Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney

  1. Yuka Takemon
  2. Joel M Chick
  3. Isabela Gerdes Gyuricza
  4. Daniel A Skelly
  5. Olivier Devuyst
  6. Steven P Gygi
  7. Gary A Churchill
  8. Ron Korstanje  Is a corresponding author
  1. The Jackson Laboratory, United States
  2. Vividion Therapeutics, United States
  3. University of Zurich, Switzerland
  4. Harvard Medical School, United States

Abstract

Little is known about the molecular changes that take place in the kidney during the aging process. In order to better understand these changes, we measured mRNA and protein levels in genetically diverse mice at different ages. We observed distinctive change in mRNA and protein levels as a function of age. Changes in both mRNA and protein are associated with increased immune infiltration and decreases in mitochondrial function. Proteins show a greater extent of change and reveal changes in a wide array of biological processes including unique, organ-specific features of aging in kidney. Most importantly, we observed functionally important age-related changes in protein that occur in the absence of corresponding changes in mRNA. Our findings suggest that mRNA profiling alone provides an incomplete picture of molecular aging in the kidney and that examination of changes in proteins is essential to understand aging processes that are not transcriptionally regulated.

Data availability

Source data and analysis scripts have been deposited with FigShare (10.6084/m9.figshare. 12894146). In addition, the transcript and protein data are available in an online tool that supports genetic mapping analysis (https://churchilllab.jax.org/qtlviewer/JAC/DOKidney).The RNA-seq data have been deposited in NCBI's Gene Expression Omnibus, accession number GSE121330 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121330). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD023823.

The following data sets were generated

Article and author information

Author details

  1. Yuka Takemon

    The Jackson Laboratory, Bar Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3538-4409
  2. Joel M Chick

    Proteomics, Vividion Therapeutics, San Diego, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Isabela Gerdes Gyuricza

    The Jackson Laboratory, Bar Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7969-1910
  4. Daniel A Skelly

    The Jackson Laboratory, Bar Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2329-2216
  5. Olivier Devuyst

    Institute of Physiology, Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3744-4767
  6. Steven P Gygi

    Department of Cell Biology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Gary A Churchill

    The Jackson Laboratory, Bar Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9190-9284
  8. Ron Korstanje

    The Jackson Laboratory, Bar Harbor, United States
    For correspondence
    Ron.Korstanje@jax.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2808-1610

Funding

National Institutes of Health (AG038070)

  • Ron Korstanje

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

Ethics

Animal experimentation: The Jackson Laboratory's Institutional Animal Care and Use Committee approved all reported mouse studies.(AUS#06005).

Reviewing Editor

  1. Jing-Dong Jackie Han, Chinese Academy of Sciences, China

Publication history

  1. Received: September 1, 2020
  2. Accepted: March 6, 2021
  3. Accepted Manuscript published: March 9, 2021 (version 1)
  4. Version of Record published: May 4, 2021 (version 2)
  5. Version of Record updated: February 2, 2022 (version 3)

Copyright

© 2021, Takemon 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

  • 5,854
    Page views
  • 753
    Downloads
  • 19
    Citations

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

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. Yuka Takemon
  2. Joel M Chick
  3. Isabela Gerdes Gyuricza
  4. Daniel A Skelly
  5. Olivier Devuyst
  6. Steven P Gygi
  7. Gary A Churchill
  8. Ron Korstanje
(2021)
Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney
eLife 10:e62585.
https://doi.org/10.7554/eLife.62585

Further reading

    1. Computational and Systems Biology
    Robert West, Hadrien Delattre ... Orkun S Soyer
    Research Article Updated

    Cycling of co-substrates, whereby a metabolite is converted among alternate forms via different reactions, is ubiquitous in metabolism. Several cycled co-substrates are well known as energy and electron carriers (e.g. ATP and NAD(P)H), but there are also other metabolites that act as cycled co-substrates in different parts of central metabolism. Here, we develop a mathematical framework to analyse the effect of co-substrate cycling on metabolic flux. In the cases of a single reaction and linear pathways, we find that co-substrate cycling imposes an additional flux limit on a reaction, distinct to the limit imposed by the kinetics of the primary enzyme catalysing that reaction. Using analytical methods, we show that this additional limit is a function of the total pool size and turnover rate of the cycled co-substrate. Expanding from this insight and using simulations, we show that regulation of these two parameters can allow regulation of flux dynamics in branched and coupled pathways. To support these theoretical insights, we analysed existing flux measurements and enzyme levels from the central carbon metabolism and identified several reactions that could be limited by the dynamics of co-substrate cycling. We discuss how the limitations imposed by co-substrate cycling provide experimentally testable hypotheses on specific metabolic phenotypes. We conclude that measuring and controlling co-substrate dynamics is crucial for understanding and engineering metabolic fluxes in cells.

    1. Computational and Systems Biology
    2. Neuroscience
    Hossein Shahabi, Dileep R Nair, Richard M Leahy
    Research Article

    Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80-200 Hz) for identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the epileptogenic zone (EZ) as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in early to mid-seizure. We showed that aging and duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but the possible factor which correlates with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we asserted the necessity of collecting sufficient data for identifying the epileptogenic networks.