Capture and Manipulation

130 irregularly positioned nest-boxes (height: 2–3 m) in the Vienna Woods, Austria (48°05′N, 15°54′E, altitude 400–600 m a.s.l.) were checked at fortnightly intervals (for further details about the study site, nest-box distribution and position see Hoelzl et al.50 and Lebl et al.51). Animals occupying the nest-boxes (i.e., using nest-boxes as a sleeping site and to rear their young) were captured during the active season (April–October). Newly captured individuals were sexed, weighed to the nearest 2 g using a 300-g spring balance (Pesola®, Baar, Switzerland) and marked with subcutaneous transponders (BackHome BioTec®, Virbac Limited, Bury St. Edmunds, UK; Tierchip Dasmann®, Greven, Germany). Dormice can be reliably classified as juveniles, yearlings (after their first hibernation) and adults (after their second hibernation) from their size, tibia length and fur colour52,53. Only animals for which an accurate determination of birth year was possible were used in this study. Reproductive activity was determined on a yearly basis. Males were classified as reproductively active when they developed their testes to a tangible and measureable size (for details see Lebl et al.54). Females were classified as reproductively active when they gave birth to a litter or if they were captured with visible, enlarged mammae. If a female was captured at least twice within the time of young-rearing (week 31–39 of the year, c.f. Lebl et al.51) without young and/or visible mammae, it was classified as non-reproductive. A total 49 individuals were investigated (7 animals were sampled 5 times, 12 individuals 4 times, 15 individuals 3 times, and 15 individuals 2 times) in this study. The mean timespan between consecutive sampling dates was 178.3 days. At first sampling date 15 individuals were 1 year old, 20 were 2 years, 1 was 3 years, 4 were 4 years, 4 were 5 years, 2 were 6 years, 1 was 7 years and 2 were 8 years old.

The study was approved by the institutional ethics committee (University of Veterinary Medicine, Vienna) and the national authority (Federal Ministry of Science, Research and Economy, BMWF; permit number: BMWF-68.205/0112-II/3b/2011). All experimental protocols were carried out in accordance with the approved guidelines and the Law for Animal Experiments § 8ff, Tierversuchsgesetz – TVG.

Determination of Relative Telomere Length

To obtain cells for DNA extraction we collected buccal mucosa. This tissue was chosen because it could be obtained using minimally invasive protocols. The alternative, that is, repeated collection of adequate quantities of blood (containing non-nucleated blood cells) is not feasible for free-living edible dormice. Further, numbers and ratios of the different fractions of white blood cells in edible dormice vary within and between seasons55, which make this cell type impractical for longitudinal studies. Cell turnover rates of the buccal mucosa cells are relatively constant and unlikely to vary within and between seasons56. Buccal mucosa was sampled throughout the active season by twirling Gynobrush® brushes (Heinz Herenz Medizinalbedarf, Hamburg, Germany) on the inner cheek for 15–20 seconds. The heads of the brushes were individually placed into separate 1.7 ml Mμlti®-SafeSeal®Tubes (Carl Roth GmbH+Co. KG, Karlsruhe, Germany) containing 1 ml BC-buffer57 and stored at 4 °C for subsequent DNA extraction, which was always carried out within 24 hours after tissue cell collection. Therefore the cells were pelleted and the brushes were removed. The brushes were centrifuged (3000 rpm) for 10 min and the remaining cell pellets (after removal of the brushes) were centrifuged again (5000 rpm) for 2.5 min and 800 μl of the supernatant was discarded. DNA was extracted using a DNeasy Blood&Tissue Kit® (Qiagen) according to the manufacturer’s protocol. Extracted DNA was stored at −20 °C for further analysis.

For measuring Relative Telomere Length (RTL) we used the real-time PCR approach58 adapted for edible dormice. As a reference non-variable copy number (non-VCN) gene we used a 54 bp portion of the c-myc proto-oncogene, which was tested for non-variability in copy number in edible dormice by Turbill et al.12 as described by Smith et al.59. Primer Sequences for the non-VCN gene were 5′-GAG GGC CAA GTT GGA CAG TG-3′ (c-mycF), and 5′-TTG CGG TTG TTG CTG ATC TG-3′ (c-mycR) and telomeric primer sequences were 5′-CGG TTT GTT TGG GTT TGG GTT TGG GTT TGG GTT TGG GTT-3′ (tel 1b) and 5′-GGC TTG CCT TAC CCT TAC CCT TAC CCT TAC CCT TAC CCT-3′ (tel 2b), respectively. Telomere and non-VCN gene PCRs were carried out in separate runs with 20 ng DNA per reaction, 400 nM of each primer (Tel1b/Tel2b or c-mycF/c-mycR) in a final volume of 20 μl containing 10 μl of SensiMix SYBR No-ROX-MasterMix (Bioline). PCR conditions for the telomere primers were 10 min at 95 °C followed by 40 cycles of 10 sec at 95 °C, 20 sec at 56 °C and 20 sec at 72 °C. For c-myc, PCR conditions were 10 min at 95 °C followed by 40 cycles of 10 sec at 95 °C, 20 sec at 61 °C and 20 sec at 72 °C. A final melting step was included in each run with the temperature ramping from 65 °C to 95 °C in 1 °C steps to check for target specificity via unimodal melt dissociation peaks. All ratios of telomere to non-VCN gene were compared to a reference standard sample (RTL = 1), which was included in every run along with a further calibration standard to monitor inter-run variation. A negative (no-template) control was also included in each run. To validate the low inter run variability of the qPCR approach one run was performed twice (both, qPCR and non-VCN reaction) under the same conditions as outlined above. Furthermore samples of dormice with different ages were evenly assigned to runs to avoid run-to-run bias associated with age. Reactions were prepared using the Qiagility PCR robot (Qiagen, Germany) to minimize pipetting errors and cycling was performed on a Rotorgene Q quantitative thermocycler (Qiagen, Germany). All samples and controls were run in triplicate. We used the software LinRegPCR (2012.0)60 for analysis of non-baseline-corrected raw qPCR data, exported from the instrument59. RTL was calculated using equation 1, that is, a formula modified for RTL measurements described previously in Ruijter et al.61 containing RTL = relative telomere length, E = qPCR efficiency, Ct = cycle threshold, T = telomere reaction of target sample, ST = telomere reaction of standard sample, C = control gene (c-Myc) reaction of target sample and SC = control gene reaction of standard sample.

