New Results

Elevated epigenetic age in wild compared to lab-raised house mice

Eveliina Hanski, Susan Joseph, Aura Raulo, Klara M Wanelik, Áine O’Toole, Sarah C L Knowles, Tom J Little
doi: https://doi.org/10.1101/2023.10.25.563913

Abstract

Age is a key parameter in population ecology, with myriad biological processes changing with age as organisms develop early in life and later senesce. As age is often hard to accurately measure with non-lethal methods, epigenetic methods of age estimation (epigenetic clocks) have become a popular tool in animal ecology, and are often developed or calibrated using captive animals of known age. However, few studies have directly compared epigenetic age estimates between wild and captive or lab-reared animals of the same species, even though age-related epigenetic changes can be influenced by environmental conditions. Here, we built an epigenetic clock from standard laboratory house mice (C57BL/6, Mus musculus) and then used it to estimate age in a population of wild mice (Mus musculus domesticus) of unknown age. We show that this clock accurately predicts adult wild mice to be older than juveniles and that wild mice typically increase in epigenetic age over time, but with wide variation in epigenetic ageing rate among wild individuals. We further show that, for a given body mass, wild mice are epigenetically older than lab mice, and that this difference is not explained by accelerated ageing post-capture but is observed even among the smallest juvenile mice. This suggests different epigenetic age profiles in mice with contrasting genetic and environmental backgrounds arise very early in life and may be driven by peri- and postnatal effects on offspring DNA methylation.

Introduction

Age is a key characteristic for any organism, with numerous biological processes from immune maturation to reproduction being age-related. Yet, measuring age in wild individuals can be challenging as date of birth is often unknown. Classic methods for assessing age in wild animals are often imprecise (e.g., body size) or destructive (e.g., eye lens weight), raising ethical concerns and preventing longitudinal studies. An alternative approach for measuring the age of wild individuals relies on the measurement of epigenetic marks in particular genomic regions. Specifically, at some CpG sites (those where a cytosine is followed by a guanine; Moore et al., 2013) the proportion of methylated cytosines appears to change linearly with age, and together these sites can be used to derive an ‘epigenetic clock’. An epigenetic clock trained using samples from individuals of known age can then be used to predict age in individuals of unknown age. Such epigenetic clocks can provide a more accurate estimate of chronological age among wild animals than visible characteristics (Mayne et al., 2022; Larison et al., 2021). Epigenetic clocks have now been developed for a wide range of animal species including baboons, chimpanzees, humpback whales, wolves, green turtles, and zebras (Jarman et al., 2015; Anderson et al., 2021; Pinho et al., 2022; De Paoli-Iseppi et al., 2017; Polanowski et al., 2014; Thompson et al., 2017; Wright et al., 2018; Mayne et al., 2022; Larison et al., 2021; Bors et al., 2021; Sullivan et al., 2022; Tangili et al., 2023; Ito et al., 2018; Fairfield et al., 2021; Wilkinson et al., 2021), as well as plants (Gardner et al., 2023).

Alongside measuring chronological age, epigenetic clocks also appear to capture signals of biological age, typically considered to reflect the accumulated damage and functional decline in cells, tissues, and organs (Yousefzadeh et al., 2021). Accelerated epigenetic ageing has been linked to various communicable and non-communicable diseases in both humans and laboratory mice (Joyce et al., 2021; Morales Berstein et al., 2022; Ambatipudi et al., 2017; Harvanek et al., 2021; Cao et al., 2022; Peng et al., 2019). Insights have also come from the wild: high social rank is associated with accelerated epigenetic ageing in wild baboons (Anderson et al., 2021), and hibernation slows down ageing in marmots and bats (Pinho et al., 2022; Sullivan et al., 2022). Thus, the use of epigenetic clocks may provide a means of estimating chronological age among wild animals while simultaneously providing insight into biological ageing in natural settings.

Previous studies using epigenetic clocks have focused on humans (Joyce et al., 2021; Morales Berstein et al., 2022; Ambatipudi et al., 2017; Harvanek et al., 2021; Cao et al., 2022; Peng et al., 2019), laboratory (Han et al., 2018; Kerepesi et al., 2022) or wild animals (Prado et al., 2021; Polanowski et al., 2014; Anderson et al., 2021; De Paoli-Iseppi et al., 2018; Lemaître et al., 2022). A number of studies have also included both wild and captive (e.g., from a zoo) individuals (Mayne et al., 2022; Robeck et al., 2021; Ito et al., 2018; Fairfield et al., 2021; Wilkinson et al., 2021). However, a comparison of wild and laboratory individuals has not been previously conducted. Comparing epigenetic age and ageing between lab and wild individuals of the same species could help us understand drivers of biological ageing and its variability in individuals from contrasting genetic and environmental backgrounds.

