Introduction
Senescence is a within-individual decline in survival probability
(actuarial senescence) and/or reproductive performance (reproductive
senescence) with age. Although the rate and shape of the decline vary
both among and within species, this detrimental process occurs in
species across the tree of life (Shefferson et al. 2017). It is
hypothesized to have evolved because unavoidable extrinsic mortality
reduces the strength of selection against poor performance with age
(Fisher 1930; Medawar 1952; Williams 1957; Hamilton 1966). From a
genetic point of view, this so-called ‘selective shadow’ could allow for
the accumulation of late-acting deleterious mutations over evolutionary
time (mutation accumulation, Medawar 1952), or for selection favouring
alleles with beneficial effects early, but detrimental effects late in
life (antagonistic pleiotropy, Williams 1957). Moreover, early-life
investment of limited resources in reproduction over that in perfect
somatic maintenance and repair would also be evolutionarily beneficial,
such that senescence could be a ‘best-of-a-bad-job’ consequence of
accumulated, unrepaired damage (disposable soma, Kirkwood 1977).
Although the classical theories of mutation accumulation and
antagonistic pleiotropy assume a purely sequence-level genetic basis to
senescence, the disposable soma theory does not. Given its firm
foundation in life history theory, resource acquisition and allocation
(sensu van Noordwijk & de Jong 1986) are important facets to
consider. Both are known to show phenotypic plasticity in response to
environmental variation (e.g.
Erikstad et al. 1998; Descamps et al. 2016), and
experimental manipulation of one of these facets without adjustment of
the other is known to affect rates of senescence (e.g. Boonekampet al. 2014). As such, part of the senescence process is also
expected to be underpinned by more flexible, regulatory processes
(Wilson et al. 2008). In line with this, numerous studies across
taxa have shown that senescence is underpinned by both genetic and
epigenetic processes (Sen et al. 2016; Melzer et al.2020).
Epigenetic processes are those that affect the regulation of gene
expression (Holliday 2006). This regulation is complex and encompasses
several mechanisms, a major one of which is DNA methylation, the
addition of a methyl-group to the fifth carbon site of a cytosine in a
CpG (5’-C-phosphate-G-3’) dinucleotide context (Jaenisch & Bird 2003;
Miranda & Jones 2007; Suzuki & Bird 2008). Age-specific DNA
methylation has been described for many model species (e.g. Maegawaet al. 2010; Hannum et al. 2013; Tharakan et al.2020), and the methylation status of specific CpGs has been shown to be
a powerful predictor of both chronological and biological age, i.e. to
function as an epigenetic clock (Bocklandt et al. 2011). Using a
cross-sectional analytical approach, epigenetic clocks have meanwhile
been characterized for a variety of organisms, including humans (Weidneret al. 2014), dogs and wolves (Thompson et al. 2017) and
cetaceans (Robeck et al. 2021). Importantly and additionally, a
rare longitudinal study following the same set of human twins across 10
years, revealed (i) the methylation of CpG sites to change with agewithin subjects , (ii) this change to mostly occur in gene-sets
involved in ageing-related degenerative disorders, and (iii) most of the
change to be explained by individual-specific environmental factors
rather than inherited (genetic) factors (Tan et al. 2016).
Although age-specific methylation seems the norm at least in model
organisms, the direction of methylation changes with age is harder to
predict: both passive non-directional changes (representing epigenetic
drift, Fraga et al. 2005; Tan et al. 2016), and active
directional changes (hyper- and hypomethylation, Zampieri et al.2015; Ciccarone et al. 2018; but see Unnikrishnan et al.2019) have been reported.
Research on senescence, including its epigenetic underpinning, has so
far mostly focused on humans or model organisms kept under controlled
laboratory conditions. Extending the taxonomic range and incorporating
field studies is crucial to understand the evolutionary ecology of
senescence (Monaghan et al. 2008). For this extension, birds are
an especially interesting taxon. They have longer life spans relative to
their body size than mammals (e.g. Lindstedt & Calder 1976), and
various populations of birds with vastly different life histories have
been studied over several decades, such that many basic insights into
their senescence patterns can now be obtained (Bouwhuis & Vedder 2017).
