Evolution


We are all descendents of an unbroken line of cell divisions, dating back to the last common ancestor of all life on Earth. At some point, long after our lineage had acquired features like nuclei and mitochondria, a less distant ancestor stumbled on a major innovation: it grew a body, bringing with it the advantages of cell and tissue specialization.

For many multicellular organisms, this specialization included a distinction between the mortal cells (the “soma”) and the potentially immortal cells (the “germ line”) that are capable of participating in the creation of new organisms. When you look at us, most of what you see is soma — the germ line is safely tucked away in the gonad, which is (usually) itself tucked away someplace safe.

But both the germ line and soma are made of cells. How is it that the soma is mortal while the germ line is, for practical purposes, immortal?

The disposable soma theory of aging begins from the premise that an organism has access to a finite amount of resources (broadly, energy and matter), and that it must distribute these resources in a way that maximizes reproductive fitness. First dibs goes to the germ line (without which it doesn’t matter, in a fitness sense, what becomes of the rest of the organism) and the rest gets divided among the cells of the soma.

For the moment, all we really need to take away from this model is that the germ line and soma are maintained in different ways, either in quality or extent. The germ line is doing something differently than the soma, the upshot of which is that the germ line is immortal. (A strict interpreter of the theory would presume that this “something” is resource-intensive, so that it wouldn’t be possible to apply the strategy to the soma. It’s also possible, however, that it’s simply inconsistent with optimal somatic functions — e.g., that making a muscle the best muscle it can be requires that myocytes not partake of the germ line strategy for immortality, for some structural reason that has nothing to do with resource allocation per se.)

One oh-wow corollary of this model is that if somatic cells could be made more like germ line cells, they would live longer. This prediction has a deliciously outrageous quality — yet is so simple that upon first hearing it, I reached for the nearest journal with the intention of rolling it up and smacking myself repeatedly on the forehead. Fortunately, there was a copy of Nature handy.

To be honest, it didn’t really happen that way. That copy of Nature contained the very article that introduced me to this concept: Curran et al. have shown that in long-lived mutants of the worm C. elegans, somatic tissues start acting like germ line cells:

A soma-to-germline transformation in long-lived Caenorhabditis elegans mutants

Unlike the soma, which ages during the lifespan of multicellular organisms, the germ line traces an essentially immortal lineage. Genomic instability in somatic cells increases with age, and this decline in somatic maintenance might be regulated to facilitate resource reallocation towards reproduction at the expense of cellular senescence. Here we show that Caenorhabditis elegans mutants with increased longevity exhibit a soma-to-germline transformation of gene expression programs normally limited to the germ line. Decreased insulin-like signalling causes the somatic misexpression of the germline-limited pie-1 and pgl family of genes in intestinal and ectodermal tissues. The forkhead boxO1A (FOXO) transcription factor DAF-16, the major transcriptional effector of insulin-like signalling, regulates pie-1 expression by directly binding to the pie-1 promoter. The somatic tissues of insulin-like mutants are more germline-like and protected from genotoxic stress. Gene inactivation of components of the cytosolic chaperonin complex that induce increased longevity also causes somatic misexpression of PGL-1. These results indicate that the acquisition of germline characteristics by the somatic cells of C. elegans mutants with increased longevity contributes to their increased health and survival.

Just to be clear: the somatic tissues of the long-lived mutants had not actually transformed into germ line cells as such, nor were the mutant worms festooned with extra gonads (though admittedly, that would be totally awesome). Rather, the somatic tissues exhibited gene expression patterns ordinarily found only in the germ line.

On the correlation vs. causation issue: The authors showed, using RNAi knockdowns, that the germ line-restricted genes were required for the longevity enhancement due to the mutation in daf-2 (worm insulin/IGF). There’s a bit of a wrinkle: in wildtype animals, blocking these same genes actually resulted in an increase in lifespan. How to explain that? The proffered rationale is that in the wildtype, germ line-restricted genes are only present in the germ line. Knocking them down has no effect on somatic tissue, but might reduce the activity of germ line cells; it’s been known for some time that ablating part of the gonad has life-extending consequences in wildtype animals.

