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

  • 2,979
    views
  • 458
    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. Genetics and Genomics
    Khanh B Trang, Matthew C Pahl ... Struan FA Grant
    Research Article

    The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci, we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18, and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 – an inflammation-responsive gene in nerve nociceptors – was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

    1. Cell Biology
    2. Genetics and Genomics
    Jisun So, Olivia Strobel ... Hyun Cheol Roh
    Tools and Resources

    Single-nucleus RNA sequencing (snRNA-seq), an alternative to single-cell RNA sequencing (scRNA-seq), encounters technical challenges in obtaining high-quality nuclei and RNA, persistently hindering its applications. Here, we present a robust technique for isolating nuclei across various tissue types, remarkably enhancing snRNA-seq data quality. Employing this approach, we comprehensively characterize the depot-dependent cellular dynamics of various cell types underlying mouse adipose tissue remodeling during obesity. By integrating bulk nuclear RNA-seq from adipocyte nuclei of different sizes, we identify distinct adipocyte subpopulations categorized by size and functionality. These subpopulations follow two divergent trajectories, adaptive and pathological, with their prevalence varying by depot. Specifically, we identify a key molecular feature of dysfunctional hypertrophic adipocytes, a global shutdown in gene expression, along with elevated stress and inflammatory responses. Furthermore, our differential gene expression analysis reveals distinct contributions of adipocyte subpopulations to the overall pathophysiology of adipose tissue. Our study establishes a robust snRNA-seq method, providing novel insights into the biological processes involved in adipose tissue remodeling during obesity, with broader applicability across diverse biological systems.