative dietary treatments that may also be viewed as CR mimetic strategies (e.g., Orentreich et al., 1993; Miller et al., 2005). This approach may facilitate the development of effective CR mimetic strategies, which may prove to be of considerable value in the realm of preventative medicine.

Supplementary Material

1

Supplemental Data File 1. Differential expression signature similarity matrix. This file displays a similarity matrix based on differential expression signatures analyzed in this study. Signature ids are listed along rows and columns of the matrix. Each square within the matrix indicates similarity between signatures associated with each row and column. Above Diagonal: Similarity between signatures as defined by Equation (1). Light colors indicate weak similarity (s near 0) and dark colors indicate strong similarity (s near 1). Below Diagonal: Filled squares indicate statistically significant overlap between differential expression signatures (P < 0.05). The test statistic used is defined by Equation 2. Null distributions were generated by simulation analysis and p-values were adjusted for multiple comparisons using the Benjamini-Hochberg method (see Methods).

2

Supplemental Data File 2. Effects of CR and aging in liver: Differential expression signatures. This file displays differential expression signatures associated with the effects of CR and aging in liver. The dendrogram associated with differential expression signatures is shown in Figure 7. Red represents genes upregulated with respect to CR, while green represents genes downregulated with respect to CR. Light-yellow areas represent genes not significantly induced by CR, and blank areas indicate that no data is available. Differential expression signatures were filtered to include only 1738 probesets that were differentially expressed with respect to at least two contrasts.

3

Supplemental Data File 3. Effects of CR and aging in liver: Differential expression signature similarity matrix. This file displays a similarity matrix based on differential expression signatures associated with CR and aging in liver. Signature ids are listed along rows and columns of the matrix. Each square within the matrix indicates similarity between signatures associated with each row and column. Above Diagonal: Similarity between signatures as defined by Equation 1. Light colors indicate weak similarity (s near 0) and dark colors indicate strong similarity (s near 1). Below Diagonal: Filled squares indicate statistically significant overlap between differential expression signatures (P < 0.05). The test statistic used is defined by Equation 2 (see Methods). Null distributions were generated by simulation analysis and p-values were adjusted for multiple comparisons using the Benjamini-Hochberg method (see Methods).

Acknowledgments

This work was supported by NIA training grant AG000114 and the University of Michigan Department of Pathology. Helpful comments and suggestions were provided by two anonymous reviewers. The author thanks laboratories for providing microarray data to the Gene Expression Omnibus and ArrayExpress databases, as well as researchers who responded to requests for experimental data (Yoshikazu Higami, Yinghe Hu, Patricia L. Mote, Thomas A. Prolla, Steven R. Spindler, James M. Vann, Richard Weindruch, Pu Wu).

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