19 June 2014

Metabolomics: epigenetic markers of aging

Epigenetic clock: how old is your methyl?

Yuri Stefanov, "Biomolecule" based on Nature News – Biomarkers and aging: The clock-watcher

The genome is constantly accumulating changes. And if a couple of decades ago the ideas about these changes inspired thoughts about the evolutionary timescales, now we are learning more and more about how our genetic material is changing right before our eyes. Molecular clocks, a technique for estimating the time of evolutionary divergence of species by analyzing substitutions in DNA, have already become a familiar topic for popular science literature and journalism, but the assessment of the age of individual cells and tissues of the body has attracted attention quite recently. And it turns out that the search for molecular markers that allow you to tell how old you and your cells are without looking at the passport can be interesting from many points of view: from the diagnosis of diseases to the search for criminals and the study of the aging process itself.

Illustration of the cytosine methylation process and its position in the DNA molecule.
A picture from the website of the Laboratory of Theoretical and Computational Biophysics of the University of Illinois.

DNA methylation is one of the mechanisms of gene activity regulation. Most often, cytosine residues in the CpG dinucleotide are methylated. Moreover, cytosines of both complementary DNA chains are methylated. Proteins can then bind to these structures in the genome, attracting enzymes capable of transferring chromatin to an inactive state, which causes the level of gene expression in these regions to decrease. The distribution of such sites across the genome, as well as the process of methylation and its removal, is important to study at once for many reasons. It has been shown that the methylation mechanism is involved in genomic imprinting, X-chromosome inactivation, suppression of the activity of mobile genetic elements and carcinogenesis [1]. It has also been clear for some time that the DNA methylation profile changes with age [2]. However, it is not yet completely clear how and why certain CpG sites are methylated in certain situations, although there are methods for genome-wide analysis of the distribution of methylated nucleotides (the most popular technologies and whales are developed by Illumina).

Despite the existence of a link between the methylation pattern and the age of a person, until recently it was not possible to accurately determine the second from the first. And last year a breakthrough work in this field was published [3]. Its author Steve Horvath is a specialist in mathematics and biological statistics from the University of California at Los Angeles. In order to obtain an algorithm capable of estimating the age of tissue by methylation, Horvath used a machine learning technique. To do this, he took previously published data from the analysis of methylation of the genomes of healthy people of different ages. A total of 82 data sets were analyzed, which included 7844 samples of tissues and cells of 51 types. At the same time, 39 data sets from the entire array were taken to "train" the algorithm. During the training, the program is given both methylation sites and age data, as a result of which the algorithm identifies universal CpG sites for all sets, which from a statistical point of view are most likely to correlate with age. At the second stage, Horvath used 32 more sets so that the algorithm could estimate the age itself, and the scientist could evaluate the effectiveness of his work. The last 11 sets were needed to assess the age of embryonic stem and induced pluripotent cells.

Induced pluripotent cells (IPCs) [4, 5] are pluripotent cells obtained artificially as a result of transformation, for example, of fibroblasts, by genes associated with pluripotency, or so–called "reprogramming factors". For the first time, the IPC was obtained in 2006 by a Japanese scientist named Shinya Yamanaka. For this work, he was awarded the Nobel Prize in Physiology or Medicine in 2012 [6].

As a result of the work carried out, Horvath's algorithm identified 353 methylation sites, which eventually formed the basis of the "epigenetic clock". Analysis of the methylation profile in these sites allows us to estimate the age of tissues with an error not exceeding three years. This result turned out to be many times better than what previously existing biomarkers of aging (for example, telomere length) could give. It turned out that for embryonic stem cells and IPC, Horvath's algorithm gives values close to zero; at the same time, a correlation was found between the age being determined and the number of cell passages.

The importance of having accurate biomarkers of tissue age is difficult to overestimate. Firstly, it can shed light on the very nature of aging, since it indicates the area in which it is possible to look for the processes that lead to it. So, among these sites, 193 had a positive correlation with aging and acquired methylation over time, and 160, on the contrary, lost methylation over time. Among the genes that are adjacent to the sites of the "epigenetic clock", the genes of regulatory proteins involved in the regulation of cell death, proliferation, differentiation, and also associated with carcinogenesis predominate.

Also, as shown in Horvath's work, the age of some tissues tended to differ from the age of the individual himself. It turned out that breast cells in women, according to methylation, age several years faster than other tissues. Cancer cells showed a much greater acceleration: based on the samples examined by Horvath, the average tumor was "older" than the owner by a little over 36 years. Therefore, the analysis of the age of the methylome of various tissues can be useful from a diagnostic point of view. It is quite possible that it will be possible to link anomalies of epigenetic age with some other diseases.

Another area in which it may be important to study the methylation patterns of epigenetic clocks may be criminology, since thanks to this technique it will be possible to determine the age of a suspect from biological tissue samples left at the crime scene.

After the publication of Steve Horvath, the methodology began to be tested in other laboratories. The team of Zymo Research in California even managed to show that Horvath's program works on the genetic material of cells isolated from urine samples. Such tissues were not represented in the initial sample on which the method was developed. Following Horvath's publication, works began to appear in which the epigenetic age is estimated even by a smaller number of methylation sites [7, 8]. Dr. Horvath himself provided temporary open access to his algorithm by posting the necessary data and download links on the laboratory's website.


  1. Jones P.A. (2012). Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13, 484-492;
  2. Richardson B. (2003). Impact of aging on DNA methylation. Ageing Res. Rev. 2, 245-261;
  3. Horvath S. (2013). DNA methylation age of human tissues and cell types. Genome Biol. 14, R115;
  4. biomolecule: "There was a simple cell, it became a stem cell";
  5. Biomolecule: "Stem and branches: stem cells";
  6. Page about Shinya Yamanaka on the website of the Nobel Committee;
  7. Hannum G et al. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell. 49, 359-367;
  8. Weidner C.I. et al. (2014). Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 15, R24.

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