The critical observation, in any case, is that the germ line genes are turned on in daf-2 mutants, and this activation is necessary in order for daf-2 mutation to extend lifespan.

Next questions, in rough order of difficulty:

  1. Does the soma-to-germ line transition occur in other long-lived mutants, or in calorie restricted animals?
  2. By what mechanisms are the germ line-restricted genes extending the somatic lifespan?
  3. Will this finding generalize to other metazoans?
  4. Do the germ line genes expressed in daf-2 soma contribute to germ line immortality?

ResearchBlogging.orgCurran, S., Wu, X., Riedel, C., & Ruvkun, G. (2009). A soma-to-germline transformation in long-lived Caenorhabditis elegans mutants Nature DOI: 10.1038/nature08106

How did aging evolve? Some evolutionary theories invoke tradeoffs between maintenance/repair and reproduction. Others postulate that genes that cause age-related decline can be positively selected, so long as these same genes confer a fitness advantage early in life.

A common feature of these theories is that they operate at the level of the individual organism, rather than the species. Models based on group selection usually have logical problems. For example, suppose that aging evolved in order to eliminate post-reproductive old organisms to preserve resources for the reproductively competent young. This is circular: Why are the old organisms were post-reproductive in the first place? i.e., the model presupposes some age-related decline in organ system function in order to rationalize the evolution of aging.

OK, so suppose that the old remain fertile, but eliminate themselves to avoid competition with their own offspring; reproductive senescence then evolves later since there’s no positive selection pressure for maintaining reproductive function over the long term. Problem: What’s the point? If both old and young are making copies of the same genes, there’s no fitness advantage in eliminating the old — especially in light of the fact that most of the offspring’s competition would be coming not from their own parents and grandparents but from more distantly related members of the same species. (And in sexual organisms, you are a better copy of your own genes than your offspring, who have only half of your alleles. Far better to stick around and show the kids how it’s done, than ride off into the sunset to clear the path for these dilutions of oneself.)

Group selection of aging is also vulnerable to “defectors” — mutants who take advantage of the situation to spread their own selfish genes. Suppose that there is some species-level advantage to aging, such that it emerges as a positively selected trait. As organisms age, they actively decrease their own viability in such a way that they have an increased mortality. The species benefits (somehow) at the cost of the individual fitness of these “cooperators.” But then along comes a defector mutant, who doesn’t age and continues to reproduce while the cooperators are pushing up the daisies. Unless the species-level advantage is overwhelming, it’s clear that the defector trait will spread within the population.

Ultimately, then, the reason why group selection models don’t satisfactorily explain the evolution of aging is that it’s hard to imagine a scenario in which a species-level advantage conferred by aging could outweigh the organism-level advantage conferred by not aging.

Such a scenario might now have been imagined. Mitteldorf and Pepper postulate that senescence could have evolved in order to prevent the spread of disease epidemics in populations:

Senescence as an adaptation to limit the spread of disease

Population density is a robust measure of fitness. But, paradoxically, the risk of lethal epidemics which can wipe out an entire population rises steeply with population density. We explore an evolutionary dynamic that pins population density at a threshold level, above which the transmissibility of disease rises to unacceptable levels. Population density can be held in check by general increases in mortality, by decreased fertility, or by senescence. We model each of these, and simulate selection among them. In our results, senescence is robustly selected over the other two mechanisms, and we argue that this faithfully mirrors the action of natural selection. This picture constitutes a mechanism by which senescence may be selected as a population-level adaptation in its own right, without mutational load or pleiotropy. The mechanism closely parallels the ‘Red Queen hypothesis’, which is widely regarded as a viable explanation for the evolution of sex.

OK, so, how might this work?

Epidemiology is, by definition, a population-level issue, and there’s already precedent for selection pressure based on disease susceptibility guiding evolution at the species level (e.g., the diversity of major histocompatibility loci).

