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Keywords:

  • human;
  • lifespan;
  • longevity;
  • model organism

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. The evidence
  5. Discussion
  6. Future directions
  7. Acknowledgments
  8. References

Ample evidence from model organisms has indicated that subtle variation in genes can dramatically influence lifespan. The key genes and molecular pathways that have been identified so far encode for metabolism, maintenance and repair mechanisms that minimize age-related accumulation of permanent damage. Here, we describe the evolutionary conserved genes that are involved in lifespan regulation of model organisms and humans, and explore the reasons of discrepancies that exist between the results found in the various species. In general, the accumulated data have revealed that when moving up the evolutionary ladder, together with an increase of genome complexity, the impact of candidate genes on lifespan becomes smaller. The presence of genetic networks makes it more likely to expect impact of variation in several interacting genes to affect lifespan in humans. Extrapolation of findings from experimental models to humans is further complicated as phenotypes are critically dependent on the setting in which genes are expressed, while laboratory conditions and modern environments are markedly dissimilar. Finally, currently used methodologies may have only little power and validity to reveal genetic variation in the population. In conclusion, although the study of model organisms has revealed potential candidate genetic mechanisms determining aging and lifespan, to what extent they explain variation in human populations is still uncertain.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. The evidence
  5. Discussion
  6. Future directions
  7. Acknowledgments
  8. References

Over the last century, the mean life expectancy in Western societies has increased dramatically (Oeppen & Vaupel, 2002). In Japan, for instance, the mean life expectancy has increased from 50 years to 80 years in no more than six decades. It is unlikely that changes in the population genome over this time-period can explain for the observed increase in lifespan which is more likely to be attributable to the improvement of environmental conditions and medical care. The increase in mean life expectancy of the total population, however, has left the marked interindividual variance in lifespan unaltered. Socio-economic factors can in part explain this phenomenon, but ample evidence suggests that genetic factors are also at play. Studies of twins and long-lived families have estimated that 20–30% of the variation in human lifespan is determined by genetic factors, which impact becomes more important for survival at older ages (Herskind et al., 1996; Mitchell et al., 2001; Hjelmborg et al., 2006). Furthermore, siblings of centenarians have a significantly higher chance of becoming a centenarian themselves when compared to other members of their birth cohort (Perls et al., 2002). The survival benefits of family members of these long-lived subjects are lifelong and persist up to the highest age categories (Perls et al., 2002; Hjelmborg et al., 2006). Offspring of long-lived sibling pairs have a lower mortality risk already at middle age, whereas their spouses, with whom they have shared in part a common environment, do not show this survival benefit (Schoenmaker et al., 2006).

As for lifespan, aging is under moderate genetic control influencing the rate at which stochastically induced damaged molecules accumulate. Such damage is caused by various endogenous and exogenous biological and biochemical stresses. As a result, over the life course there is a constant rise in vulnerability of the body, leading to a continuously increasing risk of disease and death. Longevity and the maintenance of health in old age can be ensured via two principally different strategies that minimize the risk of permanent damage to occur, that is, by a decrease of environmental hazards or an increase of the durability of the body. Pathways that influence metabolism, maintenance and repair mechanisms, and prevent the accumulation of permanent damage thus represent key molecular candidates for the preservation of health and longevity.

Experiments in model organisms have demonstrated that a series of induced mutations in various genes that make up an integrated molecular pathway can dramatically increase lifespan. The most prominent example includes the Caenorhabditis elegans daf-2 and clk double mutants that live nearly five times longer than wild-type worms (Lakowski & Hekimi, 1996). Most of the genes of model organisms are evolutionarily conserved and present in humans. Here, we will briefly review the genes and mechanisms that have been shown to regulate lifespan in model organisms, but limiting ourselves to those genes, which have human homologues, and have been studied for association with human health and/or longevity (Table 1). In addition, it is addressed whether we can expect to find single genes or molecular pathways that substantially affect lifespan in humans, whether the information obtained from model organisms can be translated to variation in lifespan in humans and whether present genetic surveys are able to pick up this genetic variation. Here, for the first time, we address these questions and suggest approaches for studies in model organisms and in humans that would lead to results that are more easily translatable from model organisms to humans. Results are different for the various species. Therefore, we explore the critical importance of the environment in which organisms live, including humans, because same genes in different environmental settings can lead to different phenotypical effects.

Table 1.  Selected examples of genes identified to influence lifespan in model organisms
Organism*Gene name/descriptionFunctionReference
  • *

    Organism in which the gene was first shown to influence lifespan.

Saccharomyces cerevisiae
 Sir2NAD(+)-dependent deacetylaseRegulation of metabolism, stress resistanceKaeberlein et al., 1999
Caenorhabditis elegans
 age-1Phosphatidylinositol kinaseInsulin signallingMorris et al., 1996
 daf-2Insulin receptor-like geneInsulin signallingKimura et al., 1997
 daf-12Nuclear hormone receptorRegulation of metabolic and developmental pathwaysLarsen et al., 1995
 daf-16Forkhead transcription factorRegulation of metabolic and developmental pathwaysOgg et al., 1997
Drosophila melanogaster
 CatCatalaseAntioxidant activityOrr & Sohal, 1994
 ChicoInsulin receptor substrateInsulin signallingClancy et al., 2001
 Sod1Superoxide dismutaseAntioxidant activityParkes et al., 1998
 Sod2Superoxide dismutaseAntioxidant activitySohal et al., 1995
 Mei-41Phosphatidylinositol kinase, ATR kinase othologueDNA repairSymphorien & Woodruff, 2003
 PcmtProtein carboxyl methyltransferaseProtein repairChavous et al., 2001
Mus musculus
 GhGrowth hormoneInsulin signalling, tissue proliferationBartke, 2005
 KlothoBeta-glucuronidaseInhibits IIS signallingKuro-o et al., 1997
 p53Tumour protein p53Tumour suppressionTyner et al., 2002

The evidence

  1. Top of page
  2. Summary
  3. Introduction
  4. The evidence
  5. Discussion
  6. Future directions
  7. Acknowledgments
  8. References

Insulin/IGF-1 signalling (IIS)

The first evidence for genetic regulation of lifespan came from studies with C. elegans. It was discovered that worms with mutations in the dauer formation (Daf) genes, such as daf-2 and age-1, were able to bypass dauer formation, and become long-lived adults (Larsen, 2001). The molecular characterization of the daf-2 and age-1 genes revealed that daf-2 shows homology to the mammalian genes encoding insulin receptor (IR) and insulin-like growth factor 1 receptor (IGF-1R) (Kimura et al., 1997); and age-1 to the mammalian phosphatidylinositol-3-OH kinase catalytic subunits, which are located downstream of the IR and IGF-1R (Morris et al., 1996). Next, it was shown that, similar to C. elegans, reduced insulin signalling extends lifespan in Drosophila melanogaster. The increase in lifespan was observed for flies with mutated insulin-like receptor (InR) or its substrate (chico), and for flies with ablated insulin-producing cells (Giannakou & Partridge, 2007). In the latter case, the adult flies also exhibited increased storage of lipids and carbohydrates, reduced fecundity and increased stress and starvation resistance.

In vertebrates, the insulin signalling system is more complex and contains separate receptors for insulin (IR) and IGF-1 (IGF-1R) (Navarro et al., 1999). Data from mice indicate that both these receptors are involved in lifespan regulation. The IGF-1 branch, which acts through growth-hormone-releasing hormone, growth hormone (GH) and IGF-1, influences body composition and is involved in the regulation of gonad function (Bartke, 2005). The Ames and Snell dwarf mice, which are deficient in GH, thyroid hormone and prolactin, are infertile but long lived (Brown-Borg et al., 1996). A similar phenotype is observed for GH receptor knockout mice (Bartke, 2005). Furthermore, mice mutated for the IGF-1 receptor hint at a direct role for reduced IGF-1 signalling in mammalian longevity: Igf1r+/– females, but not males, exhibit a long-lived phenotype (Holzenberger et al., 2003). In contrast, complete disruption of the IR gene leads to insulin resistance, diabetes and shortened lifespan (Okamoto & Accili, 2003). Likewise, tissue-specific IR knockout mouse models develop obesity, insulin resistance and impaired glucose regulation, with the exception of fat-specific IR knockout mice (FIRKO) (Okamoto & Accili, 2003). These mice have reduced fat mass, are protected against age-related obesity and live longer than their littermates. Taken together, the evidence in mouse models shows that reduced IIS can extend lifespan also in mammals.

