- Open Access
Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species
- Robert Lowe†1Email author,
- Carl Barton†1,
- Christopher A. Jenkins2,
- Christina Ernst3,
- Oliver Forman2,
- Denise S. Fernandez-Twinn4,
- Christoph Bock5, 6, 7, 8,
- Stephen J. Rossiter9,
- Chris G. Faulkes9,
- Susan E. Ozanne4,
- Lutz Walter10,
- Duncan T. Odom3,
- Cathryn Mellersh2 and
- Vardhman K. Rakyan1, 11Email author
© The Author(s). 2018
- Received: 27 September 2017
- Accepted: 19 January 2018
- Published: 16 February 2018
Mammalian species exhibit a wide range of lifespans. To date, a robust and dynamic molecular readout of these lifespan differences has not yet been identified. Recent studies have established the existence of ageing-associated differentially methylated positions (aDMPs) in human and mouse. These are CpG sites at which DNA methylation dynamics show significant correlations with age. We hypothesise that aDMPs are pan-mammalian and are a dynamic molecular readout of lifespan variation among different mammalian species.
A large-scale integrated analysis of aDMPs in six different mammals reveals a strong negative relationship between rate of change of methylation levels at aDMPs and lifespan. This relationship also holds when comparing two different dog breeds with known differences in lifespans. In an ageing cohort of aneuploid mice carrying a complete copy of human chromosome 21, aDMPs accumulate far more rapidly than is seen in human tissues, revealing that DNA methylation at aDMP sites is largely shaped by the nuclear trans-environment and represents a robust molecular readout of the ageing cellular milieu.
Overall, we define the first dynamic molecular readout of lifespan differences among mammalian species and propose that aDMPs will be an invaluable molecular tool for future evolutionary and mechanistic studies aimed at understanding the biological factors that determine lifespan in mammals.
The large variation in lifespan among different mammalian species is a fascinating yet poorly understood phenomenon. For example, mice, on average, live for only two years, whereas other species such as humans and whales can live for > 100 years. Thus far, a variety of different factors have been proposed to correlate with mammalian species lifespan such as body mass, metabolic rate and age of menarche (reviewed in ). However, in each case there are exceptions leading to confusion. For example, although body mass is positively correlated with lifespan across mammalian species, this relationship is not true within species such as dogs . Furthermore, these factors are not dynamic molecular correlates of lifespan.
Recently, several studies have reported genome-scale profiles of ageing-associated differentially methylated positions (aDMPs) in the human genome – CpG sites at which DNA methylation dynamics shows a significant correlation with age (a few of the papers in this area are listed in the references [3–8]). Currently, aDMPs represent the most accurate known molecular markers of age in humans. aDMPs are not limited to humans, with other recent studies showing similar effects in mice [9–14] and whales . In the former, it was also observed that lifespan-altering interventions can change the rate of ageing-associated DNA methylation dynamics at aDMPs [10–14]. Inspired by these recent results, we hypothesised that not only are aDMPs pan-mammalian, but they could also represent the first known dynamic molecular readout of lifespan variation among different mammalian species.
The rate of change of ageing-associated DNA methylation is faster in the mouse relative to human
aDMP dynamics are related to mammalian species lifespan
Sample information for those used in the paper
Age range (weeks)
Hannum et al.
Petkovich et al.
Polanowski et al.
Qiagen PyroMark assays
The correlation between rate of change at aDMPs and lifespan is observed between two different dog breeds
To examine if the negative correlation between rate of change of methylation at aDMP sites and reported lifespan also holds within a species, we analysed two different dog breeds. Dogs have lived alongside humans for thousands of years and shared similar environmental influences. Artificial selection has led to the generation of > 200 varieties (‘pure breeds’) with strikingly different but well characterised phenotypes and attributes, including lifespan which can be studied outside of artificial laboratory conditions. We examined two different pure breeds with contrasting lifespan – the miniature long-haired dachshund (MLHD) (average life expectancy of 12–15 years) and flat-coated retriever (FCR) (average life expectancy of 8–10 years) ( and www.thekennelclub.org.uk/pedigreebreedhealthsurvey)). Only animals that were disease-free at time of sampling were included in our analysis. From the 15 different dog aDMP regions, six regions were identified as aDMPs in both breeds. For all six of these aDMP regions, we found that the shorter-lived FCR showed a significantly faster rate of change of methylation relative to the longer-lived MLHD (P value = 0.0068, Wilcoxon rank-sum test) (Fig. 2b). This difference remains unchanged even after removing animals that were aged < 2 years (a conservative estimate of sexual maturity in dogs). Overall, this provides an example of the negative relationship between rate of change of methylation at aDMP sites and lifespan within a mammalian species.