The trick is to get the pressures at the individual and group levels to point in the same direction: If I (an organism) am more susceptible than average to a given disease, and that susceptibility has a genetic component, then my closest relatives (who share most of my genes) are likelier than the general population to be susceptible as well. Therefore, my continued existence poses a risk for my progeny, because I represent one more potential host for a pathogen that might infect them – potentially killing us all and ending the line altogether. One way to deal with that problem is to eliminate hosts, and the authors’ model shows that senescence is a reasonable way to achieve that end.

ResearchBlogging.orgMitteldorf, J., & Pepper, J. (2009). Senescence as an adaptation to limit the spread of disease Journal of Theoretical Biology DOI: 10.1016/j.jtbi.2009.05.013

Here’s the latest in our (infrequent and irregular) series of “review roundups” — links, without extensive further comment, to the reviews I found most intriguing over the past few weeks. For the previous foray into the secondary literature, see here.

Remember, each Review Roundup is guaranteed to contain at least one link to a review you will find highly educational, or your money back.

Autophagy:

Chaperones:

Evolution:

Glycation:

Immunology:

Mitochondria:

Neurodegeneration:

Resveratrol:

Senescence:

I’m in Fort Worth for meeting of the Comparative Biogerontology Initiative consortium, which now has a new acronym (“Comparative Longevity and Aging Determinants across Evolution”) and a much larger team.

Last fall a small group (essentially the original grant applicants) got together to think about the kinds of expertise we would need to assemble in order to pursue a broad study of comparative biogerontology. The motivation behind this idea is that longevity has been “tuned” many times over evolution, often ranging over more than an order of magnitude among species that share the same basic body plan. By studying longevity across species, we hope to be able to identify mechanisms of longevity determination that might reveal as-yet-unexplored strategies for slowing or reversing aging.

We brainstormed, kicked around names, and ended up inviting ~50 people with expertise ranging from aging in wild ungulates to zoo database management. Around 25 have signed on; this week’s meeting represents the first time the group has gotten together in the same place.

I’m probably not going to liveblog this meeting the way I’ve done with other conferences — it’s distracting at the best of times, and more so when I’m expected to be an active participant in the proceedings. Instead, I am going to try to Twitter about interesting points that arise, so check out the tweet feed if you’re interested in following along.

(For Twitter cognoscenti: I’ve created the hashtag #CLADE for all entries pertaining to this meeting. Unfortunately it looks like the hashtags site itself is not updating properly at the moment, so this may not end up being useful in real time. Update: Looks like the Twitter search function on the site is sufficient to the task of aggregating tweets that share a given hashtag.)

The genome era and the advent of high-throughput technologies have brought about a huge increase in the amount of data available to biologists: each genome contains tens of thousands of genes, whose products can potentially interact with each other in an astronomical number of ways. This quantitative change has created a need for a qualitative change in the way we perform analyses: the human brain is not very good at understanding thousands of things at once, let alone millions or billions, so we must find new ways to extract comprehensible patterns from torrents of data.

Many of the techniques being developed to analyze large biological networks fall under the umbrella of systems biology. Some of the newest tools have been used guide genetic perturbation studies in yeast, resulting in the discovery of novel lifespan control genes. What can such network analysis tell us about human aging?

To address this question, Bell et al. compiled a list of gerontogenes (i.e., genes whose wildtype function is associated with accelerated aging, and whose loss-of-function mutants are associated with longer life) from model systems, and studied the connectivity of these genes within the context of interaction data obtained from a large-scale (though not comprehensive) two-hybrid screen of human proteins.

A Human Protein Interaction Network Shows Conservation of Aging Processes between Human and Invertebrate Species
We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

The authors’ focus on genes studied in model organisms is well motivated; genes that control aging in one species are more likely than one would expect from chance to affect aging in another species, even if those species are as diverged as yeast and worms.

The findings: compared to the genome as a whole, longevity genes tend to be more highly connected network, often acting as “hubs” within the network; furthermore, these genes are more connected to one another than the average gene, forming a “longevity network” that stands out against the web of all interactions.

