• ageing;
  • C. elegans;
  • genome;
  • lifespan;
  • yeast


  1. Top of page
  2. Abstract.
  3. Introduction
  4. From genes to genomes
  5. Programmed ageing and implications for genetics
  6. Are pathways modulating longevity conserved amongst eukaryotes?
  7. Are classical genetic interpretations sufficient – a look into the future
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgements
  11. References

Intense effort has been directed at understanding pathways modulating ageing in invertebrate model organisms. Prior to this decade, several longevity genes had been identified in flies, worms and yeast. More recently, with the development of RNAi libraries in worms and the yeast open reading frame (ORF) deletion collection, it has become routine to perform genome-wide screens for phenotypes of interest. A number of worm screens have now been performed to identify genes whose reduced expression leads to longer lifespan, and two ORF deletion longevity screens have been performed in yeast. Interestingly, these screens have linked previously unidentified cellular pathways to invertebrate ageing. More surprising, however, is the sheer number of longevity genes in worms and yeast. In this review, I discuss data from genome-wide screens in the context of evolutionary theories of ageing and raise issues regarding the increasing complexity associated with the genetics of longevity.


  1. Top of page
  2. Abstract.
  3. Introduction
  4. From genes to genomes
  5. Programmed ageing and implications for genetics
  6. Are pathways modulating longevity conserved amongst eukaryotes?
  7. Are classical genetic interpretations sufficient – a look into the future
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgements
  11. References

Over the last three decades, studies in invertebrate model organisms have become a major driving force in ageing research. Amongst the most commonly used systems are fruit flies (Drosophila melanogaster) [1], nematodes (Caenorhabditis elegans) [2, 3] and yeast (Saccharomyces cerevisiae), for which two ageing assays have been developed [4–7]. In addition to short lifespan and ease of manipulation, a major advantage to using these model organisms is the availability of advanced genetic tools making possible the identification of an ever-increasing number of genes that modulate lifespan.

Studies of ageing in invertebrates are advancing at a rapid pace. With exceptions, many early attempts to identify longevity genes were based on educated guesswork. Forward genetic approaches in which mutations having a desired phenotype are generated and attempts are then made to identify the mutated gene have also been employed [8, 9]. However, given that longevity is a difficult phenotype to measure even in invertebrates, these screens have not generally been exhaustive. With the generation of the yeast open reading frame (ORF) deletion collection [10] and RNAi libraries in C. elegans [11] and (more recently) D. melanogaster [12], reverse genetic studies of longevity on a genome-wide scale have become possible. Here, phenotypes are measured in a strain or collection of strains with known genotype.

The purpose of this review is not to provide an exhaustive record of the genetics of invertebrate ageing. Interested readers are referred to many other excellent reviews for this purpose [1, 2, 4, 13, 14]. Rather, my focus here will be to highlight new discoveries related to invertebrate ageing that are arising from large-scale studies, interpret them in the context of ageing theory and speculate on the future of genetic studies of longevity in these model organisms. The interface between genetic alteration and environmental manipulations known to affect longevity, such as dietary restriction (DR), will also be discussed.

From genes to genomes

  1. Top of page
  2. Abstract.
  3. Introduction
  4. From genes to genomes
  5. Programmed ageing and implications for genetics
  6. Are pathways modulating longevity conserved amongst eukaryotes?
  7. Are classical genetic interpretations sufficient – a look into the future
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgements
  11. References

The first genes linked to longevity in invertebrate model organisms were identified by a variety of approaches. For instance, mutations in age-1 were identified by directly screening mutagenized C. elegans for extended lifespan [8, 9, 15]. The gene was cloned several years later and found to encode a PI-3-kinase that acts in the insulin/IGF-1 signalling pathway [16–18], which regulates worm lifespan and a number of other processes [19, 20]. The insulin/IGF-1 pathway had already been linked to worm ageing by the finding that mutating the sole insulin/IGF-1 receptor, daf-2, leads to pronounced lifespan extension [21, 22]. daf-2 mutations were tested for their effects on adult lifespan as a result of another phenotype that was speculated to be linked to longevity, constitutive dauer formation during development [22–25].

