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).

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

  • 8,766
    views
  • 1,043
    downloads
  • 84
    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. 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

Share this article

https://doi.org/10.7554/eLife.62585

Further reading

    1. Cancer Biology
    2. Computational and Systems Biology
    Rosalyn W Sayaman, Masaru Miyano ... Mark A LaBarge
    Research Article Updated

    Effects from aging in single cells are heterogenous, whereas at the organ- and tissue-levels aging phenotypes tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages: luminal epithelial and myoepithelial cells. Mammary luminal epithelia exhibit substantial stereotypical changes with age that merit attention because these cells are the putative cells-of-origin for breast cancers. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increase susceptibility to cancer initiation. We generated and analyzed transcriptomes from primary luminal epithelial and myoepithelial cells from younger <30 (y)ears old and older >55 y women. In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and involved altered regulation of chromatin and genome organizers such as SATB1. Epithelial expression variance of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue, and that their emergent phenotype both endows cells with the ability to become cancer-cells-of-origin and represents a biosensor that presages cancer susceptibility.

    1. Computational and Systems Biology
    David B Blumenthal, Marta Lucchetta ... Martin H Schaefer
    Research Article Updated

    Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.