In conjunction with expression data, this network has predictive power: genes that interact with components of the longevity network and exhibit increased expression in aging muscle are very likely to function as gerontogenes in C. elegans. This finding demonstrates once again the significant conservation of lifespan control systems across large evolutionary distances. Perhaps more importantly, it also shows that applying network analyses to large data sets can do more than merely catalog information. With the right combination of high-throughput data, a good network model and the right kinds of statistics, the tools of systems biology can reveal new biology that otherwise would have taken us a very long time to discover.

Today is the bicentennial of the birth of one of the most influential scientists of all time: Charles Robert Darwin.

I’m down with the flu today, so there will be no long tribute posted here. Instead, I will refer you to our previous posts on evolution, and to elsewhere in the blogosphere:

and the mildly hilarious

OK, that’s it for now. Wish me well – and happy Darwin Day, everyone!

I’m always on the lookout for stories about evolution of negligible senescence — situations in which organisms from mortal/aging clades evolve out from under the necessity of growing older. This generally happens because of unusual life histories: increasing fecundity with age is one way to trick natural selection into helping you out, as is finding a niche that forces young and old into a waiting game.

The selection pressure that drives such evolutionary events tells us very little about the molecular and cellular mechanisms by which the effective immortality is actually achieved, but it does point us in the direction of the right organisms to study.

Here’s a new one on me*: Turritopsis nutricula, a hydrozoan that is effectively able to reverse the aging process and revert to a juvenile state after becoming sexually mature. I learned about it in this breathless Telegraph piece (“‘Immortal’ jellyfish swarming across the world”), which appears to have been derived from the more sedate piece in the Times (“Turritopsis nutricula: the world’s only ‘immortal’ creature”) by the removal of complex sentences and the insertions of dumb lines like

Marine biologists say the jellyfish numbers are rocketing because they need not die.

Really? Not surprising that they weren’t able to get an attributed quote on that one. Evolved negligible senescence does not mean biological immortality, or the ability to grow arbitrarily well in any environment. Turritopsis is spreading because it’s an effective invasive species, not because it’s “immortal”.

Still, we’d like to know how it does its little trick. I’m not, however, holding my breath hoping it will generalize to vertebrates.

*All the more embarrassing for me since one of Ouroboros’ regular contributors uses the genus name as her handle; you’d think I would have looked it up.

Natural selection can modify the rate of aging. Often, the evolution of profoundly delayed (or negligible) senescence can be explained by thinking in reproductive terms: Organisms want to maximize production of descendants who are themselves well-situated to maximize their own reproductive success. Hence whales live long enough to help out their grandchildren, and the long lifespans of certain sessile species probably evolved because young organisms have to wait for older individuals to die before they can settle down to grow large and multiply (a similar phenomenon is likely operating on eusocial colonies).

In those examples, long life facilitates reproductive success. But what about the converse? Do species that evolve mechanisms to delay reproduction (e.g. under suboptimal conditions) achieve this goal by delaying aging, or do they let their biological clocks run on unhindered during reproductive arrest? At least in Drosophila, it appears that reproductive delay is also accompanied by a delay in the aging process. From Tatar et al.:

Negligible Senescence during Reproductive Dormancy in Drosophila melanogaster

Some endemic Drosophila overwinter in a state of adult reproductive diapause where egg maturation is arrested in previtellogenic stages. When maintained at cool temperatures, adult Drosophila melanogaster enter reproductive dormancy, that is, diapause or diapause-like quiescence. The ability to survive for extended periods is a typical feature of diapause syndromes. In adults this somatic persistence may involve reduced or slowed senescence. Here we assess whether reproductively dormant D. melanogaster age at slow rates. Adults were exposed to dormancy-inducing conditions for 3, 6, or 9 wk. After this period, demographic parameters were measured under normal conditions and compared to the demography of newly eclosed cohorts. The age-specific mortality rates of postdormancy adults were essentially identical to the mortality rates of newly eclosed, young flies. Postdormancy reproduction, in contrast, declined with the duration of the treatment; somatic survival during dormancy may tradeoff with later reproduction. Adults in reproductive dormancy were highly resistant to heat and to oxidative stress. Suppressed synthesis of juvenile hormone is known to regulate reproductive diapause of many insects. Treatment of dormant D. melanogaster with a juvenile hormone analog restored vitellogenesis, suppressed stress resistance, and increased demographic senescence. We conclude that D. melanogaster age at slow rates as part of their reproductive dormancy syndrome; the data do not agree with an alternative hypothesis based on heat-dependent “rate of living.” We suggest that low temperature reduces neuroendocrine function, which in turn slows senescence as a function of altered stress response, nutrient reallocation, and metabolism.

