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Updated: Jun 23, 2026
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A Suppressor Screen for the Characterization of Genetic Links Regulating Chronological Lifespan in Saccharomyces cerevisiae
Published on: September 17, 2020
Aging defined by a chronologic-replicative protein network in Saccharomyces cerevisiae: an interactome analysis
Fernanda Barea1, Diego Bonatto
1Instituto de Biotecnologia, Universidade de Caxias do Sul (UCS), RS, Brazil.
Mechanisms of Ageing and Development
|May 13, 2009
Summary
This study introduces a new model for yeast aging, the chronologic-replicative protein network (CRPN), by integrating replicative and chronological lifespan data. It reveals key protein networks involved in yeast aging mechanisms.
Area of Science:
- Gerontology
- Systems Biology
- Molecular Biology
Background:
- Aging is a complex process involving loss of organismal fitness.
- Existing aging theories lack synthesis, and proteomics data is increasing.
- Yeast (Saccharomyces cerevisiae) is a model organism for studying aging, with distinct replicative (RLS) and chronological (CLS) lifespans.
Purpose of the Study:
- To synthesize existing aging theories using systems biology.
- To define a combined model for yeast aging by integrating RLS and CLS.
- To identify protein networks common to both yeast aging states.
Main Methods:
- Applied systems biology tools to mine yeast aging-associated proteins.
- Constructed an interactome network representing both RLS and CLS.
- Identified subgraphs within the interactome network.
Main Results:
- Developed a novel yeast aging model: the chronologic-replicative protein network (CRPN).
- Identified four key protein network subgraphs within the CRPN.
- These subgraphs are involved in ubiquitin-dependent proteasome/regulation of cell growth, nucleic acid metabolism, carbohydrate metabolism/RNA metabolism, and carbohydrate-organic acid-amino acid/DNA metabolism.
Conclusions:
- The CRPN provides a unified model for yeast aging, integrating RLS and CLS.
- This network highlights the interconnectedness of protein functions in aging.
- The identified subgraphs offer insights into specific molecular mechanisms driving yeast aging.
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