In humans, defects in GH–GHR–IGF-1 axis result in major pathologies (e.g. Laron Syndrome), but despite the pathologies these patients have a long lifespan, reaching ages of 80–90 years (Laron, 2005). In addition, there is evidence that long-lived subjects, such as centenarians, have decreased plasma IGF-1 levels and preserved insulin action, thus indicating that insulin responsiveness influences human longevity (Paolisso et al., 1997). Furthermore, polymorphisms in the IGF-1R locus, which lower plasma IGF-1 levels, were shown to be enriched among Italian centenarians (Bonafe et al., 2003). This finding was not replicated in a prospective follow-up study of elderly Dutch subjects, but in the same study it was found that a polymorphism in the GH1 gene, which controls IGF-1 activity, associates with longevity (van Heemst et al., 2005a). In addition, a combined effect of variation at the GH1, IGF-1 and IRS1 loci was observed, suggesting an additive effect of multiple variants associated with reduced IIS signalling on human longevity. These data, however, do not reveal a modulation of human lifespan in a magnitude that would come close to what is seen by analogous defects in some of the model organisms.

Klotho

The Klotho gene, which was identified in mouse models (Kuro-o et al., 1997), encodes a mammalian-specific hormone that negatively regulates the activity of IR and IGF-1R through repressing their autophosphorylation (Kurosu et al., 2005). In mice, genetic variation in the Klotho gene results in an early onset of various age-related disorders, including ectopic calcification, skin and muscle atrophy, osteopenia, medial calcification of the aorta and pulmonary emphysema (Kuro-o et al., 1997). On the other hand, over-expression of Klotho in mice leads to inhibition of insulin and IGF1 signalling and increased lifespan (Kurosu et al., 2005). In humans, a haplotype allele called KL-VS, which contains six sequence variants that are in complete linkage disequilibrium, has been associated with KLOTHO expression and shown to be underrepresented in elderly individuals (Arking et al., 2002). Additional studies have demonstrated that the survival advantage is only present in heterozygous KL-VS allele carriers, whereas in homozygous allele carriers a disadvantage for high-density lipoprotein (HDL) cholesterol levels, systolic blood pressure, stroke and longevity was observed (Arking et al., 2005).

Forkhead transcription factors

In C. elegans, the IIS pathway negatively regulates the activity of DAF-16, which is its main downstream target. The long-lived phenotype of the IIS mutants depends on the presence of an active DAF-16 protein (Mukhopadhyay & Tissenbaum, 2007). In mammals, the DAF-16 homologues are forkhead transcription factors (FOXOs): FOXO1a, FOXO3a, FOXO4 and FOXO6 (Furuyama et al., 2000). Similar to DAF-16 in C. elegans, in mammals the FOXO proteins relay the effects of insulin on lifespan, influence fertility and play a role in complex diseases such as diabetes (Carter & Brunet, 2007). In humans, only few studies have analysed the role of FOXO proteins in the development of age-related diseases, fertility and lifespan. In most studies, no associations between genetic variance in the FOXO1a and FOXO3a genes, and lifespan have been detected (Bonafe et al., 2003; Kojima et al., 2004). These findings, contrast with recent studies where genetic variance in the FOXO1a gene was linked to increased glucose levels (Karim et al., 2006; Kuningas et al., 2007a) increased risk of diabetes and decreased lifespan (Kuningas et al., 2007a). For genetic variance in the FOXO3a gene, associations were observed with increased risks of stroke and mortality, but not with fertility (Kuningas et al., 2007a).

Daf-12

In C. elegans, DAF-12 is a member of the evolutionarily conserved nuclear hormone receptor (NHR) superfamily (Mangelsdorf et al., 1995), and it has been implicated in dauer diapause, developmental timing, metabolism, fertility and longevity. Current data have positioned DAF-12 downstream of the insulin and germline signalling, as the long-lived phenotype of germline-ablated mutants and of some IIS mutants depends on DAF-12 activity, but the exact position is still unknown (Rottiers & Antebi, 2006). In humans, the NHRs most similar to DAF-12 are the liver X receptors (LXRs) (alpha and beta), which have cholesterol breakdown products (oxysterols) as ligands. Upon activation, LXRs regulate various processes that result in cholesterol excretion from the body (Zelcer & Tontonoz, 2006). Recently, a common haplotype of the LXRA gene was associated with increased survival, predominantly because of lower mortality from cardiovascular causes and infection (Mooijaart et al., 2007a). A possible mechanism through which LXR could lead to the observed beneficial effects includes involvement of its target gene apolipoprotein E (APOE). ApoE is an anti-atherosclerotic protein involved in the efflux of cholesterol from macrophages. Genetic variation in APOE has consistently been associated with cognitive decline and cardiovascular disease mortality. Moreover, we have recently shown that independent of genetic variation in APOE, high plasma ApoE levels associate with increased risk of stroke (van Vliet et al., 2007), increased risk of cardiovascular mortality (Mooijaart et al., 2006) and decreased cognitive functioning (Mooijaart et al., 2007b). These data support previous observations that lipoprotein metabolism is critical for exceptional longevity. It has been shown that families of Ashkenazi Jewish centenarians have larger particles of HDL and low-density lipoprotein (LDL), which are associated with a decreased incidence of metabolic syndrome, cardiovascular disease and hypertension (Barzilai et al., 2003). Also, in the Dutch Caucasian population, offspring of long-lived sibling pairs have larger LDL particles than their age-matched partners, again suggesting that larger LDL particles confer a survival benefit (Heijmans et al., 2006).

Sirtuins

The Sirtuins represent an evolutionarily conserved family of silent information regulator 2 (Sir2) NAD-dependent protein deacetylases that interact with and influence the activity of various transcription factors and co-regulators (Bordone & Guarente, 2005). Increased expression of the Sir2 gene, either because of an extra copy of the gene or to caloric restriction, has been shown to prolong lifespan in various model organisms (Haigis & Guarente, 2006). Likewise, the administration of resveratrol, which is normally synthesized by plants in response to stress, increases the activity of Sir2, and the lifespan of yeast, worms and fruit flies (Howitz et al., 2003; Wood et al., 2004). However, the effect of resveratrol on lifespan extension in model organisms has been recently put under question (Bass et al., 2007). A study of resveratrol found no significant effects on lifespan in seven independent trials in D. melanogaster and found slight increases in lifespan in some trials but not others in C. elegans (Bass et al., 2007).

In mammals, there are seven Sir2 homologues (SIRT1-7), of which SIRT1 is the most closely related to Sir2 (Frye, 2000). In mouse models, SIRT1 and SIRT3 have been studied the most. SIRT1 has been associated with glucose and fat metabolism, stress resistance and cell survival (Haigis & Guarente, 2006), whereas SIRT3 regulates the activity of acetyl-CoA synthetase, and thereby the entry of carbons from acetate into central metabolism (Haigis & Guarente, 2006). In humans, polymorphisms within SIRT1 and SIRT3 genes have been analysed for association with age-related diseases and longevity. In case of SIRT1, no associations have been found (Flachsbart et al., 2006; Kuningas et al., 2007b), whereas of SIRT3, a G477T polymorphism and a variable number of tandem repeats have been associated with increased lifespan (Rose et al., 2003; Bellizzi et al., 2005). These results suggest that at least one member of the SIRT family is involved in human lifespan regulation.

The life-extending benefits of resveratrol are also being studied in mammals. It has been published that the health and survival of mice on a high-calorie diet can be improved by resveratrol supplementation (Baur et al., 2006). While these results seem promising, there is no evidence that demonstrates the influence of resveratrol supplementation on human health, even though it is being sold as a nutritional supplement.

