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ClockBase: a comprehensive platform for biological age profiling in human and mouse

View ORCID ProfileKejun Ying, View ORCID ProfileAlexander Tyshkovskiy, View ORCID ProfileAlexandre Trapp, Hanna Liu, View ORCID ProfileMahdi Moqri, View ORCID ProfileCsaba Kerepesi, View ORCID ProfileVadim N. Gladyshev
doi: https://doi.org/10.1101/2023.02.28.530532
Kejun Ying
1Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
2T. H. Chan School of Public Health, Harvard University, Boston, MA
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Alexander Tyshkovskiy
1Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
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Alexandre Trapp
1Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
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Hanna Liu
1Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
3Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
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Mahdi Moqri
1Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
4Department of Obstetrics & Gynecology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
5Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA
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Csaba Kerepesi
1Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
6Institute for Computer Science and Control (SZTAKI), Budapest, Hungary
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Vadim N. Gladyshev
1Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
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  • For correspondence: vgladyshev@rics.bwh.harvard.edu
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ABSTRACT

Aging represents the greatest risk factor for chronic diseases and mortality, but to understand it we need the ability to measure biological age. In recent years, many machine learning algorithms based on omics data, termed aging clocks, have been developed that can accurately predict the age of biological samples. However, there is currently no resource for systematic profiling of biological age. Here, we describe ClockBase, a platform that features biological age estimates based on multiple aging clock models applied to more than 2,000 DNA methylation datasets and nearly 200,000 samples. We further provide an online interface for statistical analyses and visualization of the data. To show how this resource could facilitate the discovery of biological age-modifying factors, we describe a novel anti-aging drug candidate, zebularine, which reduces the biological age estimates based on all aging clock models tested. We also show that pulmonary fibrosis accelerates epigenetic age. Together, ClockBase provides a resource for the scientific community to quantify and explore biological ages of samples, thus facilitating discovery of new longevity interventions and age-accelerating conditions.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://www.clockbase.org/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 01, 2023.
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ClockBase: a comprehensive platform for biological age profiling in human and mouse
Kejun Ying, Alexander Tyshkovskiy, Alexandre Trapp, Hanna Liu, Mahdi Moqri, Csaba Kerepesi, Vadim N. Gladyshev
bioRxiv 2023.02.28.530532; doi: https://doi.org/10.1101/2023.02.28.530532
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ClockBase: a comprehensive platform for biological age profiling in human and mouse
Kejun Ying, Alexander Tyshkovskiy, Alexandre Trapp, Hanna Liu, Mahdi Moqri, Csaba Kerepesi, Vadim N. Gladyshev
bioRxiv 2023.02.28.530532; doi: https://doi.org/10.1101/2023.02.28.530532

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