Identification of longevity genes with systems biology approaches

Hydrogen peroxide induced loss of heterozygosity correlates with replicative lifespan and mitotic asymmetry in Saccharomyces cerevisiae

Cellular aging in Saccharomyces cerevisiae can lead to genomic instability and impaired mitotic asymmetry. To investigate the role of oxidative stress in cellular aging, we examined the effect of exogenous hydrogen peroxide on genomic instability and mitotic asymmetry in a collection of yeast strains with diverse backgrounds. We treated yeast cells with hydrogen peroxide and monitored the changes of viability and the frequencies of loss of heterozygosity (LOH) in response to hydrogen peroxide doses. The mid-transition points of viability and LOH were quantified using sigmoid mathematical functions. We found that the increase of hydrogen peroxide dependent genomic instability often occurs before a drop in viability. We previously observed that elevation of genomic instability generally lags behind the drop in viability during chronological aging. Hence, onset of genomic instability induced by exogenous hydrogen peroxide treatment is opposite to that induced by endogenous oxidative stress during chronological aging, with regards to the midpoint of viability. This contrast argues that the effect of endogenous oxidative stress on genome integrity is well suppressed up to the dying-off phase during chronological aging. We found that the leadoff of exogenous hydrogen peroxide induced genomic instability to viability significantly correlated with replicative lifespan (RLS), indicating that yeast cells' ability to counter oxidative stress contributes to their replicative longevity. Surprisingly, this leadoff is positively correlated with an inverse measure of endogenous mitotic asymmetry, indicating a trade-off between mitotic asymmetry and cell's ability to fend off hydrogen peroxide induced oxidative stress. Overall, our results demonstrate strong associations of oxidative stress to genomic instability and mitotic asymmetry at the population level of budding yeast.

A network-based approach on elucidating the multi-faceted nature of chronological aging in S. cerevisiae

BACKGROUND

Cellular mechanisms leading to aging and therefore increasing susceptibility to age-related diseases are a central topic of research since aging is the ultimate, yet not understood mechanism of the fate of a cell. Studies with model organisms have been conducted to ellucidate these mechanisms, and chronological aging of yeast has been extensively used as a model for oxidative stress and aging of postmitotic tissues in higher eukaryotes.

METHODOLOGY/PRINCIPAL FINDINGS

The chronological aging network of yeast was reconstructed by integrating protein-protein interaction data with gene ontology terms. The reconstructed network was then statistically "tuned" based on the betweenness centrality values of the nodes to compensate for the computer automated method. Both the originally reconstructed and tuned networks were subjected to topological and modular analyses. Finally, an ultimate "heart" network was obtained via pooling the step specific key proteins, which resulted from the decomposition of the linear paths depicting several signaling routes in the tuned network.

CONCLUSIONS/SIGNIFICANCE

The reconstructed networks are of scale-free and hierarchical nature, following a power law model with γ  =  1.49. The results of modular and topological analyses verified that the tuning method was successful. The significantly enriched gene ontology terms of the modular analysis confirmed also that the multifactorial nature of chronological aging was captured by the tuned network. The interplay between various signaling pathways such as TOR, Akt/PKB and cAMP/Protein kinase A was summarized in the "heart" network originated from linear path analysis. The deletion of four genes, TCB3, SNA3, PST2 and YGR130C, was found to increase the chronological life span of yeast. The reconstructed networks can also give insight about the effect of other cellular machineries on chronological aging by targeting different signaling pathways in the linear path analysis, along with unraveling of novel proteins playing part in these pathways.