Mean qPCR efficiencies were 94.86% and 96.30% for the non-VCN gene and telomere reactions respectively. The mean coefficient of variation among replicates (intra-assay variation) for Ct-values of the non-VCN gene and telomere assay were 0.020% and 0.016% respectively. Among runs (inter-assay variation) we found a high correlation of RTL (R = 0.96) and a slope (1.12) close to the theoretical optimum of 1.0 between two runs with identical samples (Fig. 5). It should be noted that the inter-assay variation in our study was substantially smaller (≤ 0.02%) than in many other qPCR studies (0.9–7%; review in Steenstrup et al.62). Therefore, and because there is no logical explanation why errors should depend on the animals ages, we can rule out that measurement error alone could have led to observations of RTL increases, as suggested by Steenstrup et al.62.

Figure 5: Relative telomere length (RTL) of two qPCR runs with identical samples showing the low inter-run variability of the qPCR method used in this study. Full size image

Arguably, the main factors that enabled us to achieve extremely low inter-assay variation were (i) running all qPCRs in a laboratory with controlled constant air temperature. Fluctuations in ambient temperature during the cooling phase of amplification cycles strongly affect the results, as the used qPCR device uses ambient temperature for cooling, which leads to differences in the duration of the different steps (especially primer annealing and elongation). (ii) Additionally we performed a pre-run (run without samples in the instrument) to preheat the thermocycler to avoid temperature fluctuations when several runs were performed in a row. (iii) Furthermore all assays were run with exactly the same reagents and with a strict temporal protocol.

Statistics

All statistical tests were carried out using R 3.2.163. As RTL were determined at least twice (median 3, range 2–5) in each animal, we used linear mixed effects models (lme; library ‘lme4’64 and ‘lmerTest’65) that adjust for repeated measures. First, we used an lme model to investigate the effect of sex, age, body mass, reproductive activity and time of the year of taking the DNA sample on RTL in 158 samples from 49 edible dormice. Because data plots gave some indication for nonlinear effects of age, we also entered the square and cube of age as predictors in the full model. As the cubic term was not significant, it was subsequently removed, but quadratic terms were significant and retained. Second, to investigate factors affecting RTL-changes, that is shortening or elongation, we computed an lme model with RTL at time point t as the response variable, and RTL at time t-1 (see below), as well as the above variables entered as fixed effects. This analysis of RTL-changes additionally contained the independent variable time-interval (in days) between subsequent sampling points. Age at time t was used in this model. To correct for repeated measures, animal ID and qPCR plate ID were used as random factors for all models. These random effects were chosen to adjust for repeated measures and possible qPCR plate effects. Year was not used as a random factor as the AICc of the models increased when it was used to correct for effects of year-to-year variation in environmental conditions. Excluding year as a random effect did not notably affect the p-values of the significant terms.

A potential problem in the statistical analysis of changes in RTL over time is the “regression to the mean”, that is, extreme values measured in a subject during a first trial are likely to lie closer to the mean in subsequent trials66. Consequently, one would expect stronger apparent shortening of RTL in animals in which initial RTL was high, and vice versa. This effect would also be expected because the relationship between initial RTL and RTL-loss represents a so-called part-whole correlation67,68. Verhulst et al.66 have devised a numerical method to correct for these statistical effects. However, it can be shown that RTL values identical to the values obtained by the correction method of Verhulst et al.66 can be obtained by simply computing the residuals of a linear model of RTL-loss as a function of initial RTL (Supplementary Information). Therefore, we preferred to include initial RTL (i.e. RTL at t-1) as a covariate in the model of RTL changes. This approach has the advantage that, unlike a residual analysis, all predictor variables are entered simultaneously and the correct degrees of freedom are used.

To further separate between-subject and within-subject effects of age we used within-subject centering as outlined in van de Pol & Wright69. To differentiate effects we used both the mean age of individuals (capturing the between-subjects effects) and the difference between age at sampling and mean individual age (capturing within-subjects effects) as fixed effect predictors. Our initial analysis (see above) indicated a significantly nonlinear pattern with RTL decreasing in younger and increasing in older dormice. Thus, between-subjects and within-subjects effects could not be meaningfully computed from a single model with all ages pooled together. Also, there was a strong tendency for a continuous interaction between mean age and delta age (t = 1.99. P = 0.050). Therefore we computed separate models four younger and older individuals for all possible points of separation between these groups. This iterative procedure showed that the combined residual sum of squares of these models was minimised when regressions were computed separately for animals with a mean age of ≤5.3 years and >5.3 years, respectively.

To investigate the relationship between age and reproduction, we analysed an extended dataset (years 2006–2014) obtained from the same study population using the same methods as outlined above. This analysis was restricted to females only, because determination of reproductive effort (encountered with young or in lactating state) seemed more reliable than in males (small versus large testes). To see if the yearly probability to reproduce was affected by age we fitted a mixed-effects logistic regression, using age as a fixed predictor and animal-ID nested within study-year as random effects. This model was fitted using function glmmPQL from the R-library MASS70. We use n to denote the number of animals investigated and N to indicate the numbers of observations.