Discussion
To the best of our knowledge, this is the first study to establish a
method for epigenetic age estimation in bears. We constructed a single
regression model using one CpG, designated “SLC12A5-4”, which showed
the strongest correlation between age and methylation level.
Additionally, we constructed six elastic net regression models and six
SVR models using various combinations of 13 CpGs. Evaluation based on
the MAE of LOOCV suggested that the best SVR model is the model using
four CpGs, designated “SLC12A5-1, -2, -3, and -4”. This model had the
smallest median absolute error (MedAE) and the second smallest root mean
square error (RMSE) for LOOCV. The four CpGs included in this model can
be covered by a single PCR amplification, suggesting that this model
provides the greatest benefits in terms of both accuracy and
cost-effectiveness among all tested models. Although the best model in
this study targeted only four CpG sites, the accuracy of the model was
comparable with that of models developed previously using genome-wide
approaches (targeting more than 30 CpG sites) in other carnivore species
with shorter life spans than bears. For example, the MedAE values were
approximately 0.8 years in studies of dogs, wolves (Thompson et al.
2017), and cats (Raj et al. 2021), whose life spans are approximately
one-half to two-thirds of the brown bear lifespan. Furthermore, the DNA
methylation levels in seven individuals that were sampled multiple times
showed similar increasing trends to those of other samples included in
the model. These repeated measurements were all obtained from wild
individuals, and the data are valuable because changes in DNA
methylation levels were tracked in the wild under highly variable
environmental conditions. As shown in Table 1, the MAE, MedAE, and RMSE
values were small even when age estimation was performed for wild
samples that were not included in the model, indicating that the models
we constructed were highly accurate.
The current method is superior to other commonly used age estimation
methods in bear species in the following respects. First, this method
provides accurate and precise age estimates. Traditionally, tooth-based
age estimation has been commonly used for bears (Mundy & Fuller 1964;
Marks & Erickson 1966; Stoneberg & Jonkel 1966). Ages estimated using
this method are 80–90% consistent with the actual age of the animal.
However, if the observer is inexperienced, the accuracy and precision of
age estimation are reduced (Mclaughlin et al. 1990). In addition, the
error is greater for older than younger bears (Mclaughlin et al. 1990;
Harshyne et al. 1998; Costello et al. 2004), as the annuli of older
bears are less distinct and interpretable. The current method based on
pyrosequencing can avoid such human errors and overcome the technical
difficulties facing age estimation. Second, this method is less costly
in terms of time, money, and human resources compared with traditional
tooth-based age estimation, which requires multiple steps, including
decalcification, neutralization, section preparation, staining, and
counting of cementum annuli (Tochigi et al. 2018). Depending on the
protocol, these steps may take several days (Matson et al. 1993). Third,
the current method requires only 100 µL blood, which is much less
invasive to the animal than removal of a tooth. Additionally, blood
sampling is much easier than tooth removal, as pulling the tooth without
breaking the root requires skill (Costello et al. 2004).
In the best model identified in this study, sex had no effect on Δage
(predicted age − chronological age) or |Δage|
(absolute difference between predicted age and chronological age). This
finding indicates that no sex difference existed in age-dependent DNA
methylation changes or individual variability and further suggests that
our method can be applied regardless of sex. Among humans, DNA
methylation levels change faster in males than in females (Nussey et al.
2013). Adult males have shorter lifespans than those of adult females in
many long-lived vertebrates (Clutton-Brock & Isvaran 2007), although it
remains unclear whether the shorter lifespans of males are associated
with faster epigenetic aging (Hägg & Jylhävä. 2021). Notably, we had
small sample sizes for males, especially for wild bears. In contrast to
females, which are philopatric, males born in our study region leave the
area at the age of 2–3 years (i.e., dispersal behavior (Blanchard &
Knight 1991; Shirane et al. 2019)), hindering sample collection from
adult males of known ages. In addition, only three captive males over 10
years of age were included in the analysis. Therefore, further study is
needed to determine the influence of sex on DNA methylation.
The error represented by |Δage| was larger for wild
bears than for captive bears (Table 1), indicating that individual
differences in DNA methylation are greater in wild than captive bears.
One possible reason for this difference is that captive bears are fed
the same type and quantity of food throughout the year in a stable
environment, whereas wild bears consume a variety of foods in differing
quantities depending on the season (Naves et al. 2006; Shirane et al.
