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.