Epigenetic marks on the genome, such as DNA methylation at CpG sites, determine its structure. That in turn determines which regions of DNA are exposed to transcriptional machinery, which proteins are manufactured, and thus the behavior of the cell. Epigenetic marks and the structure of the genome change constantly in response to circumstances, but some of these changes have been found to be characteristic of aging, leading to the development of epigenetic clocks to measure biological age.
In today's open access paper, the authors report on a novel approach to the development of a clock that measures the burden of aging. Rather than using unbiased machine learning across as many DNA methylation sites on the genome as can be usefully measured, the researchers consider only DNA methylation sites that show little tendency to change with age, under the assumption that these are functionally important to normal cell function and tissue health. Should any of these sites in fact change methylation status, which does appear to occur to a growing degree with advancing age, then something is going wrong as a consequence. The results are interesting, and seem worthy of further exploration.
This study shows that Elastic Net (EN) DNA methylation (DNAme) clocks have low accuracy of predictions for individuals of the same age and a low resolution between healthy and disease cohorts; caveats inherent in applying linear models to non-linear processes. We found that change in methylation of cytosines with age is, interestingly, not the determinant for their selection into the clocks. Moreover, an EN clock's selected cytosines change when non-clock cytosines are removed from the training data; as expected from optimization in a machine learning (ML) context, but inconsistently with the identification of health markers in a biological context.
To address these limitations, we moved from predictions to measurement of biological age, focusing on the cytosines that on average remain invariable in their methylation through lifespan, postulated to be homeostatically vital. We established that dysregulation of such cytosines, measured as the sums of standard deviations of their methylation values, quantifies biological noise, which in our hypothesis is a biomarker of aging and disease. We term this approach a "noise barometer" - the pressure of aging and disease on an organism.
We describe a new quantification of biological age from DNAme, based on the noise of the most-regulated, age invariable cytosines. This approach fits well with the importance of age-specific increase in biological noise and it is the quantification of primary data without numerical adjustments, which improves on ML predictions. The points of increased DNAme noise turned out to be 49-52 and 64-67 years of age, and it would be very interesting to probe global omics at these transitional ages. Our noise barometer distinguishes health from disease and can potentially distinguish one pathology from another completely different pathology. The different time-shapes of different diseases might enable epidemiology of a specific disease in a population, based on the curve of epigenetic noise.
The biological significance of noise-detector cytosines is clear and the effects of their deregulation with time and disease are expected to be many and deleterious. Namely, the likely reason for the noise-detectors cytosines to be on average invariable in their methylation with age is that they are in the regulatory regions of genes that are vital at constant levels.