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\begin{document}

\title{Associations between DNA methylation and telomere length during early
life: insight from wild zebra finches (Taeniopygia guttata)}



\author[1]{Elizabeth Sheldon}%
\author[2]{Riccardo  Ton}%
\author[3]{Winnie Boner}%
\author[3]{Pat Monaghan}%
\author[3]{Shirley Raveh}%
\author[4]{Aaron Schrey}%
\author[2]{Simon Griffith}%
\affil[1]{University of South Florida}%
\affil[2]{Macquarie University}%
\affil[3]{Glasgow University}%
\affil[4]{Georgia Southern University}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Telomere length and DNA methylation (DNAm) are two promising biomarkers
of biological age. Environmental factors and life history traits are
known to affect variation in both these biomarkers, especially during
early life, yet surprisingly little is known about their reciprocal
association. Here, we present the first study on a natural population to
explore how variation in DNAm, growth rate and early-life conditions are
associated with telomere length changes during development. We tested
these associations by collecting data from wild, nestling zebra finches
in the Australian desert. We found that increases in the level of DNAm
were negatively correlated with telomere length changes across early
life. We also confirm previously documented effects of post hatch growth
rate and clutch size on telomere length in a natural ecological context
for a species that has been extensively studied in the laboratory.
However, we did not detect any effect of ambient temperature during
developmental on telomere dynamics. We also found that the absolute
telomere length of wild zebra finches, measured using the in-gel TRF
method, was similar to that of captive birds. Our findings highlight
exciting new opportunities to link and disentangle potential
relationships between environmental, epigenetic and telomere length
dynamics during early life.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Telomeres are highly conserved, non-coding DNA sequences that form
protective caps at the end of eukaryotic chromosomes (Blackburn,1991,
Blackburn \& Epel 2012). In the absence of telomerase (a reverse
transcriptase that adds telomeric repeats de novo after each cell
division), telomeres shorten with each round of cell division (Harley,
Futcher, \& Greider, 1990). When a critical length is lost, telomeres
become dysfunctional and cells enter a state of replicative senescence
(Hornsby, 2003; Verdun \& Karlseder, 2007, see also Victorelli \&
Passos, (2017) for length independent damage to telomeres triggering
cell senescence). The accumulation of senescent cells is known to
contribute to age-related declines in tissue and organ function (Wong et
al., 2003). Accordingly, within species, relatively short telomeres and
accelerated rates of telomere shortening have been associated with
fitness costs, predominantly via reduced lifespan, at the individual
level (Monaghan, 2010; Eastwood et al., 2019; Boonekamp, Mulder,
Salomons, Dijkstra, \& Verhulst, 2014, Wilbourn et al., 2018). Given the
relationship with lifespan, telomeres have been widely used as
biomarkers of biological age (Jylhava, Pedersen, \& Hagg, 2017). This
has also led to a recent focus in understanding how inter-individual
variation in telomere length arises. Early life telomere length is
partly determined by genetic factors (Olsson et al., 2011; Dugdale \&
Richardson 2018), however, accumulating evidence suggests that
environmental cues also impact telomere length dynamics across the life
course (Kotrschal, Ilmonen, Penn, 2007; Vedder, Verhulst, Zuidersma, \&
Bouwhuis, 2018; Dupoue et al., 2017; Boonekamp, et al., 2014).
Information on the relationship between environmental and telomere
length variation is steadily increasing, yet surprisingly little is
known about whether other DNA modifications mirror telomere dynamics.
Insight on how telomere length can vary in concert with other genetic
traits can expand our understanding of DNA based biological markers of
aging, and of physiological reactions to environmental change.

DNA methylation (DNAm) is an epigenetic mechanism that appears to be an
important component of telomere length regulation (Blasco, 2007), and is
also considered a promising biological clock (Horvath \& Raj 2018). Yet,
the relationship between DNAm and telomere length has not yet been
explored in an ecological context in any species. DNAm usually refers to
the addition of a methyl group to a cytosine base at a CG dinucleotide
(a `CpG' site) on the DNA sequence, but can occur at other sites in
different taxa (Anglers et al., 2010). When DNAm occurs at a CpG site
close to a gene regulatory region, it can modulate phenotypic variation
through its effects on gene expression. Evidence suggests that DNAm
could be involved in two, key telomere regulatory processes; those
involving telomerase (Buxton et al., 2014), and `alternative mechanisms'
relying on homologous recombination between telomeric sequences such as
alternative lengthening of telomeres (Gonzalo et al., 2006). Indeed,
while telomeres themselves do not contain CpG sites, decreases in global
and subtelomeric DNAm are known to be concomitant with increased
homologous recombination between telomeric sequences and dramatically
elongated telomeres in mouse cells (Gonzalo et al., 2006). Additionally,
a negative relationship between telomere length and genome-wide (Lee et
al., 2019) and gene-specific (Lee et al., 2019; Buxton et al., 2014)
DNAm has been described in humans (however, this relationship may be
complicated as a positive relationship has been detected in a different
correlative study (Dong et al., 2018)). Evidence also suggests that
short telomeres have specific epigenetic marks that may facilitate their
preferential elongation (Hemann, Strong, Hao, \& Greider, 2001; de
Lange, 2005). These studies suggest the existence of a direct
relationship between DNAm and telomere length. However, there is also
plenty of scope for indirect relationships between DNAm and telomere
length to occur given that they are associated with a range of the same
biological and ecological factors (Figure 1) and are also both
independently identified as molecular measures of age and ageing (Lu et
al., 2019; Banszerus, Vetter, Salewsky, K\selectlanguage{ngerman}önig \& Demuth, 2019).