Antioxidative enzymes

Antioxidative enzymes, such as catalase and superoxide dismutase (SOD), prevent damage from reactive oxygen species (ROS), but the evidence from model organisms on the beneficial effects of antioxidative enzymes on lifespan has been controversial. Studies with D. melanogaster have demonstrated that over-expression of CuZn–SOD (SOD1), Mn–SOD (SOD2) and catalase leads to lifespan extension (Sohal et al., 1995; Orr & Sohal, 2003). Additional experiments, however, showed that this effect depends on genetic background of the used lines (Orr & Sohal, 2003). Likewise, the extended lifespan of C. elegans by administration of synthetic SOD/catalase mimetics was shown to depend on laboratory conditions (Melov et al., 2000; Keaney & Gems, 2003).

In mammals, one catalase and three SOD genes have been characterized: SOD1, SOD2 and SOD3, of which catalase and SOD2 seem to influence lifespan. In mice, disruption of the SOD2 gene is lethal because of neurodegeneration and damage to the heart (Li et al., 1995; Melov et al., 1998). In contrast, over-expression of SOD2 leads to increased lifespan (Hu et al., 2007), as does over-expression of catalase targeted to mitochondria (Schriner et al., 2005). Mice heterozygous for the mitochondrial form of SOD2 (Sod2 ± mice) showed high levels of DNA oxidation in multiple organs. In spite of their abnormally oxidized DNA, these animals showed no decline in lifespan and no acceleration in the hallmarks of aging, such as cataracts, immune dysfunction and protein modifications (Van Remmen et al., 2003). These data suggest that mice can live reasonably long and healthy lives despite unusually high levels of oxidative damage. On the other hand, a recent study showed that enhanced longevity is associated with low free radical production by mitochondria in vertebrate homeotherms. In that study, no correlations with SOD activity and lifespan were found (Lambert et al., 2007). Altogether, these data illustrate the existence of a balance between the production of free radicals and of antioxidative enzymes.

The evidence for the role of antioxidative enzymes in the preservation of human health is not well established. It has been shown that RNA interference (RNAi) of SOD1 induces senescence in human fibroblasts (Blander et al., 2003), which suggests that SOD1 may play a role in the regulation of cellular lifespan. However, genetic variants in the SOD1 gene have never been studied for that relationship. In contrast, genetic variants in the SOD2 gene have been studied and associated with a number of phenotypes including increased risk for prostate and breast cancer, immunosenescence profile and DNA damage (Liu et al., 2004; Taufer et al., 2005), but not with mortality (De Benedictis et al., 1998; van Heemst et al., 2005a). Likewise, no associations between genetic variants in the catalase gene and mortality have been found (Christiansen et al., 2004).

Macromolecule repair mechanisms

Defects in mechanisms that repair damage to cellular components, such as DNA, proteins and membranes, have been shown to reduce lifespan in various model organisms. Even though these mechanisms are evolutionarily conserved (Eisen & Hanawalt, 1999), systematic comparative genomic analyses across species have not been conducted. In addition, within species, there are many studies demonstrating detrimental effects of impaired repair systems on lifespan, but only few demonstrating beneficial effects of increased repair capacity on lifespan. The only evidence for the latter has come from experiments with D. melanogaster, where the absence of mei-41 excision repair reduces lifespan, whereas flies with one or two extra copies of the gene have significantly increased lifespan (Symphorien & Woodruff, 2003). Likewise, over-expression of protein carboxyl methyltransferase (PCMT), which is a protein repair enzyme, has been correlated with enhanced longevity in a temperature-dependent manner (Chavous et al., 2001). Both of these genes, mei-41 and Pcmt, have homologues in mammals, which are ataxia telangiectasia and Rad3 related (ATR) and PCMT, respectively. In mice, the disruption of the ATR gene leads to chromosomal fragmentation and early embryonic lethality (Brown & Baltimore, 2000), and in humans to a rare Seckel syndrome (Casper et al., 2004). The Pcmt1-null mice, on the other hand, display a fatal seizure disorder and retarded growth (Kim et al., 1999), and die at a mean age of 42 days (Lowenson et al., 2001). In contrast, none of the heterozygous or wild-type littermates died during this period.

Compared to other repair mechanisms, DNA repair has been studied the most in relation to aging and lifespan. The DNA repair-deficient mouse models that have been generated, display a common phenotype of progeria, or cancer predisposition, or both, and have a reduced lifespan (Hasty et al., 2003). Similarly, in humans, all mutations identified in DNA repair genes severely compromise health. For instance, mutations in the components of transcription coupled repair have been associated with the progeroid syndromes of Cokayne syndrome and trichothiodystrophy (Hoeijmakers, 2001; Cleaver, 2005). Likewise, mutations in the RecQ-like DNA helicase genes, WRN, BLM and RecQ4, lead to the progeroid syndromes of Werner, Bloom and Rothmund–Thomson syndrome, respectively (Navarro et al., 2006). In contrast to the strong phenotypes associated with loss-of-function mutations in the RecQ helicases, common polymorphisms with subtle effects on the functionality of these genes do not seem to influence the aging trajectories and survival in the general population (Castro et al., 2000; Bohr et al., 2004; Kuningas et al., 2006). The RecQ helicases are highly conserved throughout evolution, but in higher eukaryotes, the different homologues seem to have distinct functions because failure of one given RecQ gene cannot be complemented by another RecQ gene. These observations underpin the importance of DNA repair in all organisms. The key question that has yet to be answered is whether subtle variants in the DNA repair genes contribute to different lifespans, and whether above-average repair makes for a lifespan extension. In addition, it is still under question, whether subtle variations in the genes that assure genomic integrity influence disease susceptibility and lifespan in the general population. From an aging point of view, their study is important as defective or reduced DNA damage recognition and repair capacity, together with decreased ROS scavenging can greatly compromise human health.

Cellular responses to damage

In response to unrepaired damage, cells trigger either apoptosis or cell cycle arrest. The most well-known protein implicated in the maintenance of genomic stability is p53. Recently, p53 homologues were identified in C. elegans and D. melanogaster. In contrast to mammalian p53, which elicits apoptosis or cell cycle arrest (Attardi, 2005), the p53 in C. elegans and D. melanogaster affects only apoptosis (Derry et al., 2001; Schumacher et al., 2001; Brodsky et al., 2004). Nevertheless, in all of these organisms, reduced p53 activity leads to lifespan extension (Bauer & Helfand, 2006). In mammals, this extension comes at the cost of increased cancer risk (Campisi, 2003). In humans, it has been shown that Pro/Pro carriers of the TP53 codon 72 polymorphism have a significantly lower apoptotic potential than Arg/Arg carriers, both in p53-inducible human cell lines (Dumont et al., 2003; Pim & Banks, 2004; Sullivan et al., 2004) and in normal diploid fibroblasts (Bonafe et al., 2004). Later on, it was shown that despite an increased mortality from cancer, carriers of the same polymorphism have a significantly increased survival at old age in line with the experimental models (van Heemst et al., 2005b). Altogether, these observations support the hypothesis that reduced p53-mediated induction of apoptosis can have beneficial effects on lifespan if tumour formation can be avoided. This might hold true also for other genes that mediate cellular responses to damage.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. The evidence
  5. Discussion
  6. Future directions
  7. Acknowledgments
  8. References

Experiments in model organisms have demonstrated that changes in genes can dramatically increase their lifespan. In some cases, mean and maximum lifespan is extended up to fivefold. The equivalent life-extending effect in humans would result in an average lifespan of 400 years and a maximum lifespan of over 600 years. Many of the pathways regulating lifespan in model organisms are conserved throughout evolution. Why, then, have we not yet identified genetic determinants that could increase human lifespan by more than a few years? Are we looking at the right genes? Is it fair to expect such dramatic effects? Do we have the tools to observe genetic determinants of human lifespan?

Increased complexity

In vertebrate organisms, novel genes and signalling components have appeared during evolution, contributing to increased complexity of the genomes (Long, 2001). Among several molecular mechanisms, gene duplication plays a major role in genome evolution (Long, 2001; Britten, 2006). Often, a mammalian genome contains several homologues of a single invertebrate gene with similar or distinct functions and expression patterns. This can hinder the assessment of the role of a specific candidate gene, because genetic variance in duplicated genes is likely to have less dramatic effects than in the original single gene in invertebrate organisms (Gu et al., 2003; Conant & Wagner, 2004). Furthermore, because vertebrates have acquired pathways that are more diverse, the homologous genes in phylogenetically distant animals could be involved in different pathways.

