The rate of change of ageing-associated DNA methylation is faster in the mouse relative to human

We first re-analysed published ageing-associated genome-scale methylation datasets for human and mouse. This involved calling ageing-associated differentially methylated positions (aDMPs) from array-based Illumina 450 K data for 656 human samples from Hannum et al. [3] and Reduced Representation Bisulphite Sequencing (RRBS) data for 153 mice from Petkovich et al. [11] (see ‘Methods’). At q-value < 0.01, we found 172,365 CpGs in human and 43,909 CpGs in the mouse that were called aDMPs. Analysis of conserved CpG sites only revealed that approximately 70% were called as aDMPs in both species, suggesting that short-range cis sequence is unlikely to be the only driving factor in determining whether any given CpG site behaves as an aDMP (Fig. 1b). As mice have a considerably shorter lifespan than humans, the ability to detect aDMPs in mice suggests that the rate of DNA methylation change must be considerably faster. For example, in Fig. 1a, the mouse aDMP shows a methylation change of ~ 50% that occurs within just 35 weeks. To confirm this was a general feature of detected aDMPs, we calculated the rate of change of methylation per week for both human and mouse aDMPs and found that irrespective of whether we use conserved or non-conserved aDMP sites, mouse aDMPs showed a significantly (P value < 2.2 × 10–16) faster rate of change than human aDMPs (Fig. 1c, d), consistent with Stubbs et al. [10]. One potential caveat when comparing species with very different lifespans such as mouse and human is that if there are mouse aDMPs which show similar slow rates of methylation dynamics to those of human aDMPs, they would be difficult to detect as their dynamics would be much too slow to be detected within the lifetime of a mouse. Nevertheless, we can confidently state that at least a significant proportion of aDMPs show considerably different dynamics between a short-lived (mouse) and long-lived (human) mammalian species.

Fig. 1 a Example of called aDMPs. Top: A significant aDMP in human samples (top left) but not in mouse samples (top right). Bottom: A significant aDMP in mouse samples (bottom right) but not in human samples (bottom left). Thick coloured boxes represent a genome-wide significance aDMP in either human (purple) or mouse (red). b A Venn diagram representing the overlap between called aDMPs in mouse and human. c A density plot of the negative log transformed gradients for those aDMPs in non-sequence conserved regions in either mouse (red) or human (purple). d A density plot of the negative log transformed gradients for those aDMPs in regions showing sequence conservation between mouse (red) and human (purple) Full size image

aDMP dynamics are related to mammalian species lifespan

To extend the above findings, we investigated aDMP methylation dynamics in six different mammalian species spanning a range of documented maximum lifespans (T max ), which is a commonly used estimate for the rate of ageing: mouse (Mus musculus, T max = 4 years), dog (Canis familiaris, T max = 24 years), naked mole rat (NMR) (Heterocephalus glaber, T max = 31), rhesus macaque (Macaca mulatta, T max = 40), humpback whale (Megaptera novaeangliae, T max = 95) and human (Homo sapien, T max = 122) (all T max values are from ‘AnAge’, http://genomics.senescence.info/species). Due to the high cost of generating sequencing-based deep coverage genome-scale methylation data across many samples, and given that cost-effective commercial DNA methylation arrays are only available for human, we analysed 48 different targeted bisulphite polymerase chain reaction (Bis-PCR) sequencing amplicons for dog and NMR that were chosen based on sequence conservation with human aDMP sites (we show above that human vs mouse differences in methylation ageing rates hold for both conserved and non-conserved aDMP sites). For macaque, due to its close evolutionary distance to human, we generated aDMP profiles using the Illumina 450 K array (previous studies have successfully used this cost-effective microarray platform for assaying genome-scale methylation differences in a variety of primate species [16, 17]). For humpback whale, we used existing targeted Bis-PCR sequencing data from Polanowski et al. [15]. For human and mouse, we used previously published genome-scale datasets [3, 14]. Detailed sample statistics are available in Table 1. For dog, we identified 68 aDMPs that clustered in 15 different targeted aDMP regions (adjusted P value < 0.05) and for NMR we identified 30 aDMPs that clustered in 11 different targeted aDMP regions (adjusted P value < 0.05). For macaque, we determined 29 distinct aDMP regions (P value < 5 × 10–5). From each of these CpGs, we determined the rate of dynamic change in methylation levels per week for each species. This yielded a significant negative correlation between rate of change of methylation at aDMP sites and reported T max across the different species (rho = 1, P = 0.0028, Spearman correlation) (Fig. 2a note that in this figure we plot the ‘–log gradient’ of methylation ageing rate). There are four key points to note about these findings: (1) this relationship holds even when comparing mammalian species such as dog, NMR and rhesus macaque, i.e. species with more similar T max values to each other, compared with the extreme differences between mouse and human; (2) this relationship is with lifespan per se and not confounded with body mass differences as aDMP methylation dynamics are faster in whales relative to humans even though the former is a much bigger species in terms of mass; (3) our use of different tissues across the different species has negligible influence on the relationship with mammalian lifespan we report here, as analysis of previously published aDMP data from various human tissues reveals that they display similar rates of change with age, and these are significantly greater than between-species differences we report here (Additional file 1: Figure S1); (4) although females show slightly slower ageing-associated methylation dynamics relative to males in human [3], this difference is again smaller than the differences we find among mammalian species.

