Hibernation slows epigenetic ageing in yellow-bellied marmots

Species that hibernate generally live longer than would be expected based solely on their body size. Hibernation is characterized by long periods of metabolic suppression (torpor) interspersed by short periods of increased metabolism (arousal). The torpor–arousal cycles occur multiple times during hibernation, and it has been suggested that processes controlling the transition between torpor and arousal states cause ageing suppression. Metabolic rate is also a known correlate of longevity; we thus proposed the ‘hibernation–ageing hypothesis’ whereby ageing is suspended during hibernation. We tested this hypothesis in a well-studied population of yellow-bellied marmots (Marmota flaviventer), which spend 7–8 months per year hibernating. We used two approaches to estimate epigenetic age: the epigenetic clock and the epigenetic pacemaker. Variation in epigenetic age of 149 samples collected throughout the life of 73 females was modelled using generalized additive mixed models (GAMM), where season (cyclic cubic spline) and chronological age (cubic spline) were fixed effects. As expected, the GAMM using epigenetic ages calculated from the epigenetic pacemaker was better able to detect nonlinear patterns in epigenetic ageing over time. We observed a logarithmic curve of epigenetic age with time, where the epigenetic age increased at a higher rate until females reached sexual maturity (two years old). With respect to circannual patterns, the epigenetic age increased during the active season and essentially stalled during the hibernation period. Taken together, our results are consistent with the hibernation–ageing hypothesis and may explain the enhanced longevity in hibernators.

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Study description
We tested the hypothesis that aging is suspended during hibernation in a well-studied population of yellow-bellied marmots (Marmota flaviventer), which spend 7-8 months per year hibernating. We used two approaches to estimate epigenetic age: the epigenetic clock and the epigenetic pacemaker. Variation in epigenetic age of 149 samples collected throughout the life of 73 females was modeled using generalized additive mixed models (GAMM), where season (cyclic cubic spline) and chronological age (cubic spline) were fixed effects.

Research sample
All samples were collected as part of a long-term study of a free-living population of yellow-bellied marmots in the Gunnison National Forest, Colorado (USA), where marmots were captured and blood samples collected biweekly during their active season (May to August). Individuals were monitored throughout their lives, and chronological age was calculated based on the date at which juveniles first emerged from their natal burrows. We only used female samples because precise age for most adult males is unavailable since males are typically immigrants born elsewhere.

Sampling strategy
We selected 160 blood samples from 78 females with varying ages. From these, DNA methylation profiling passed quality control for 149 samples from 73 females with ages varying from 0.01 to 12.04 years. We used two simulations approaches to estimate the type 1 error and the power to detect a hibernation-ageing effect given the limitations of our sample collection. Specifically, blood samples could only be collected during the active season, instead of throughout the year. Our earliest sample was collected on 27 April and the latest on 20 August. In our first approach, we simulated two traits ( Figure S1): (1) a trait that increases linearly with age independently of the season; and (2) a trait that increases during the summer but not during the winter. The daily rate of increase for the first trait was set at 0.004, to simulate data with a similar range to the observed EPM data. For the second trait, the rate of increase was set to zero during winter (16 Sept to 17 April, days 139-352 using 1 May as reference). The simulation assumed that the active season was 150 days long starting on 18 April (day 353) and finishing on 15 Sept (day 138). The rate of increase during the active season was set as 0.0164 (0.004 / 365 * 150) so that the annual rate of increase was similar between the two simulated traits. Our simulation was parametrized using among-individual and residual variance from the EPM. We performed these simulations using field data (day of sample collection, age in days, birth date, and number of samples), and estimated the significance of the seasonal effect with the GAMM that best explained the marmot data (model selection described above). We repeated this procedure 1,000 times for both traits. The proportion of simulations on trait 1 (no seasonal effect) that were significant indicated our type 1 error. The proportion of simulations on trait 2 (seasonal effect) that were significant was an indication of the power to detect this effect. In the second approach, we simulated 1,000 data sets where day of year was randomly shuffled. The type 1 error was estimated as the proportion of simulations with significant seasonal effects in the GAMMs.

Data collection
Since the data came from a long-term study, many generations of postdoctoral researchers, grad students, research assistants and undergrads have collected samples. Since 2002, Professor Daniel Blumstein has been the leader of the project, and all members of the field team are trained on animal trapping, sample collection and animal observation prior to starting field activities.
Timing and spatial scale The samples used in this manuscript have been collected from 2004 to 2018. The free living yellow-bellied marmots in the Gunnison National Forest, Colorado (USA), are studied every year from May to August.

Data exclusions
We selected 160 blood samples from 78 females with varying ages. We used two different unsupervised hierarchical clustering procedures to identify technical outliers. The first clustering procedure was based on imputed SNPs. Toward this end, we used MethylToSNP v.0.99.0 to identify CpG sites that corresponded to SNPs. The SNP data was used for unsupervised hierarchical clustering based on Euclidean distances. Branches (clusters) of the cluster tree corresponded to multiple samples from the same animal. This allowed us to identify a small platemap error probably caused by human pipetting error. To err on the side of caution we removed putative platemap errors from the data set. Second, we carried out average linkage hierarchical clustering based on the inter-array correlation to identify technical outliers due to an insufficient amount of DNA. The DNAm profiling from 149 samples passed quality control. These samples were collected from 73 females (1 to 8 samples per individual) with ages varying from 0.01 to 12.04 years.

nature research | reporting summary
April 2020

Reproducibility
All the DNA methylation data, detailed sample information, and code have been made available.

Randomization
The order in which blood samples have been extracted for DNA and analyzed with the methylation array have been randomized.
Blinding DNA extraction and methylation array have been performed by individuals without knowledge about the samples.
Did the study involve field work?

Yes No
Field work, collection and transport

Field conditions
The study was performed in a highly seasonal environment, where marmots hibernate from 7 to 8 months per year. Individuals reside in either 'up-valley' or 'down-valley' colonies that differ by an elevational gradient of 165 m.

Location
The wild population of yellow-bellied marmots is located in and around the Rocky Mountain Biological Laboratory (38°57'N, 106°5 9'W; 2900 m elevation) in Colorado, USA.