The genomes of vertebrate organisms also contain genes, which have no homologues in invertebrates. The appearance of novel genes, or ‘add-ons’, probably contributed to the arise of elaborate systems that regulate the activity of the conserved or ‘shared’ genes (Levine & Tjian, 2003). The GH control over IGF-1 and the regulation of IR and IGF1-R activity by KLOTHO have arisen relatively recently in vertebrate evolution (Mian, 1998; Forsyth & Wallis, 2002). Genetic variance in these genes has been associated with lower IIS signalling and with increased lifespan, corroborating the findings from invertebrates. However, in these organisms, this phenotype was attained by modifying the ‘shared’ genes. This demonstrates that in humans, we should not limit ourselves to studying the evolutionarily conserved genes that have been implicated in lifespan regulation in invertebrates, but also include, and maybe even focus on, ‘add-ons’ that regulate their activity. Taken together, the increased complexity of signalling systems in vertebrates adds robustness to the signalling pathways, which is the ability to maintain its functions despite changes in its components or environment (Lenski et al., 2003; Soyer & Bonhoeffer, 2006). In that respect, lifespan regulation can be regarded as a complex genetic trait, for which it is unlikely that single alterations in the genetic machinery will have dramatic effects. Furthermore, genes that have appeared later in evolution, ‘add-ons’, such as microcephalin gene and glucocorticoid receptor, are interesting candidates to determine longevity in vertebrate organisms.

Environmental influences

Environmental influences have played a major role in shaping and patterning the genomes of all organisms throughout evolutionary history. Changes in environment can lead to different expressions of genotypic information, and thereby complicate the comparability of results between model organisms and humans (Partridge & Gems, 2007). Most research on model organism has been performed under laboratory conditions, where temperature, presence of pathogens, food availability and population density are tightly controlled. In most cases, these conditions poorly mimic the evolutionary niche in which the genes come to expression, and it may therefore be questioned how well the results obtained in these conditions are applicable to species under ‘natural’ conditions (Walker et al., 2000; Clancy et al., 2001; Marden et al., 2003).

Even for humans, the environment in which the genome effectively evolved has changed. The genes that were originally selected for survival in adverse environments are now expressed under completely new, affluent conditions. For instance, the IIS system was selected and fine tuned in times when food abundance and famine alternated. The genotypes that increased the efficiency to store energy in times of abundance and use these storages in times of famine had a survival advantage. In modern Western societies, where food is constantly abundant, these ‘thrifty genotypes’ lead to increased prevalence of storage diseases, such as obesity and diabetes (Neel, 1962). This reinforces the idea that our genomes have been optimized to increase fitness under adverse conditions and not under modern affluent conditions, resulting in new interactions with outcomes that are both unknown and unpredictable. Taken together, caution should be taken in extrapolating results on genetic variation obtained in model organisms to the human situation, because the environments in which the genes come to expression are often markedly different. Moreover, research into human aging should include various environmental conditions that can explain different phenotypes despite an equal genetic background. Furthermore, several studies have already documented the dominance of nurture over the nature within the context of human lifespan (Fraser & Shavlik, 2001; Peeters et al., 2003).

Mutants versus natural genetic variants

The majority of candidate genes of longevity have been identified by studies with mutant model organisms, such as the worm, fruit fly and mouse models. These approaches are extremely powerful to disentangle biological pathways. However, to date it is largely unknown to what extent these mutations affect fitness in natural environments and whether these candidate loci contain genetic variation, which would contribute to phenotypic variance for lifespan in natural populations (Fig. 1). These questions are of importance because not all candidate loci with major effects on longevity in laboratory conditions may exhibit variation in natural populations. Currently, only few studies have tried to disentangle this question. For instance, it has been shown that the long-lived mutant fruit fly methuselah (mth) underperforms in most cases under conditions that resemble a more natural situation (Baldal et al., 2006). This illustrates that the mth locus would never have been identified to influence lifespan if natural populations of the fruit fly would have been analysed. A similar observation is obtained for the Chico variant that outlives the wild type under food affluence, but becomes short lived when exposed to food-restricted conditions (Clancy et al., 2002). In case of humans, most information on the effect of genetic variation on longevity is being obtained by studying naturally occurring variants in candidate genes identified in mutant model organisms. Hence, the discrepancy between the data from mutant model organism and standing genetic variance in natural populations contributes to the difficulty of translating results from model organisms to humans (Fig. 1). Therefore, to facilitate translation of the results from mutant model organisms to humans, the analysis of standing variation in the same loci in natural populations of model organisms should be encouraged. In addition, in all the studies with model organisms, the genetic background (rest of the genome) should be taken into account, as it has been shown that in some cases lifespan differences disappear when the background is made identical in mutant and controls (Partridge & Gems, 2007).

image

Figure 1. The amount of information obtained from different organisms on genetic mechanisms on aging and longevity.The four quadrants indicate the amount of available information on the effect of genetic variation on lifespan in organisms of different complexity. As can be seen, in humans most information on the effect of genetic variation on longevity is being obtained by studying naturally occurring variants, while in model organisms, most information on the effect of genetic variation on longevity has been obtained by studying mutant invertebrate models, and, to a lesser extent, mutant vertebrate models. In contrast, only few studies have analysed the contribution of standing natural genetic variation on lifespan in model organisms. The direct translation of the importance of candidate genes identified in mutant invertebrate models for variation in human lifespan is complicated by two possible discrepancies. The first includes the influences of mutations and standing (subtle) genetic variations in the candidate loci, and the second possible discrepancy includes the differences in genome complexity between the organisms.

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Methodological considerations

The lack of very strong effects of the evolutionarily conserved genes in humans can have other reasons than those suggested earlier. Some of these include study design and methodology in human studies. Most of the genetic association studies of longevity have been performed in case–control settings, where genotype or allele frequencies between elderly and a younger population are compared. The main advantage of case–control study design is that cases are readily obtainable and can be efficiently genotyped and compared with control populations. Cases can be nonagenarians, centenarians or long-lived sibpairs. By collecting long-lived sibpairs instead of long-lived singletons, an enrichment of genetic factors contributing to longevity in this population can be expected, while the likelihood of having reached a long life because of exceptional environmental conditions or chance is lower. The difficulty of case–control studies is in selecting controls. In longevity studies, the ideal control group should be composed of participants from the same birth cohort who were not long lived, to minimize the effect of environmental differences caused by cohort differences. However, obtaining such a control group is impossible in a retrospective study design. Such case–control studies are only possible if one is studying in long-lasting population surveys. The longest ongoing studies (e.g. Framingham Heart Study) now allow a difference of 30 years between subjects that survived and those that did not. However, in most studies so far, controls have been selected from the general population of younger generations. This, can lead to biases where allele frequency differences between cases and controls can appear as an association, even if they only reflect the results of changes to the source population because of changes in environment, migratory history, gender differences or other independent processes (Cardon & Palmer, 2003; Manolio et al., 2006). An alternative approach would be prospective cohort studies, which suffer less from population stratification but are more expensive and time consuming (Manolio et al., 2006).

Another consideration for the methodology includes the selection and analysis of genetic variants in the candidate genes. Commonly, a selected number of polymorphisms from the coding region of candidate genes are analysed, leaving aside genetic variants in regulatory regions. In addition, besides analyzing the individual polymorphisms, only few studies have undertaken haplotype analyses. Given the amount of information that has recently become available through the International HapMap project, polymorphisms that tag common haplotypes can easily be identified. The analysis of haplotypes can be more powerful because this analysis captures the joint effect of all unknown gene variants that are in linkage disequilibrium with the markers forming the haplotype (Johnson et al., 2001).

Finally, similar to other association studies, also the results from longevity association studies have been rarely replicated. Explanations for lack of reproducibility include poor study design, small sample size, incorrect assumptions about the underlying genetic architecture and over-interpretation of the data. In addition, for a number of associations (mainly negative), no replication has been undertaken, leaving open the reproducibility. Replication of even negative results is necessary, because the lack of associations in the first study could likewise have been caused by poor study design, population stratification or other reasons. Therefore, before discarding a candidate gene from the list of possible candidates, replication in different cohorts with more thorough genetic analysis is necessary.