Here, we build an epigenetic clock using samples from laboratory mice (Mus musculus) and use this lab-based clock to predict age in house mice (Mus musculus domesticus) from a wild population. Our aims were threefold: (1) to see whether we could develop an epigenetic clock from lab-based animals capable of accurately capturing differences in chronological age within a wild population (2) to compare estimates of epigenetic age in lab compared to wild mice of a given size, to gain insight into biological ageing in lab vs wild settings and (3) to assess the extent of variability in biological (epigenetic) ageing rate among wild mice. We used faecal samples as a source of DNA, to develop a non-invasive method that allows longitudinal sampling without ethical or logistical limitations on sampling frequency, and allows applications of epigenetic age estimates in contexts where animal capture or handling are not possible. To our knowledge, this is the first epigenetic clock built with faecal samples. Our results show the potential of such an approach, but also indicate substantial differences in the estimated epigenetic age of an inbred laboratory population and an outbred wild population, even from very early in life.

Materials and Methods

Sample collection

A total of 137 faecal samples were collected from 65 individual Mus musculus C57BL/6 laboratory mice (30 females, 35 males) from two animal facilities. The samples were collected in May– November 2021 at the Biomedical Services Building, Oxford, UK (Animal facility A), and King’s College, London, UK (Animal facility B). The chronological age of the mice varied from 7 to 339 days. The mice were kept in standard housing and were not subject to any interventions before or during sampling. During sample collection, body mass was recorded for mice from Animal facility B but not for mice from Animal facility A. However, body mass is tightly correlated with age among juvenile house mice (Spangenberg et al., 2014; Jax, 2022a; Jax, 2022b) allowing accurate estimation of mass from age. As such, we estimated body mass for 25 mice under seven weeks of age from Animal Facility A (for older mice from this facility (n=8) body mass was not estimated and consequently samples from those mice were not included in analyses including body mass). Body mass estimation was done based on Spangenberg et al. (2014) for 7–20-day old pups and The Jackson Laboratory C57BL/6 body mass references for 3–7-week-old pups (Jax, 2022a; Jax, 2022b). For the latter age group, estimation was done separately for females and males using the Jackson Laboratory sex-specific data (Jax, 2022a; Jax, 2022b). To collect faecal samples, mice were briefly placed on a sterile surface until defecation. Faecal pellets were collected in a sterile manner, immediately preserved in DNA/RNA Shield, and stored frozen at -80°C until further processing.

Wild house mouse (Mus musculus domesticus) sampling was conducted in April–May 2019, July 2019, September–October 2019, August–September 2020, and April–May 2021 on Skokholm Island, Wales, UK. Mice were trapped overnight using small Sherman live traps baited with peanuts and non-absorbent cotton wool for bedding, and with a spray of sesame oil outside the trap as a lure. Across each of two broad sampling areas (one near the coast and one in the island interior, named “Quarry” and “Observatory” respectively), on each trapping night 150 traps were set at dusk and checked at dawn. To prevent cross-contamination, any traps showing signs of mouse presence were washed and sterilised before being reset using bleach solution (including a ≥60 min soak in 20% bleach) to destroy bacterial cells and DNA.

All newly captured mice were permanently identified by subcutaneous injection of a passive integrated transponder (PIT) tag. Upon each capture, each mouse was either tagged or identified (if a recapture), aged, sexed, and measured before being released within 3m of its trapping point. Age category was assigned based on size and pelage characteristics: small (typically <15g of mass and <80mm of length) mice were classified as juveniles and full-sized mice (typically >20g and >80mm of length) were classified as adults. Mice falling between these two size categories were ranked as sub-adults. Sex was determined using anogenital distance and reproductive state. Reproductive state was recorded as either non-perforate, perforate, suspected pregnant or lactating for females, and testes abdominal, small or large for males. At each capture, body mass and body condition were recorded. Body condition was scored from 1 to 4 by palpating the lower spine and hips to estimate the amount of subcutaneous fat.

