Replicative Aging in Yeast: The Means to the End

Steinkraus, Katherine A.; Kaeberlein, Matt; Kennedy, Brian K.

doi:10.1146/annurev.cellbio.23.090506.123509

Cited by 254 publications

(233 citation statements)

References 193 publications

(239 reference statements)

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“…The yeast RLS (RLS) has been extensively studied and refers to the number of daughter cells an asymmetrically dividing mother cell can produce prior to senescence. 2 A molecular cause of replicative aging was described more than a decade ago, with the discovery that extrachromosomal rDNA circles accumulate specifically in the mother cell with replicative age and are sufficient to induce cell senescence. 3 In addition to accumulation of extrachromosomal rDNA circles, other molecular determinants of replicative aging have been proposed, including mitochondrial degeneration, proteotoxic stress, oxidative damage and epigenetic changes.…”

Section: Introductionmentioning

confidence: 99%

A genomic analysis of chronological longevity factors in budding yeast

Burtner

Murakami²,

Olsen³

et al. 2011

Cell Cycle

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Section: Introductionmentioning

confidence: 99%

A genomic analysis of chronological longevity factors in budding yeast

Burtner

Murakami²,

Olsen³

et al. 2011

Cell Cycle

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“…For example, studies of replicative aging in yeast or human fibroblasts typically track at least 25 or 45 cell division events prior to the onset of senescence, respectively (14). Lineage mapping during worm development frequently tracks at least 10 differentiation events (15), while research with mouse and human systems considers up to several hundred cell divisions (16).…”

mentioning

confidence: 99%

Rewritable digital data storage in live cells via engineered control of recombination directionality

Bonnet

Subsoontorn²,

Endy³

2012

Proc. Natl. Acad. Sci. U.S.A.

326

350

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The use of synthetic biological systems in research, healthcare, and manufacturing often requires autonomous history-dependent behavior and therefore some form of engineered biological memory. For example, the study or reprogramming of aging, cancer, or development would benefit from genetically encoded counters capable of recording up to several hundred cell division or differentiation events. Although genetic material itself provides a natural data storage medium, tools that allow researchers to reliably and reversibly write information to DNA in vivo are lacking. Here, we demonstrate a rewriteable recombinase addressable data (RAD) module that reliably stores digital information within a chromosome. RAD modules use serine integrase and excisionase functions adapted from bacteriophage to invert and restore specific DNA sequences. Our core RAD memory element is capable of passive information storage in the absence of heterologous gene expression for over 100 cell divisions and can be switched repeatedly without performance degradation, as is required to support combinatorial data storage. We also demonstrate how programmed stochasticity in RAD system performance arising from bidirectional recombination can be achieved and tuned by varying the synthesis and degradation rates of recombinase proteins. The serine recombinase functions used here do not require cell-specific cofactors and should be useful in extending computing and control methods to the study and engineering of many biological systems.DNA inversion | synthetic biology | genetic engineering | standard biological parts M ost engineered genetic data storage systems use auto-or cross-regulating bistable systems of transcription repressors or activators to define and hold state via continuous gene expression (1-4). Such epigenetic storage systems can be subject to evolutionary counter selection due to resource burdens placed on the host cell or spontaneous switching due to putatively stochastic fluctuations in cellular processes, including gene expression. Moreover, heterologous expression-based systems are difficult to redeploy given differences in gene regulatory mechanisms across organisms.Another approach for storing data inside organisms is to code extrinsic information within genetic material (5). Nucleic acids have undergone natural selection to serve as heritable data storage material in organismal lineages. Moreover, DNA provides attractive features in terms of data storage robustness, scalability, and stability (6). In addition, engineered transmission of DNA molecules could support data exchange between organisms as needed to implement higher-order multicellular behaviors within programmed consortia (6, 7).Practically, researchers have begun to use enzymes that modify DNA, typically site-specific recombinases, to study and control engineered genetic systems. For example, recombinases can catalyze strand exchange between specific DNA sequences and enable precise manipulation of DNA in vitro and in vivo (8). Depending on the relative location or...

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“…In S. cerevisiae, lifespan can either be defined by the time cells remain viable in absence of nutrients, called chronological lifespan, or by the number of daughter cells a yeast cell produces during its life, which is referred to as replicative lifespan (RLS) (14). Although RLS can vary greatly between individual yeast cells, the average RLS (i.e., across a population of cells) is assumed to be a genotypic trait (15)(16)(17)(18)(19).…”

mentioning

confidence: 99%

Calorie restriction does not elicit a robust extension of replicative lifespan inSaccharomyces cerevisiae

Huberts

González

Lee

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Calorie restriction (CR) is often described as the most robust manner to extend lifespan in a large variety of organisms. Hence, considerable research effort is directed toward understanding the mechanisms underlying CR, especially in the yeast Saccharomyces cerevisiae. However, the effect of CR on lifespan has never been systematically reviewed in this organism. Here, we performed a meta-analysis of replicative lifespan (RLS) data published in more than 40 different papers. Our analysis revealed that there is significant variation in the reported RLS data, which appears to be mainly due to the low number of cells analyzed per experiment. Furthermore, we found that the RLS measured at 2% (wt/vol) glucose in CR experiments is partly biased toward shorter lifespans compared with identical lifespan measurements from other studies. Excluding the 2% (wt/vol) glucose experiments from CR experiments, we determined that the average RLS of the yeast strains BY4741 and BY4742 is 25.9 buds at 2% (wt/vol) glucose and 30.2 buds under CR conditions. RLS measurements with a microfluidic dissection platform produced identical RLS data at 2% (wt/vol) glucose. However, CR conditions did not induce lifespan extension. As we excluded obvious methodological differences, such as temperature and medium, as causes, we conclude that subtle methodspecific factors are crucial to induce lifespan extension under CR conditions in S. cerevisiae.I n the last decades, a nutritious diet low in calories, commonly referred to as calorie restriction (CR), has been reported to extend the lifespan of a broad range of organisms, such as yeast, worms, fruit flies, mice, rats, and monkeys (1-6). This seemingly evolutionary conserved effect of CR on aging has sparked intense investigation into its underlying molecular mechanisms, especially in the budding yeast Saccharomyces cerevisiae (7-10). Some studies suggest that nutrient-responsive pathways, such as target of rapamycin (TOR), protein kinase A (PKA), and Sch9 (11), mediate CR-induced lifespan extension, whereas others hypothesize that CR increases the activity of sirtuin deacetylases (e.g., SIR2) extending lifespan by suppressing ribosomal DNA recombination (9, 12, 13).In S. cerevisiae, lifespan can either be defined by the time cells remain viable in absence of nutrients, called chronological lifespan, or by the number of daughter cells a yeast cell produces during its life, which is referred to as replicative lifespan (RLS) (14). Although RLS can vary greatly between individual yeast cells, the average RLS (i.e., across a population of cells) is assumed to be a genotypic trait (15-19). However, besides CR, other environmental conditions, such as pH and carbon source, are known to have a strong impact on RLS (20, 21).Here, through a systematic analysis of RLS data of S. cerevisiae using data from more than 27,000 cells, we obtained indication that small sample sizes might be the cause for the often observed significant variation between RLS data. Further, we identified that partly biased data exis...

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Replicative Aging in Yeast: The Means to the End

Cited by 254 publications

References 193 publications

A genomic analysis of chronological longevity factors in budding yeast

A genomic analysis of chronological longevity factors in budding yeast

Rewritable digital data storage in live cells via engineered control of recombination directionality

Calorie restriction does not elicit a robust extension of replicative lifespan inSaccharomyces cerevisiae

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