Future directions

  1. Top of page
  2. Summary
  3. Introduction
  4. The evidence
  5. Discussion
  6. Future directions
  7. Acknowledgments
  8. References

Pathway analyses and epigenetic variation

Given the current feasibility of high throughput genotyping and increasing knowledge on cellular mechanisms, pathway analyses instead of analysing individual loci separately could be performed. The appropriate tools are likely to be available soon, because the analysis of complex traits, which are under the influence of multiple and possibly interacting genes, has become a subject of new statistical methodological research (Kristensen et al., 2006). However, besides genetic variation, other mechanisms influence the expression of genomic information. For instance, epigenetic modifications, which are differences in gene expression that cannot be accounted for by changes in the primary DNA sequence, have a significant impact on gene function, and may explain how iso-genetic organisms are phenotypically very distinct. It has been noticed that during aging, epigenetic alterations occur, which could have functional and biological significances. An emerging field, ‘aging epigenetics’, aims to answer these questions (Fraga & Esteller, 2007). Besides epigenetics, different levels of transcriptional and post-transcriptional control through RNA interference, or other mechanisms, can account for phenotypic differences. A family of small, non-coding RNAs, known as microRNAs (miRNA), has recently emerged as sequence-specific regulators of gene expression. miRNAs have been shown to affect a broad spectrum of biological activities, including developmental fate determination, cell signalling, oncogenesis and metabolism (Boehm & Slack, 2006). In addition, miRNAs have arisen as potential modulators of age-related decline. In C. elegans, it was found that the expression of certain miRNAs declines with age, suggesting a general reduction of message-specific translational inhibition during aging (Ibanez-Ventoso et al., 2006; Wang, 2007). Because many miRNAs are conserved across species, such mechanism might be at play also in humans. Altogether, various modifications can contribute to the differences in lifespan between and within species.

Genes and pathways for future analyses

A number of interesting candidate genes, with or without homologues in model organisms, remain to be investigated in humans. The most interesting and so far not very thoroughly analysed genes in respect to longevity include those involved in fertility. In model organisms, fertility and lifespan are closely linked (Partridge et al., 2005). In C. elegans, ablation of germline precursor cells of the gonad abolishes reproduction and extends lifespan (Hsin & Kenyon, 1999), as do mutations that reduce germline proliferation (Arantes-Oliveira et al., 2002). In D. melanogaster, a reduction in fecundity extends lifespan in females (Sgro & Partridge, 1999) and long-lived heterozygous chico mutant females exhibit reduced fecundity, with the homozygotes being almost sterile (Clancy et al., 2001). In mice, Ames and Snell dwarfs are long lived and sterile (Bartke, 2005). The observational studies in historical human populations living under pre-affluent conditions have provided similar evidence. In the English and Finnish aristocracy, women with the longest lifespan had the smallest number of offspring (Westendorp & Kirkwood, 1998; Korpelainen, 2000). Despite this evidence, the genetic determinants for the trade-off between fertility and lifespan are unknown.

Acknowledgments

  1. Top of page
  2. Summary
  3. Introduction
  4. The evidence
  5. Discussion
  6. Future directions
  7. Acknowledgments
  8. References

