Cell
Volume 186, Issue 1, 5 January 2023, Pages 63-79.e21
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Article
Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan

https://doi.org/10.1016/j.cell.2022.12.007Get rights and content
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Highlights

  • Yeast cells exchange metabolites across generations

  • Metabolite exchange interactions determine cellular lifespan

  • Metabolic reconfigurations result in the export of protective metabolites

  • Multiple pathways of aging are altered by a lifespan-extending exometabolome

Summary

Metabolism is deeply intertwined with aging. Effects of metabolic interventions on aging have been explained with intracellular metabolism, growth control, and signaling. Studying chronological aging in yeast, we reveal a so far overlooked metabolic property that influences aging via the exchange of metabolites. We observed that metabolites exported by young cells are re-imported by chronologically aging cells, resulting in cross-generational metabolic interactions. Then, we used self-establishing metabolically cooperating communities (SeMeCo) as a tool to increase metabolite exchange and observed significant lifespan extensions. The longevity of the SeMeCo was attributable to metabolic reconfigurations in methionine consumer cells. These obtained a more glycolytic metabolism and increased the export of protective metabolites that in turn extended the lifespan of cells that supplied them with methionine. Our results establish metabolite exchange interactions as a determinant of cellular aging and show that metabolically cooperating cells can shape the metabolic environment to extend their lifespan.

Keywords

metabolite exchange interactions
chronological aging
metabolic microenvironment
eukaryotic longevity

Data and code availability

  • The data supporting the findings of this study are available within the paper, its supplemental information and is deposited within publicly accessible repositories. The mass spectrometry proteomics data of chronologically aging SeMeCo, relevant to data shown in Figures 4, 6, S5, and S6, have been deposited to the ProteomeXchange Consortium via the PRIDE86 partner repository with the dataset identifier PRIDE: PXD036444. The mass spectrometry proteomics data of wild-type yeast supplemented with/out amino acids, relevant to data shown in Figure S3C, have been deposited to the Mendeley repository with dataset identifier Mendeley Data: https://doi.org/10.17632/sd8zthmrk4.1.

  • Yeast gene functions and GO slim term mapper can be accessed at the Saccharomyces Genome Database (SGD: https://www.yeastgenome.org/). Protein sequence databases used for the identification and mapping of proteins from proteomics can be accessed via Uniprot:https://www.uniprot.org/ and KEGG: https://www.genome.jp/kegg/pathway.html. No custom software codes were generated as part of this study. All analyses conducted in R v4.1.3. used standard, publicly accessible packages obtained either through GitHub (https://github.com/), the Comprehensive R Archive Network (CRAN, https://cran.r-project.org/) or via Bioconductor (https://www.bioconductor.org/).

  • Any additional information required to reanalyse the data reported in this paper is available from the lead contact upon request.

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