Faecal samples were collected from traps in a sterile manner (shortly after mice were collected from traps the following day), preserved in DNA/RNA Shield and stored in a -20°C freezer until the end of fieldwork (maximum 6 weeks after sample collection). At this point they were returned to the laboratory frozen and stored at -80°C until DNA extraction. A total of 215 samples were selected from all collected samples (>900) for further processing, by first selecting a longitudinal dataset (mice sampled ≥2 times over time; total of 54 individuals) with as much variation as possible in morphometric variables (age, body mass, sex, and reproductive status), as well as environmental variables (sampling season and sampling area) and then supplementing this with additional (equally variable) cross-sectional samples (one sample per animal) to increase number of individuals for cross-sectional analyses up to 130. Variation in variables was achieved by randomly selecting approximately equal numbers of samples across categories e.g., across juvenile, sub-adult and adult mice.

DNA extraction, bisulfite conversion and PCR amplification

DNA was extracted from faecal samples using the ZymoBIOMICS DNA MiniPrep Kit according to the manufacturer’s protocol (Zymo Research, Irvine, California, USA). DNA was then bisulfite-converted using the Zymo EZ DNA Methylation-Gold Kit to convert unmethylated cytosines to uracil and then thymine (Zymo Research, Irvine, California, USA). PCR amplification was conducted for five genes previously reported to correlate with chronological age in Mus musculus; Prima1, Hsf4, Kcns1, Gm9312, and Gm7325 (Han et al., 2018). Amplification was conducted using the PyroMark PCR Kit according to manufacturer’s instructions and primers for the five genes (QIAGEN, Hilden, Germany; Table S2; Han et al., 2018). PCR conditions were as follows: 15 min initial denaturation at 95℃, 50 cycles of 30 sec denaturation at 58℃, 30 sec primer annealing at 58℃ and 30 sec extension at 72℃, followed by a 10 minute final extension at 72℃. Amplification success was confirmed using gel electrophoresis. PCR was repeated with the same conditions for any reaction that did not produce a band on the gel. The five amplicons (PCR products) were pooled for each sample into 5-gene libraries by combining all PCR products per sample. DNA was quantified with Qubit Fluorometer High Sensitivity dsDNA kit and normalised to 6.25 ng/μl (Thermo Fisher Scientific, Waltham, Massachusetts, USA).

Sequencing, basecalling, and demultiplexing

The Oxford Nanopore Technology (ONT) platform was used for library sequencing, and all ONT procedures were conducted according to manufacturer’s instructions and ONT protocol NBA_9102_v109_revl_09Jul2020 (Oxford Nanopore Technologies, Oxford, UK). We first used the ONT Ligation Sequencing Kit (SQK-LSK109) to repair and dA-tail the DNA ends, followed by ligation of sequencing adaptors to the prepared ends. We then barcoded libraries using the ONT Native Barcoding Expansion kit (EXP-NBD104 or EXP-NBD196). Approximately 15 ng of the prepared library was loaded onto a prepared ONT MinION Mk1B R9.4.1 flow cell and sequenced using the ONT MinKNOW software v21.10.4. Libraries were sequenced across a total of six runs resulting in a mean of 49,969 reads per sample. A negative control (where DNAse free H2O was used instead of pooled amplicons at the start of Nanopore pipeline) was included in three sequencing runs, and these generated a mean of 200 (range 17–519) reads. One flow cell was used twice, and washed between runs with the ONT Flow Cell Wash kit (EXP-WSH003). Different barcodes were used for negative controls across the two sequencing runs where the same flow cell was used to enable testing for carry-over of reads (only 17 potential carry-over reads were detected). Raw sequencing data was basecalled and demultiplexed using High Accuracy basecalling on the ONT Guppy software v5.0.11. The basecalled FASTQ files were then run through the Apollo pipeline v0.1 (https://github.com/WildANimalClocks/apollo, DOI: 10.5281/zenodo.8426692) to acquire methylation rates for each CpG site within the five genes. Using the alignment with the reference genes, target sites (cytosines within CpGs) were identified in each read and determined as either methylated (cytosine) or unmethylated (uracil). The process was continued for each read, resulting in a proportion of methylated cytosines at each CpG site.