In Drosophila, multiple hypomorphic mutations in several genes result in lifespan extension [1]. These were identified through direct screens [26, 27], or by testing specific gene mutations [28, 29], including those in the insulin/IGF-1 pathway [30, 31].

As stated, two ageing assays have been developed for yeast. The replicative assay is a measure of the proliferative capacity of one yeast cell, taking advantage of asymmetric division in yeast in which a daughter cell can be easily differentiated from the mother [32]. By tabulating and removing successive daughters, the age of the mother (number of daughter cells produced) can be determined. Initial genes linked to replicative ageing were identified either by studying yeast orthologues of mammalian genes linked to cell proliferation [33], by focusing on genes differentially expressed with age [34], or by an indirect screen identifying stress-resistant mutations and subsequently testing for enhanced replicative lifespan [35]. Chronological ageing measures the time a yeast cell can maintain viability in a postreplicative state [7]. Chronologically long-lived mutants were first identified by direct screening [36].

These groundbreaking studies propelled ageing research, identifying longevity pathways in simple eukaryotes that can be tested in mammals. However, they do not provide any direct measure of the total number of genes that regulate lifespan in invertebrates. Have all of the pathways been identified or are there large regions of undiscovered terrain? Recently, scientists have employed new tools to conduct genome-wide (or near genome-wide) screens for genes whose reduced expression confers lifespan extension. These unbiased approaches provide a better assessment of the total number of longevity genes and are more likely to identify pathways with no prior link to ageing. These screens (described below) have been conducted in worms and yeast and are leading to surprising new insights into the genetics of longevity in invertebrates [37]. Large-scale genomic screens for long-lived animals were first reported by two groups studying C. elegans [38, 39], where RNAi constructs can be delivered to worms through bacterial feeding [40] (Table 1). Genes on chromosomes I and II were surveyed, and both groups discovered that a reduced expression of a number of genes involved in mitochondrial function including electron transport chain components leads to enhanced longevity [38, 39]. A number of other longevity genes were also reported. This unexpected finding reinforces the value of unbiased approaches to study lifespan modulation.

Table 1.   Genome-wide searches for ageing mutants
ReportMethod of screeningGenes interrogatedPotential longevity genes identifiedPer cent long lived
Caenorhabditis elegans
 Lee et al. [39]RNAi screen of genes on chromosome I and II for increased lifespan. RNAi initiated at LI stage569052 on Chr. I of 2663 genes screened1.8
 Hamilton et al. [41]Genome-wide RNAi screen for increased lifespan. RNAi initiated at LI stage16 745890.5
 Hansen et al. [42]  See also Ref. [38] for  partial results from an  earlier screenGenome-wide RNAi screen for increased lifespan. RNAi initiated from time of hatching16 757230.1
 Chen et al. [44]RNAi screen for increased lifespan amongst genes reported to be essential for development. RNAi initiated in old L4 larvae572442.1
 Curran and Ruvkun [45]RNAi screen foe increased lifespan of genes essential for growth and development. RNAi initiated in L4 larvae2700642.4
 Kim and Sun [46]Primary RNAi screen for resistance o ROS amongst genes on Chr. Ill and IV, secondary screen for increased lifespan. RNAi initiated at LI−600084 lead to lifespan increase1.4
 Kaeberlein et al. [47]Screen for yeast deletion strains with increased replicative lifespan564132.3
 Powers et al. [48]Screen of yeast open reading frame deletion collection for increased chronological lifespan−4800−90−1.9

Subsequent screening of the C. elegans RNAi library that targets 80% of the ∼19 000 genes led not only to further insights, but also to a number of questions. Hamilton et al. reported the identification of 90 RNAi clones targeting 89 genes that confer reproducible lifespan extension [41]. Hansen et al. identified 23 genes [42]. Similar to the earlier reports, these studies provided fascinating new information, and through epistasis analysis both teams were able to place novel ageing genes in known longevity pathways. Perhaps the most surprising finding, however, was the almost complete lack of overlap (only three genes were identified by both groups) in the results of the screens. Possible reasons for this are discussed in detail in a recent review [43]. To summarize, differences are likely to arise due in part to the subtle differences in screening methodology, but largely because of the inefficiency of RNAi. Whilst extremely effective in a relative sense, RNAi delivered through bacterial feeding results in variable degrees of reduced target gene expression. The likely result of such a scenario is a large number of false negatives: true longevity genes that escape discovery due to inefficient knockdown of expression. This explanation is by far the most plausible; however, it raises the possibility that many more longevity genes are yet to be discovered and begs the question of how many longevity genes there are in worms. Why does reduced expression of so many genes confer lifespan extension?

