Naked DNA fragments that occur within the bloodstream are referred to as cell-free DNA; this study was designed to test whether changes in these fragments could predict an individual’s functional status, with cfDNA testing aimed at providing a better insight into epigenetic changes and their significance.
cfDNA samples were taken from 12 participants from Bologna in 3 groups: those in their 20’s; those in their 70s; and those over 100 years of age; those in the 100+ group were further subdivided into healthy and unhealthy. Differences in nucleosome spacing of cfDNA were found to vary depending on age and health status; those in their 20s nucleosome spacing was regular but spacing deteriorated with age, being least regular in the unhealthy 100+ group.
There was an interesting similarity of nucleosome spacing between the healthy 100+ group and those in their 20s rather than with the group in the 70s; differences suggest that healthy aging results in epigenomic characteristics similar to those of younger, although the reasons behind it are not clear as many factors are involved in epigenomic changes and all aspects of age related changes.
Computational analysis was used in combination with next generation sequencing by the scientists to recreate spacing of the nucleosomes across various regions of the genome which included both regions of euchromatin and heterochromatin.
State of cellular components involved in maintaining regular nucleosome spacing degenerates with age; changing seen in spacing reflects deterioration rather than being result of movement of the nucleosomes themselves or increases in density.
When nucleosome positioning changes different parts of the genome become less or more accessible worsening functioning of the component, such as the liberation of normally immobile transposons DNA segments.
At the start of 2 frequently seen transposons cfDNA signals were found to decline with advancing age; inference may be that unhealthy centenarians as well as those in their 70s have transposons which are more mobile, replicating themselves into the genome and disrupting normal genetic processes.
In order to correlate biological age with epigenetic markers additional studies are needed using a much larger sample; studying a larger number of participants could make it possible to find associations between epigenomic differences and the individual’s diet, medical conditions, and lifestyle.The team suggests the best way is to look at cfDNA from a large population over 2 or 3 decades which would enable tracking of age related epigenomic alterations taking place within each individual’s DNA as well as rate of such changes.