Analyses

The data was analysed and visualised in R v4.1.2 (R Core Team, 2023). The epigenetic clock was built using the cross-sectional data of 50 samples from C57BL/6 mice housed in two facilities. Using CpG site specific methylation rates, we used the package glmnet v4.1–3 (Friedman et al., 2010) to perform a cross-validated elastic net regulatisation using the cv.glmnet function using a LASSO model (mixing parameter alpha=1) and a leave-one-out cross validation (nfolds=nrow). We then fitted a final glmnet model using optimal lambda value determined by the cross-validation. Epigenetic age was then predicted based on the glmnet model using the predict() function. This clock was validated on an additional 30 samples from C57BL/6 mice (n=15 mice, two samples per animal). We assessed clock performance for estimating chronological age of lab mice using mean absolute error (MAE; Tangili et al., 2023). We then built another epigenetic clock as described above using only those CpG sites that (1) showed parallel trends in their methylation rates against body mass in lab and wild mice (i.e., ones that increased in methylation with body mass in both systems; Fig. S2–5) and (2) had methylation rates for the majority of wild mouse samples (we failed to acquire sufficient read counts for methylation rate measurement for all 11 CpG sites from the gene Gm7325 in 9% of all wild mouse samples). Following these principles, we chose in total 53 CpG sites. This revised clock was similarly validated with the independent lab dataset and then used to estimate epigenetic age for all wild house mice for which methylation rates at the CpG sites included in the clock were successfully measured (n=201; 93% of all wild mouse samples) using linear modeling.

To test for the effect of covariates on predicted epigenetic age in the lab mouse validation dataset, we fit a linear model with epigenetic age as the dependent variable and chronological age, sex, cage, and sequencing run ID as predictor variables. A Wilcoxon rank sum test with 1,000 permutations was used to test whether the predicted epigenetic age of wild mice ranked as adults was older than that of wild mice ranked as juveniles. The ability of the clock to detect an increase in age among wild mice sampled on two consecutive occasions was tested using a binomial test. This test used a null hypothesis p=0.5 that predicted age was higher at the later time-point than the earlier time point (i.e. mice are equally likely to increase or decrease in epigenetic age over time). We also used a linear model to test whether time between sampling points predicted absolute change in epigenetic age. Age category at the first time-point and sex were also included as predictors. Mice with less than 25 days between time-points were excluded from this longitudinal analysis as the mean absolute error (MAE) of the clock in the validation dataset was 25 days (Fig. 1B).

Figure 1.
Figure 1. The relationship between DNA methylation-based (epigenetic) age and chronological age in C57BL/6 mice

(A, C) used to train the epigenetic clock model or (B, D) an independent set of mice (a validation set) not used in building the epigenetic clock model. Two epigenetic clocks were built: one with 73 input CpG sites from five genes (A, B), and one with 53 input CpG sites from four genes (C, D). Circles represent individual mice, circle colour in A indicates animal facility (grey= facility A, black = facility B). Solid lines are linear regression lines, while dashed lines are a reference line of y=x (the hypothetical relationship if chronological age and epigenetic age estimates were exactly equivalent).

To explore whether lab and wild mice might differ in epigenetic age for a given chronological age, we made use of the fact that body mass is a good predictor of age when mice are young, in both lab and wild settings (Spangenberg et al., 2014; Jax, 2022a; Jax, 2022b; Gerber et al., 2021; Gray et al., 2015). We used a linear model to test whether source (lab/wild) predicted epigenetic age when controlling for the effects of body mass (a proxy of age). Epigenetic age was the dependent variable and source, body mass, sex and an interaction between body mass and sex (as the relationship between mass and age is somewhat sex-dependent, Jax, 2022a; Jax, 2022b; Gerber et al., 2021; Gray et al., 2015) were fitted as predictor variables. Female wild mice with signs of ongoing or recent pregnancy (those recorded as suspected pregnant) were excluded, as body mass will be a less accurate age proxy in these individuals, who are significantly heavier than other females (one sample per Animal ID; non-pregnant n=48, suspected pregnant n=9; linear model, F1,54= 29.28, p<0.001). Lastly, to explore whether the rate of epigenetic ageing differed between lab and wild mice we used two methods. First, we used a linear model which tested whether the increase in epigenetic age observed between two consecutive time-points in repeat-sampled mice varied as a function of the time between samples, source population (lab/wild) and their interaction. Second, we used Levene’s test to ask whether variation in the rate of epigenetic ageing differed according to source. Here, we used the ratio between change in epigenetic age and change in time as the response variable, and source (lab/wild) as the explanatory variable.

Ethical statement

Wild mouse work was conducted under Home Office license PPL PB0178858 held at the University of Oxford, and with research permits from the Islands Conservation Advisory Committee (ICAC), and Natural Resources Wales.