Postdormancy flies have the same mortality curve as young flies that never underwent the reproductive arrest — thus, they’ve delayed aging (in the sense of “the increased risk of dying per unit time as a function of chronological age”).

But not every aspect of the flies’ physiology is equally well preserved: Even though they’re surviving at the same rate, postdormancy flies are less fertile than young flies that have not experienced diapause — perhaps the endocrine systems that help preserve the somatic tissues are less efficient at maintaining the germ line. (The aging is of the fly germ line has been well studied in its own right, and is understood at sufficient molecular detail to allow very directed questions about how diapause affects the gonadal stem cell niche.)

That might seem to contradict the principle outlined above — that the purpose of delayed aging would be to increase reproductive success. If an organism’s fertility declines, who cares — in an evolutionary sense — how long it ultimately lives? The answer, I think, is to make the right comparison: The appropriate “control” for a postdormancy fly isn’t a young, well-fed compatriot that never encountered enviornmental conditions adverse enough to initiate diapause; rather, it’s the fly that died because it was dumping resources into reproduction when it should have been bolstering its stress responses and lining its body with fat in order to ride out the bad times. That fly’s fertility, obviously, is zero.

Many insects live a long time as larvae and only briefly as sexually mature adults — extreme examples include the mayfly, the cicada, and some crane flies, though there are countless others. In most cases, the brevity of adult life is not because of rapid onset of decrepitude but rather because the adult morph lacks some essential tool (like a mouth).

Such life histories are vanishingly rare among vertebrates — though they do exist, as revealed by this fascinating (and, to my mind, somewhat poignant) tale from Karsten et al., in which the short-lived adult does appear to be undergoing accelerated senescence:

A unique life history among tetrapods: An annual chameleon living mostly as an egg

The ≈28,300 species of tetrapods (four-limbed vertebrates) almost exclusively have perennial life spans. Here, we report the discovery of a remarkable annual tetrapod from the arid southwest of Madagascar: the chameleon Furcifer labordi, with a posthatching life span of just 4–5 months. At the start of the active season (November), an age cohort of hatchlings emerges; larger juveniles or adults are not present. These hatchlings grow rapidly, reach sexual maturity in less than 2 months, and reproduce in January–February. After reproduction, senescence appears, and the active season concludes with population-wide adult death. Consequently, during the dry season, the entire population is represented by developing eggs that incubate for 8–9 months before synchronously hatching at the onset of the following rainy season. Remarkably, this chameleon spends more of its short annual life cycle inside the egg than outside of it. Our review of tetrapod longevity (>1,700 species) finds no others with such a short life span. These findings suggest that the notorious rapid death of chameleons in captivity may, for some species, actually represent the natural adult life span. Consequently, a new appraisal may be warranted concerning the viability of chameleon breeding programs, which could have special significance for species of conservation concern. Additionally, because F. labordi is closely related to other perennial species, this chameleon group may prove also to be especially well suited for comparative studies that focus on life history evolution and the ecological, genetic, and/or hormonal determinants of aging, longevity, and senescence.

If nothing else, an apt reminder of the crazy games evolution plays in determining the genetic control of lifespan.

But examples like this are more than curiosities — per the final sentence of the abstract (emphasis mine), the vast diversity of life histories generated over the course of evolution provide an ideal laboratory in which to investigate the determinants of lifespan. Specifically, what can we learn from organisms with similar body plans and overall metabolism but significantly different lifespans? At least one project that will explore and exploit the longevity differences between related species is already underway.

