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www.aging-us.com 24817 AGING
INTRODUCTION
Ageing on a cellular level involves the decline of
molecular functions resulting in a cumulative decrease in
cell maintenance [1]. Biological ageing at the organism
level is at least partly driven by molecular and cellular
changes [2]. Biological ageing is observed in most
animal species [3, 4] and several epigenetic changes
occur during the ageing process [5, 6]. DNA methylation
at cytosine-phosphate-guanine (CpG) sites is known to
change with age.
A growing number of studies have shown that changes
in DNA methylation at a set of specific CpG sites are
predictive of age [7]. These studies have constructed
what is often referred to as epigenetic clocks and can be
used to predict chronological or biological age [8–11].
Epigenetic clocks have predominantly been derived for
mammalian species but has also been developed for a
sea bird (Ardenna tenuirostris) [7, 12]. There has been
little exploration of whether DNA methylation can be
used for age estimation in other vertebrate groups. The
identification of DNA methylation biomarkers for age
estimation has typically relied on data-intensive reduced
representation bisulfite sequencing (RRBS) or Illumina
Infinium microarrays [13–16]. While effective for
marker identification and model development, these
methods are not well-suited to high-throughput
characterisation of age for large numbers of non-human
samples. RRBS generates a high volume of data that
makes it computationally expensive. Microarrays have
not been developed for most species outside of humans
www.aging-us.com AGING 2020, Vol. 12, No. 24
Research Paper
A DNA methylation age predictor for zebrafish
Benjamin Mayne1, Darren Korbie2, Lisa Kenchington3, Ben Ezzy3, Oliver Berry1, Simon Jarman4
1Environomics Future Science Platform, Indian Ocean Marine Research Centre, Commonwealth Scientific and
Industrial Research Organisation (CSIRO), Crawley, Western Australia, Australia
2Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia,
Queensland, Australia
3Western Australian Zebrafish Experimental Research Centre (WAZERC)
, University of Western Australia, Perth,
Western Australia, Australia
4School of Biological Sciences, The University of Western Australia, Perth, Western Australia, Australia
Correspondence to:
Benjamin Mayne; email: benjamin.mayne@csiro.au
Keywords: age estimation, CpG sites, DNA methylation, multiplex PCR, sequencing
Received: October 14, 2020 Accepted: November 30, 2020 Published: December 23, 2020
Copyright: © 2020 Mayne et al. This is an open access article distributed under the terms of the Creative Commons
Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.
ABSTRACT
Changes in DNA methylation at specific CpG sites have been used to build predictive models to estimate animal
age, predominantly in mammals. Little testing for this effect has been conducted in other vertebrate groups, such
as bony fish, the largest vertebrate class. The development of most age-predictive models has relied on a genome-
wide sequencing method to obtain a DNA methylation level, which makes it costly to deploy as an assay to
estimate age in many samples. Here, we have generated a reduced representation bisulfite sequencing data set of
caudal fin tissue from a model fish species, zebrafish (Danio rerio), aged from 11.9-60.1 weeks. We identified
changes in methylation at specific CpG sites that correlated strongly with increasing age. Using an optimised
unique set of 26 CpG sites we developed a multiplex PCR assay that predicts age with an average median absolute
error rate of 3.2 weeks in zebrafish between 10.9-78.1 weeks of age. We also demonstrate the use of multiplex
PCR as an efficient quantitative approach to measure DNA methylation for the use of age estimation. This study
highlights the potential further use of DNA methylation as an age estimation method in non-mammalian
vertebrate species.
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and model organisms such as mice because of the large
expense involved in development.
Age is fundamental information in fisheries as it can be
used to estimate abundance [17], sustainable harvests,
and population growth rates [18]. In short lived species
the length of the fish is often used as a substitute for age
[19, 20] otherwise otoliths (ear bones) are used for most
species [21–23]. It is possible to obtain daily increments
by otoliths for short lived fish [24]. However, this
becomes difficult for fish greater than 10 years old,
where only annual increments can be measured [25].