More recent targeted RNAi screens have further added to the list of C. elegans longevity genes [44–46]. Genes that are essential for worm development are unlikely to be identified from RNAi screens in which the worms are exposed to the RNAi from embryonic or early larval stages. However, a great benefit of the RNAi feeding regimen is that it can be initiated at time points after development when worms are entirely postmitotic (except for the germline). Two screens have been performed in which worms were exposed to bacteria expressing RNAi clones that were identified as essential for development [44, 45]. Both screens uncovered a large number of new longevity genes in worms (80 in sum with three identified in both screens). Interestingly, a greater percentage of developmentally essential genes confer lifespan extension when knocked down by RNAi than nonessential genes. This was interpreted by both studies as consistent with evolutionary theories of ageing (see below for a discussion).

A final screen used a two-step approach, first identifying RNAi clones representing genes on two worm chromosomes that conferred resistance to paraquat, which induces reactive oxygen species [46]. As a second step, resistant clones were then examined for their effects on worm lifespan, resulting in the identification of 84 new potential longevity genes, of which four were previously identified.

In total, over 300 genes in which reduced expression is associated with lifespan extension have been identified and there is no evidence that this is a complete list. Whilst differential RNAi effectiveness, timing of RNAi induction, RNAi sensitivity of worm strain, methodology for lifespan determination and choice of RNAi screening set all play a role in these divergent results, it is easy to question why so many genes act to limit longevity. In the section below, I propose that such a large number of longevity genes in one organism may not be so surprising when interpreted in the context of evolutionary theories of ageing.

In collaboration with the laboratories of Matt Kaeberlein and Stan Fields, my research group has been involved in genomic studies of ageing in yeast. A yeast ORF deletion collection has been created containing ∼4800 yeast strains, each lacking one nonessential gene [10]. This collection has been used to screen against a wide variety of phenotypes yielding a multitude of new insights into the biology of yeast ( We have used this collection in screens for chronologically and replicatively long-lived gene deletions [47, 48]. Amongst the 90 longest lived deletion strains in the chronological screen, 16 have been implicated in TOR signalling [48]. The tor1Δ strain was also identified as long lived in the replicative screen [47], and reduced levels of this nutrient-responsive kinase have been associated with long lifespan in worms and flies as well [29, 49]. Increasing evidence has identified reduced TOR signalling as a potential longevity-promoting effect of DR (see below) [29, 47, 50–52].

In addition to tor1Δ, an unbiased replicative lifespan screen of 564 yeast deletions (representing ∼12% of nonessential genes) yielded 13 with a long lifespan, suggesting that a gene deletion chosen at random has a 2% chance of being long lived [47]. This number is similar to that observed in the worm screens described above. Although this estimate came from the analysis of a relatively small percentage of the yeast genome, replicative lifespan data have now been collected for greater than 98% of the nonessential gene deletions, and results appear to be consistent with the 2% prediction made by the preliminary study (B.K. Kennedy and M. Kaeberlein, unpublished data). Interestingly, 20% of gene deletions have short replicative lifespan. These deletions tend to lack proteins associated with specific cellular processes conditionally important for viability (e.g. DNA damage repair). Whether these mutants undergo accelerated ageing or die in an age-independent manner (e.g. stochastic chance of irreparable DNA damage) is difficult to ascertain.

Programmed ageing and implications for genetics

  1. Top of page
  2. Abstract.
  3. Introduction
  4. From genes to genomes
  5. Programmed ageing and implications for genetics
  6. Are pathways modulating longevity conserved amongst eukaryotes?
  7. Are classical genetic interpretations sufficient – a look into the future
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgements
  11. References

Ageing is often described as a decline in fitness over time, ultimately culminating in mortality. It is ubiquitous or nearly ubiquitous across species and fully penetrant within a species. Dating back to Darwin and Wallace, evolutionary biologists have attempted to understand ageing in the context of natural selection. Why would a seemingly deleterious process affecting all individuals in a species not be counter-selected? Readers are directed to several comprehensive reviews regarding the evolutionary biology of ageing [53–59]. Here I cover the topic briefly, focusing on points pertinent to the genetics of ageing and longevity.

