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DNA methylation clocks tick in naked mole rats but queens age more slowly than nonbreeders

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Author(s): Steve Horvath ¹ ^, ² ^, ³ ^, ⁷ , Amin Haghani ¹ ^, ⁷ , Nicholas Macoretta ⁴ , Julia Ablaeva ⁴ , Joseph A. Zoller ² , Caesar Z. Li ² , Joshua Zhang ¹ , Masaki Takasugi ⁴ , Yang Zhao ⁴ , Elena Rydkina ⁴ , Zhihui Zhang ⁴ , Stephan Emmrich ⁴ , Ken Raj ⁵ , Andrei Seluanov ⁴ ^, ⁸ , Chris G. Faulkes ⁶ ^, ⁸ , Vera Gorbunova ⁴ ^, ⁸

Publication date (Electronic): 23 December 2021

Journal: Nature aging

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Abstract

Naked mole rats (NMRs) live an exceptionally long life, appear not to exhibit age-related decline in physiological capacity and are resistant to age-related diseases. However, it has been unknown whether NMRs also evade aging according to a primary hallmark of aging: epigenetic changes. To address this question, we profiled n = 385 samples from 11 tissue types at loci that are highly conserved between mammalian species using a custom array (HorvathMammalMethylChip40). We observed strong epigenetic aging effects and developed seven highly accurate epigenetic clocks for several tissues (pan-tissue, blood, kidney, liver, skin clocks) and two dual-species (human–NMR) clocks. The skin clock correctly estimated induced pluripotent stem cells derived from NMR fibroblasts to be of prenatal age. The NMR epigenetic clocks revealed that breeding NMR queens age more slowly than nonbreeders, a feature that is also observed in some eusocial insects. Our results show that despite a phenotype of negligible senescence, the NMR ages epigenetically.

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Most cited references 55

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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Regularization Paths for Generalized Linear Models via Coordinate Descent

Jerome H. Friedman, Trevor J. Hastie, Robert Tibshirani … (2011)

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DNA methylation age of human tissues and cell types

Steve Horvath, Ilias Lagkouvardos (2014)

Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101773306

Journal ID (pubmed-jr-id): 50167

Journal ID (nlm-ta): Nat Aging

Journal ID (iso-abbrev): Nat Aging

Title: Nature aging

ISSN (Electronic): 2662-8465

Publication date Nihms-submitted: 2 March 2022

Publication date (Print): January 2022

Publication date (Electronic): 23 December 2021

Publication date PMC-release: 01 April 2022

Volume: 2

Issue: 1

Pages: 46-59

Affiliations

[1 ]Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.

[2 ]Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.

[3 ]Altos Labs, San Diego, CA, USA.

[4 ]Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA.

[5 ]Radiation Effects Department, Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Chilton, Didcot, UK.

[6 ]School of Biological and Chemical Sciences, Queen Mary University of London, London, UK.

[7 ]These authors contributed equally: Steve Horvath, Amin Haghani.

[8 ]These authors jointly supervised this work: Andrei Seluanov, Chris G. Faulkes, Vera Gorbunova.

Author notes

Correspondence and requests for materials should be addressed to Steve Horvath, Chris G. Faulkes or Vera Gorbunova. shorvath@ 123456mednet.ucla.edu ; c.g.faulkes@ 123456qmul.ac.uk ; vera.gorbunova@ 123456rochester.edu

Author contributions

V.G., C.G.F., A.S., N.M., J.A., M.T., Y.Z., E.R., Z.Z. and S.E. contributed DNA samples and phenotypic data. A.H., J.A.Z., S.H., C.Z.L. and J.A.Z. performed statistical analyses. Drafting and initial editing of the article was performed by S.H., A.H., V.G., C.G.F., K.R. and A.S. All authors edited the article. S.H. conceived of the study.

Author information

Steve Horvath http://orcid.org/0000-0002-4110-3589

Joseph A. Zoller http://orcid.org/0000-0001-6309-0291

Caesar Z. Li http://orcid.org/0000-0002-3907-7983

Joshua Zhang http://orcid.org/0000-0002-4488-1175

Masaki Takasugi http://orcid.org/0000-0001-9880-1225

Yang Zhao http://orcid.org/0000-0002-8703-3678

Stephan Emmrich http://orcid.org/0000-0002-0537-4242

Ken Raj http://orcid.org/0000-0002-3744-265X

Andrei Seluanov http://orcid.org/0000-0003-3400-538X

Chris G. Faulkes http://orcid.org/0000-0002-5228-9075

Vera Gorbunova http://orcid.org/0000-0001-8979-0333

Article

Manuscript ID: NIHMS1784575

DOI: 10.1038/s43587-021-00152-1

PMC ID: 8975251

PubMed ID: 35368774

SO-VID: 46274ab6-bb1a-418f-a6be-0cbbe187f3f5

License:

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/..

Reprints and permissions information is available at www.nature.com/reprints.

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