To date, surprisingly little work has explored the potential links
between telomere length and DNAm, although a few studies have indicated
some conceptual connections (Horvath et al 2018; Blasco, 2007; Figure
1). DNAm and telomere length are highly responsive to environmental cues
(Feil \& Fraga, 2012; Monaghan, 2014), particularly during early life
(Watson, Bolton \& Monaghan, 2019; Boonekamp et al., 2014). For example,
studies have detected associations between DNAm or telomere length and
clutch/brood size (Noguera \& Velando, 2020; Jimeno, Hau, Gomez-Diaz, \&
Verhulst, 2019; Sheldon, Schrey, Ragsdale \& Griffith 2018; Nettle et
al., 2016; Reichert et al., 2014; Costanzo et al., 2016; Boonekamp et
al., 2014); ambient temperature (Stier, Metcalfe \& Monaghan, 2020;
Sheldon, Schrey, Hurley, \& Griffith, 2020; Yan et al., 2015); and body
size/growth rate (Young et al., 2017; Vedder et al., 2018). It is
therefore important to account for these influences when testing the
association between DNAm and telomere length. The present study is the
first examination, to date, of the relationship between DNAm and
telomere length dynamics in a species in the wild in which individuals
are exposed to natural variation in early life conditions. Specifically,
we collected longitudinal measures of telomere length (using qPCR) and
genome-wide levels of DNAm (using methylation sensitive- amplification
fragment length polymorphisms (MS-AFLP)) across the early life of wild
zebra finches (\emph{Taeniopygia guttata} ). Although MS-AFLP analyses
do not provide inference on the functional, gene regulatory consequences
of DNAm differences, they do provide a useful tool to compare the
relationship between telomere length dynamics and changes in the
percentage of a consistent subset of CpG loci that are methylated among
individuals (Schrey et al., 2013). Our study aims to provide an initial
exploration into the relationship between early life DNAm changes and
telomere length changes in wild animals as well as potential
associations with ambient temperature and life history effects. This can
establish a base for future research to link and/or disentangle the
relationship between early life conditions, DNA based biomarkers of age,
and individual fitness parameters.

In addition to considering the links between telomere length and DNAm,
our study is also of value in providing the first investigation of
telomere length dynamics in \emph{wild} zebra finches \emph{Taenopygia
guttata} . Characterisations of telomere dynamics in the wild have
become increasingly common for a wide range of bird species
(Supplementary Table 1a and 1b). In contrast, laboratory studies on
birds are largely performed using the zebra finch, in which
paradoxically, telomere dynamics have yet to be explored in the wild
(Supplementary Table 1a). Studies of ecologically relevant populations
of wild zebra finches are thus of value in helping to contextualize and
interpret the controlled laboratory tests that have made such a
significant contribution to our understanding of telomere biology in
birds.

\textbf{MATERIALS AND METHODS}

\textbf{Field work and blood sampling}

Fieldwork was conducted at Fowlers Gap Arid Research Station, in
far-western New South Wales (31\textsuperscript{o}05'S,
`141\textsuperscript{o}42'E) during the main part of the Austral
breeding season (August--December) in 2016. Data were collected from
zebra finch nestlings in nest boxes in their natural habitat at `Fowlers
Gap' (details regarding field site characteristics can be found in
Griffith, Pryke, \& Mariette, 2008). Given the mobility of the species
in the wild, we were working with a population in which most adults were
not banded, and we were unable to ascertain parents' age or reproductive
history. Nest boxes were monitored periodically (every 2 days) during
the nest-building stage, and after the first egg was laid nest boxes
were monitored each morning until the entire clutch was laid, enabling
us to ascertain clutch size and projected hatch date. In the wild, we
have previously shown that parent zebra finches only initiate incubation
on the day that the last egg is laid (Gilby, Mainwaring, \& Griffith,
2013). Consequently, we used the day of the last laid egg to represent
the first day of embryonic development and the usual incubation period
of \textasciitilde{}12 days after the onset of incubation, to predict
clutch hatch date. Two days prior to a clutch's estimated hatch date, we
monitored the nest three times per day between 06.00 and 17.00; our
first nest check was at \textasciitilde{}06.00, our second nest check
was at \textasciitilde{}11.30 and our third nest check was at
\textasciitilde{}17.00, which allowed us to identify the hatch date of
each nestling. The hatch date was used to calculate the post-hatch age
of each nestling (in days). Synchronous hatching meant that we could not
tie nestlings to specific eggs, we were unable to account for the order
in which eggs/nestlings were produced, something that has been shown to
impact telomere dynamics in the zebra finch (Noguera, Metcalfe, Reichert
\& Monaghan, 2016).