2021). Additionally, foraging strategies differ among individuals even
within a population (Servheen & Gunther 2022; Jimbo et al. 2022).
Factors linked to lifestyle, including obesity, weight reduction, and
overfeeding, have been suggested to affect DNA methylation (Samblas et
al. 2019; Yamazaki et al. 2021). Wild brown bears show cyclical annual
body mass patterns, with a continuous decrease from the beginning of
winter hibernation to summer, and a rapid increase during autumnal
hyperphagia (McLellan 2011; Schwartz et al. 2014). In addition, the
annual fluctuation in food availability affects body condition (Shirane
et al. 2021). The dietary diversity of wild bears and annual
fluctuations in food availability may cause greater individual
differences in DNA methylation levels compared with captive bears.
Interestingly, Δage tended to decrease with age in wild individuals.
This result indicates that older wild individuals have lower age
estimates and contradicts the fact that captive animals generally live
longer than wild animals (Müller et al. 2010; Lemaître et al. 2013).
Animals with short lifespans and high reproductive rates are reported to
live longer in captivity than in the wild, but this trend is not always
true for species with long lifespans and low reproductive rates (Tidière
et al. 2016). Brown bears have a long lifespan and low reproductive
rate, which may explain the current results. Another possible
explanation is the presence or absence of hibernation periods. Wild
brown bears spend 3–7 months hibernating in a reduced metabolic state
(González-Bernardo et al. 2020), whereas captive bears in Noboribetsu
Bear Park are fed throughout winter and do not hibernate. In general,
hibernators live longer than similar-sized non-hibernators (Wilkinson &
South 2002; Turbill et al. 2011; Wilkinson and Adams 2019). Recent
studies of yellow-bellied marmots, Marmota flaviventris , (Pinho
et al. 2022) and big brown bats, Eptesicus fuscu s, (Sullivan et
al. 2022) suggest that this difference arises in part because
hibernation slows epigenetic aging. Similarly, in bears, the low
metabolic state characteristic of hibernation (Tøien et al. 2011) may
reduce the rate of DNA methylation changes, lowering the epigenetic ages
of aged individuals. However, this study included a limited number of
wild bears, especially aged bears. Further study is needed to clarify
this issue.
Among the four genes located adjacent to the CpGs whose methylation
levels showed significant correlations with age in bears, SLC12A5and POU4F2 have been reported to show similar correlations in
other carnivores (Ito et al. 2017; Raj et al. 2021). VGF andSCGN show changes in expression during aging at the protein level
in T lymphocytes (Busse et al., 2014) and at the mRNA level in blood
mononuclear cells (Tan et al. 2012) in humans, although whether those
changes are due to changes in the DNA methylation level remains unknown.
The gene SLC12A5 , located adjacent to the CpG site whose
methylation levels were used for the best age estimation model, encodes
an integral membrane KCl co-transporter that regulates cell volume, net
trans-epithelial salt movement, and maintenance of a low intracellular
Cl− concentration in neurons (Payne et al. 1996). As
far as we know, age-related changes in expression at the mRNA or protein
levels have never been reported. In humans, the DNA methylation level of
the CpG site proximal to this gene is a frequently used target for age
estimation in a variety of samples, including blood, saliva, buccal
swabs, and hair (Florath et al. 2014; Hong et al. 2017; Hao et al. 2021;
Schwender et al. 2021). This finding suggests that age-dependent
methylation changes at CpG sites adjacent to SLC12A5 occur in a
tissue-independent manner. In addition, CpGs located in the promoter
region of SLC12A5 showed strong age-related methylation changes
in cats (Raj et al. 2021). Although no positive relationship between age
and the methylation level of this site was found in dogs (Ito et al.
2017), the present findings suggest that CpG sites adjacent toSLC12A5 are a useful methylation marker for age estimation in
other mammals.
In conclusion, we established an epigenetic age estimation model for
brown bears using SVR models and obtained an MAE value of 1.3 years.
This value is comparable with those from models established for other
animals. The current method is more accurate, easier to perform, and
less invasive than conventional tooth-based methods. Notably, our
long-term field study enabled the establishment of age estimation models
for bears, including both captive and wild bears. Furthermore, this age
estimation model may be applicable to other bear species, such as Asian
black bears (Ursus thibetanus ), American black bears (Ursus
americanus ), and polar bears (Ursus maritimus ). The current
study will contribute to ecological research, conservation, and
management of bear species.