Rate of change of aDMPs is related to the cellular milieu
Here we have shown that ageing-associated DNA methylation is a dynamic correlate of lifespan among mammalian species and that these methylation dynamics are measuring cellular ageing as opposed to just chronological age. Crucially, our work shows that, in the context of different mammalian species, the rate of methylation dynamics at aDMPs predict lifespan. That is, aDMPs are more than just a measure of chronological age. Although recent studies have shown that lifespan-altering interventions are associated with changes in dynamics of the epigenetic ageing clock, our work [10–14], in particular the Tc1 experiments, highlights the significant extent to which the dynamics of the clock can be modulated by the trans-nuclear environment. It is also important to note that in recently published comparisons of human with mouse [10–14] and/or macaque epigenetic clocks , it was impossible to determine whether the differences in aDMP dynamics among the different species were due to lifespan or body mass differences. On the other hand, in our manuscript we can categorically state that the rate of the epigenetic clock is a measure of lifespan per se and not body mass.
Our work raises two key questions that need to be addressed in future research. First, what is the biological basis of the link between aDMPs and mammalian lifespan? The data from the Tc1 mice show that aDMP dynamics are a consequence of cellular ageing. Ageing comprises a multitude of different processes and one could speculate that the overall aDMP signature reflects information integrated from different sources, as opposed to a single cause. These different upstream processes have stable, cumulative (and measurable) influences on independent subsets of CpG sites, that collectively represent the aDMP signature. Such a model is also consistent with the idea that of the few known factors thought to influence mammalian lifespan, including body mass, none can solely explain aDMPs. It would be interesting to specifically modulate known signalling pathways to establish how they alter dynamics at specific aDMPs. In this regard, it will be worth exploring whether aDMPs are also found in non-mammalian species that harbour DNA methylation (such as many insects), as these organisms typically have much shorter lifespans and hence provide a more tractable system. Second, it will be important to investigate the functional impact of aDMPs. Within this context, relevant questions include whether there are broader changes in epigenetic state, and/or gene expression, and how such changes might influence ageing-associated processes. Recent large-scale integrative analyses suggest only modest correlations between ageing-associated DNA methylation and gene expression dynamics [3, 23]. This is largely consistent with the complex relationship between DNA methylation and gene expression levels, and it may be that ageing-associated DNA methylation dynamics influence the response of a gene as opposed to steady-state levels. Recent advances in epigenetic engineering methodologies may allow the creation of aDMP signatures in cells (e.g. ), thus enabling a more direct assessment of their function including effects on gene expression.
Although future research will undoubtedly address the questions discussed above, it is already clear that aDMPs are a readout of mammalian cellular ageing and, to the best of our knowledge, the first dynamic molecular correlate of lifespan differences among mammalian species. Consequently, they have great potential as molecular markers for studying evolutionary and mechanistic aspects of mammalian ageing.
We generated DNA methylation data for dogs, NMRs, macaques and Tc1 mice. All animals did not show any obvious signs of disease at time of sampling. NMRs were maintained at Queen Mary University of London in the Biological Services Unit, in compliance with institutional guidelines. Macaques were maintained at the German Primate Center, Leibniz-Institute for Primate Research, as a self-sustaining colony of rhesus macaques (Macaca mulatta).