To measure ambient developmental temperature, we obtained hourly
atmospheric temperature data in the shade (i.e. air temperature) from
the Australian Bureau of Meteorology's automated weather station at
Fowlers Gap, located within 20km of the study site. We calculated the
mean temperature during each day of zebra finch development using hourly
data from 7am to 7pm. These data represent the period of the day when
the adults are periodically away from the nest foraging together
(Mariette \& Griffith 2015), and the nestlings (or embryos) are
therefore subject to the greatest exposure to ambient (rather than
brooding/ incubation) temperatures; the duration of nestling exposure to
ambient temperature increases as the nestlings grow, and parents reduce
brooding. The daily temperature values were then averaged from day 3 to
day 11 of post-hatch development (prior to day 3, the ectothermic
nestlings are typically brooded or incubated for long periods during the
day, thus are not exposed to ambient temperatures, day 3 was also the
age at which the earliest blood sample was taken, see below). During our
sampling period (Aug-Nov), average temperatures across post-hatch
development ranged from 14.6- 27.6\textsuperscript{o}C (average
20.3\textsuperscript{o}C, SEM: 0.295). At day 3 a small patch of down
feathers was trimmed from a different area on each nestling allowing us
to identify individual nestlings throughout development. Trimming the
small patch of hair (approximately 0.5cm\textsuperscript{2}) from each
nestling was unlikely to impose thermoregulatory costs.

At day 3 and day 11, we extracted blood (\textless{}20\selectlanguage{greek}µ\selectlanguage{english}l) from the
metatarsal vein of younger (day 3) nestlings, and the brachial vein of
older (day 11) nestlings with a hypodermic needle and capillary tube.
Blood was preserved in 0.5ml of 95\% ethanol and stored at room
temperature in an Eppendorf tube that was labelled with a unique ID.
Growth rate (a measure of cell proliferation) can affect telomere
dynamics (Boonekamp et al., 2020; Ringsby et al., 2015). To account for
variation in growth rate between individual nestlings and broods we
measured tarsus length (a reliable metric of body size) at day 3 and day
11 using digital calipers. The associated effects of clutch and brood
size (i.e. parental care and resource acquisition) can also affect
telomere length (Costanzo et al., 2016; Boonekamp et al., 2014) and DNAm
changes (Sheldon et al., 2018), thus, we included clutch and brood size
in our model. We collected data on the clutch size of each nest daily
from the day of first lay to the onset of incubation, and on the two
days prior to the estimated day of hatch (clutch size ranged from 2 to
11 eggs). We collected data on the brood size of each nest on the day
the entire clutch hatched, day 3, and day 11 of post-hatch development
(brood size ranged from 2 to 8 nestlings). Due to egg/nestling
mortality, clutch/brood size often varied across development, thus we
averaged the clutch/brood size of each nest across the two time-points.

\textbf{Sample size}

Two blood samples were available for analysis from 91 zebra finches from
62 nests - a total of 182 samples. 73\% of these samples yielded enough
DNA for assaying telomere length successfully using qPCR -- a total of
133 samples from 83 individuals from 57 nests, 50 of these individuals
from 38 nests were measured successfully for telomere length at both day
3 \emph{and} day 11. Of the 83 individuals analysed successfully for
telomere length, 78 individuals from 60 nests, a total of 106 samples,
were analysed successfully for MS-AFLP. 28 of these individuals from 23
nests were measured successfully for MS-AFLP at both day 3 \emph{and}day
11. Sample failures during qPCR analysis were generally due to an
insufficient DNA yield from a small initial blood sample. Sample
failures during MS-AFLP analysis were generally due to the sensitivity
of the MS-AFLP protocol to DNA concentration during the restriction
digest step. Due to logistical constraints during field work, tarsus
length and/or clutch size was not recorded for six nestlings from five
nests, leading to the final sample sizes reported in Table 1 and 2.

\textbf{DNA methylation measurements}

The MS-AFLP protocol used modifies the standard AFLP protocol by
substituting the MseI enzyme with the methylation-sensitive
isoschizomeric enzymes MspI and HpaII (New England Biolabs). See Salmon
et al., (2008) for further details on the MS-AFLP method. Together, four
types of variation can be scored: Type 1 is when both enzymes cut at the
restriction site and indicates no methylation, Type 2 is when MspI does
cut and HpaII does not cut, indicating the restriction site has a
methylated internal cytosine C; Type 3 is when MspI does not cut and
HpaII does cut indicating the restriction site has hemi-methylation; and
Type 4 is when neither enzyme cuts indicating either both cytosines are
methylated or the restriction site has mutated (Richards, Schrey \&
Pigliucci, 2012) (conservatively, we treated Type 4 as missing data
because the underlying methylation state cannot be determined; for
example see Richards et al., 2012).