In any species where there is a significant mortality rate over time, even if mortality occurs in a manner entirely unrelated to ageing, it follows that there will be stronger selection against mutations with deleterious effects early in life than those late in life [60, 61]. This reasoning led Peter Medawar to propose a ‘mutation accumulation’ model, whereby germline mutations would accumulate that have late-acting deleterious effects [62, 63]. Over a long period of selection, these mutations would together result in an age-associated increase in mortality. George Williams developed the theory further, defining antagonistic pleiotropy, whereby genes would have beneficial effects on early survival and/or reproduction at the expense of detrimental effects late in life [64]. In support, many mutations conferring enhanced longevity also reduce organismal fitness (see below). The natural extension of these theories is that ageing is not the result of a programme; rather, it is more a consequence of reduced natural selection in late life.

One prediction based on these theories is that organisms are not optimized for maximum lifespan [65]; instead they are optimized for maximum fitness. Fitness simply put is the ability of an individual to survive and reproduce subsequent generations. Results from genome-wide longevity studies warrant consideration in the context of these evolutionary theories. As reproductive fitness (and not maximum longevity) is under selection, mutations that extend lifespan may not be as rare as one might first imagine. However, the majority of these lifespan-extending mutations may have adverse effects on organismal fitness. Even though it can be difficult to accurately model fitness in a laboratory setting, this appears to be the case for many mutations extending lifespan in worms [66–70] and flies [71–74]. Whilst it is hard to imagine reduced gene function leading to increased fitness, increased lifespan may not be so difficult to achieve, but at a cost.

Are pathways modulating longevity conserved amongst eukaryotes?

  1. Top of page
  2. Abstract.
  3. Introduction
  4. From genes to genomes
  5. Programmed ageing and implications for genetics
  6. Are pathways modulating longevity conserved amongst eukaryotes?
  7. Are classical genetic interpretations sufficient – a look into the future
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgements
  11. References

Many cellular processes are highly conserved amongst disparate eukaryotic species (e.g. cell cycle regulators, DNA damage repair processes, secretory components). Whilst these processes are modified to suit the particular needs of each organism, their underlying core components have been maintained throughout evolution, presumably due to their central importance to organismal survival and reproduction. This conservation underlies the exploitation of invertebrate model organisms to understand mammalian biology and has proved essential for many major advances in biomedical research. Why, however, should this principle apply to processes like ageing which are not under direct natural selection? Are invertebrate models valuable mainly as paradigms to understand the concept of ageing or do they provide direct insight into the mechanisms underlying human ageing?

Many reviews of ageing research highlight the strong conservation of ageing mechanisms throughout eukaryotic species. However, no direct and quantitative tests have been performed. There are certainly examples that support conservation. First and foremost is dietary (also called calorie) restriction, defined as reduced dietary intake without malnutrition [50, 75–78]. DR was shown to extend lifespan in rodents over seven decades ago [79], and has since been reported have similar effects in worms [80], flies [81, 82] and yeast [83–85]. In terms of genes, over- expression of SIR2 and its orthologues extend lifespan in yeast (replicative lifespan) [86], worms [87] and flies [88]. Reduced levels of nutrient-responsive kinases lead to lifespan extension in yeast [36, 47, 48, 89, 90], worms [22, 29, 41], flies [31, 49] and (in one case) mice [91, 92]. Superoxide dismutase over-expression extends lifespan in yeast (chronological lifespan) [89] and flies [93, 94]. But given the large number of longevity genes that exist, is it really more likely that the orthologues of a longevity gene in one organism would itself affect lifespan in another organism and, if so, how much more likely? And why are longevity pathways conserved if maximum lifespan is not under direct selection?