This work was supported by an Innovative Orientated Research grant from the Dutch Ministry of Economic Affairs (grant number IGE010114), by the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research (NWO) (grant number 911-03-016) and by the Centre for Medical Systems Biology, which is a centre of excellence approved by the NWO. This work was also supported by and carried out within the EU funded Network of Excellence Lifespan (FP6 036894). All co-authors have seen and agreed with the contents of the manuscript, and none of the co-authors has any financial interests to disclose.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. The evidence
  5. Discussion
  6. Future directions
  7. Acknowledgments
  8. References
  • Arantes-Oliveira N, Apfeld J, Dillin A, Kenyon C (2002) Regulation of life-span by germ-line stem cells in Caenorhabditis elegans. Science 295, 502505.
  • Arking DE, Krebsova A, Macek M Sr, Macek M Jr, Arking A, Mian IS, Fried L, Hamosh A, Dey S, McIntosh I, Dietz HC (2002) Association of human aging with a functional variant of klotho. Proc. Natl. Acad. Sci. USA 99, 856861.
  • Arking DE, Atzmon G, Arking A, Barzilai N, Dietz HC (2005) Association between a functional variant of the KLOTHO gene and high-density lipoprotein cholesterol, blood pressure, stroke, and longevity. Circ. Res. 96, 412418.
  • Attardi LD (2005) The role of p53-mediated apoptosis as a crucial anti-tumor response to genomic instability: lessons from mouse models. Mutat. Res. 569, 145157.
  • Baldal EA, Baktawar W, Brakefield PM, Zwaan BJ (2006) Methuselah life history in a variety of conditions, implications for the use of mutants in longevity research. Exp. Gerontol. 41, 11261135.
  • Bartke A (2005) Minireview: role of the growth hormone/insulin-like growth factor system in mammalian aging. Endocrinology 146, 37183723.
  • Barzilai N, Atzmon G, Schechter C, Schaefer EJ, Cupples AL, Lipton R, Cheng S, Shuldiner AR (2003) Unique lipoprotein phenotype and genotype associated with exceptional longevity. JAMA 290, 20302040.
  • Bass TM, Weinkove D, Houthoofd K, Gems D, Partridge L (2007) Effects of resveratrol on lifespan in Drosophila melanogaster and Caenorhabditis elegans. Mech. Ageing Dev. 128, 546552.
  • Bauer JH, Helfand SL (2006) New tricks of an old molecule: lifespan regulation by p53. Aging Cell 5, 437440.
  • Baur JA, Pearson KJ, Price NL, Jamieson HA, Lerin C, Kalra A, Prabhu VV, Allard JS, Lopez-Lluch G, Lewis K, Pistell PJ, Poosala S, Becker KG, Boss O, Gwinn D, Wang M, Ramaswamy S, Fishbein KW, Spencer RG, Lakatta EG, Le CD, Shaw RJ, Navas P, Puigserver P, Ingram DK, De CR, Sinclair DA (2006) Resveratrol improves health and survival of mice on a high-calorie diet. Nature 444, 337342.
  • Bellizzi D, Rose G, Cavalcante P, Covello G, Dato S, De Rango F, Greco V, Maggiolini M, Feraco E, Mari V, Franceschi C, Passarino G, De Benedictis G (2005) A novel VNTR enhancer within the SIRT3 gene, a human homologue of SIR2, is associated with survival at oldest ages. Genomics 85, 258263.
  • Blander G, De Oliveira RM, Conboy CM, Haigis M, Guarente L (2003) Superoxide dismutase 1 knock-down induces senescence in human fibroblasts. J. Biol. Chem. 278, 3896638969.
  • Boehm M, Slack FJ (2006) MicroRNA control of lifespan and metabolism. Cell Cycle 5, 837840.
  • Bohr VA, Metter EJ, Harrigan JA, Von Kobbe C, Liu JL, Gray MD, Majumdar A, Wilson DM III, Seidman MM (2004) Werner syndrome protein 1367 variants and disposition towards coronary artery disease in Caucasian patients. Mech. Ageing Dev. 125, 491496.
  • Bonafe M, Barbieri M, Marchegiani F, Olivieri F, Ragno E, Giampieri C, Mugianesi E, Centurelli M, Franceschi C, Paolisso G (2003) Polymorphic variants of insulin-like growth factor I (IGF-I) receptor and phosphoinositide 3-kinase genes affect IGF-I plasma levels and human longevity: cues for an evolutionarily conserved mechanism of life span control. J. Clin. Endocrinol. Metab. 88, 32993304.
  • Bonafe M, Salvioli S, Barbi C, Trapassi C, Tocco F, Storci G, Invidia L, Vannini I, Rossi M, Marzi E, Mishto M, Capri M, Olivieri F, Antonicelli R., Memorandum M, Uberti D, Nacmias B, Sorbi S, Monti D, Franceschi C (2004) The different apoptotic potential of the p53 codon 72 alleles increases with age and modulates in vivo ischaemia-induced cell death. Cell Death Differ. 11, 962973.
  • Bordone L, Guarente L (2005) Calorie restriction, SIRT1 and metabolism: understanding longevity. Nat. Rev. Mol. Cell Biol. 6, 298305.
  • Britten RJ (2006) Almost all human genes resulted from ancient duplication. Proc. Natl. Acad. Sci. USA 103, 1902719032.
  • Brodsky MH, Weinert BT, Tsang G, Rong YS, McGinnis NM, Golic KG, Rio DC, Rubin GM (2004) Drosophila melanogaster MNK/Chk2 and p53 regulate multiple DNA repair and apoptotic pathways following DNA damage. Mol. Cell Biol. 24, 12191231.
  • Brown EJ, Baltimore D (2000) ATR disruption leads to chromosomal fragmentation and early embryonic lethality. Genes Dev. 14, 397402.
  • Brown-Borg HM, Borg KE, Meliska CJ, Bartke A (1996) Dwarf mice and the ageing process. Nature 384, 33.
  • Campisi J (2003) Cancer and ageing: rival demons? Nat. Rev. Cancer 3, 339349.
  • Cardon LR, Palmer LJ (2003) Population stratification and spurious allelic association. Lancet 361, 598604.
  • Carter ME, Brunet A (2007) FOXO transcription factors. Curr. Biol. 17, R113R114.
  • Casper AM, Durkin SG, Arlt MF, Glover TW (2004) Chromosomal instability at common fragile sites in Seckel syndrome. Am. J. Hum. Genet. 75, 654660.
  • Castro E, Edland SD, Lee L, Ogburn CE, Deeb SS, Brown G, Panduro A, Riestra R, Tilvis R, Louhija J, Penttinen R, Erkkola R, Wang L, Martin GM, Oshima J (2000) Polymorphisms at the Werner locus: II. 1074Leu/Phe, 1367Cys/Arg, longevity, and atherosclerosis. Am. J. Med. Genet. 95, 374380.
  • Chavous DA, Jackson FR, O’Connor CM (2001) Extension of the Drosophila lifespan by overexpression of a protein repair methyltransferase. Proc. Natl. Acad. Sci. USA 98, 1481414818.
  • Christiansen L, Petersen HC, Bathum L, Frederiksen H, McGue M, Christensen K (2004) The catalase -262C/T promoter polymorphism and aging phenotypes. J. Gerontol. A Biol. Sci. Med. Sci. 59, B886B889.
  • Clancy DJ, Gems D, Harshman LG, Oldham S, Stocker H, Hafen E, Leevers SJ, Partridge L (2001) Extension of life-span by loss of CHICO, a Drosophila insulin receptor substrate protein. Science 292, 104106.
  • Clancy DJ, Gems D, Hafen E, Leevers SJ, Partridge L (2002) Dietary restriction in long-lived dwarf flies. Science 296, 319.
  • Cleaver JE (2005) Cancer in xeroderma pigmentosum and related disorders of DNA repair. Nat. Rev. Cancer 5, 564573.
  • Conant GC, Wagner A (2004) Duplicate genes and robustness to transient gene knock-downs in Caenorhabditis elegans. Proc. Biol. Sci. 271, 8996.
  • De Benedictis G, Carotenuto L, Carrieri G, De Luca M, Falcone E, Rose G, Cavalcanti S, Corsonello F, Feraco E, Baggio G, Bertolini S, Mari D, Mattace R., Yashin AI, Bonafe M, Franceschi C (1998) Gene/Longevity association studies at four autosomal loci (REN, THO, PARP, SOD2). Eur. J. Hum. Genet. 6, 534541.
  • Derry WB, Putzke AP, Rothman JH (2001) Caenorhabditis elegans p53: role in apoptosis, meiosis, and stress resistance. Science 294, 591595.
  • Dumont P, Leu JI, Della PA III, George DL, Murphy M (2003) The codon 72 polymorphic variants of p53 have markedly different apoptotic potential. Nat. Genet. 33, 357365.
  • Eisen JA, Hanawalt PC (1999) A phylogenomic study of DNA repair genes, proteins, and processes. Mutat. Res. 435, 171213.
  • Flachsbart F, Croucher PJ, Nikolaus S, Hampe J, Cordes C, Schreiber S, Nebel A (2006) Sirtuin 1 (SIRT1) sequence variation is not associated with exceptional human longevity. Exp. Gerontol. 41, 98102.
  • Forsyth IA, Wallis M (2002) Growth hormone and prolactin – molecular and functional evolution. J. Mammary Gland Biol. Neoplasia 7, 291312.
  • Fraga MF, Esteller M (2007) Epigenetics and aging: the targets and the marks. Trends Genet. 23, 413418.
  • Fraser GE, Shavlik DJ (2001) Ten years of life: is it a matter of choice? Arch. Intern. Med. 161, 16451652.