We performed MS-AFLP following the protocol used by Richards et al.,
(2012). For the MS-AFLP analysis, DNA was extracted using the Gentra
Puregene tissue kit (Qiagen, Valencia, CA, USA) and was stored in 40 \selectlanguage{greek}µ\selectlanguage{english}l
of TE buffer.~ For both day 3 and day 11 samples, we digested
approximately 250ng of genomic DNA at 37\selectlanguage{ngerman}º C for 3 h in paired reactions:
one with EcoRI and MspI, the other with EcoRI and HpaII. We immediately
followed the restriction digest with adaptor ligation with EcoRI and
MspI/HpaII adaptors at 16-20 h at 16º C (Supplementary Material Table 2,
all primer and adapter sequences). After adaptor ligation, we conducted
pre-selective PCR with EcoRI+1, MspI/HpaII+0 pre-selective primers
(Supplementary Material Table 2) at the following PCR conditions: 75º C
for 2 min; 20 cycles of 94º C for 30 s, 56º C for 30s, 75º C for 2 min,
final extension at 60º C for 30 min and 4º C hold. Following
pre-selective PCR, we conducted selective PCR by multiplexing 6-FAM
fluorescently labelled EcoRI+AGC primers with HEX fluorescently labelled
EcoRI+ACG primers and unlabeled primers HpaII/MspI+TCAT (Supplemental
Table 2) at the following PCR conditions 94º C for 2 min, 8 cycles of
94º C 30 s, 65º C 30 s 72º C 2 min (dropping the annealing temperature
1º each cycle), 31 cycles of 94º C 30 s, 56º C 30 s 72º C 2 min, final
extension of 60º C 5 min and a 4º C hold. We sent the selective PCR
products to Macrogen Facilities (South Korea) for fragment analysis on
an ABI 3130XL.

We used PEAKSCANNER v 1.0 (Applied Biosystems) to analyse resultant gel
files and define fragment sizes, and RAWGENO (Arrigo et al., 2012) to
define bands. We pooled data into two categories: methylated (Type 2 and
Type 3) or not methylated (Type 1). Throughout, we refer to a MS-AFLP
locus to indicate a particular sized band resolved in the selective PCR.

To ensure scores were consistent, we validated our MS-AFLP results by
duplicating the entire protocol for 30 random individuals. We identified
bands that consistently occurred, and we eliminated bands that were
inconsistently amplified or occurred at highly variable intensities. We
conducted all analyses using a binary haplotype-binding pattern
(methylated 1, not methylated 0) for 92 verified consistent CpG sites
between 50 and 500 base pairs for each nestling. We calculated
percentage of genome wide DNAm as the proportion of the 92 loci that
were methylated for each sample, we refer to this throughout as DNAm.

\textbf{Telomere measurements}

Genomic DNA (gDNA) was extracted using the NucleoSpin blood kit
(Macherey-Nagel) with some minor modifications. 2 \selectlanguage{greek}µ\selectlanguage{english}l of whole blood was
removed from the sample tube and allowed to air-dry, to evaporate the
ethanol, and subsequently added to 198\selectlanguage{greek}µ\selectlanguage{english}l of PBS. From this stage, the
manufacturer's protocol was followed, with the gDNA eluted into 35\selectlanguage{greek}µ\selectlanguage{english}l of
BE buffer. The quantity and purity of the gDNA was measured on a
Nanodrop 8000 and all samples were within the accepted parameters;
A\textsubscript{260/280} [?]1.7, A\textsubscript{260/230} [?] 1.8 (Thermo
Fisher). The gDNA was stored at -20\textsuperscript{o}C until telomere
length analysis was performed.

Telomere length was assayed using the qPCR method as described
previously (Criscuolo 2009). Briefly, the telomere length of each sample
was measured by determining the ratio (T:S) of telomere repeat copy
number (T) to a single copy or non-variant control gene (S), relative to
a five pooled DNA reference sample from wild zebra finches at day 11
that was run on all plates. For this study, Glyceraldehyde-3-phosphate
dehydrogenase (GAPDH) was used as the control gene. A standard curve (6
serial dilutions of zebra finch gDNA from 40 to 1.25ng/well) was also
included on each plate and all samples (ran in triplicate) fell within
standard curve boundaries. Mean reaction efficiencies were within the
acceptable range for telomere and control gene (mean \selectlanguage{ngerman}± SE, TEL: 97.43 ±
4.54\%; GAPDH: 87.73 ± 3.75\%). The average inter\selectlanguage{english}-plate variation of the
Ct values was 1.47 for the telomere assay and 2.6 for the GAPDH assay.
Intra-plate coefficient of variation for the telomere and GAPDH assays
for the raw Ct values were 12.44 and 27.08 respectively . The average
intra-plate variation of the Ct values was 0.74 for the telomere assay
and 0.47 for the GAPDH assay.

Telomere length measurements were calculated using the method by Pfaffl
(2001). The mean values were used to calculate the telomere length (T:S
ratio) using the formula: ((1 + E telomere)\^{} \selectlanguage{greek}Δ\selectlanguage{english}Cq telomere (control --
sample) / (1 + E GAPDH)\^{} \selectlanguage{greek}Δ\selectlanguage{english}Cq GAPDH (control -- sample)).

In addition we had sufficient DNA from a small number of 11 day samples
(5) that enabled us to measure absolute telomere length using the in-gel
TRF method (see Nussey et al., 2014 for details of this method). This
enabled us to compare telomere length in wild and captive zebra finches.