DNA methylation-based age-estimation may offer a
robust alternative in fishes, and further, provide sub-
annual age increments. Age estimation from otoliths is
also lethal as it involves the removal of the inner ear
bone. Non-lethal age estimation methods are ethically
preferable and compatible with multiple live capture
survey methods such as genetic capture-recapture studies
[26]. In this study, we use zebrafish as a model species
to develop a cost-effective approach for age estimation
in fish. Zebrafish are an ideal species for this work as
individuals with known ages are readily available.
Zebrafish are a short-lived species that can reproduce at
10 weeks and with an average lifespan in captivity of 150
weeks [27]. This short life cycle, their regenerative
ability, and their senescent phenotype with increasing age
makes them a valuable model for vertebrate ageing
research [28]. Zebrafish are highly fertile and are
generally inexpensive to maintain making them an ideal
model species for experimental research on any aspect of
fish biology [29]. Moreover, zebrafish have a long
history as a genetic model organism, with a well-
characterised genome that can be genetically manipulated
[30–32]. The zebrafish epigenome experiences variation
due to environmental factors similar to other species [33].
DNA methylation also has a pivotal role in zebrafish
embryonic development orchestrating the transcriptome
and regulating cell development [33, 34]. However, it is
unknown if DNA methylation is predictive of age during
post-embryonic development, as seen in mammals [7,
35]. Here, using RRBS and a known age series we
identify DNA methylation biomarkers that accurately
predict the age of zebrafish from caudal fin tissue. We
develop a multiplex PCR assay for the affordable and
efficient measurement of DNA methylation to estimate
age with high accuracy and precision.
RESULTS
Age marker identification by reduced representation
bisulfite sequencing
Full details on the maintenance of the zebrafish colony
can be found in Supplementary File 1. On average, 45.1
million reads per RRBS library (Supplementary File 1
Data) was aligned to the zebrafish genome with an
alignment rate of 87.4%. This resulted in a total of
524,038 CpG sites with adequate coverage in at least
90% of all samples (see Methods). Of these sites, 60.9%
were within gene bodies such as exons (Supplementary
Figure 1). Global CpG methylation level was on average
79.5%, similar to what has been observed in other
zebrafish tissues [36–38]. We found no correlation
between global CpG methylation and age (Pearson
correlation = 0.030, p-value = 0.77). However, we
identified methylation at 1,311 CpG sites to significantly
correlate (p-value < 0.05) with increasing age, similar to
what has been found in mice [15]. This suggests specific
CpG sites are associated with ageing but not epigenetic
drift as indicated by global methylation [39].
An elastic net regression model was used to regress age
over the 70% of the RRBS samples (67 samples). The
regression model returns the minimum number of sites
required to estimate age (see Methods). Our model to
estimate age in zebrafish using RRBS data was based
on a total of 29 CpG sites (Supplementary Table 2). In
the training data set a high correlation (Pearson
correlation = 0.95, p-value < 2.20 x 10-16) between the
chronological and predicted age was observed (Figure
1A). In addition, a high correlation (Pearson correlation
= 0.92, p-value = 9.56 x 10-11) between these variables
in the testing data set was also observed (Figure 1B). A
median absolute error (MAE) rate of 3.7 weeks was
found in the testing data set (Figure 1C) and no
statistical difference was observed between the
absolute error rate between the training and testing data
sets (p-value = 0.14, t-test, two-tailed). The similar
performance between the training and testing data sets
suggests a low possibility of overfitting. A PCA was
used to visualise the separation of samples by age using
the methylation levels of the 29 CpG sites (Figure 1D).
