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DNA methylation rates scale with maximum lifespan across mammals

Samuel. J. C. Crofts, Eric Latorre-Crespo, Tamir Chandra

Posted on: 13 June 2023 , updated on: 14 June 2023

Preprint posted on 15 May 2023

Article now published in Nature Aging at http://dx.doi.org/10.1038/s43587-023-00535-6

Tik-Tok of the Methylation clock: does DNA methylation impact on our lifespan?

Selected by Jennifer Ann Black

Categories: genetics

Background:

Eukaryotes are capable of regulating gene expression in many ways, including via epigenetic changes. Epigenetic changes are broadly defined as chemical modifications that may alter gene expression but do not change the underlying DNA sequence (1). DNA methylation is a type of epigenetic change that involves specialised enzymes called methyltransferases which add methyl groups (-CH3) to specific regions of DNA. A common example is the methylation of the 5th carbon of the DNA base cytosine (C), generating 5-methylcytosine (or 5mC). When this cytosine is directly followed by a guanine (G), we refer to this combination of DNA bases as a CpG site (or 5’-C-phosphate-G-3’). By modifying the cytosine base in this manner, eukaryotes can alter the gene expression of the methylated DNA sequence. Curiously, the patterns of DNA methylation at these CpG sites that emerged correlate with ageing, suggesting we each possess an internal methylation-associated ‘molecular clock’. Additionally, aberrant DNA methylation can occurs in cancers (2). Thus, by diving deeper into DNA methylation, we may gain important insights not only into how organisms age and develop diseases, but potentially how we could encourage healthy ageing.

 

In this preprint article, Crofts et al. focus specifically on the role of DNA methylation in ageing, investigating whether there is a scaling relationship between the rate of DNA methylation and maximum lifespan across a range of different eukaryotic species. A scaling law mathematically describes an association between two physical quantities over several orders of magnitude and can reflect shared evolutionary constraints across species.

 

Key Findings:

 

1) Increased DNA methylation rates in conserved sites is associated with lower maximum lifespan

First, the authors examine DNA methylation rates within the blood of 7 different species of mammal. They selected species that reflect a range of maximum lifespan lengths, with the house mouse (less than 5 years) as the organism with the shortest lifespan to humans with the longest (over 100 years). To do this, they focus on a carefully selected group of conserved CpG sites whose methylation has been shown to correlate with ageing (i.e., age-related CpGs). They show that:

  • The shorter the maximum lifespan of an organism, the higher the rate of DNA methylation in blood or in skin samples
  • This relationship is not influenced by the size of the animal (i.e it’s body mass), which is unusual as many previously described scaling relationships with lifespan have been driven by mass.

What does this mean? This means that the maximum lifespan and the rate of DNA methylation scale proportionally i.e, if animal X lives 100 x longer than animal Y, the rate of DNA methylation for animal X is 100 x less than animal Y.

Figure shows a selection of data from Crofts et al. (a) Graph shows the relationship between maximum lifespan and DNA methylation in the blood of different mammalian species. Data is log transformed. (d) Graph shows the relationship between maximum lifespan and DNA methylation in the skin samples of different mammalian species. Data is log transformed. (f) Graph shows the relationship between DNA methylation and maximum lifespan from both blood and skin samples. Additionally, the somatic mutation rate is plotted in relation to maximum lifespan. (Adapted from the preprint under a CC-BY 4.0 International License).

 

2) The DNA methylation rate and the somatic mutation rate share a similar relationship with maximum lifespan

In a recent study, the rate of somatic mutation (i.e., mutations in cells that are not germline associated, and are therefore not inherited by offspring) was shown to scale proportionally with the lifespan of an organism, i.e., longer living organisms have reduced rates of somatic mutation (3). As this data shows a similar trend to the findings in this study, the authors suggest as one of the possible explanations that there may be a connection between somatic mutations and DNA methylation and, ultimately, an organism’s maximum lifespan.

What could this mean? 1) Aberrant DNA methylation limits maximum lifespan perhaps by affecting the health of the organism. Organisms that live longer may be better at resolving these issues, thereby extending their lifespan and/or, 2) the rates of somatic mutation and DNA methylation are connected by underlying biological processes that are still to be determined.

 

Why I like this preprint:

The human population is shifting towards a more ‘aged’ population, but we still understand very little about all the complex factors that play roles in the ageing process. Studies like this and many others now suggest that common processes operate across mammals that could contribute to ageing, and moreover, link these processes to certain regulatory events/chemical changes that happen inside our cells. If we could find ways to control these events ourselves, we may find new ways to limit the effects of ageing and age-related diseases on the human body.

 

Questions for the Authors:

Q1: Have you tried using your model to predict the lifespan of any species?

Q2: The largest organisms in your dataset are humans, but of course human lifespans can now be influenced by additional factors like medical science. Can you control for these effects in your model? What about other longer living species like land tortoises or elephants? Does your model hold true for these organisms?

Q3: Why are most of the skin samples derived from bats? Any why did you select skin samples for this study? Do you have any plans to test other tissue types?

 

References:

  1. David Allis, C., & Jenuwein, The Molecular hallmarks of epigenetic control. Nature Review Genetics, 2016.
  2. Greenberg, M.V.C., & Bourc’his, D. The diverse roles of DNA methylation in mammalian development and disease. Nature Reviews Mol. Cell Bio, 2019.
  3. Cagan et al., Somatic mutation rates scale with lifespan across mammals. Nature, 2022.

Tags: dna methylation, lifespan, mammals

doi: https://doi.org/10.1242/prelights.34858

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Author's response

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Q1: Have you tried using your model to predict the lifespan of any species?

We have not tried this as we wanted to use all datasets for inferring the scaling law. In theory, however, this would easily done by comparing the methylation rates of any new species with that of a species of known lifespan.

 

Q2: The largest organisms in your dataset are humans, but of course human lifespans can now be influenced by additional factors like medical science. Can you control for these effects in your model? What about other longer living species like land tortoises or elephants? Does your model hold true for these organisms?

We have not controlled for any other factors, but any variations in estimates of maximum lifespan are unlikely to make a large difference given our analysis is on a log scale. For example, a difference between a maximum lifespan in humans of 120 years compared to 100 is only 0.079 on a log scale.

We used all datasets publicly available to the best of our knowledge. As such, we cannot test our model on these species, although we see no reason why it would not hold in theory for the elephant. The tortoise, on the other hand, is a non-mammalian species and so it would be interesting to explore whether our results can be extended to these animals.

 

Q3: Why are most of the skin samples derived from bats? Any why did you select skin samples for this study? Do you have any plans to test other tissue types?

Unfortunately, we were limited by datasets publicly available, and one of the largest datasets happened to come from bats. Similarly, skin samples were the next best sampled tissue we had access to, after blood. If or when more data becomes available, our findings could be easily corroborated.

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