\textbf{Statistical analysis}

All statistical analyses were performed in R 4.0.3 (R Core Team, 2017).
Linear mixed models (LMMs) were run using the package lmer (Bates,
Machler, Bolker, \& Walker , 2015), with package lmerTest (Kuznetsova,
Brockhoff, \& Christensen 2016) to calculate degrees of freedom
and\emph{p} -values. We calculated marginal R\textsuperscript{2} values
for the LMMs using the method described by Nakagawa and Schielzeth
(2013). We initially performed a series of linear regression analyses to
test for possible effects of body size on telomere length, possible
effects of body size on percent DNAm changes and possible effects of
temperature on growth rate/body size (Supplementary Analysis 1). We also
performed a LMM to examine the effect of baseline telomere length (at
day 3) on change in telomere length after adjusting for the effects of
regression to the mean (RTM) (Verhulst, Aviv, Benetos, Berenson, \&
Kark, 2013; Supplementary Analysis 2).

In a first, longitudinal analysis, we used a LMM (Model 1) to explore
the effects of temperature, clutch size, change in DNAm and change in
tarsus (growth from day 3 to day 11) (all as fixed effects) on the rate
of changes in telomere length from day 3 to day 11 (dependent variable).
Our preliminary analysis (Supplementary analysis 2c) detected a negative
correlation between tarsus length at day 3 and telomere length at day 3.
Therefore, we also included tarsus length at day 3 as a fixed effect to
account for potential effects of body size on telomere length changes.
Nest ID was included as a random effect in this model to control for
possible familial/genetic effects, and the repeated sampling of the same
individuals within a brood. Only individuals with data for both day 3
and day 11 were included in this longitudinal analysis. Change between
day 3 and day 11 was calculated using the equation (\emph{change =
(value at day 11 -- value at day 3)).} Models were built on the
biological variables of interest.

In our cross-sectional analyses, we performed two LMMs to examine a)
Model 2: the effects of temperature, clutch size, DNAm at day 3, and
tarsus length at day 3 (fixed effects) on telomere length at day 3 (the
dependent variable), and b) Model 3: the effects of temperature, brood
size, DNAm at day 11, and tarsus length at day 11 (fixed effects) on
telomere length at day 11. We accounted for genetic and/or familial
effects by including Nest ID as a random effect.

\textbf{Literature search}

We compiled a list of peer-reviewed scientific publications on avian
telomere dynamics from 2000 to 2020 using Google Scholar and Web of
Science as online search engines. We simultaneously used ``telomere'',
``zebra finch'', and ``birds'' as keywords, including
``\textasciitilde{}'' in front of every word to consider also potential
synonyms. A further search was performed also using the same keywords
individually to cross check the comprehensiveness of our results. We
performed the web search first considering the entire 20-year interval
and then on a year-by-year basis. We only recorded studies published in
peer reviewed journals and excluded thesis', reviews, metanalyses and
preprints. We divided our total sample into two categories: 1) telomere
studies using the zebra finch, and 2) telomere studies using any species
other than zebra finch. For each category we listed the studies in
ascending chronological order, and for multiple publications within each
year we listed authors in alphabetical order. The results of our
literature search are summarized in Table 1 of Supplementary Materials.

\textbf{RESULTS}

The telomere length of the five wild zebra finches measured using the
in-gel TRF method was similar to that of captive bred birds (range
12.2-13.59kb, mean 12.77kb+/-0.24. Overall, there was no change in
telomere length between day 3 and day 11 of post-hatch development,
however on average Mean telomere length was 0.84 (range: 0.29-2.59, SEM:
0.059) on day 3, and 0.74 (range: 0.44-2.16, SEM: 0.041) on day 11. This
decrease in telomere length was not statistically significant (paired
t-test: \emph{t} \textsubscript{49} = 1.59, \emph{p} = 0.116) (Figure
2). Thirty-three individuals showed a reduction in telomere length
between day 3 and day 11 post-hatch, while 17 were found to have
telomeres that increased in length. Telomere length decreased by an
average of 0.28 (range: -0.64 to -0.02, SEM: 0.03), and increased by
0.25 (range: \textless{}0.01 to 1.92, SEM: 0.16).

Our MS-AFLP analyses verified 92 consistent CpG sites between 50 and 500
base pairs. (\%) DNAm was 29.2\% at day 3 (Standard deviation (SD) =
6.5), and 34.9\% at day 11 (SD=8.3). We found a significant, negative
relationship between changes in the level (\%) of DNAm across these 92
CpG sites and change in telomere length between day 3 and day 11 (Table
1, Figure 3a). We also found a significant, negative relationship
between change in tarsus length and change in telomere length between
day 3 and day 11 (Table 1, Figure 3b). Tarsus length at day 3, brood
size and post-hatch ambient temperature did not significantly affect
change in telomere length (Table 1). The fixed factors explained 43.73\%
(Marginal R\textsuperscript{2}=0.437), while the random effect of natal
nest ID explained \textless{}1\% of the variance in the data
(Conditional R\textsuperscript{2}=0.441; this value describes the
proportion of variance explained by both the fixed and the random
factor, thus most variance in this model was explained by the fixed
factors) (Table 1)).

In our second LMM we detected a significant, positive association
between clutch size and telomere length at day 3 (i.e., larger clutches
were associated with longer telomeres at day 3 (Table 2)), while percent
DNAm at day 3 and other fixed factors (pre-hatch ambient temperature and
tarsus length at day 3) were not related to telomere length. In this
model, the fixed factors explained 28.57\% of the variance in telomere
length at day 3 (Marginal R\textsuperscript{2}=0.285), while the random
effect of natal nest ID explained 40.19\% of variance in telomere length
at day 3 (Conditional R\textsuperscript{2}=0.687) (Table 2).