Table 1 Sample information for those used in the paper Full size table

Fig. 2 a A bar plot of the mean negative log gradients for six different species. This shows that the rate of methylation change at aDMPs is proportional to the longevity of the species. b A plot showing the gradient of significant aDMPs for the two dog breeds profiled (FCR and MLHD). In each of the six aDMPs (two points have similar gradients and overlap each other), the gradient of the FCR is larger than that of the MLHD. Dashed line is the line which represents equal gradient in both breeds, e.g. y = x Full size image

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) ([18] 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

Given the lack of conserved aDMPs across species, it is unlikely that short-range cis-sequence plays a major role. Rather, we hypothesised that the ageing cellular environment per se influences aDMP methylation dynamics. Since this cannot be addressed just by comparing different mammalian species, we used a transchromosomic mouse strain – ‘Tc1’ – that harbours a freely segregating and largely intact functional human chromosome 21 (h-chr21) [19]. We profiled the DNA methylation of 8- to 12-week-old and 44- to 52-week-old Tc1 mice using the human Illumina 450 K array. This allowed us to determine the methylation state of 3158 CpGs on h-chr21. We restricted our analysis to h-chr21 probes that have no significant sequence similarity in the mouse, hence minimising any cross-hybridisation artefacts associated with aDMPs. Furthermore, it has been previously shown that 13,715 CpGs on the 450 K array also bind mouse DNA [20]. Since the DNA samples only contained human DNA from chromosome 21, we removed those probes from human chromosome 21 that also robustly bind mouse DNA, allowing us to confidently assay the methylation state of 12,358 CpGs in the mouse genome and thus permitting a valuable control analyses. A number of previous studies have shown that the majority of functional attributes of h-chr21in the Tc1 mouse are similar to those found in human cells [21]. Indeed, we confirmed that both mouse and h-chr21CpGs showed lower methylation levels in CpG Islands and promoters compared to open sea, gene bodies and intergenic regions (Fig. 3a, b). Given the relatively small number of Tc1 mice available for our analyses, we did not have the statistical power to perform de novo aDMP calling, but we were able to compare previously reported aDMP profiles with methylation differences between young and old TC1 mice. We therefore initially attempted to investigate the aDMP signature on h-chr21 using existing human liver data (n = 117) ([22] and GSE61258) but found only four aDMPs. Therefore, to provide a more robust set of aDMPs we called pan-tissue aDMPs using samples from multiple human tissues (n = 350) [23]. This yielded 15 aDMPs on human chr 21. Analysis of these in the Tc1 mouse revealed a significant correlation of directionality in ageing-associated dynamics between aDMPs on human chr21 in human cells and the corresponding sites on h-chr21 in the Tc1 mouse (P = 0.011, Fig. 3c). More strikingly, fitting a linear model across all 15 points revealed that the rate of change of methylation with age at these chr21 aDMP sites is approximately 21 times faster in the mouse relative to what is observed in humans (note the differences in scale between the x- and y-axes in Fig. 3c). Calculating the methylation rate of change for just those aDMPs with the largest change in the Tc1 mouse showed a mean increased rate of ~ 35 times that of the same aDMP in humans (Fig. 3d). Therefore, aDMPs on a chromosome from a long-lived species (human) show greatly accelerated methylation dynamics in a short-lived species (mouse), demonstrating that such aDMPs are measuring the rate of cellular ageing.