The first principal component explains 23.4% of the
variation by age. This unsupervised clustering shows
separation of the samples solely on increasing age,
suggesting the 29 CpG sites are ideal candidates to
estimate age. Samples did not cluster by sex suggesting
the age associated sites are not sex specific
(Supplementary Figure 2). No significant gene
ontology (GO) enrichment was observed for the 29
CpG sites using Enrichr. Using a zebrafish background
set of genes, four genes (meis2a, gnptab, hoxb3a,
mab21l2) relating to embryonic skeletal system
morphogenesis were identified (adjusted p-value =
0.06). It should be noted that this is not a significant
GO result. However, it is a similar pattern to what has
been observed in age associated CpG sites in humans
as many are related to embryonic development [14].
From here on the 29 CpG sites will be referred to as the
zebrafish clock sites.
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Multiplex PCR followed by sequencing
Multiplex PCR has been used previously to measure
DNA methylation at select CpG sites [40]. In
comparison to RRBS, it provides a 100-fold decrease in
sequencing cost, making a more cost-effective approach
for targeted sequencing. For each zebrafish clock CpG
site, primers were designed to amplify an approximate
140bp amplicon inclusive of the CpG site
(Supplementary Table 3). Three CpG sites were
removed from the model due to lack of amplification by
primers (see Methods). Caudal fin tissue from 96
zebrafish that were not part of the initial RRBS and
aged between 10.9 - 78.1 weeks was used to test the
multiplex PCR assay. Samples were assayed in
triplicate to determine reproducibility of the method. On
average, 459,000 reads were aligned to the reference
genome, with 15,248 reads per amplicon, with an
alignment rate of 98.5%. By having a saturation of high
read coverage per amplicon reduces any potential of
read variation on methylation levels. We found a high
average correlation across the replicates (Supplementary
Figure 3) between the chronological and predicted age
(Pearson correlation = 0.97) and a low average (mean)
MAE of 3.18 weeks (Figure 2). No statistically
significant difference was found between the absolute
Figure 1. Zebrafish age estimation from DNA methylation of 29 CpG sites. Performance of the model in the (A) training data set,
(B) testing data set. Colour represents the sample sex in the correlation plots. (C) Boxplots show the absolute error rate in the training and
testing data sets. (D) Unsupervised clustering of samples using the 29 CpG sites show separation based on age in the first principle
component.
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error rates between replicates (p-value = 0.366, one-way
ANOVA), suggesting the method is highly reproducible.
In addition, no statistically significant difference was
found between the absolute error rate in the RRBS
testing data set and the multiplex PCR samples (p-value
= 0.23, t-test, two-tailed). This suggests RRBS and
multiplex PCR return similar sensitivities in methylation
values. A median relative error of 8.2% was also
observed in the multiplex PCR assay. No significant
difference was found between the residuals and
increasing age (Supplementary Figure 4) suggesting a
consistent relative error rate with age.
Methylation-sensitive PCR
Methylation sensitive PCR (msPCR) can be deployed as
a rapid and cost-effective method to assay methylation of
selected CpG sites, but has not been used to quantify
methylation for the estimation of age [41]. We used
msPCR as a potential alternative non-sequencing
methodology to quantify DNA methylation for age
prediction. Primers (Supplementary Table 4) were
designed to target the zebrafish clock sites (see
Methods). Despite a significant correlation between the
chronological and predicted age (Pearson correlation =
0.62, p-value = 0.00028) the MAE rate increased 261%
(13.4 weeks) compared to the RRBS MAE rate
(Supplementary Figure 5). This analysis suggests msPCR
is not sensitive enough as the relative error was 36.2%.
Epigenetic drift
The elastic net regression model returns the minimum
number of CpG sites required to estimate age. However,
these sites differ in terms of importance for age
prediction. Each CpG site has a different weight (Figure
3A), but collectively can be used to estimate age. This
demonstrates that despite each CpG site having a
different degree of age-association, collectively multiple
CpG sites can be used to accurately estimate age [14].
To determine the level of age-association in other CpG
sites we used a ridge model (α-parameter = 0 in glmnet)
and randomly selected 29 CpG sites out of the possible
524,038 CpG sites. This was repeated 10,000 times and
produced an average MAE of 15.1 weeks (Figure 3B).