In our third LMM, DNAm at day 11 was not related to telomere length at
day 11, and, similarly none of the other fixed factors (brood size,
temperature and tarsus length at day 11) included in our model appeared
to influence telomere length at day 11 (Table 3). The fixed factors in
our model (Table 3), explained 8.63\% of the variance in the data
(Marginal R\textsuperscript{2}=0.086), while the random effect of natal
nest ID explained 59.43\% of variance in telomere length at day 11
post-hatch (Conditional R\textsuperscript{2}=0.6807) (Table 3). After
visual inspection of figures 2, 3a and 3b, we identified an individual
with a particularly high telomere length value. We re-ran all our models
after excluding this anomalous data point, however, excluding this data
point did not alter our results significantly. Since there was no
biological reason to omit this individual from the data set it was
included in our final analyses.

\textbf{DISCUSSION}

Our study is the first investigation of the relationship between
telomere length and DNAm dynamics in wild animals exposed to naturally
variable early life conditions. In our longitudinal analysis we found a
negative relationship between individual changes in telomere length and
changes in the level of methylated CpG loci (92 CpG loci in our study)
across early life, such that individuals with accelerated rates of
telomere shortening were associated with increases in DNAm levels. The
observational nature of our field data means that we were unable to
establish whether the negative relationship between telomere length and
DNAm changes reflected coincidental or causal effects. That is, whether
DNAm and telomere length respond comparably to the same
intrinsic/extrinsic conditions experienced across early life and/or
whether potential regulatory effects between DNAm and telomere length
may exist. How changes in telomere length and DNAm relate to each other
and respond to early life conditions warrants further investigation
since it may help identify potentially distinct aspects of biological
age that DNAm and telomere length reflect. It is important to note that
while variation in DNAm levels may reflect variation in the early life
environment (Angers, Castonguay, \& Massicotte, 2010; Sheldon et al.,
2018; 2020; Makinen, van Oers, Eeva, Laine \& Ruuskanen, 2020), the
relevance of early-life, `genome-wide' hypo or hyper methylation in the
context of biological age remains unclear. Thus, while our MS-AFLP
analyses are useful to compare DNAm and telomere length associations in
different environments, next-generation sequencing data (used to
generate an `epigenetic clock') are necessary to compare DNAm and
telomere length predictors of biological age in different environments
(Horvath \& Raj 2018; Banszerus et al., 2019). We did not detect a
relationship between cross-sectional measures of DNAm and telomere
length at either day 3 or day 11 post-hatch. However, telomere length at
day 3 was weakly, but significantly positively associated with clutch
size. Given this relationship was positive rather than negative, it
suggests that the within clutch decline in telomere length across the
laying order that has been seen in other studies did not have much
effect here. The positive relationship could perhaps be due to parental
age if older parents had a reduced clutch size and their offspring
inherited shorter telomere lengths or lost more up to this point
(Monaghan and Metcalfe, 2019).

Evidence is accumulating to suggest that longitudinal measures of
within-individual change in telomere length may represent a more useful
biomarker of ageing/individual condition than static, cross-sectional
telomere length measures (Wood and Young, 2019; Boonekamp et al., 2014;
Tricola et al., 2018; Wilbourn et al., 2018. However, to our knowledge,
no other study has conducted longitudinal analyses on the relationship
between within-individual change in telomere length and DNAm across
early development, in any species. Although it may be expected that
factors associated with telomere length would also be associated with
telomere length \emph{changes} , telomere length at a given timepoint is
the outcome of both initial telomere length and subsequent
attrition/elongation, which may not be associated. Indeed, in our
supplementary analysis (Supplementary Analysis 2) the initial length of
telomeres (at day 3) did not affect the rate of telomere length change
across early life after controlling for effects of regression to the
mean (Verhulst et al., 2013) (however, other studies have detected this
trend (Salomons et al., 2009; Aviv et al., 2009)). Hence, variation in
initial telomere length, which may be more influenced by heritable
genetic factors (Broer et al., 2013, but see Voillemot et al., 2012),
may confound the relationship between environmental factors associated
with telomere length versus telomere length \emph{change} , and indeed,
may confound the accuracy of telomere length as a biomarker of
individual condition later in life (Boonekampp et al., 2014; Wood \&
Young 2019). In line with this, in our study we found that genetic
and/or familial effects (represented by nest ID) explained the greatest
amount of variance in our cross-sectional telomere length measures at
both day 3 and day 11, while telomere length \emph{change} was better
described by DNAm changes and growth rate, factors that are extremely
sensitive to the environment.

Contrary to other studies (Stier et al., 2020; Fitzpatrick et al.,
2019), we did not find an effect of ambient temperature on telomere
length or telomere length changes across development. Temperature has
been shown to effect within individual changes in DNAm (Sheldon et al.,
2020) and telomere length (Stier et al 2020; Fitzpatrick et al 2019)
however, it is unclear if temperature effects these DNA modifications
comparably. The temperatures (average daily maximum of
27.6\textsuperscript{o}C) in our study may not have been `stressful'
enough to impact telomere dynamics, and indeed temperature has not
affected telomere length in studies on other taxa (McLennan \emph{et
al.,} 2018; Boonekamp et al, 2020).