Bird genomes are rather compact, show high levels of synteny across
species (Zhang et al. 2014), and, similar to those of other
vertebrates, are globally methylated (Suzuki & Bird 2008; Li et
al. 2011, Sepers et al. 2019). With respect to age-specific DNA
methylation in birds, we are aware of the existence of an epigenetic
clock for short-tailed shearwaters (Ardenna tenuirostris ; De
Paoli Iseppi et al. 2019), and of findings of early-life
within-individual increases in global methylation in both great tit
(Parus major ; Watson et al. 2019) and zebra finch
(Taeniopygia guttata ; Sheldon et al. 2020) nestlings.
However, we are not aware of any study reporting age-specific DNA
methylation patterns in avian adulthood, or in avian late-life
specifically.
When studying age-specific trait expression, many studies, including
most of those producing epigenetic clocks or otherwise studying
age-specific differences in DNA methylation patterns, use
cross-sectional samples and analysis tools. Patterns revealed by
cross-sectional approaches, however, are the result of a combination of
within- and among-individual processes. If we aim for understanding the
within-individual process of senescence, we need to account for the
effect of compositional changes of a population, for example when birds
with a certain level of DNA methylation are more likely to die and
selectively disappear from the study population (e.g. Vaupel & Yashin
1985; Forslund & Part 1995). Mixed-effect models applied to (partly)
longitudinal data to specifically test whether within-individual
patterns and population-level patterns are the same, or differ, are a
powerful analytical tool to do so (van de Pol & Wright 2009). Across
taxa, efforts have increasingly been made to validate and complement
findings from cross-sectional analyses of senescence through
longitudinal studies (Nussey et al. 2008; Gaillard et al.2017), and this methodological turn is also reflected in studies of
human DNA methylation (Bollati et al. 2009; Tan et al.2016). Longitudinal studies of DNA methylation and ageing in model
species and natural populations are, however, missing and needed (Bellet al. 2019).
Here, we report on a longitudinal study of autosomal methylation levels
in the common tern. Common terns are long-lived migratory seabirds whose
patterns of senescence have been the topic of various studies. Although
breeders of both sexes show little sign of reproductive senescence -
they breed earlier in the year and fledge more offspring as they grow
older (Nisbet et al. 2002; Zhang et al. 2015c; Nisbetet al. 2020) - breeding and survival probabilities are known to
decline with age (Zhang et al. 2015b; Vedder et al.2021b). In addition, there is evidence for sex-specific
transgenerational senescence, with daughters of older mothers and sons
of older fathers suffering from reduced lifetime reproductive success
(Bouwhuis et al. 2015). Studies aiming to identify a molecular
basis for these within- and transgenerational effects have so far
focused on telomere dynamics, and found that: (i) telomeres shorten with
age (Bichet et al. 2020); (ii) telomere length is genetically
correlated with lifespan (Vedder et al. 2021a); and (iii)
paternal age is negatively correlated with offspring telomere length
(Bouwhuis et al. 2018). The explanatory power of telomere length,
however, is very low for all of these patterns (e.g. 1.1% of phenotypic
variation in lifespan is explained by additive genetic variation in
telomere length, Vedder et al. 2021a), such that additional
mechanisms are expected. To evaluate whether age-specific changes in
global DNA methylation could be such a mechanism, we sequenced andde novo assembled a chromosome-scale high-quality reference
genome and used it to compare within-individual age-specific changes in
DNA methylation at shared sites across the genome. Although rates of
within-generational senescence do not differ between the sexes (Zhanget al. 2015a), transgenerational effects are known to be
sex-specific (Bouwhuis et al. 2015), such that we also considered
sex-specificity of any patterns in autosomal methylation status.
Materials and
Methods