Abstract

Age-associated DNA methylation in blood cells convey information on health status. However, the mechanisms that drive these changes in circulating cells and their relationships to gene regulation are unknown. We identified age-associated DNA methylation sites in six purified blood borne immune cell types (naïve B, naïve CD4+ and CD8+ T cells, granulocytes, monocytes and NK cells) collected from healthy individuals interspersed over a wide age range. Of the thousands of age-associated sites, only 350 sites were differentially methylated in the same direction in all cell types and validated in an independent longitudinal cohort. Genes close to age-associated hypomethylated sites were enriched for collagen biosynthesis and complement cascade pathways, while genes close to hypermethylated sites mapped to neuronal pathways. In-silico analyses showed that in most cell types, the age-associated hypo- and hypermethylated sites were enriched for ARNT (HIF1β) and REST transcription factor motifs respectively, which are both master regulators of hypoxia response. To conclude, despite spatial heterogeneity, there is a commonality in the putative regulatory role with respect to transcription factor motifs and histone modifications at and around these sites. These features suggest that DNA methylation changes in healthy aging may be adaptive responses to fluctuations of oxygen availability.

Data availability

DNA methylation EPIC 850k data are available at GEO under accession number GSE184269

The following previously published data sets were used

Article and author information

Author details

  1. Roshni Roy

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Pei-Lun Kuo

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Julián Candia

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5793-8989
  4. Dimitra Sarantapoulou

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Ceereena Ubaida-Mohien

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4301-4758
  6. Dena Hernandez

    Laboratory of Neurogenetics, National Institute on Aging, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Mary Kaileh

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2314-312X
  8. Sampath Arepalli

    Laboratory of Neurogenetics, National Institute on Aging, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Amit Singh

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Arsun Bektas

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Jaekwan Kim

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Ann Z Moore

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Toshiko Tanaka

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4161-3829
  14. Julia McKelvey

    Clinical Research Core, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Linda Zukley

    Clinical Research Core, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Cuong Nguyen

    Flow Cytometry Unit, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  17. Tonya Wallace

    Flow Cytometry Unit, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  18. Christopher Dunn

    Flow Cytometry Core, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7899-0110
  19. William Wood

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  20. Yulan Piao

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  21. Christopher Coletta

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  22. Supriyo De

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  23. Jyoti Sen

    Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  24. Nan-ping Weng

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  25. Ranjan Sen

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  26. Luigi Ferrucci

    Translational Gerentology Branch, National Institute on Aging, Baltimore, United States
    For correspondence
    ferruccilu@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:" 0000-0002-6273-1613

Funding

No external funding was received for this work.

Ethics

Human subjects: GESTALT study was approved by the institutional review board of the National Institutes of Health. Informed consent as well as the consent to publish the data collected was obtained from every participant in the study. Since the study of gene expression and epigenetic regulation are essential aims of GESTALT, all participants were required to consent to DNA/RNA testing and storage at all visits in order to participate in the study. the GESTALT IRB approval number is 15-AG-0063.

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

  • 1,387
    views
  • 199
    downloads
  • 5
    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. Roshni Roy
  2. Pei-Lun Kuo
  3. Julián Candia
  4. Dimitra Sarantapoulou
  5. Ceereena Ubaida-Mohien
  6. Dena Hernandez
  7. Mary Kaileh
  8. Sampath Arepalli
  9. Amit Singh
  10. Arsun Bektas
  11. Jaekwan Kim
  12. Ann Z Moore
  13. Toshiko Tanaka
  14. Julia McKelvey
  15. Linda Zukley
  16. Cuong Nguyen
  17. Tonya Wallace
  18. Christopher Dunn
  19. William Wood
  20. Yulan Piao
  21. Christopher Coletta
  22. Supriyo De
  23. Jyoti Sen
  24. Nan-ping Weng
  25. Ranjan Sen
  26. Luigi Ferrucci
(2023)
Epigenetic signature of human immune aging in the GESTALT study
eLife 12:e86136.
https://doi.org/10.7554/eLife.86136

Share this article

https://doi.org/10.7554/eLife.86136

Further reading

    1. Genetics and Genomics
    Junhong Choi, Wei Chen ... Jay Shendure
    Research Article

    One of the goals of synthetic biology is to enable the design of arbitrary molecular circuits with programmable inputs and outputs. Such circuits bridge the properties of electronic and natural circuits, processing information in a predictable manner within living cells. Genome editing is a potentially powerful component of synthetic molecular circuits, whether for modulating the expression of a target gene or for stably recording information to genomic DNA. However, programming molecular events such as protein-protein interactions or induced proximity as triggers for genome editing remains challenging. Here, we demonstrate a strategy termed ‘P3 editing’, which links protein-protein proximity to the formation of a functional CRISPR-Cas9 dual-component guide RNA. By engineering the crRNA:tracrRNA interaction, we demonstrate that various known protein-protein interactions, as well as the chemically induced dimerization of protein domains, can be used to activate prime editing or base editing in human cells. Additionally, we explore how P3 editing can incorporate outputs from ADAR-based RNA sensors, potentially allowing specific RNAs to induce specific genome edits within a larger circuit. Our strategy enhances the controllability of CRISPR-based genome editing, facilitating its use in synthetic molecular circuits deployed in living cells.

    1. Biochemistry and Chemical Biology
    2. Genetics and Genomics
    Kira Breunig, Xuifen Lei ... Luiz O Penalva
    Research Article

    RNA binding proteins (RBPs) containing intrinsically disordered regions (IDRs) are present in diverse molecular complexes where they function as dynamic regulators. Their characteristics promote liquid-liquid phase separation (LLPS) and the formation of membraneless organelles such as stress granules and nucleoli. IDR-RBPs are particularly relevant in the nervous system and their dysfunction is associated with neurodegenerative diseases and brain tumor development. Serpine1 mRNA-binding protein 1 (SERBP1) is a unique member of this group, being mostly disordered and lacking canonical RNA-binding domains. We defined SERBP1’s interactome, uncovered novel roles in splicing, cell division and ribosomal biogenesis, and showed its participation in pathological stress granules and Tau aggregates in Alzheimer’s brains. SERBP1 preferentially interacts with other G-quadruplex (G4) binders, implicated in different stages of gene expression, suggesting that G4 binding is a critical component of SERBP1 function in different settings. Similarly, we identified important associations between SERBP1 and PARP1/polyADP-ribosylation (PARylation). SERBP1 interacts with PARP1 and its associated factors and influences PARylation. Moreover, protein complexes in which SERBP1 participates contain mostly PARylated proteins and PAR binders. Based on these results, we propose a feedback regulatory model in which SERBP1 influences PARP1 function and PARylation, while PARylation modulates SERBP1 functions and participation in regulatory complexes.