In our longitudinal analyses, we also detected a significant, negative
relationship between tarsus growth and change in telomere length, such
that individuals that grew faster lost more telomere length across the
measured period of their early life. This relationship has been detected
in previous studies, and could be due to increases in cell division,
energy expenditure and oxidative stress associated with increased growth
rate (Monaghan \& Ozanne, 2018). The relationship between telomere
length and tarsus growth occurred independently of temperature in our
study. However, previous studies have shown that embryonic growth rate,
manipulated by small variations (+-1\textsuperscript{o}C) in incubation
temperature, negatively affect telomere length in the laboratory (Stier
et al., 2020). We did not detect a relationship between cross-sectional
measures of telomere length and tarsus length in our attempt to explore
proximate effects within individuals. However, in a study examining the
same relationship (between telomere length and tarsus length) at an
ultimate level, across generations, in a selection experiment on body
size in the house sparrow, a relationship was detected (Ringsby et al.,
2015).

In our cross-sectional analysis we detected a positive correlation
between telomere length at day 3 and clutch size. We have previously
shown that DNAm was affected by brood size during early life of wild
zebra finches (Sheldon et al., 2018), and experimental work has also
shown that telomeres are generally shorter when brood size is increased
during early life (Voillemot \emph{et al.,} 2012; Reichert et al.,
2014), potentially as a response to the stress of lower per capita food
delivery rates to offspring (Costanzo et al., 2016). The positive
relationship detected in our analysis is thus interesting and may
reflect higher quality parental care (Bichet et al., 2020), lower rates
of nestling/egg mortality, or optimal environmental factors among chicks
developing in larger (unmanipulated) clutches (Van Noordwijk \& de Jong,
1986).

In our study, telomere length was found to increase as well as decrease
from day 3 to day 11 of post-hatch development. Increasing evidence from
a range of avian taxa suggests this could be a somewhat common
occurrence (Wood \& Young, 2019; Kotrschal et al., 2007; Young et al.,
2013; Uivari \& Madsen, 2009; Haussmann \& Mauck, 2008) possibly due to
the actions of telomerase (Haussmann, Winkler, Huntington, Nisbet, \&
Vleck, 2004; 2007; Jaskelioff et al., 2011; Bateson \& Nettle, 2017;
Hoelzl et al., 2016). The extent to which mechanisms underlying telomere
length elongation may weaken the utility of within-individual telomere
length variation and, indeed, the utility of telomere length as a
biomarker of biological ageing, is unclear, as telomere repair by
telomerase may itself be inhibited by the same factors effecting
telomere length (e.g. oxidative damage; Ahmed et al. 2008).

Our measurements of absolute telomere length in wild zebra finches gave
telomere lengths similar to those in captive birds measured by the same
in-gel TRF method; measurements in the same lab and using the same
equipment and protocol for laboratory birds (varying in age from 7 to
120 days) ranged from 10.1 to 16.35kb (Millet \& Salmon, pers comm).
These data suggest that, in contrast to the laboratory mouse which
typically has telomeres several times longer than its wild counterparts
possibly due to inbreeding (Manning et al 2002), but this is not the
case for the zebra finch.

In conclusion, our study of wild zebra finches has confirmed previously
documented effects in captive zebra finches of both growth rate and
clutch size on telomere length dynamics during early life. This
consistency is important given the differences in resources and
variability in the environment of wild compared to captive animals. Most
significantly, our study detected a negative relationship between early
life telomere and DNAm dynamics, such that individuals with accelerated
rates of telomere shortening have associated increases in DNAm levels.
The nature of this relationship warrants further investigation since it
may shed light on potentially clinically/ecologically distinct aspects
of biological age reflected by DNAm and telomere length. It would be
valuable for future work to focus on next-generation sequencing
techniques (e.g., reduced representative bisulfite sequencing (RRBS)) to
compare DNAm and telomere length measures of biological age across
different environments and/or to compare potentially functional relevant
or regulatory associations between DNAm and telomere length across
different environments. Our results provide a base for further
investigations to link and/or disentangle the relationship between the
early life environment and DNA biomarkers of biological age.

\textbf{ACKNOWLEDGMENTS}

We would like to acknowledge funding from an Australian Research Council
grant (DP170103619) to SCG and PM. Thanks also to Caroline Millet and
Pablo Salmon for the TRF measurements.

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\textbf{DATA ACCESSIBILITY}

Authors have archived their data in the publicly accessible repository
FigShare. DOI: 10.6084/m9.figshare.13670044

\textbf{STATEMENT OF CONFLICTING INTEREST}

The authors declare no conflict of interest.

\textbf{AUTHOR CONTRIBUTIONS}

E.L.S., P.M. and S.C.G. conceived the study. E.L.S. collected the field
data. E.L.S. and A.W.S. performed the MS-AFLP assay. W.B., R.T., S.R.,
and P.M. performed the qPCR assay. E.L.S. and R.T. conducted the
statistical analyses. The manuscript was written by E.L.S. and commented
on and edited by all authors. S.C.G provided funding.