Results

Construction of a non-invasive epigenetic clock

We used faecal samples from C57BL/6 laboratory mice (Mus musculus, n=50, one sample per animal) from two animal facilities to generate a DNA methylation-based epigenetic clock. We first built an epigenetic clock using 73 targeted CpG sites from five genes that were previously associated with age in laboratory mice (Hsf4, Gm9312, Kcns1, Gm7325, and Prima1; Table. S1; Han et al., 2018). Elastic net regression identified 22 CpG sites from the five targeted genes; three from Hsf4, six from Gm9312, six from Kcns1, one from Gm7325 and one from Prima1 (Table S2). The epigenetic clock built using methylation patterns across these sites had a mean absolute error (MAE) of 7 days (Pearson’s r=0.996, p<0.001; Fig. 1A). We validated the clock by applying it to an independent set of C57BL/6 mice that were not used in training the clock (n=15, one sample per animal). Among these lab mice, predicted (epigenetic) age was also strongly correlated with chronological age (Pearson’s r=0.935, p<0.001, MAE=23 days; Fig. 1B). Neither sex, cage nor sequencing run had a significant effect on epigenetic age (linear model: chronological age F1,8=63.797, p<0.001; sex F1,8=0.328, p=0.583; cage F3,8=0.040, p=0.989; sequencing run F1,8=0.057, p=0.817). This demonstrates that non-invasive faecal samples can be used to generate an epigenetic clock in laboratory mice with equivalent or higher accuracy in estimating chronological age compared to a clock previously derived using blood samples (Han et al., 2018; MAE=35–41 days in two validation datasets).

As our aim was to develop a lab-based epigenetic clock that could be used to predict age in wild mice, we next inspected methylation rates of targeted CpG sites (n=73) against body mass (a proxy for age) in lab and wild mice. The majority of CpG sites showed similar trends (i.e., increase in methylation rate with body mass; Fig. S2–5). However, nine CpG sites from genes Prima1 and Gm9312 showed contradicting patterns, where methylation rate increased in wild but decreased in lab mice with body mass (all CpG sites from Prima1, CpG sites at positions 10, 33, 116, 218, and 224 from Gm9312; Fig. S2, S3), and as such we excluded these CpG sites. Further, we failed to acquire sufficient read counts and thus methylation rates for the 11 CpG sites from the gene Gm7325 in 20 wild mouse samples (9% of all wild mouse samples), and consequently decided to remove this gene from the clock model. We then used the above described approach to build an epigenetic clock with the remaining 53 CpG sites from four genes (Table S3), whose methylation rates showed a positive linear relationship with body mass (Fig. S2–4). For this clock, elastic net regression identified 11 CpG sites from genes Hsp4 and Kcns1 (Table S3). Here, the slope deviated more from 1 (where 1 would indicate perfect positive linear relationship between chronological and epigenetic age) than did the first clock (slope estimate 0.81 ± 0.02 standard error vs 0.98 ± 0.01 standard error in training set; Fig 1A, 1C). However, the clock still had a high accuracy with a MAE of 19 days in the training set (Pearson’s r=0.979, p<0.001; Fig. 1C) and 24 days in the validation set (Pearson’s r=0.939, p<0.001; Fig. 1D). This second clock was then used for further analyses in the present study.

Chronological age prediction in wild mice

We next applied the lab-mouse derived epigenetic clock (Fig. 1C, 1D) to 205 faecal samples from 119 wild house mice to test if it can be used to estimate chronological age in wild individuals of unknown age. Mice of all available body sizes were included with the aim of capturing as much age variation as possible (body mass range 5.9–43.0g, mean 19.0, median 19.8). The epigenetic age of wild mice varied from -26 to 460 days (mean 227, median 207; 2 out of 205 samples (1%) had a negative epigenetic age).

Mice assigned as adults in the field using external characteristics were estimated by the epigenetic clock model to be significantly older than those assigned as juveniles (permutational Wilcoxon rank sum test, p<0.001, n=166; Fig. 2A). Moreover, among 35 wild mice sampled twice between 30 and 340 days apart (mean 129, median 81), a great majority (31; 89%) were epigenetically older at the latter timepoint (binomial test for H0 p=0.5, p<0.001). In general, the number of days between sampling time-points positively predicted change in epigenetic age (linear model, F1,17=8.516, p=0.010; Pearson’s r=0.406, p=0.01, n=35), but there was wide variation in the slope observed among individuals (Fig. 2B). Rate of epigenetic ageing Embedded Image was not predicted by any investigated variables (sex F1,18=0.366, p=0.552; reproductive status F11,18=0.653, p=0.763, body mass F1,18=0.046, p=0.834, season F2,18=0.370, p=0.696, and trapping area F1,18=1.986, p=0.176 at first sampling point). Together these results indicate that an epigenetic clock trained with samples from inbred lab mice can be used to provide an estimate of chronological age in outbred wild mice, though not one that is highly precise.