Our understanding of aging in animals owes a great debt to a large body of careful work in a single-celled organism, the brewer’s yeast Saccharomyces cerevisiae. Indeed, as I’ve argued before, yeast is one of the two organisms with the strongest credible claim to have started modern biogerontology. An unusually large crop of yeast aging papers have appeared over the last few months, and I thought it would be appropriate to spend a few paragraphs describing them — in honor of this humble organism that rises our bread, ferments our beer, and has done so much to open our eyes to the fundamental mechanisms of aging.

For those unfamiliar with the yeast field or simply wishing a clearly written and nearly comprehensive summary, Steinkraus et al. provide the historical perspective. The piece thoroughly reviews the development of yeast as a model system in aging, as well as the arguments in favor of a connection between results in yeast and well-established (but sometimes hard-to-test) hypotheses in animals.

Based on the influence that yeast has already had on biogerontology as a whole, it seems fair to claim that it will continue to reveal fundamentals of aging that are conserved across evolution. Now, however, there is quantitative evidence to back up that claim: Smith et al. have used bioinformatic and genomic approaches to study the conservation between known longevity genes in yeast and worm, and they show that yeast mutants in worm longevity genes are significantly more likely to be long-lived than randomly chosen mutants — suggesting that

genes that modulate aging have been conserved not only in sequence, but also in function, over a billion years of evolution.

Given this functional conservation, it is reasonable to use yeast to help answer questions about aging in general, so long as these questions are cell-biological in scope.

For instance: NAD+/NADH ratios are thought to be an important metric of the cellular energy balance, and appear to have effects both within the mitochondria and the cytosol. The mitochondrial inner membrane, however, is impermeable to both NAD+ and NADH. How, then, is information about energy balance communicated between the two cellular compartments? Easlon et al. report that two components of the malate-aspartate NADH shuttle (which transports metabolites across the mitochondrial membrane, resulting in equilibration of the cytosolic and mitochondrial NAD+/NADH pools) are involved in controlling longevity. The two proteins, Mdh1 and Aat1, are required for longevity enhancement by calorie restriction (CR), and overexpression of both proteins can increase lifespan independent of caloric conditions (but in a Sir2-dependent manner, about which see more below).

Another outstanding question involves how cellular energy balance is coordinated with the rates of catabolic and anabolic processes, and how this coordination impinges on regulation of longevity. We know that in yeast, the effects of CR are mediated by pathways involving the nutrient sensor TOR and the kinase Sch9. (Brief aside: longevity-enhancing mutations of Sch9 can also suppress genomic instability; new results from Qin et al. show that genomic instability is also associated with lifespan variation in yeast). Sch9 regulates, among other things, ribosome biogenesis; both CR and Sch9 mutation cause ribosome synthesis to decrease — but are the ribosome and longevity phenotypes related? Very likely yes: Steffen et al. report that multiple means of downregulating ribosome synthesis all extend lifespan, implying that reducing production of ribosomes is essential in order to reap the benefits of CR.

As the tools of biology have adapted, so has the yeast field (sometimes leading the charge, as in the case of the earliest microarray-based expression profiling experiments). Murakami et al. have developed a high-throughput method for measuring yeast lifespan. In this first report, the authors primarily demonstrate the use of their method on known mutants, arguing that their results are similar but with lower variance. (Brief aside: they also demonstrate that CR-induced lifespan extension does not require SIR2 or any other yeast sirtuin, adding fuel to the controversy about whether sirtuins play any role in CR in yeast; for more, see here and here.) The increased precision of their technique will allow detection of subtler aging-related phenotypes than were previously detectable, very likely allowing us to add to the list of genes known to regulate lifespan. The high-throughput aspects of the method, of course, open the door to testing small-molecule drugs that could delay aging in yeast — historically a fruitful approach though not without its potential pitfalls.

If you’ve made it this far, feel free to toast S. cerevisiae, perhaps with a beer.

(Before I depart, I just want to mention — since it’s not necessarily clear from the first authors’ names — that four of the papers mentioned above, as well as many of the papers described in earlier Ouroboros posts linked above, are the result of the combined work of the Kaeberlein and Kennedy labs at U-Wash Seattle. Both of them worked together in the Guarente lab back in the day, and they’ve been in the yeast aging field from its very beginning. Clearly, their combined work is continuing to advance the field.)

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