As genome-wide screens begin to provide more quantitative assessments of the lifespans of strains lacking (or with reduced expression of) each gene, cross species comparisons will become possible and it will be feasible to determine the levels of conservation at the gene level between disparate eukaryotic species. As to why there might high levels of conservation amongst longevity pathways, one possibility stems from the increasing awareness that lifespan may be intimately linked to cellular processes which are under tight selection (e.g. metabolism, DNA repair, response to oxidative damage) within an organism. Genes controlling these processes are highly conserved and their effects on longevity may be a by-product of their roles which are under greater selective pressure.

Are classical genetic interpretations sufficient – a look into the future

  1. Top of page
  2. Abstract.
  3. Introduction
  4. From genes to genomes
  5. Programmed ageing and implications for genetics
  6. Are pathways modulating longevity conserved amongst eukaryotes?
  7. Are classical genetic interpretations sufficient – a look into the future
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgements
  11. References

As more and more longevity genes get discovered, genetic interpretations become more complex. The challenge is to translate many genes into a lesser number of critical pathways modulating lifespan. A standard and proven valuable approach has been to perform epistasis analysis. For example, lifespan extension in daf-2 mutant worms requires daf-16 [22], a FOXO transcription factor regulated by insulin/ IGF-1 signalling [95, 96]. Epistasis can be misleading, however. For instance, deletion of SIR2 in yeast shortens replicative lifespan and blocks lifespan extension by DR [83]. This latter finding was a major lynchpin supporting the model that DR extended yeast lifespan through upregulation of Sir2 activity. However, in a mutant background lacking SIR2 and FOB1, another gene linked to longevity in yeast [97], DR caused a robust increase in lifespan [98]. Whilst the debate continues concerning whether Sirtuins have a role in DR-induced longevity (see references for details) [99, 100], the interaction between DR and Sirtuins in yeast is clearly more complex than the simple conclusion suggested by the initial epistasis experiment.

A theoretical example is provided to illustrate possible complexities associated with simple epistasis analysis as it applies to lifespan (Fig. 1). Four pathways (1–4) are described that all contribute to organismal ageing and age-induced mortality (Fig. 1a). For simplicity, the relative contribution of each to mortality in the wild-type organism is considered equal. A histogram of the relative contribution of each pathway to mortality is provided (Fig. 1b, left panel). Strains lacking gene X are short-lived due to an increased contribution of pathway 2 to mortality. Strains lacking gene Y are long lived and the question is whether Y inhibits activity of X (affecting pathway 2) or whether Y affects another pathway. The standard approach, described above for epistasis involving both SIR2 and daf-16, would be to generate a double mutant lacking genes X and Y. If the double mutant is long lived relative to gene X mutants, then gene Y is said to be affecting lifespan independently of gene X. If double mutants lacking gene X and Y have the same lifespan as single X mutants, then X is said to be epistatic and Y upstream. At best, the data are consistent with this interpretation. Consider an alternative whereby the relative contribution of pathway 2 to mortality in the absence of gene X becomes so high that nearly all individuals of the X mutant strain succumb for one reason (Fig. 1b, right panel). The causes of age-induced mortality have been dramatically altered. In this scenario, a double mutant lacking gene X and Y would be expected to have virtually the same lifespan as a single mutant lacking X even though Y may promote ageing through pathway 4 (Fig. 1a, right). Loss of Y has little effect as pathway 4 is not a big contributor to mortality in the absence of X. This problem is likely to be most severe when the X mutation shortens lifespan on its own, the case for a number of genes used for epistasis in yeast and worms.


Figure 1.  Complexity of interpretation in the genetics of longevity. Hypothetical pathways modulating ageing and results from epistasis experiments are presented and described in detail in the text. (a) A hypothetical set of pathways modulating lifespan. Blue boxes depict cellular or organismal events that accelerate ageing and reduce lifespan. Gene X encodes a protein which reduces the contribution of pathway 2. In question is whether the protein encoded by gene Y accelerates ageing by inhibiting the product of gene X or whether Y controls the contribution of another pathway to ageing. (b) In the left graph is a histogram depicting the relative contribution of each pathway to age-induced mortality. Right-hand graph shows the change in relative contribution of each pathway in the absence of gene X. (c) Two graphs showing simple (left) or complex (right) changes in the contribution of each pathway to age-induced mortality when the contribution of pathway 2 is eliminated.