Figure 2.
Figure 2.

(A) Epigenetic age of wild mice phenotypically characterised as adult (n=109) and juvenile (n=57) predicted with an epigenetic clock built using C57BL/6 lab mice. Age category was assigned in the field using body size and appearance (see Methods). Median epigenetic age was 316 days in adults and 110 days in juveniles. Circles are individual samples. (B) Change in epigenetic age between two timepoints in wild mice sampled twice between 30 and 340 days apart (n=35). Lines represent individual mice. Epigenetic age increased with time for 31 out of 35 (89%) mice.

Wild mice are multiple times older epigenetically than laboratory mice

We next assessed whether epigenetic age for a given chronological age differed between wild and laboratory mice. In the absence of known chronological age for wild mice, we used body mass to provide an upper limit age estimate for individuals classed as juveniles. Others have reported that 12–13-day-old wild house mice from mainland Europe weigh around 7g (range 3.6–10.5g, mean 6.8; Gerber et al., 2021) and another study showed that 14-day-old wild-derived but captive house mice from Gough Island (home to the largest wild house mice recorded) weigh around 8.5g (range ∼7–10.5g, raw data not available; Gray et al., 2015; Fig. S1). Thus, irrespective of context, house mice between 12–14 days typically are expected to weigh 7–10.5g (Gerber et al., 2021; Gray et al., 2015). We therefore examined epigenetic age from a cross-sectional set of juvenile wild Skokholm Island mice that fall within this body mass range and thus which we expect to be no more than 25 days old (n=19, body mass 6.1–10.4g with a mean of 8.4g). Among these individuals, the mean epigenetic age estimate was 106 days (range -26–173, median 110; 1 sample had a negative epigenetic age), which is more than four times their expected chronological age (Fig. S1).

To explore whether wild mice were epigenetically older than lab mice beyond early life, we explored the relationship between epigenetic age and body mass across mice of all sizes. While the reliability of body mass as an indicator of age declines after initial growth during first few weeks of life, it continues to increase with chronological age in both C57BL/6 lab and wild mice beyond this time and thus can be used as a rough estimate of age in adults as well (Jax, 2022a; Jax, 2022b; Gerber et al., 2021; Gray et al., 2015). In a cross-sectional subset of 57 lab and 99 wild mice (excluding wild mice with visual indication of ongoing/recent pregnancy, see Methods, and those with unknown sex), source (lab/wild) predicted epigenetic age (linear model, F1,135=93.0143, p<0.001; controlling for an interaction term between sex and body mass, F1,135=0.8519, p=0.357), with wild mice having a higher mean epigenetic age than that of lab mice consistently across investigated range of body mass (Fig. 3A). In line with this finding, wild mice also had higher rates of DNA methylation in the targeted genes (Fig. S2–4), explaining the higher epigenetic age profile.

Figure 3.
Figure 3. Wild mice have elevated epigenetic age compared to lab mice for a given body mass, but do not show a different rate of epigenetic ageing in adulthood

(A) Epigenetic age of wild (n=99) and laboratory mice (n=57) in relation to body mass. Empty circles are wild mice and filled circles are lab mice; black circles are lab mice for which body mass was recorded during sample collection, and grey circles are lab mice for which body mass was estimated post hoc (see Methods). (B) Change in epigenetic age in relation to time elapsed in laboratory (n=15) and wild mice (n=35) sampled twice a minimum 25 days apart. Body mass varies 19.2–26.3g for lab mice and 7.0–37.7g for wild mice. Empty circles are wild mice and filled circles are lab mice. Lines are linear regression lines (dashed = wild mice, solid = lab mice) and shading indicates 95% confidence intervals.

