A cross-sectional study of functional and metabolic changes during aging through the lifespan in male mice.

  1. Michael A Petr
  2. Irene Alfaras
  3. Melissa Krawcyzk
  4. Woei-Nan Bair
  5. Sarah J Mitchell
  6. Christopher H Morrell
  7. Stephanie A Studenski
  8. Nathan L Price
  9. Kenneth W Fishbein
  10. Richard G Spencer
  11. Morten Scheibye-Knudsen
  12. Edward G Lakatta
  13. Luigi Ferrucci
  14. Miguel A Aon
  15. Michel Bernier
  16. Rafael de Cabo  Is a corresponding author
  1. University of Copenhagen, Denmark
  2. University of Pittsburgh, United States
  3. National Institute on Aging, NIH, United States
  4. University of Sciences, United States
  5. ETH Zürich, Switzerland
  6. Yale University, United States

Abstract

Aging is associated with distinct phenotypical, physiological, and functional changes, leading to disease and death. The progression of aging-related traits varies widely among individuals, influenced by their environment, lifestyle, and genetics. In this study, we conducted physiologic and functional tests cross-sectionally throughout the entire lifespan of male C57BL/6N mice. In parallel, metabolomics analyses in serum, brain, liver, heart, and skeletal muscle were also performed to identify signatures associated with frailty and age-dependent functional decline. Our findings indicate that declines in gait speed as a function of age and frailty are associated with a dramatic increase in the energetic cost of physical activity and decreases in working capacity. Aging and functional decline prompt organs to rewire their metabolism and substrate selection and towards redox-related pathways, mainly in liver and heart. Collectively, the data provide a framework to further understand and characterize processes of aging at the individual organism and organ levels.

Data availability

This study did not generate datasets or code. However, we are citing one of our previously published raw microarray datasets that was deposited to the NCBI GeneExpressionOmnibus database under accession number GSE81959.

The following previously published data sets were used

Article and author information

Author details

  1. Michael A Petr

    Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  2. Irene Alfaras

    Aging Institute, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Melissa Krawcyzk

    Laboratory of Cardiovascular Science, National Institute on Aging, NIH, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Woei-Nan Bair

    Department of Physical Therapy, University of Sciences, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Sarah J Mitchell

    Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  6. Christopher H Morrell

    Laboratory of Cardiovascular Science, National Institute on Aging, NIH, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Stephanie A Studenski

    Department of Medicine, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Nathan L Price

    Department of Comparative Medicine, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Kenneth W Fishbein

    Laboratory of Clinical Investigator, National Institute on Aging, NIH, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Richard G Spencer

    Laboratory Clinical Investigation, National Institute on Aging, NIH, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Morten Scheibye-Knudsen

    Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  12. Edward G Lakatta

    Laboratory of Cardiovascular Science, National Institute on Aging, NIH, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Luigi Ferrucci

    Intramural Research Program, National Institute on Aging, NIH, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6273-1613
  14. Miguel A Aon

    Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Michel Bernier

    Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Rafael de Cabo

    Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    For correspondence
    decabora@grc.nia.nih.gov
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2830-5693

Funding

National Institute on Aging (AG000335-04)

  • Rafael de Cabo

The research was supported by the Intramural Research Program of the National Institute on Aging

Reviewing Editor

  1. Jamie Justice, Wake Forest School of Medicine, United States

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (ACUC) protocol of the National Institute on Aging (Protocol Number: TGB-277-2022)

Version history

  1. Received: September 10, 2020
  2. Accepted: April 13, 2021
  3. Accepted Manuscript published: April 20, 2021 (version 1)
  4. Version of Record published: May 5, 2021 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 5,255
    views
  • 829
    downloads
  • 52
    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. Michael A Petr
  2. Irene Alfaras
  3. Melissa Krawcyzk
  4. Woei-Nan Bair
  5. Sarah J Mitchell
  6. Christopher H Morrell
  7. Stephanie A Studenski
  8. Nathan L Price
  9. Kenneth W Fishbein
  10. Richard G Spencer
  11. Morten Scheibye-Knudsen
  12. Edward G Lakatta
  13. Luigi Ferrucci
  14. Miguel A Aon
  15. Michel Bernier
  16. Rafael de Cabo
(2021)
A cross-sectional study of functional and metabolic changes during aging through the lifespan in male mice.
eLife 10:e62952.
https://doi.org/10.7554/eLife.62952

Share this article

https://doi.org/10.7554/eLife.62952

Further reading

    1. Computational and Systems Biology
    Maksim Kleverov, Daria Zenkova ... Alexey A Sergushichev
    Research Article

    Transcriptomic profiling became a standard approach to quantify a cell state, which led to accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here we present Phantasus - a user-friendly web-application for interactive gene expression analysis which provides a streamlined access to more than 96000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed on-line at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under MIT license.

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Ryan T Bell, Harutyun Sahakyan ... Eugene V Koonin
    Research Article

    A comprehensive census of McrBC systems, among the most common forms of prokaryotic Type IV restriction systems, followed by phylogenetic analysis, reveals their enormous abundance in diverse prokaryotes and a plethora of genomic associations. We focus on a previously uncharacterized branch, which we denote coiled-coil nuclease tandems (CoCoNuTs) for their salient features: the presence of extensive coiled-coil structures and tandem nucleases. The CoCoNuTs alone show extraordinary variety, with three distinct types and multiple subtypes. All CoCoNuTs contain domains predicted to interact with translation system components, such as OB-folds resembling the SmpB protein that binds bacterial transfer-messenger RNA (tmRNA), YTH-like domains that might recognize methylated tmRNA, tRNA, or rRNA, and RNA-binding Hsp70 chaperone homologs, along with RNases, such as HEPN domains, all suggesting that the CoCoNuTs target RNA. Many CoCoNuTs might additionally target DNA, via McrC nuclease homologs. Additional restriction systems, such as Type I RM, BREX, and Druantia Type III, are frequently encoded in the same predicted superoperons. In many of these superoperons, CoCoNuTs are likely regulated by cyclic nucleotides, possibly, RNA fragments with cyclic termini, that bind associated CARF (CRISPR-Associated Rossmann Fold) domains. We hypothesize that the CoCoNuTs, together with the ancillary restriction factors, employ an echeloned defense strategy analogous to that of Type III CRISPR-Cas systems, in which an immune response eliminating virus DNA and/or RNA is launched first, but then, if it fails, an abortive infection response leading to PCD/dormancy via host RNA cleavage takes over.