  • Frye RA (2000) Phylogenetic classification of prokaryotic and eukaryotic Sir2-like proteins. Biochem. Biophys. Res. Commun. 273, 793798.
  • Furuyama T, Nakazawa T, Nakano I, Mori N (2000) Identification of the differential distribution patterns of mRNAs and consensus binding sequences for mouse DAF-16 homologues. Biochem. J. 349, 629634.
  • Giannakou ME, Partridge L (2007) Role of insulin-like signalling in Drosophila lifespan. Trends Biochem. Sci. 32, 180188.
  • Gu Z, Steinmetz LM, Gu X, Scharfe C, Davis RW, Li WH (2003) Role of duplicate genes in genetic robustness against null mutations. Nature 421, 6366.
  • Haigis MC, Guarente LP (2006) Mammalian sirtuins – emerging roles in physiology, aging, and calorie restriction. Genes Dev. 20, 29132921.
  • Hasty P, Campisi J, Hoeijmakers J, Van Steeg H, Vijg J (2003) Aging and genome maintenance: lessons from the mouse? Science 299, 13551359.
  • Van Heemst D, Beekman M, Mooijaart SP, Heijmans BT, Brandt BW, Zwaan BJ, Slagboom PE, Westendorp RG (2005a) Reduced insulin/IGF-1 signalling and human longevity. Aging Cell 4, 7985.
  • Van Heemst D, Mooijaart SP, Beekman M, Schreuder J, De Craen AJ, Brandt BW, Slagboom PE, Westendorp RG (2005b) Variation in the human TP53 gene affects old age survival and cancer mortality. Exp. Gerontol. 40, 1115.
  • Heijmans BT, Beekman M, Houwing-Duistermaat JJ, Cobain MR, Powell J, Van Der Blauw GJOF, Westendorp RG, Slagboom PE (2006) Lipoprotein particle profiles mark familial and sporadic human longevity. PLoS Med. 3, e495.
  • Herskind AM, McGue M, Holm NV, Sorensen TI, Harvald B, Vaupel JW (1996) The heritability of human longevity: a population-based study of 2872 Danish twin pairs born 1870–1900. Hum. Genet. 97, 319323.
  • Hjelmborg J, Iachine I, Skytthe A, Vaupel JW, McGue M, Koskenvuo M, Kaprio J, Pedersen NL, Christensen K (2006) Genetic influence on human lifespan and longevity. Hum. Genet. 119, 312321.
  • Hoeijmakers JH (2001) DNA repair mechanisms. Maturitas 38, 1722.
  • Holzenberger M, Dupont J, Ducos B, Leneuve P, Geloen A, Even PC, Cervera P, Le Bouc Y (2003) IGF-1 receptor regulates lifespan and resistance to oxidative stress in mice. Nature 421, 182187.
  • Howitz KT, Bitterman KJ, Cohen HY, Lamming DW, Lavu S, Wood JG, Zipkin RE, Chung P, Kisielewski A, Zhang LL, Scherer B, Sinclair DA (2003) Small molecule activators of sirtuins extend Saccharomyces cerevisiae lifespan. Nature 425, 191196.
  • Hsin H, Kenyon C (1999) Signals from the reproductive system regulate the lifespan of C. elegans. Nature 399, 362366.
  • Hu D, Cao P, Thiels E, Chu CT, Wu GY, Oury TD, Klann E (2007) Hippocampal long-term potentiation, memory, and longevity in mice that overexpress mitochondrial superoxide dismutase. Neurobiol. Learn. Mem. 87, 372384.
  • Ibanez-Ventoso C, Yang M, Guo S, Robins H, Padgett RW, Driscoll M (2006) Modulated microRNA expression during adult lifespan in Caenorhabditis elegans. Aging Cell 5, 235246.
  • Johnson GC, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G, Ueda H, Cordell HJ, Eaves IA, Dudbridge F, Twells RC, Payne F, Hughes W, Nutland S, Stevens H, Carr P, Tuomilehto-Wolf E, Tuomilehto J, Gough SC, Clayton DG, Todd JA (2001) Haplotype tagging for the identification of common disease genes. Nat. Genet. 29, 233237.
  • Kaeberlein M, McVey M, Guarente L (1999) The SIR2/3/4 complex and SIR2 alone promote longevity in Saccharomyces cerevisiae by two different mechanisms. Genes Dev. 13, 25702580.
  • Karim MA, Craig RL, Wang X, Hale TC, Elbein SC (2006) Analysis of FOXO1A as a candidate gene for type 2 diabetes. Mol. Genet. Metab. 88, 171177.
  • Keaney M, Gems D (2003) No increase in lifespan in Caenorhabditis elegans upon treatment with the superoxide dismutase mimetic EUK-8. Free Radic. Biol. Med. 34, 277282.
  • Kim E, Lowenson JD, Clarke S, Young SG (1999) Phenotypic analysis of seizure-prone mice lacking 1-isoaspartate (d-aspartate) O-methyltransferase. J. Biol. Chem. 274, 2067120678.
  • Kimura KD, Tissenbaum HA, Liu Y, Ruvkun G (1997) daf-2, An insulin receptor-like gene that regulates longevity and diapause in Caenorhabditis elegans. Science 277, 942946.
  • Kojima T, Kamei H, Aizu T, Arai Y, Takayama M, Nakazawa S, Ebihara Y, Inagaki H, Masui Y, Gondo Y, Sakaki Y, Hirose N (2004) Association analysis between longevity in the Japanese population and polymorphic variants of genes involved in insulin and insulin-like growth factor 1 signaling pathways. Exp. Gerontol. 39, 15951598.
  • Korpelainen H (2000) Fitness, reproduction and longevity among European aristocratic and rural Finnish families in the 1700s and 1800s. Proc. Biol. Sci. 267, 17651770.
  • Kristensen VN, Tsalenko A, Geisler J, Faldaas A, Grenaker G, Lingjaerde OC, Fjeldstad S, Yakhini Z, Lonning PE, Borresen-Dale AL (2006) Multilocus analysis of SNP and metabolic data within a given pathway. BMC Genomics 7, 5.
  • Kuningas M, Slagboom PE, Westendorp RG, Van Heemst D (2006) Impact of genetic variations in the WRN gene on age related pathologies and mortality. Mech. Ageing Dev. 127, 307313.
  • Kuningas M, Magi R, Westendorp RG, Slagboom PE, Remm M, Van Heemst D (2007a) Haplotypes in the human Foxo1a and Foxo3a genes; impact on disease and mortality at old age. Eur. J. Hum. Genet. 15, 294301.
  • Kuningas M, Putters M, Westendorp RGJ, Slagboom EP, Van Heemst D (2007b) SIRT1 gene, age-related diseases and mortality: The Leiden 85-plus study. J. Gerontol. A Biol. Sci. Medical Sci. 62, 960965.
  • Kuro-o M, Matsumura Y, Aizawa H, Kawaguchi H, Suga T, Utsugi T, Ohyama Y, Kurabayashi M, Kaname T, Kume E, Iwasaki H, Iida A, Shiraki-Iida T, Nishikawa S, Nagai R, Nabeshima YI (1997) Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nature 390, 4551.
  • Kurosu H, Yamamoto M, Clark JD, Pastor JV, Nandi A, Gurnani P, McGuinness OP, Chikuda H, Yamaguchi M, Kawaguchi H, Shimomura I, Takayama Y, Herz J, Kahn CR, Rosenblatt KP, Kuro-o M (2005) Suppression of aging in mice by the hormone Klotho. Science 309, 18291833.
  • Lakowski B, Hekimi S (1996) Determination of life-span in Caenorhabditis elegans by four clock genes. Science 272, 10101013.
  • Lambert AJ, Boysen HM, Buckingham JA, Yang T, Podlutsky A, Austad SN, Kunz TH, Buffenstein R, Brand MD (2007) Low rates of hydrogen peroxide production by isolated heart mitochondria associate with long maximum lifespan in vertebrate homeotherms. Aging Cell doi. DOI: 10.1111/j.1474-9726.2007.00312.x.
  • Laron Z (2005) Do deficiencies in growth hormone and insulin-like growth factor-1 (IGF-1) shorten or prolong longevity? Mech. Ageing Dev. 126, 305307.
  • Larsen PL (2001) Asking the age-old questions. Nat. Genet. 28, 102104.
  • Larsen PL, Albert PS, Riddle DL (1995) Genes that regulate both development and longevity in Caenorhabditis elegans. Genetics 139, 15671583.
  • Lenski RE, Ofria C, Pennock RT, Adami C (2003) The evolutionary origin of complex features. Nature 423, 139144.
  • Levine M, Tjian R (2003) Transcription regulation and animal diversity. Nature 424, 147151.
  • Li Y, Huang TT, Carlson EJ, Melov S, Ursell PC, Olson JL, Noble LJ, Yoshimura MP, Berger C, Chan PH, Wallace DC, Epstein CJ (1995) Dilated cardiomyopathy and neonatal lethality in mutant mice lacking manganese superoxide dismutase. Nat. Genet. 11, 376381.
  • Liu G, Zhou W, Park S, Wang LI, Miller DP, Wain JC, Lynch TJ, Su L, Christiani DC (2004) The SOD2 Val/Val genotype enhances the risk of nonsmall cell lung carcinoma by p53 and XRCC1 polymorphisms. Cancer 101, 28022808.
  • Long M (2001) Evolution of novel genes. Curr. Opin. Genet. Dev. 11, 673680.
  • Lowenson JD, Kim E, Young SG, Clarke S (2001) Limited accumulation of damaged proteins in 1-isoaspartyl (d-aspartyl) O-methyltransferase-deficient mice. J. Biol. Chem. 276, 2069520702.
  • Mangelsdorf DJ, Thummel C, Beato M, Herrlich P, Schutz G, Umesono K, Blumberg B, Kastner P, Mark M, Chambon P, Evans RM (1995) The nuclear receptor superfamily: the second decade. Cell 83, 835839.
  • Manolio TA, Bailey-Wilson JE, Collins FS (2006) Genes, environment and the value of prospective cohort studies. Nat. Rev. Genet. 7, 812820.
