While research never ends, more than enough is known about the mechanisms of aging to develop therapies that can potentially slow or reverse facets of aging. That said, establishing that a mechanism exists is one thing, but determining how important that mechanism is to aging or any specific age-related disease is quite another. Cellular metabolism is enormously complex and incompletely mapped. It is impossible to theorize effectively on whether mechanism A causes more dysfunction than mechanism B. In many cases it is even hard to comment on the degree to which mechanism A causes mechanism B, or vice versa.
There is a definitive, best way to figure out the importance of a mechanism: remove it, in isolation of all other aspects of aging. Unfortunately there is only one mechanism of aging for which that can be achieved at present, the presence of senescent cells, which can be destroyed by senolytic therapies. Thus we now know a great deal about how important cellular senescence is to aging and specific age-related diseases, in mice at least. But all of the other approaches to slowing aging, such as the well-studied practice of calorie restriction, change many mechanisms and tell us little about relative importance. If developing therapies to target the mechanisms of aging, we need a way to measure their outcome rapidly. If every potential approach must be laboriously run through life span studies in mice, or equally lengthy and costly experiments, then progress will necessarily be slow. Even focusing funding and researchers on approaches that are better rather than worse will take far too long.
The most promising work when it comes to rapid assessment of aging is the production of epigenetic clocks. Some of the epigenetic marks on the genome change in characteristic ways with age, and evidence shows that accelerated epigenetic aging tends to correlate with a greater mortality and disease risk. But no-one yet understands how these epigenetic changes connect to the underlying mechanisms of aging. Thus without calibrating a specific clock against a specific therapy in life span studies, in mice at least, one can't say whether or not the results are real and meaningful. In order to use clocks to rapidly assess new therapies that potentially slow or reverse aging, the clocks must be understood. Today's open access paper is an example of the first steps in that direction, but a great deal more work is needed.
Alterations to the epigenome are one of the central molecular hallmarks of aging, with potentially vast consequences for the physical and functional characteristics of cells. While a cell's genetic code is essentially fixed, the epigenome is a dynamic master conductor, directing information encoded in DNA to generate the diversity of cells and tissues. In many ways, it is akin to the 'operating system of a cell', controlling cell turnover rate, propagating cellular stress response, and supporting the maintenance and stability of cell populations in tissues. Unfortunately, the epigenetic program is also rewired over the lifespan, leading some to hypothesize that epigenetic change may be the root source of aging-related phenotypes.
One of the most extensively studied epigenetic aging phenomena is the alteration in the pattern of DNA methylation (DNAm). Starting in 2011, DNAm patterns were found to be systematic to a degree that enable their use for developing 'clocks' aimed at estimating aging in cells and tissues. To date, there are more than a dozen such epigenetic clocks being applied to answer questions about aging, disease risk, and determinants of health. Overall, epigenetic clocks have been shown to strongly track with age across a vast array of tissue and cell types - even when trained using only data from blood.
Recently, much of the focus on epigenetic clocks has shifted towards examining them in the context of cellular reprogramming. Intriguingly, the conversion of somatic cells into induced pluripotent stem cells (iPSCs) via expression of Yamanaka factors can reverse the epigenetic aging signal - taking cells all the way back to a predicted age of around zero. However, it remains to be shown to what extent this truly represents an aging rejuvenation event. It is also unclear whether all DNAm age changes that accumulated within a cell are reversed, and if not, what the specific relevance is for those that are, versus are not, "reprogrammed".
This lack of insight stems from an overall deficiency in mechanistic understanding of the changes captured by epigenetic clocks - what initiates these epigenetic changes and how or why are they implicated in disease etiology? Moreover, the debate over whether they are causal drivers versus casual passengers of aging has yet to be settled. The major obstacle we observe in uncovering mechanistic understanding relates to the way epigenetic clocks have been constructed. Epigenetic clocks are composite variables developed from a top-down perspective that combines input from typically hundreds to thousands of CpGs that appear to change with aging, without regard to the underlying biology. As such, they likely are comprised of many different subtypes of methylation patterns-each with its own causal explanations and functional consequences.
In this paper we combined computational and experimental approaches to deconstruct epigenetic clocks and group CpGs into smaller functionally related modules, from which epigenetic aging mechanisms can be more easily discovered. We demonstrate that not all signals captured in the clocks are equal when it comes to morbidity/mortality risk. We also show that reprogramming is concentrated on a few specific modules, yet the discrepancy in response across CpGs is not decipherable at the level of the whole clock.
Overall, two modules stand out in terms of their unique features. The first is one of the most responsive to epigenetic reprogramming; is the strongest predictor of all-cause mortality; and shows increases with in vitro passaging up until senescence burden begins to emerge. The second module is moderately responsive to reprogramming; is very accelerated in tumor versus normal tissues; and tracks with passaging in vitro even as population doublings decelerate. Overall, we show that clock deconstruction can identify unique DNAm alterations and facilitate our mechanistic understanding of epigenetic clocks.