This analysis demonstrates that collectively CpG sites
can be predictive of age, however certain CpG sites are
better candidates as biomarkers of age.
Figure 2. Performance of age estimation by multiplex PCR showing the absolute error rate for the 96 samples in triplicate.
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DISCUSSION
We have developed the first epigenetic clock for
zebrafish. By developing a high-resolution DNA
methylation map for zebrafish caudal fin tissue, we were
able to identify age associated CpG sites that can be
collectively used to estimate age. Previous studies have
developed assays to target small numbers of CpG sites
to estimate age [42, 43] or used data-intensive genome-
wide approaches [15, 16]. This is the first study to
both develop a genome-wide characterisation of age-
associated CpG sites in zebrafish and to construct a high-
throughput assay deployable in a basic molecular biology
laboratory for specific CpG sites that estimate age.
The changes in DNA methylation at specific CpG sites
with increasing age are generally small and it is rare for
methylation levels to go from completely unmethylated
to fully methylated or vice versa. Therefore, when
measuring methylation at specific CpG sites it is
essential for the assay to be highly sensitive. The
efficacy of msPCR was investigated because of its
modest technical requirements in comparison to DNA
sequencing approaches. In other applications msPCR
has delivered sufficient sensitivity to cancer prognosis
assays [44]. However, msPCR was insufficiently
sensitive for the detection of the small changes in
methylation during ageing in zebrafish. In contrast,
multiplex PCR appears to be an ideal cost-effective
approach to estimate age from CpG methylation. As
other age-estimation studies have shown, fewer than
110 CpG sites is required to estimate age [16, 42, 43].
Therefore, if estimating age is the only requirement, it is
only necessary to perform sequencing on CpG sites that
are predictive of age rather than the more data intensive
and costly RRBS. Multiplex PCR followed by
sequencing is an ideal tool to estimating age as it can
focus on genomic regions of interest and still provide
the same sensitivity as RRBS. This method for age
estimation has an advantage over array-based methods
for many researchers in that arrays are only available for
a limited range of species and the technology for
implementing array-based epigenetic clocks is not
available in many laboratories.
The aim of the study was to demonstrate the use of a
cost-effective method of age estimation in a model fish.
However, just as similar clocks in mice and humans
have been used, there is the potential for it also to be
used as a proxy for zebrafish health [14, 15]. Known age
profiles are key data used to inform stock assessments in
harvested fishes, and existing methods to age fish is
lethal and subject to error. There is the potential of
applying the age associated CpG sites in zebrafish in
other bony fish where there is the conservation of DNA
sequence. This approach has been used with success in
mammals [42, 43]. Unlike human and mice studies,
which were multi-tissue, the zebrafish epigenetic clock
is tissue specific, and therefore may not accurately
predict age from methylation data collected from other
tissues. Caudal fin was selected because it is a widely
sampled in fisheries management context and can be
Figure 3. Importance of specific CpG sites in estimating the age of zebrafish. (A) Weighting and directionality of each of the 29 CpG
age associated sites. (B) Distribution of the performance of 10,000 age-estimation models in the form of median absolute error (weeks).
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collected non-lethally [45]. Establishing a clock for fish
caudal fin is therefore a necessary proof of concept for
application to other species.
The first age-predictive model for a fish was developed
by [46], who used multiplex PCR to estimate the age of
European Seabass (Dicentrarchus labrax). That study
also produced a highly accurate model (Pearson
correlation = 0.824, MAE= 2.149 years), based on
targeting candidate genes involved in tissue specific
development [46]. Our study took an approach that has
previously been used to identify age biomarkers in mice
and dogs [15, 16] and that is less dependent on prior
knowledge. The zebrafish model produced a more
accurate model than the European Seabass (Pearson
correlation = 0.97). However, it’s unclear whether this
reflects differences between the species, or the method
used to identify predictive CpG sites. Regardless of the
performance of the models, the addition of the zebrafish
epigenetic clock demonstrates the possibility of DNA
methylation being predictive of age in a wide variety of
fish. Under ideal conditions zebrafish live up to an
average of 182 weeks in captivity [27]. This study used
zebrafish up to 78.1 weeks as older individuals were
unavailable. It is therefore unknown how well the
model will perform on older individuals. The model
was also developed with one zebrafish strain (AB). In
any type of machine learning, the model will generally
perform best on data similar characteristics to what it
was trained on. To establish the generality of our
results, in future the zebrafish model we have developed
should ideally be tested in a subset of known age older
fish and from other strains, including wild caught fish.
