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Epigenetic aging waves: Artificial intelligence detects clustering of switch points in DNA methylation rate in defined sex-dependent age periods

View ORCID ProfileElad Segev, View ORCID ProfileTamar Shahal, View ORCID ProfileThomas Konstantinovsky, View ORCID ProfileYonit Marcus, View ORCID ProfileGabi Shefer, View ORCID ProfileYuval Ebenstein, View ORCID ProfileMetsada Pasmanik-Chor, View ORCID ProfileNaftali Stern
doi: https://doi.org/10.1101/2022.10.02.510495
Elad Segev
1The Sagol Center for Epigenetics of Aging and Metabolism, Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv-Sourasky Medical Center; Sackler Faculty of Medicine, Tel Aviv University, Israel
2Department of Applied Mathematics, Holon Institute of Technology, Israel
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Tamar Shahal
1The Sagol Center for Epigenetics of Aging and Metabolism, Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv-Sourasky Medical Center; Sackler Faculty of Medicine, Tel Aviv University, Israel
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Thomas Konstantinovsky
1The Sagol Center for Epigenetics of Aging and Metabolism, Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv-Sourasky Medical Center; Sackler Faculty of Medicine, Tel Aviv University, Israel
3Department of Engineering, Bar Ilan University, Israel
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Yonit Marcus
1The Sagol Center for Epigenetics of Aging and Metabolism, Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv-Sourasky Medical Center; Sackler Faculty of Medicine, Tel Aviv University, Israel
4The Sackler Faculty of Medicine, Tel-Aviv University, Israel
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Gabi Shefer
1The Sagol Center for Epigenetics of Aging and Metabolism, Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv-Sourasky Medical Center; Sackler Faculty of Medicine, Tel Aviv University, Israel
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Yuval Ebenstein
1The Sagol Center for Epigenetics of Aging and Metabolism, Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv-Sourasky Medical Center; Sackler Faculty of Medicine, Tel Aviv University, Israel
5Department of Chemistry, Tel Aviv University, Israel
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Metsada Pasmanik-Chor
6Bioinformatics Unit, The George S. Wise Faculty of Life Science, Tel Aviv University, Israel
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Naftali Stern
1The Sagol Center for Epigenetics of Aging and Metabolism, Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv-Sourasky Medical Center; Sackler Faculty of Medicine, Tel Aviv University, Israel
4The Sackler Faculty of Medicine, Tel-Aviv University, Israel
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  • For correspondence: naftalis@tlvmc.gov.il
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Abstract

Background Aging is linked to hypermethylation of CpG sites on promoters and enhancers, along with loss of methylation in intergenic zones. That such changes are not necessarily a continuous process is exemplified by the extensive changes in DNA methylation during development with another significant time of change during adolescence. However, the relation between age and DNA methylation during adult life has not been systematically evaluated. In particular, potential changes in methylation trends in the same CpGs over the years that may occur with aging remain largely unexplored.

Methods Here we set out to determine the average trends by age of the CpG sites represented in the Illumina 450 platform, based on data from 2143 subjects of the age range of 20 to 80 years, compiled from 24 different cohorts. Using several mathematical procedures, we initially separated stationary probes from probes whose methylation changes with age. Among the latter, representing ∼20% of the probes, we then focused on the identification of CpG sites with switch points, i.e., a point where a stable trend of change in the age-averaged methylation is replaced by another linear trend.

Results Using several mathematical modeling steps, we generated a machine learning model that identified 5175 CpG sites with switch points in age-related changes in the trend of methylation over the years. Switch points reflect acceleration, deceleration or change of direction of the alteration of methylation with age. The 5175 switch points were limited to 2813 genes in three waves, 80% of which were identical in men and women. A medium-size wave was seen in the early forties, succeeded by a dominant wave as of the late fifties, lasting up to 8 years each. Waves appeared∼4-5 years earlier in men. No switch points were detected on CpGs mapped to the X chromosome.

Conclusion In non-stationary CpG sites, concomitant switch points in age related changes in methylations can be seen in a defined group of sites and genes, which cluster in 3 age- and sex-specific waves.

Competing Interest Statement

The authors have declared no competing interest.

  • List of abbreviations

    ESP
    epigenetic switch points
    HDAC4
    Histone deacetylase 4
    mTOR
    Mechanistic Target of Rapamycin Kinase
    FOXO3
    Forkhead Box O3
    TXNIP
    Thioredoxin Interacting Protein
    ADCY5
    adenylate cyclase 5
    IGFR
    insulin growth factor receptor
    IGF
    insulin growth factor
    IIS
    insulin/insulin-like growth factor (IGF-1) signaling
    DNMT1
    DNA methyl transferase 1
    TRDMT1
    tRNA aspartic acid methyltransferase 1, also known as DNMT2,
    DNMT3B
    Methyltransferase 3 Beta
    TET1/2
    Ten-Eleven Translocation 1/2
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    Posted October 05, 2022.
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    Epigenetic aging waves: Artificial intelligence detects clustering of switch points in DNA methylation rate in defined sex-dependent age periods
    Elad Segev, Tamar Shahal, Thomas Konstantinovsky, Yonit Marcus, Gabi Shefer, Yuval Ebenstein, Metsada Pasmanik-Chor, Naftali Stern
    bioRxiv 2022.10.02.510495; doi: https://doi.org/10.1101/2022.10.02.510495
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    Epigenetic aging waves: Artificial intelligence detects clustering of switch points in DNA methylation rate in defined sex-dependent age periods
    Elad Segev, Tamar Shahal, Thomas Konstantinovsky, Yonit Marcus, Gabi Shefer, Yuval Ebenstein, Metsada Pasmanik-Chor, Naftali Stern
    bioRxiv 2022.10.02.510495; doi: https://doi.org/10.1101/2022.10.02.510495

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