  • Marden JH, Rogina B, Montooth KL, Helfand SL (2003) Conditional tradeoffs between aging and organismal performance of Indy long-lived mutant flies. Proc. Natl. Acad. Sci. USA 100, 33693373.
  • Melov S, Schneider JA, Day BJ, Hinerfeld D, Coskun P, Mirra SS, Crapo JD, Wallace DC (1998) A novel neurological phenotype in mice lacking mitochondrial manganese superoxide dismutase. Nat. Genet. 18, 159163.
  • Melov S, Ravenscroft J, Malik S, Gill MS, Walker DW, Clayton PE, Wallace DC, Malfroy B, Doctrow SR, Lithgow GJ (2000) Extension of life-span with superoxide dismutase/catalase mimetics. Science 289, 15671569.
  • Mian IS (1998) Sequence, structural, functional, and phylogenetic analyses of three glycosidase families. Blood Cells Mol. Dis. 24, 83100.
  • Mitchell BD, Hsueh WC, King TM, Pollin TI, Sorkin J, Agarwala R, Schaffer AA, Shuldiner AR (2001) Heritability of life span in the Old Order Amish. Am. J. Med. Genet. 102, 346352.
  • Mooijaart SP, Berbee JF, Van Heemst D, Havekes LM, De Craen AJ, Slagboom PE, Rensen PC, Westendorp RG (2006) ApoE plasma levels and risk of cardiovascular mortality in old age. PLoS Med. 3, e176.
  • Mooijaart SP, Kuningas M, Westendorp RG, Houwing-Duistermaat JJ, Slagboom PE, Rensen PC, Van Heemst D (2007a) Liver X receptor alpha associates with human life span. J. Gerontol. A Biol. Sci. Med. Sci. 62, 343349.
  • Mooijaart SP, Van Vliet P, Van Heemst D, Rensen PC, Berbee JF, Jolles J, De Craen AJ, Westendorp RG (2007b) Plasma levels of apolipoprotein E and cognitive function in old age. Ann. NY Acad. Sci. 1100, 148161.
  • Morris JZ, Tissenbaum HA, Ruvkun G (1996) A phosphatidylinositol-3-OH kinase family member regulating longevity and diapause in Caenorhabditis elegans. Nature 382, 536539.
  • Mukhopadhyay A, Tissenbaum HA (2007) Reproduction and longevity: secrets revealed by C. elegans. Trends Cell Biol. 17, 6571.
  • Navarro CL, Cau P, Levy N (2006) Molecular bases of progeroid syndromes. Hum. Mol. Genet. 15, R151R161.
  • Navarro I, Leibush B, Moon TW, Plisetskaya EM, Banos N, Mendez E, Planas JV, Gutierrez J (1999) Insulin, insulin-like growth factor-I (IGF-I) and glucagon: the evolution of their receptors. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 122, 137153.
  • Neel JV (1962) Diabetes mellitus: a ‘thrifty’ genotype rendered detrimental by ‘progress’? Am. J. Hum. Genet. 14, 353362.
  • Oeppen J, Vaupel JW (2002) Demography. Broken limits to life expectancy. Science 296, 10291031.
  • Ogg S, Paradis S, Gottlieb S, Patterson GI, Lee L, Tissenbaum HA, Ruvkun G (1997) The Fork head transcription factor DAF-16 transduces insulin-like metabolic and longevity signals in C. elegans. Nature 389, 994999.
  • Okamoto H, Accili D (2003) In vivo mutagenesis of the insulin receptor. J. Biol. Chem. 278, 2835928362.
  • Orr WC, Sohal RS (1994) Extension of life-span by overexpression of superoxide dismutase and catalase in Drosophila melanogaster. Science 263, 11281130.
  • Orr WC, Sohal RS (2003) Does overexpression of Cu,Zn-SOD extend life span in Drosophila melanogaster? Exp. Gerontol. 38, 227230.
  • Paolisso G, Ammendola S, Del Buono A, Gambardella A, Riondino M, Tagliamonte MR, Rizzo MR, Carella C, Varricchio M (1997) Serum levels of insulin-like growth factor-I (IGF-I) and IGF-binding protein-3 in healthy centenarians: relationship with plasma leptin and lipid concentrations, insulin action, and cognitive function. J. Clin. Endocrinol. Metab. 82, 22042209.
  • Parkes TL, Elia AJ, Dickinson D, Hilliker AJ, Phillips JP, Boulianne GL (1998) Extension of Drosophila lifespan by overexpression of human SOD1 in motorneurons. Nat. Genet. 19, 171174.
  • Partridge L, Gems D (2007) Benchmarks for ageing studies. Nature 450, 165167.
  • Partridge L, Gems D, Withers DJ (2005) Sex and death: what is the connection? Cell 120, 461472.
  • Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Al MA, Bonneux L (2003) Obesity in adulthood and its consequences for life expectancy: a life-table analysis. Ann. Intern. Med. 138, 2432.
  • Perls TT, Wilmoth J, Levenson R, Drinkwater M, Cohen M, Bogan H, Joyce E, Brewster S, Kunkel L, Puca A (2002) Life-long sustained mortality advantage of siblings of centenarians. Proc. Natl. Acad. Sci. USA 99, 84428447.
  • Pim D, Banks L (2004) p53 Polymorphic variants at codon 72 exert different effects on cell cycle progression. Int. J. Cancer 108, 196199.
  • Rose G, Dato S, Altomare K, Bellizzi D, Garasto S, Greco V, Passarino G, Feraco E, Mari V, Barbi C, Bonafe M, Franceschi C, Tan Q, Boiko S, Yashin AI, De Benedictis G (2003) Variability of the SIRT3 gene, human silent information regulator Sir2 homologue, and survivorship in the elderly. Exp. Gerontol. 38, 10651070.
  • Rottiers V, Antebi A (2006) Control of Caenorhabditis elegans life history by nuclear receptor signal transduction. Exp. Gerontol. 41, 904909.
  • Schoenmaker M, De Craen AJ, De Meijer PH, Beekman M, Blauw GJ, Slagboom PE, Westendorp RG (2006) Evidence of genetic enrichment for exceptional survival using a family approach: the Leiden longevity study. Eur. J. Hum. Genet. 14, 7984.
  • Schriner SE, Linford NJ, Martin GM, Treuting P, Ogburn CE, Emond M, Coskun PE, Ladiges W, Wolf N, Van Remmen H, Wallace DC, Rabinovitch PS (2005) Extension of murine life span by overexpression of catalase targeted to mitochondria. Science 308, 19091911.
  • Schumacher B, Hofmann K, Boulton S, Gartner A (2001) The C. elegans homolog of the p53 tumor suppressor is required for DNA damage-induced apoptosis. Curr. Biol. 11, 17221727.
  • Sgro CM, Partridge L (1999) A delayed wave of death from reproduction in Drosophila. Science 286, 25212524.
  • Sohal RS, Agarwal A, Agarwal S, Orr WC (1995) Simultaneous overexpression of copper- and zinc-containing superoxide dismutase and catalase retards age-related oxidative damage and increases metabolic potential in Drosophila melanogaster. J. Biol. Chem. 270, 1567115674.
  • Soyer OS, Bonhoeffer S (2006) Evolution of complexity in signaling pathways. Proc. Natl. Acad. Sci. USA 103, 1633716342.
  • Sullivan A, Syed N, Gasco M, Bergamaschi D, Trigiante G, Attard M, Hiller L, Farrell PJ, Smith P, Lu X, Crook T (2004) Polymorphism in wild-type p53 modulates response to chemotherapy in vitro and in vivo. Oncogene 23, 33283337.
  • Symphorien S, Woodruff RC (2003) Effect of DNA repair on aging of transgenic Drosophila melanogaster: I. mei-41 locus. J. Gerontol. A Biol. Sci. Med. Sci. 58, B782B787.
  • Taufer M, Peres A, De Andrade VM, De Oliveira G, Sa G, Do Canto ME, Dos Santos AR, Bauer ME, Da Cruz IB (2005) Is the Val16Ala manganese superoxide dismutase polymorphism associated with the aging process? J. Gerontol. A Biol. Sci. Med. Sci. 60, 432438.
  • Tyner SD, Venkatachalam S, Choi J, Jones S, Ghebranious N, Igelmann H, Lu X, Soron G, Cooper B, Brayton C, Hee PS, Thompson T, Karsenty G, Bradley A, Donehower LA (2002) p53 Mutant mice that display early ageing-associated phenotypes. Nature 415, 4553.
  • Van Remmen H, Ikeno Y, Hamilton M, Pahlavani M, Wolf N, Thorpe SR, Alderson NL, Baynes JW, Epstein CJ, Huang TT, Nelson J, Strong R, Richardson A (2003) Life-long reduction in MnSOD activity results in increased DNA damage and higher incidence of cancer but does not accelerate aging. Physiol. Genomics 16, 2937.
  • Van Vliet P, Mooijaart SP, De Craen AJ, Rensen PC, Van Heemst D, Westendorp RG (2007) Plasma levels of apolipoprotein E and risk of stroke in old age. Ann. NY Acad. Sci. 1100, 140147.
  • Walker DW, McColl G, Jenkins NL, Harris J, Lithgow GJ (2000) Evolution of lifespan in C. elegans. Nature 405, 296297.
  • Wang E (2007) MicroRNA, the putative molecular control for mid-life decline. Ageing Res. Rev. 6, 111.
  • Westendorp RG, Kirkwood TB (1998) Human longevity at the cost of reproductive success. Nature 396, 743746.
  • Wood JG, Rogina B, Lavu S, Howitz K, Helfand SL, Tatar M, Sinclair D (2004) Sirtuin activators mimic caloric restriction and delay ageing in metazoans. Nature 430, 686689.
  • Zelcer N, Tontonoz P (2006) Liver X receptors as integrators of metabolic and inflammatory signaling. J. Clin. Invest. 116, 607614.