1. Biochemistry and Chemical Biology
  2. Computational and Systems Biology

Protein biogenesis machinery is a driver of replicative aging in yeast

  1. Georges E Janssens
  2. Anne C Meinema
  3. Javier González
  4. Justina C Wolters
  5. Alexander Schmidt
  6. Victor Guryev
  7. Rainer Bischoff
  8. Ernst C Wit
  9. Liesbeth M Veenhoff
  10. Matthias Heinemann  Is a corresponding author
  1. University of Groningen, Netherlands
  2. University of Sheffield, United Kingdom
  3. University of Basel, Switzerland
https://doi.org/10.7554/eLife.08527
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  1. Georges E Janssens
  2. Anne C Meinema
  3. Javier González
  4. Justina C Wolters
  5. Alexander Schmidt
  6. Victor Guryev
  7. Rainer Bischoff
  8. Ernst C Wit
  9. Liesbeth M Veenhoff
  10. Matthias Heinemann
(2015)
Protein biogenesis machinery is a driver of replicative aging in yeast
eLife 4:e08527.
https://doi.org/10.7554/eLife.08527
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Abstract

An integrated account of the molecular changes occurring during the process of cellular aging is crucial towards understanding the underlying mechanisms. Here, using novel culturing and computational methods as well as latest analytical techniques, we mapped the proteome and transcriptome during the replicative lifespan of budding yeast. With age, we found primarily proteins involved in protein biogenesis to increase relative to their transcript levels. Exploiting the dynamic nature of our data, we reconstructed high-level directional networks, where we found the same protein biogenesis-related genes to have the strongest ability to predict the behavior of other genes in the system. We identified metabolic shifts and the loss of stoichiometry in protein complexes as being consequences of aging. We propose a model whereby the uncoupling of protein levels of biogenesis-related genes from their transcript levels is causal for the changes occurring in aging yeast. Our model explains why targeting protein synthesis, or repairing the downstream consequences, can serve as interventions in aging.

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Author details

  1. Georges E Janssens

    European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  2. Anne C Meinema

    Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  3. Javier González

    Sheffield Institute for Translational Neuroscience, Department of Computer Science, Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  4. Justina C Wolters

    Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  5. Alexander Schmidt

    Biozentrum, University of Basel, Basel, Switzerland
    Competing interests
    The authors declare that no competing interests exist.

  6. Victor Guryev

    European Research Institute for the Biology of Ageing, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  7. Rainer Bischoff

    Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  8. Ernst C Wit

    Probability and Statistics, Johann Bernoulli Institute of Mathematics and Computer Science, University of Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  9. Liesbeth M Veenhoff

    European Research Institute for the Biology of Ageing, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  10. Matthias Heinemann

    Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
    For correspondence
    m.heinemann@rug.nl
    Competing interests
    The authors declare that no competing interests exist.

Copyright

© 2015, Janssens et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Georges E Janssens
  2. Anne C Meinema
  3. Javier González
  4. Justina C Wolters
  5. Alexander Schmidt
  6. Victor Guryev
  7. Rainer Bischoff
  8. Ernst C Wit
  9. Liesbeth M Veenhoff
  10. Matthias Heinemann
(2015)
Protein biogenesis machinery is a driver of replicative aging in yeast
eLife 4:e08527.
https://doi.org/10.7554/eLife.08527

Share this article

https://doi.org/10.7554/eLife.08527

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Further reading

    1. Cell Biology
    2. Computational and Systems Biology

    Saccharomyces cerevisiae goes through distinct metabolic phases during its replicative lifespan

    Simeon Leupold, Georg Hubmann ... Matthias Heinemann
    Research Advance Updated

    A comprehensive description of the phenotypic changes during cellular aging is key towards unraveling its causal forces. Previously, we mapped age-related changes in the proteome and transcriptome (Janssens et al., 2015). Here, employing the same experimental procedure and model-based inference, we generate a comprehensive account of metabolic changes during the replicative life of Saccharomyces cerevisiae. With age, we found decreasing metabolite levels, decreasing growth and substrate uptake rates accompanied by a switch from aerobic fermentation to respiration, with glycerol and acetate production. The identified metabolic fluxes revealed an increase in redox cofactor turnover, likely to combat increased production of reactive oxygen species. The metabolic changes are possibly a result of the age-associated decrease in surface area per cell volume. With metabolism being an important factor of the cellular phenotype, this work complements our recent mapping of the transcriptomic and proteomic changes towards a holistic description of the cellular phenotype during aging.