Age-specific global methylation
Across the 74 samples, 29,044,662 RRBS reads (~ 56% mapping efficiency) could be uniquely mapped to the reference genome. The mean bisulfite conversion rate was 99.2%. When analyzing sources of variation in methylation rates across the 23,647 CpG positions occurring in at least 70% of samples, we found a significant interaction between sex and delta age (Table 1). This interaction showed that methylation rates declined with age within individual females, but did not change with age within individual males (Figure 1). The effect of average age was non-significant and the credible intervals of the average and delta age components strongly overlapped (Table 1), suggesting no selective (dis)appearance of individuals based on their autosomal methylation rates. When assessing the random effects, most variance was explained by position identity (Table 1), showing that genomic positions differ in their average level of methylation. Bird identity explained variation in methylation rates to a much lesser extent (Table 1), such that there is little evidence for consistent differences in methylation among individuals.

Discussion

DNA methylation patterns at CpG sites are increasingly used as biomarkers, so-called epigenetic clocks, to predict both chronological and biological age across species and taxa (e.g. Lu et al. 2021). How DNA methylation changes within individuals and whether it can explain phenotypic senescence patterns, however, is still largely unknown (Bell et al. 2019). Here, we used blood samples from common terns collected at 1-, 3- and/or 4-year intervals and a longitudinal analysis approach to investigate whether autosomal methylation levels changed with age within individual birds, and whether any change differed between males and females sharing environments and broods. We found female genomes to become less methylated as these females aged, whereas there was no such age-specific decline of autosomal methylation in males. Moreover, we found the estimates for the within- and among-individual components of age to be similar, such that there was no indication for selective (dis)appearance of individuals based on their methylation pattern. Finally, we provide evidence for positions in the genome to consistently differ in their methylation levels, whereas evidence for consistent differences among individuals was considerably less.
Our finding of female common terns showing a decrease in methylation as they aged fits with findings of global hypomethylation in older compared to younger mammals (Zampieri et al. 2015; Ciccarone et al.2018; Sziráki et al. 2018; but see Unnikrishnan et al. 2019). Such methylation loss is thought to partly originate from demethylation of large regions of repetitive sequences, CpG-poor promoters or large hypomethylated blocks of ”open sea” regions outside the CpG islands (Bollati et al. 2009; Heyn et al. 2012; Yuan et al.2015). Whether these changes affect chromatin configuration and thus genome (in)stability, and whether changes in promoter methylation interact with histone modifications and transcription factors to alter expression remains to be investigated (Zampieri et al. 2015; Ciccarone et al. 2018).
Male terns, in contrast, showed no signs of decreased methylation as they grew older. Although many studies developing epigenetic clocks have assumed age-related changes to be similar across the sexes and used mixed-sex datasets to obtain them (e.g. Raj et al. 2021; Horvathet al. 2021), others have found sex-differences in these clocks (e.g. in some human ethnicities (Horvath et al. 2016), baboons (Anderson et al. 2021) or elephants (Prado et al. 2021)). Moreover, a rare longitudinal study in a wild population of roe deer also showed sex-specific epigenetic clock regions, with an accelerated ageing signal in males, which are known to undergo stronger survival senescence in this species (Lemaître et al. 2021). Combined with our findings, this suggests that methylation tests for sex-specificity should best be the norm.
Interestingly, male and female terns from our study population do not differ in the onset or rate of senescence in survival or breeding probabilities (Zhang et al. 2015b; Vedder et al. 2021b), such that sex-specificity in the ageing process is only found in how parental age affects the quality of the offspring that recruit back into the population (with maternal age negatively affecting the reproductive performance of daughters and paternal age negatively affecting survival of sons (Bouwhuis et al. 2015)). As such, we did not necessarily expect sex differences in the age-specificity of the birds’ autosomal methylation level. The fact that we were able to observe them, raises the question of which site-specific methylation patterns drive the pattern observed on the global level. As mentioned above, global loss is thought to originate from the demethylation of specific regions: repetitive sequences, CpG-poor promoters or large hypomethylated blocks of ”open sea” regions outside the CpG islands (Bollati et al.2009; Heyn et al. 2012; Yuan et al. 2015). CpG island promoters, on the other hand, have been found to show age-specific increases in methylation (Heyn et al. 2012; Day et al.2013). As such, global demethylation may perhaps be compensated for by such increases in males, but not females. In combination with the fact that we found genomic positions to consistently differ in methylation levels, this stresses the need for moving from the level of global autosomal methylation assessment that we and others (e.g. Watsonet al. 2019; Sheldon et al. 2020) have started with, to fully annotating the (common tern) genome and studying patterns across genomic features and focal sites.
Besides finding a sex-specific within-individual change in autosomal methylation level with age, and significant among-position consistency in methylation level, we found little evidence for consistent among-individual levels of autosomal methylation across years, or for selective (dis)appearance of birds in relation to their methylation level. This suggests that birds differentially change their autosomal methylation from year to year, with these changes perhaps reflecting their individual-specific condition or environment, but not relating to their recruitment as a breeder in the study population or their local survival. Implementing a random regression analytical framework is data-hungry and not possible with our current dataset, but linking among-year changes in e.g. body mass or other measures of physiology seems a promising research avenue, especially when taking the analyses to a site-specific level, such that distinct genotype-methylation-phenotype correlations can be identified.
The strength of our study lies in its longitudinal sampling and analytical approach, which allows us to characterise within-individual changes, rather than infer them from cross-sectional data. At the same time, however, this sampling approach may come with some limitations. Non-destructive, longitudinal sampling in natural populations often relies on using blood as the focal tissue, but how DNA methylation of (in our case) erythrocytes translates to phenotypes mostly remains an open question that needs addressing (Husby 2020), ideally in experimental study systems. Moreover, because we used RRBS, a high-throughput and low-cost method, to assess methylation, it is important to realise that this method introduces biases towards high density CpG regions (Smith et al. 2009; Gu et al. 2011); we have focally answered the question of what happens at CpG islands during ageing (Beck et al. 2021). Keeping this in mind, however, our study has provided evidence for a sex-specific within-individual change in autosomal methylation with age and shown that different positions in the genome are consistently differentially methylated, such that future work, changeing the perspective from genome-wide average estimates to the specific genome feature or base pair level, can elucidate whether, where and how much methylation might affect ageing males and females, as such establishing a longitudinal epigenetic clock.