Evolution of natural lifespan variation and molecular strategies of extended lifespan

  1. Alaattin Kaya  Is a corresponding author
  2. Cheryl Zi Jin Phua
  3. Mitchell Lee
  4. Lu Wang
  5. Alexander Tyshkovskiy
  6. Siming Ma
  7. Benjamin Barre
  8. Weiqiang Liu
  9. Benjamin R Harrison
  10. Xiaqing Zhao
  11. Xuming Zhou
  12. Brian M Wasko
  13. Theo K Bammler
  14. Daniel EL Promislow
  15. Matt Kaeberlein
  16. Vadim N Gladyshev  Is a corresponding author
  1. Virginia Commonwealth University, United States
  2. Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore
  3. University of Washington, United States
  4. Brigham and Women's Hospital, Harvard Medical School, United States
  5. Chinese Academy of Sciences, Institute of Zoology, China
  6. Harvard medical school, United States
  7. University of Houston - Clear Lake, United States

Abstract

To understand the genetic basis and selective forces acting on longevity, it is useful to examine lifespan variation among closely related species, or ecologically diverse isolates of the same species, within a controlled environment. In particular, this approach may lead to understanding mechanisms underlying natural variation in lifespan. Here, we analyzed 76 ecologically diverse wild yeast isolates and discovered a wide diversity of replicative lifespan. Phylogenetic analyses pointed to genes and environmental factors that strongly interact to modulate the observed aging patterns. We then identified genetic networks causally associated with natural variation in replicative lifespan across wild yeast isolates, as well as genes, metabolites and pathways, many of which have never been associated with yeast lifespan in laboratory settings. In addition, a combined analysis of lifespan-associated metabolic and transcriptomic changes revealed unique adaptations to interconnected amino acid biosynthesis, glutamate metabolism and mitochondrial function in long-lived strains. Overall, our multi-omic and lifespan analyses across diverse isolates of the same species shows how gene-environment interactions shape cellular processes involved in phenotypic variation such as lifespan.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. RNA-seq data were deposited with the NCBI Gene Expression Omnibus (GEO) with accession number GSE188294.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Alaattin Kaya

    Biology and Cancer Biology, Virginia Commonwealth University, Richmond, United States
    For correspondence
    kayaa@vcu.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6132-5197
  2. Cheryl Zi Jin Phua

    Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
    Competing interests
    No competing interests declared.
  3. Mitchell Lee

    Laboratory Medicine and Pathology, University of Washington, Seattle, United States
    Competing interests
    No competing interests declared.
  4. Lu Wang

    Environmental and Occupational Health Sciences, University of Washington, Seattle, United States
    Competing interests
    No competing interests declared.
  5. Alexander Tyshkovskiy

    Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
    Competing interests
    No competing interests declared.
  6. Siming Ma

    Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
    Competing interests
    No competing interests declared.
  7. Benjamin Barre

    Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
    Competing interests
    No competing interests declared.
  8. Weiqiang Liu

    Key Laboratory of Animal Ecology and Conservation Biology, Chinese Academy of Sciences, Institute of Zoology, Beijing, China
    Competing interests
    No competing interests declared.
  9. Benjamin R Harrison

    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States
    Competing interests
    No competing interests declared.
  10. Xiaqing Zhao

    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States
    Competing interests
    No competing interests declared.
  11. Xuming Zhou

    Division of Genetics, Department of Medicine, Harvard medical school, Boston, United States
    Competing interests
    No competing interests declared.
  12. Brian M Wasko

    Biology, University of Houston - Clear Lake, Houston, United States
    Competing interests
    No competing interests declared.
  13. Theo K Bammler

    Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, United States
    Competing interests
    No competing interests declared.
  14. Daniel EL Promislow

    Department of Lab Medicine & Pathology, University of Washington, Seattle, United States
    Competing interests
    No competing interests declared.
  15. Matt Kaeberlein

    Department of Pathology, University of Washington, Seattle, United States
    Competing interests
    Matt Kaeberlein, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1311-3421
  16. Vadim N Gladyshev

    Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
    For correspondence
    vgladyshev@rics.bwh.harvard.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0372-7016

Funding

National Institutes of Health (1K01AG060040)

  • Alaattin Kaya

National Institutes of Health (AG067782)

  • Vadim N Gladyshev

National Institutes of Health (AG064223)

  • Vadim N Gladyshev

National Institutes of Health (AG049494)

  • Daniel EL Promislow

National Institutes of Health (T32 AG052354)

  • Mitchell Lee

Nathan Shock Center-University of Washington (P30AG013280)

  • Alaattin Kaya

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Kaya 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

  • 2,941
    views
  • 453
    downloads
  • 25
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Alaattin Kaya
  2. Cheryl Zi Jin Phua
  3. Mitchell Lee
  4. Lu Wang
  5. Alexander Tyshkovskiy
  6. Siming Ma
  7. Benjamin Barre
  8. Weiqiang Liu
  9. Benjamin R Harrison
  10. Xiaqing Zhao
  11. Xuming Zhou
  12. Brian M Wasko
  13. Theo K Bammler
  14. Daniel EL Promislow
  15. Matt Kaeberlein
  16. Vadim N Gladyshev
(2021)
Evolution of natural lifespan variation and molecular strategies of extended lifespan
eLife 10:e64860.
https://doi.org/10.7554/eLife.64860

Share this article

https://doi.org/10.7554/eLife.64860

Further reading

    1. Cancer Biology
    2. Genetics and Genomics
    Nicole S Arellano, Shannon E Elf
    Insight

    A new approach helps examine the proportion of cancerous and healthy stem cells in patients with chronic myeloid leukemia and how this influences treatment outcomes.

    1. Cancer Biology
    2. Genetics and Genomics
    Rebecca Warfvinge, Linda Geironson Ulfsson ... Göran Karlsson
    Research Article

    The advent of tyrosine kinase inhibitors (TKIs) as treatment of chronic myeloid leukemia (CML) is a paradigm in molecularly targeted cancer therapy. Nonetheless, TKI-insensitive leukemia stem cells (LSCs) persist in most patients even after years of treatment and are imperative for disease progression as well as recurrence during treatment-free remission (TFR). Here, we have generated high-resolution single-cell multiomics maps from CML patients at diagnosis, retrospectively stratified by BCR::ABL1IS (%) following 12 months of TKI therapy. Simultaneous measurement of global gene expression profiles together with >40 surface markers from the same cells revealed that each patient harbored a unique composition of stem and progenitor cells at diagnosis. The patients with treatment failure after 12 months of therapy had a markedly higher abundance of molecularly defined primitive cells at diagnosis compared to the optimal responders. The multiomic feature landscape enabled visualization of the primitive fraction as a mixture of molecularly distinct BCR::ABL1+ LSCs and BCR::ABL1-hematopoietic stem cells (HSCs) in variable ratio across patients, and guided their prospective isolation by a combination of CD26 and CD35 cell surface markers. We for the first time show that BCR::ABL1+ LSCs and BCR::ABL1- HSCs can be distinctly separated as CD26+CD35- and CD26-CD35+, respectively. In addition, we found the ratio of LSC/HSC to be higher in patients with prospective treatment failure compared to optimal responders, at diagnosis as well as following 3 months of TKI therapy. Collectively, this data builds a framework for understanding therapy response and adapting treatment by devising strategies to extinguish or suppress TKI-insensitive LSCs.