\textbf{AUTHOR ORCID I.D.}

Elizabeth L. Sheldon https://orcid.org/0000-0001-5002-3006

Riccardo Ton https://orcid.org/0000-0003-3897-9720

Simon C. Griffith https://orcid.org/0000-0001-7612-4999

Aaron. W. Schrey https://orcid.org/0000-0002-2165-7349

Pat Monaghan https://orcid.org/0000-0003-2430-0326;

Winnie Boner https://orcid.org/0000-0003-1702-4298

Shirley Raveh https://orcid.org/0000-0003-0406-4928

\textbf{TABLES AND FIGURES}

\textbf{Figure 1} . Conceptual description of the associations between
an environmental variable (ambient temperature), brood size, growth,
DNAm and telomere length. An exemplar citation is provided as an example
of a study exploring each association.

\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/404965/articles/516046-associations-between-dna-methylation-and-telomere-length-during-early-life-insight-from-wild-zebra-finches-taeniopygia-guttata}

\textbf{Figure 2} . Paired box plot illustrating individual changes in
telomere length across early life (from day 3 to day 11 post-hatch) for
50 individuals from 38 broods. Grey lines connect individuals across
development. The box plots show the median, lower, and upper quartile of
telomere length data.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Table 1} : Random and fixed effect estimates from a linear mixed
model for change in telomere length (\emph{n} ~=~24 individuals from 20
nests) from day 3 to day 11 post-hatch. Significant values are indicated
by * at the 95\% confidence interval. SE = standard error.

\textbf{Model 1. Dependent Variable: Early life change in telomere
length}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Fixed effect} & \textbf{Estimate} & \textbf{SE} &
\textbf{t-value} & \emph{p}\textbf{-value}\tabularnewline
Change in Tarsus Length & -1.499 & 0.580 & -2.397 &
0.021*\tabularnewline
Change in DNA methylation (\%) & -0.875 & 0.397 & -2.126 &
0.044*\tabularnewline
Post-hatch Temperature & 0.037 & 0.026 & 0.732 & 0.255\tabularnewline
Tarsus Length (day 3) & 0.388 & 0.264 & 1.469 & 0.164\tabularnewline
Brood Size & \textgreater{}0.001 & 0.083 & -0.566 & 0.999\tabularnewline
\textbf{Random effect} & \textbf{Variance} & \textbf{St Dev} &
&\tabularnewline
Nest ID & 0.002 & 0.042 & &\tabularnewline
Residual & 0.263 & 0.513 & &\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 3a} . Partial residual plot illustrating the relationship
between changes in DNA methylation levels (\%) (x-axis) and telomere
length (y-axis) from day 3-11 post-hatch while controlling for
differences in tarsus growth, post-hatch temperature, and brood size.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3b} . Partial residual plot illustrating the relationship
between tarsus growth (x-axis) and telomere length change (y-axis) from
day 3-11 post-hatch while controlling for changes in DNA methylation
levels (\%), post-hatch temperature, and brood size.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Table 2} . Random and fixed effect estimates from a linear mixed
model for telomere length at day 3 (n= 33 individuals from 26 nests).
Significant values are indicated by * at the 95\% confidence interval.
SE= Standard error.

\textbf{Model 3. Dependent Variable: Telomere length at day 3}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Fixed effect} & \textbf{Estimate} & \textbf{SE} &
\textbf{t-value} & \emph{p}\textbf{-value}\tabularnewline
Tarsus Length (day 3) & -0.132 & 0.113 & -1.168 & 0.253\tabularnewline
DNA methylation (\%) (day 3) & -1.787 & 1.105 & -1.617 &
0.117\tabularnewline
Incubation Temperature & 0.014 & 0.020 & 0.678 & 0.503\tabularnewline
Clutch Size & 0.104 & 0.114 & 2.275 & 0.032*\tabularnewline
\textbf{Random effect} & \textbf{Variance} & \textbf{St Dev} &
&\tabularnewline
Nest ID & 0.093 & 0.301 & &\tabularnewline
Residual & 0.048 & 0.221 & &\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3} . Random and fixed effect estimates from a linear mixed
model for telomere length at day 11 (\emph{n} ~=~28 individuals from 23
nests). Significant values are indicated by * at the 95\% confidence
interval. SE= Standard error.

\textbf{Model 1. Dependent Variable: Telomere length at day 11}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Fixed effect} & \textbf{Estimate} & \textbf{SE} &
\textbf{t-value} & \emph{p}\textbf{-value}\tabularnewline
Tarsus Length (day 11) & 0.001 & 0.077 & 0.017 & 0.987\tabularnewline
DNA methylation (\%) (day 11) & 0.080 & 0.786 & 0.102 &
0.920\tabularnewline
Post-hatch Temperature & 0.022 & 0.016 & 1.337 & 0.197\tabularnewline
Brood Size & -0.031 & 0.053 & -0.589 & 0.564\tabularnewline
\textbf{Random effect} & \textbf{Variance} & \textbf{St Dev} &
&\tabularnewline
Nest ID & 0.091 & 0.301 & &\tabularnewline
Residual & 0.048 & 0.221 & &\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1/Fig1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2/Fig2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3a/Fig3a}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3b/Fig3b}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

