A study recently published in Aging Cell has discovered an entirely new method of looking at methylation and experimented with multiple ways of analyzing it.
Not just a clock
While we have reported extensively on the use of epigenetic methylation clocks, the genes that become activated and deactivated with aging are not just useful measurements of it. Turning genes on and off has serious consequences, determining what proteins are and aren’t produced.
Furthermore, the genes that are measured in clocks are only a small subset of the total. Current clocks are based on a microarray technology that uses only specific sites [1], which these researchers note is less than 10% of the entire genome subject to methylation. They chose an entirely different technology, whole-genome bisulfite sequencing, which is unbiased and accounts for most of the genome.
Testing the new tool
The researchers chose two cell types in order to test their new approach. From 40 people, they collected monocytes, which turn over rapidly; from 43 people, they collected muscle cells, which turn over very slowly. The volunteers ranged greatly in age, from 22 to 83.
The data from this new method was evaluated in three different ways. First, the researchers used a standard linear regression technique, which is the basis for other clocks. Multiple pathways, including the Notch signaling pathway, were found to be affected. Interestingly, comparing this data to a similar microarray-based analysis found only 7 sites in common.
Second, they attempted to determine which methylation sites were changing within 10-year time windows. This analysis found that muscle tissue is strongly affected around ages 52 to 62 and that monocytes are most affected around ages 33 to 42. A great many signaling pathways were found, including pathways related to neuronal development.
In their third analysis, the researchers directly compared the methylation of younger and older people as large, generalized groups, with the cutoff point being age 51. This analysis found that sites related to protein translation and muscle contraction were affected in muscle tissue, with cellular adhesion, consumption of particles, DNA maintenance, and, again, neuronal development pathways being affected in monocytes.
Finally, the researchers compared the number of sites changed in each of the three analyses. While there were thousands of overlapping sites between the first two analyses, the third analysis had far less overlap. Only four specific sites were found to overlap between all three groups in muscle tissue, and only one dozen overlapped in monocytes.
A need for detail and consistency
The fact that each of these researchers’ analyses yielded entirely different areas of interest, with minimal overlap, is both interesting and a cause for concern. It is not clear which, if any, of these approaches is best suited to measure aging or which of the detected sites are most consequential for physical function.
Additionally, the authors note that their analysis was hampered by technical constraints and that they could not be as thorough as they might have liked. While it targeted all of the genome, this was a “shotgun” approach that only hit random parts of it. They suggest that a greater analytical depth might have yielded more useful information.
This technique is clearly in its infancy, and it needs far more development and adjustment before it can be considered a competitor to the microarray-based methylation analysis that current clocks use. The true end goal of development in this area is, of course, a technique that accurately and completely analyzes the methylation of the entire genome.
Literature
[1] Schumacher, A., Kapranov, P., Kaminsky, Z., Flanagan, J., Assadzadeh, A., Yau, P., … & Petronis, A. (2006). Microarray-based DNA methylation profiling: technology and applications. Nucleic acids research, 34(2), 528-542.