To further examine whether older epigenetic age profiles among wild mice might be due to accelerated ageing through exposure to environmental stressors, such as food shortage or climatic variation, we studied the rate of epigenetic ageing across lab and wild mice for which two timepoints were available. In lab mice (n=15), the rate of epigenetic ageing was similar to that observed when originally validating the clock using the cross-sectional data (longitudinal data: slope estimate = 0.77 ± 0.15 standard error; cross-sectional data: slope estimate = 0.80 ± 0.08 standard error; Fig. 1B). This rate was slightly shallower in wild mice (n=35; slope estimate = 0.49 ± 0.19 standard error), but not significantly so (linear model: F1,48=0.085, p=0.772, Fig. 3B). Lab mice included were all adults, while the longitudinal data of wild mice included juvenile mice; however, exclusion of juvenile mice (<15g of body mass) did not appear to have a strong effect on these trends (Fig. S6). Further, although the rate of epigenetic ageing appeared to be more variable among wild than lab mice, the variance in epigenetic ageing rate did not differ statistically across settings (Levene’s test, F1,48=3.140, p=0.083; correlation between change in epigenetic age and days elapsed: lab mice, Pearson’s r=0.81, p<0.001; wild mice, Pearson’s r=0.48, p=0.003; Fig. 3B).

Discussion

Here, we tested an approach for estimating age in wild house mice, by building an epigenetic clock using samples from inbred C57BL/6 laboratory mice and using it to estimate age in outbred wild mice of unknown chronological age. The clock effectively distinguished wild juveniles from adults, and typically predicted increases in age over time among repeat-captured individuals at a similar success rate (∼90% individuals predicted older at later time point) to the wild baboon study of Anderson et al (2021)).

However, while the clock accurately predicted age (with error of ± 25 days) in an independent set of laboratory mice, we observed high variation among wild mice in how their epigenetic age changed over chronological time, suggesting our clock had reduced accuracy in this different ecological context. Others have had better success in applying clocks built with captive individuals to wild individuals (e.g. Mayne et al., 2022 in green turtles; Robeck et al., 2021 in cetaceans; correlation between chronological and epigenetic age in these studies 0.67–0.98 vs 0.41 between change in time and change in epigenetic age in our study). However, these studies have built epigenetic clocks using samples from captive individuals where there is genetic and environmental variation, such as zoos or outdoor enclosures. Our clock was built with samples from inbred lab mice housed under very stable environmental conditions, but applied to wild mice that are outbred and exposed to a highly variable temperate climate. Studies of different lab strains have also confirmed that epigenetic clocks may behave differently in different genetic backgrounds. For instance, Han et al demonstrated DBA/2 mice to be up to twice as old epigenetically as C57BL/6 mice (Han et al., 2018).

Moreover, DNA methylation may be influenced by environmental factors (Zocher et al., 2021; Parrott et al., 2014; Viitaniemi et al., 2019), and wild animals are generally exposed to more variable environments. As such, the contrasting genetic and environmental backgrounds in our mouse systems may partly explain why age estimates in wild mice based on a clock from lab mice had low accuracy. Finally, while all samples from lab mice were preserved immediately after defecation, the time between defecation and sample preservation varied in wild mice (where samples were collected from traps which animals had been in overnight, up to 13 hours). It is possible some DNA degradation by nuclease and gut microbes occurred before the samples were preserved (in DNA/RNA Shield, which inactivates microbes and preserves DNA integrity), affecting the methylation profiles.

The epigenetic age of wild mice from Skokholm Island varied from -26 to 460 days (mean 227, median 207; 2 (1% of all 205) samples had negative epigenetic ages, -26 and -21 days). The presence of negative predicted ages in wild individuals may be due to measurement error, but it may also be due to the inherent biological differences between wild and lab populations. DNA methylation-based age predictions may reflect the distinct age trajectories and relative age of wild individuals compared to their lab counterparts, highlighting the importance of considering the natural variation in epigenetic ageing processes across different populations.

Despite our epigenetic clock having a very close relationship with chronological age in laboratory mice, several lines of evidence suggest these epigenetic age estimates for wild mice are overestimates of their chronological age. First, when investigating juveniles, in which body mass is an accurate predictor of age across both wild and lab mice (Jax, 2022a; Jax, 2022b; Gerber et al., 2021; Gray et al., 2015), we found that wild juveniles were predicted to be several times older than their expected chronological age from body mass (Gerber et al., 2021; Gray et al., 2015). Second, by comparing the epigenetic age estimates to body mass in mice of all sizes, we found that the estimated epigenetic age of wild mice was on average several times higher at any given body mass than that of lab mice, with similar patterns seen in CpG site methylation rates. While accelerated weight gain in ad libitum-fed lab mice may contribute to a lower epigenetic age among adult lab mice compared to wild mice, these findings suggested that the more challenging environment experienced by wild mice may accelerate epigenetic clocks. Our comparison of the epigenetic age of wild vs lab mice is specific to a comparison with the C57BL/6 strain; however, the difference between the epigenetic age of e.g., C57BL/6 and DBA/2 mice is substantially smaller than the observed difference between C57BL/6 and wild mice (DBA/2 mice are only twice as old epigenetically than are C57BL/6; Han et al., 2018). As such, while the choice of strain may have influenced our findings, it is unlikely to entirely explain the difference between lab and wild mice.

