Two prominent theories regarding aging both relate to DNA but approach the concept in distinctly different manners. Recent research suggests these theories may actually be more aligned than previously thought.
At the University of California San Diego School of Medicine, researchers have released findings that offer fresh insight into the long-standing question of what drives aging on a molecular level. Their research, published in Nature Aging, uncovers a previously unrecognized connection between the two main theories: random genetic mutations and predictable epigenetic modifications. The latter, often referred to as the epigenetic clock theory, has been extensively utilized by scientists to quantify biological aging.
However, this new study indicates that the situation may be more complicated.
“Leading research institutions and companies are focusing on reversing the epigenetic clock as a means to mitigate aging’s effects, but our findings imply that this might only address a symptom rather than the root cause of aging,” stated co-corresponding author Trey Ideker, Ph.D., a professor at UC San Diego School of Medicine and UC San Diego Jacobs School of Engineering. “If mutations are indeed linked to the observed epigenetic alterations, it could significantly alter our future strategies in combating aging.”
There are two key theories regarding the connection between aging and DNA. The somatic mutation theory posits that aging results from the buildup of mutations—permanent alterations in the DNA sequence occurring randomly. In contrast, the epigenetic clock theory suggests aging arises from the accumulation of epigenetic modifications, which are minor alterations in the chemical structure of DNA that don’t change the sequence but affect which genes are activated or deactivated. Unlike mutations, some epigenetic modifications may be reversible.
Since epigenetic modifications happen at specific locations within the genome rather than randomly, they are easier to measure and have become a primary tool for scientists to determine the “biological age” of cells. Nonetheless, researchers have long sought to comprehend the origins of these epigenetic changes.
To explore this essential query, researchers evaluated data from 9,331 patients documented in the Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes. Their analysis of genetic mutations in relation to epigenetic changes revealed a predictable correlation between mutations and shifts in DNA methylation, a form of epigenetic modification. They discovered that a single mutation could initiate a series of epigenetic changes across the genome, not limited to the mutation’s location. By utilizing this relationship, the researchers could accurately predict age based on either mutations or epigenetic alterations.
“Epigenetic clocks have existed for years, but we are just beginning to uncover the reason behind their ticking,” commented first author Zane Koch, a Ph.D. candidate in bioinformatics at UC San Diego. “Our research establishes for the first time that epigenetic modifications are intricately linked to random genetic mutations.”
The authors of the study emphasize the necessity for further investigations to completely grasp the connection between somatic mutations and epigenetic modifications relative to aging. Nonetheless, these findings represent a significant advancement in understanding the aging process and hold notable potential for developing new therapies aimed at preventing or reversing aging.
“If somatic mutations are the primary catalyst for aging while epigenetic changes merely reflect this process, reversing aging will be more challenging than we had previously assumed,” remarked co-corresponding author Steven Cummings, M.D., executive director of the San Francisco Coordinating Center at UC San Francisco and senior research scientist at Sutter Health’s California Pacific Medical Center Research Institute. “This shifts our perspective from seeing aging as a programmed process to one primarily shaped by random, cumulative changes over time.”
Along with Ideker, Cummings, and Koch, the study also included contributions from Adam Li at UC San Diego and Daniel S. Evans at California Pacific Medical Center Research Institute and UC San Francisco.
This research was supported by the National Institutes of Health (grants U54 CA274502 and P41 GM103504).