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The example above is relatively specific but the problem may be more general. Given the breadth of longevity genes, and the links between many of them and intricate processes like metabolism and translation, the interactions are likely to be complex, with interconnected behaviour amongst different pathways. For instance, nutrient-responsive signalling kinases (insulin/IGF-1, TOR and PKA) not only regulate overlapping downstream targets but regulate each other in a number of situations. Returning to the theoretical example, for epistasis to be simple, one assumes a scenario whereby removal of one pathway (2 in the model) extends lifespan and leads to higher contribution of the remaining pathways to mortality (Fig. 1c, left panel). This may be true in some cases but is also an increasingly dangerous assumption. In many cases, alternative scenarios are likely to exist, whereby ablation of one pathway by mutation changes the contributions of others in unpredictable ways (Fig. 1c, right panel). Under this latter scenario, pathway determination for longevity genes will become complicated. Ultimately, for a phenotype as complicated as ageing, or even lifespan, the solution is likely to require large arrays of mutant combinations and the end result may not be ‘n’ independent pathways, but a network of complex genetic interactions. The collision between network theory and the genetics of longevity has already started [101–105]. For now, simple epistasis analysis experiments will remain in the forefront providing useful information, but proceed with caution.


  1. Top of page
  2. Abstract.
  3. Introduction
  4. From genes to genomes
  5. Programmed ageing and implications for genetics
  6. Are pathways modulating longevity conserved amongst eukaryotes?
  7. Are classical genetic interpretations sufficient – a look into the future
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgements
  11. References

In the eyes of many, the primary goal of ageing research is to develop therapeutic strategies to slow ageing in an effort to delay the onset (or possibly progression) of age-related disease. The strategy offers promise. DR in mice delays the onset of multiple age-related pathologies including cancer, cognitive decline, diabetes and cardiovascular disease [106]. Sirt1 over-expression and reduced insulin/IGF-1 pathway signalling may also be beneficial [100, 107, 108].

How does the existence of so many longevity genes (if data from model organisms translates to humans) and the disconnect between fitness and lifespan bear on efforts slow human ageing? This question has been considered [59, 109]. A commonly held view is that it might be possible to alter gene action and extend human lifespan, but only with an accompanying (and possibly prohibitive) fitness costs. Reducing p53 activity may extend lifespan but elevated cancer risk makes it potentially unappealing [110, 111]. One interesting recent finding is that it is possible to extend fly and worm lifespan by inducing DR late in life [112] E. Smith, B.K. Kennedy and M. Kaeberlein, unpublished data). Whether this is true in mammals remains unresolved. If it is possible to intervene in a temporal manner (e.g. after reproduction), it may be feasible to extend lifespan and reduce age-related disease without significant deleterious consequences.

If there are many longevity genes in humans, that means many potential targets for therapeutic intervention. However, a complex network of genetic interactions would suggest that it may be very difficult to predict the totality of effects associated with inhibiting a gene that promotes age-induced mortality. Moreover, even though there are examples of conserved longevity genes across eukaryotic species, there are organism-specific (presumably human-specific) pathways as well. Interventions in mice and humans are likely to have overlapping effects at best. I believe the outlook for drug development is promising but the timeline is unpredictable. The opportunity to address multiple diseases which continue to increase in prominence as the percentage of aged individuals increases makes ageing a target that demands attention.


  1. Top of page
  2. Abstract.
  3. Introduction
  4. From genes to genomes
  5. Programmed ageing and implications for genetics
  6. Are pathways modulating longevity conserved amongst eukaryotes?
  7. Are classical genetic interpretations sufficient – a look into the future
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgements
  11. References

I would like to apologize in advance for any unintentionally omitted references. This review touches upon several large rich areas of research and it is impossible to thoroughly discuss and reference them all. I would also like to thank Matt Kaeberlein and Kristan Steffen for helpful comments on the manuscript. Ageing research in my laboratory is supported by a grant from the Ellison Medical Foundation and by National Institutes of Health grant R01 AG024287.


  1. Top of page
  2. Abstract.
  3. Introduction
  4. From genes to genomes
  5. Programmed ageing and implications for genetics
  6. Are pathways modulating longevity conserved amongst eukaryotes?
  7. Are classical genetic interpretations sufficient – a look into the future
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgements
  11. References
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