However, if applying this model to wild zebrafish or
applying similar tests for other wild fish species
calibrated with a preponderance of younger individual,
individuals near the maximum lifespan are generally
very rare in wild populations. For many population
biology research questions, older individuals are
grouped into a “plus class” representing a wide age
range, but very small counts [47].
The stochastic accumulation of error in the epigenome
is often described as epigenetic drift and is a
fundamental part of the ageing process [48, 49]. Using
biological clocks to measure epigenetic drift has been
previously suggested [35, 50, 51]. We found
methylation at all CpG sites captured by RRBS have
some level of age association, which is similar to results
observed in mice [15]. This supports the notion of
epigenetic drift as a factor in why DNA methylation
performs as a marker of age. Yet, while epigenetic drift
occurs as a random process across the genome, some
CpG sites are significantly better predictors of age,
suggesting additional functional drivers of age-related
methylation. In humans, 30% of age associated DNA
methylation is tissue specific [52]. There is also
evidence that age associated DNA methylation occurs
in bivalent domains and Polycomb group promoters [52,
53]. This suggests that age associated DNA methylation
occurs in conjunction with specific epigenetic and
genomic features over and above signals of epigenetic
drift [54]. This is yet to be explored in non-human
organisms such as zebrafish. Developing a better
functional understanding of the mechanisms
underpinning epigenetic clocks would enable more
targeted identification of age biomarkers, especially in
non-model organisms.
One of the limitations of using RRBS as it does not
assess all CpG sites and more predictive sites may be
missed. Furthermore, like all sequence-based approaches
it can result in inconsistent coverage across sites, which
may introduce error in the identification of age markers.
Whole genome bisulfite sequencing would enable
evaluation of all CpG sites, potentially revealing the
strongest possible set of age predictive CpG sites.
However, the high cost per sample and the large number
of samples required to develop an age-estimation model
would makes this a costly endeavour. Indeed, the high
accuracy and precision demonstrated by our model
developed from sites discovered through RRBS indicates
that this approach suffices for developing accurate
epigenetic clock for zebrafish. Similarly, the deep
sequence coverage enabled by our PCR multiplex assay
of CpG markers initially identified through RRBS shows
that despite potential instances of low coverage in the
marker identification phase, the biomarkers identified
produce highly accurate estimates of zebrafish age.
Microarrays represent an alternative means to assay CpG
sites, with high accuracy and without suffering from low
sequence coverage [55].
CONCLUSIONS
This study is the first to develop a RRBS dataset for
zebrafish caudal fin tissue from a broad range of ages
and highlights the potential to use DNA methylation as
a predictor of age in non-mammalian and non-avian
animal groups. This is a valuable resource as it provides
a time series of methylation in a species that is a model
for development studies. Using this methylation data set
we were able to identify CpG sites that collectively can
be used to estimate age very accurately. Moreover, we
were able to design a multiplex PCR assay to measure
the methylation state at 26 CpG sites, at a significantly
reduced cost and complexity of analysis compared to
RRBS. Age has a central role in regulating the
dynamics of animal populations and estimates of age-
structure underpin almost all frameworks for wildlife
and fisheries management. Yet, biomarkers for age are
lacking for most animal groups. The transfer of
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epigenetic markers between mammal groups including
humans, mice and bats indicates that similar approaches
may be feasible in other groups such as fishes. The
transfer of zebrafish epigenetic age markers to fishes
with significant commercial or conservation importance
would be of major significance considering the
importance of age structure to management and the lack
of effective non-lethal alternatives to estimating age.