For the 656 samples from Hannum et al., preprocessed samples were downloaded from GEO with accession (GSE40279). For 153 mice from Petkovich et al. , methylation and coverage values for each CpG was obtained from GEO with accession GSE80672. Those CpGs with < 50× coverage in a minimum of 133 samples were removed from the analysis. For Dog and NMR samples, raw FASTQ files were mapped to the reference canFam3 and hetGla2, respectively, using BISMARK (v0.16.3)  and Bowtie2 (v2.2.8) . Reads that mapped outside of the targeted regions were discarded from analyses and methylated and unmethylated counts for each CpG were calculated using the custom C++ program (https://bitbucket.org/lowelabqmul/methylation-extractor). Those CpGs with a coverage < 50× were also discarded from analyses. For Macaque samples, we first mapped 450 K probe sequences to the rheMac8 reference genome using BWA . Probes with no mismatch within the first 5 bp and with < 3 mismatches in total were kept for further analysis (125,102 probes). From these probes, those with a detection P value > 0.01 were removed and a quantile normalisation on the Red, Green and Type II probes was performed. For TC1 mouse samples, a list of probes previously shown to map to mouse genome was used as well as those probes mapping to h-chr21. Additional filtering was performed to remove probes mapping to h-chr21 which are either deleted or duplicated. Processing was then performed in the same manner as macaque. For Humpback whale, we downloaded methylation values from DRYAD (http://hdl.handle.net/10255/dryad.58061). Data created for this manuscript is available from GEO with accession number GSE86059. Primers used for targeted bisulphite sequencing are listed in Additional file 2.
For all aDMP calling, we used the dmpFinder function available in the R package  minfi  with default parameters, using age in weeks as phenotype variable and continuous as the type. q-values was calculated using the positive false discovery rate . This provided P values and q-values used to filter aDMPs as well as the gradient from the fitted linear model. aDMPs regions were defined by extending each aDMP by ± 100 bp. Then, overlapping regions are combined into a single region spanning the entire length of the extended aDMPs and the maximum gradient and minimum P value are assigned to the region. Therefore, each region will be a minimum of 200 bp if only a single aDMP is contained in it.
Sequence conservation analysis
For comparing between species, we used the UCSC tool liftOver  with default parameters except for setting the minimum ratio of bases that must remap to 90% using the parameter ‘-minMatch = 0.9’. To liftOver 450 k probes, we defined a region of 200 bp (± 100 bp) around the target CpG.
We thank Dr Amelie Kuchler, CeMM Austria for assisting with the preparation of RRBS libraries. This research utilised Queen Mary’s Apocrita HPC facility, supported by QMUL Research-IT. https://doi.org/10.5281/zenodo.438045.
This research was supported by Cancer Research UK (CE, DTO), the Wellcome Trust (grant 202878/Z/16/Z to DTO) and the European Research Council (grant 615584 to DTO). SEO and DFT are supported by the Medical Research Council (MRC_MC_UU_12012/4). We thank Dr Amelie Kuchler, CeMM Austria for assisting with the preparation of RRBS libraries. This research utilised Queen Mary’s Apocrita HPC facility, supported by QMUL Research-IT. https://doi.org/10.5281/zenodo.438045.
Availability of data and materials
All data generated in this manuscript are included in NCBI GEO under the accession number GSE86059 . Additional datasets used are previously published and in GEO under accession numbers GSE40279  and GSE80672 . Humpback whale data are found in DRYAD (https://doi.org/10.5061/dryad.h4b48) . Human colon and rectal cancer data are from the Genomic Data Commons (https://portal.gdc.cancer.gov) .
The study was conceived and coordinated by RL and VKR. DNA samples were provided by CAJ, CE, OF, DSFT, SJR, CGF, SEO, LW, DTO and CM. RRBS libraries were made by CB. Bisulfite PCR was done by VKR. Analysis and interpretation was led by RL, CB and VKR. The manuscript was written by RL and VKR with input from all co-authors. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The reference number for macaque blood sampling approval is 33.9-425-05-10A102 given by LAVES (Lower Saxony State Office for Consumer Protection and Food Safety). The Tc1 mouse line was housed in the Biological Resources Unit at the Cancer Research UK - Cambridge Institute under Home Office Licence PPL 70/7535. This study did not require ethics committee approval for dog analysis as DNA was collected using buccal swabs, which is a non-invasive, non-regulated procedure. No live animals were involved in the research, nor were any in vivo experiments undertaken. All samples were obtained from privately owned pet dogs with the owners’ written consent. The majority of DNA samples were obtained using non-invasive buccal/cheek swabs. Where DNA was obtained from blood, these samples were residual aliquots of blood drawn by veterinarians, under the Veterinary Procedures Act, for routine and/or diagnostic veterinary purposes, and not specifically for the purposes of research. NMRs were maintained at Queen Mary University of London in the Biological Services Unit, and tissues obtained from postmortem specimens in compliance with national (Home Office) and institutional procedures and guidelines. Because sample collection was from postmortem material, additional local ethical approval was not required for this study.
The authors declare that they have no competing interests.
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