A new experimental platform facilitates assessment of the transcriptional and chromatin landscapes of aging yeast

Abstract

Replicative aging of Saccharomyces cerevisiae is an established model system for eukaryotic cellular aging. A limitation in yeast lifespan studies has been the difficulty of separating old cells from young cells in large quantities. We engineered a new platform, the Miniature-chemostat Aging Device (MAD), that enables purification of aged cells at sufficient quantities for genomic and biochemical characterization of aging yeast populations. Using MAD, we measured DNA accessibility and gene expression changes in aging cells. Our data highlight an intimate connection between aging, growth rate, and stress. Stress-independent genes that change with age are highly enriched for targets of the signal recognition particle (SRP). Combining MAD with an improved ATAC-Seq method, we find that increasing proteasome activity reduces rDNA instability usually observed in aging cells, and contrary to published findings, provide evidence that global nucleosome occupancy does not change significantly with age.

Data availability

We've included all processed data in easily accessible tables.Sequencing data have been deposited in GEO under accession codes GSE118581

The following data sets were generated
    1. Hendrickson DG
    2. Soifer I
    (2018) Genomic analysis of aging yeast
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE118581).

Article and author information

Author details

  1. David G Hendrickson

    Calico Life Sciences, LLC, South San Francisco, United States
    Competing interests
    David G Hendrickson, Is affiliated with Calico Life Sciences. There are no other competing interests.
  2. Ilya Soifer

    Calico Life Sciences, LLC, South San Francisco, United States
    Competing interests
    Ilya Soifer, Is affiliated with Calico Life Sciences. There are no other competing interests.
  3. Bernd J Wranik

    Calico Life Sciences, LLC, South San Francisco, United States
    Competing interests
    Bernd J Wranik, Is affiliated with Calico Life Sciences. There are no other competing interests.
  4. Griffin Kim

    Calico Life Sciences, LLC, South San Francisco, United States
    Competing interests
    Griffin Kim, Is affiliated with Calico Life Sciences. There are no other competing interests.
  5. Michael Robles

    Calico Life Sciences, LLC, South San Francisco, United States
    Competing interests
    Michael Robles, Is affiliated with Calico Life Sciences. There are no other competing interests.
  6. Patrick A Gibney

    Calico Life Sciences, LLC, South San Francisco, United States
    Competing interests
    Patrick A Gibney, Is affiliated with Calico Life Sciences. There are no other competing interests.
  7. R Scott McIsaac

    Calico Life Sciences, LLC, South San Francisco, United States
    For correspondence
    rsm@calicolabs.com
    Competing interests
    R Scott McIsaac, Is affiliated with Calico Life Sciences. There are no other competing interests.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5339-6032

Funding

The authors declare that there was no funding for this work.

Reviewing Editor

  1. Matt Kaeberlein, University of Washington, United States

Version history

  1. Received: July 18, 2018
  2. Accepted: October 17, 2018
  3. Accepted Manuscript published: October 18, 2018 (version 1)
  4. Accepted Manuscript updated: October 19, 2018 (version 2)
  5. Version of Record published: November 28, 2018 (version 3)

Copyright

© 2018, Hendrickson 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.

Metrics

  • 12,679
    Page views
  • 1,316
    Downloads
  • 41
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. David G Hendrickson
  2. Ilya Soifer
  3. Bernd J Wranik
  4. Griffin Kim
  5. Michael Robles
  6. Patrick A Gibney
  7. R Scott McIsaac
(2018)
A new experimental platform facilitates assessment of the transcriptional and chromatin landscapes of aging yeast
eLife 7:e39911.
https://doi.org/10.7554/eLife.39911

Share this article

https://doi.org/10.7554/eLife.39911

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    David O'Reilly, Ioannis Delis
    Tools and Resources

    The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles ‘working together’ towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns specific functional roles to muscle couplings by characterising their task-relevance. Our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can ‘work together’ in different ways: (1) irrespective of the task at hand but also (2) redundantly or (3) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task-relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (shared task information), or synergistic (different task information). To demonstrate these types of interactions in real data, we firstly apply the framework in a simple way, revealing its added functional and physiological relevance with respect to current approaches. We then apply the framework to large-scale datasets and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar and complementary task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.

    1. Computational and Systems Biology
    Ron Sender, Elad Noor ... Yuval Dor
    Research Article

    Cell-free DNA (cfDNA) tests use small amounts of DNA in the bloodstream as biomarkers. While it is thought that cfDNA is largely released by dying cells, the proportion of dying cells' DNA that reaches the bloodstream is unknown. Here, we integrate estimates of cellular turnover rates to calculate the expected amount of cfDNA. By comparing this to the actual amount of cell type-specific cfDNA, we estimate the proportion of DNA reaching plasma as cfDNA. We demonstrate that <10% of the DNA from dying cells is detectable in plasma, and the ratios of measured to expected cfDNA levels vary a thousand-fold among cell types, often reaching well below 0.1%. The analysis suggests that local clearance, presumably via phagocytosis, takes up most of the dying cells' DNA. Insights into the underlying mechanism may help to understand the physiological significance of cfDNA and improve the sensitivity of liquid biopsies.