To test whether the older epigenetic age profile of wild mice could be explained by accelerated ageing post-weaning (i.e., from when they are trappable) we investigated the rate of epigenetic ageing using individuals captured and sampled twice over time. If anything, the rate of epigenetic age appeared slightly slower and more variable in wild mice, though this observation relied on a relatively small sample size and was not statistically significant. Moreover, as we observed heightened epigenetic age in wild compared to lab mice even during the first ∼2 weeks of life, we suggest that peri- and early postnatal effects on offspring DNA methylation may vary between laboratory and wild mice and contribute to their different epigenetic age profiles. Various human, mouse, and other animal studies have demonstrated the association between prenatal maternal experience (such as food shortage, diet, infection, substance exposure, and stress) and offspring DNA methylation patterns, with differences from the prenatal (foetal) phase still detectable in later life (Tobi et al., 2009; Heijmans et al., 2008; Lan et al., 2013; Richetto et al., 2017; Camerota et al., 2021; Joubert et al., 2016; Kertes et al., 2016; Vangeel et al., 2017).

While our approach of training an epigenetic clock with lab individuals and using it to estimate age in wild individuals did not allow precise estimation of chronological age, our results demonstrate such an approach can still be effective in distinguishing between juvenile and adult individuals. Such information may be useful in contexts where a faecal deposit is found but the individual is not observed, such as in field-based projects of animals that are hard or impossible to capture. At the same time, this method can provide interesting insights into biological ageing when applied to wild animals of known chronological age or to individuals sampled longitudinally such that changes in epigenetic age can be estimated (De Paoli-Iseppi et al., 2017; Powell & Proulx, 2003; Brivio et al., 2015). Considering the much greater variability in epigenetic ageing rates we observed in wild compared to laboratory animals, our results suggest wild systems may provide an informative environment in which to study drivers of epigenetic age acceleration. In the present study we were not able to identify drivers of epigenetic ageing rates in the wild due to a small sample size, but further work with larger samples sizes could address this important question.

We used faeces as a source of host DNA and demonstrated that an epigenetic clock can be built with faecal samples at a similar or even improved accuracy to a previously published blood-based mouse epigenetic clock (Han et al., 2018). As faecal samples can be collected non-invasively, a given individual can be sampled over time without limitations to sampling frequency. Further, it may be possible to collect faecal samples without capturing the animal, making this methodological approach particularly useful when estimating age in wild species that are endangered, hard to capture or even detect.

In summary, our data indicate the potential to use a non-invasive, DNA methylation-based epigenetic clock built with samples from laboratory mice to estimate the age of entirely wild mice. While this approach did not provide highly precise estimates of chronological age, it can be used to measure variation in biological ageing in future longitudinal studies, making it a promising tool for studies of ontogeny and senescence in wild settings.

Funding

This work was funded by The Osk. Huttunen Foundation studentship and the National Geographic Society (Early Career grant reference No. EC-58520R-19) to EH, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 851550) and a NERC fellowship (NE/L011867/1) to SCLK, and the British Ecological Society (BES) to TJL.

Author information

Contributions

EH and SCLK set up the wild mouse study system. EH, SJ, AR and KW collected the samples. AO and TJL developed the software Apollo. EH conducted the laboratory work and analysed the data with support from TJL, SCLK and AR. EH wrote the manuscript with contributions from all authors.

Data accessibility

Data Accessibility

Data will be made publicly available upon publication.

Acknowledgements

We thank Giselle Eagle and Richard Brown, the wardens of Skokholm Island, the Friends of Skokholm and Skomer, the Wildlife Trust of South and West Wales and field assistants for their help in enabling the wild mouse data collection.

Footnotes

  • Competing interests Authors declare no competing interests.

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Supplementary Material

Email
Elevated epigenetic age in wild compared to lab-raised house mice
Eveliina Hanski, Susan Joseph, Aura Raulo, Klara M Wanelik, Áine O’Toole, Sarah C L Knowles, Tom J Little
bioRxiv 2023.10.25.563913; doi: https://doi.org/10.1101/2023.10.25.563913
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