MATERIALS AND METHODS
Zebrafish ageing colony
Zebrafish (AB strain) were bred and maintained at the
Western Australian Zebrafish Experimental Research
Centre (WAZERC). Refer to the Supplementary File 1
for full details on how the zebrafish were maintained.
Animal ethics was approved by the University of
Western Australia animal ethics committee (RA/3/100/
1630). Animals aged between 10.9-78.1 weeks were
euthanized using rapid cooling. Once deceased all
organs and tissues were collected and stored into
RNAlater (Thermo Fisher). DNA was extracted using
the DNeasy Blood and Tissue Kit (QIAGEN) as
instructed in the manufacturer’s protocol.
Reduced representation bisulfite sequencing
A total of 96 RRBS libraries were prepared as
previously described with digestion of the restriction
enzyme MspI [56] at the Australian Genome Research
Facility (AGRF) and were sequenced using an Illumina
NovaSeq. Details of each zebrafish which were
sequenced by RRBS are provided in Supplementary
Table 1.
Data availability
Raw sequencing data from RRBS has been made
publicly available on the CSIRO Data Access Portal
available at https://doi.org/10.25919/5f63ce026960a.
RRBS data analysis
Fastq files were quality checked using FastQC v0.11.8
(https://www.bioinformatics.babraham.ac.uk/projects/f
astqc/). Reads were trimmed using trimmomatic v 0.38
[57] with the following options: SE -phred33
ILLUMINACLIP:TruSeq3-SE:2:30:10 LEADING:3
TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36.
Trimmed reads were aligned to the zebrafish genome
(danRer10) using BS-Seeker2 v 2.0.3 default settings
[58] and bowtie2 v2.3.4 [59]. Methylation calling was
performed using BS-Seeker2 call methylation module
with default settings. CpG sites were filtered out of the
analysis if they had a mean coverage of < 2 reads or >
100 reads as what has occurred previously [15]. On
average, each site per sample had a coverage of 16 reads.
Predicting age from CpG methylation
Samples were randomly assigned to either a training (67
samples) or a testing data set (29 samples) using the
createDataPartition function in the caret R package to
maintain equal ratios of sex in each data set [60]. Age
was transformed to natural log to fit a linear model.
Using an elastic net regression model, the age of the
zebrafish was regressed over all CpG site methylation in
the training data set. Sites with missing data in less than
10% of samples were replaced with a methylation score
of 0. By replacing the methylation score with 0 in
samples with missing sites prevents any correction bias
as the site will be removed from the analysis. The
glmnet function in the glmnet R package [61] was set to
a 10-fold cross validation with an α-parameter of 0.5
(optimal between a ridge and lasso model), which
returned a minimum λ-value based on the training data
of 0.02599415. These parameters resulted in a total of
29 CpG sites required to estimate the age of zebrafish.
These 29 sites had methylation values in 100% of all
samples. The performance of the model in the training
and testing data set were assessed using Pearson
correlations between the chronological and predicted
age and the MAE rates.
Principal component analysis and gene ontology
A principal component analysis (PCA) was used as a
form of unsupervised clustering to visualise the age
associated CpG sites in terms of separating samples by
age. PCA was performed using FactoMineR [62]. Gene
ontology (GO) enrichment was performed using the 2018
terms in the R package Enrichr and using the Generic
Gene Ontology Term finder (https://go.princeton.edu/)
[63, 64]. All analyses were performed in R using version
3.5.1 [65].
DNA bisulfite conversion
DNA was bisulfite converted using the EZ DNA
Methylation Gold Kit (Zymo Research) using
manufacturer’s protocol or using a manual protocol as
previously described [66].
Multiplex PCR
A total of 96 independent zebrafish caudal fin tissue
samples which were not part of the initial RRBS were
used for multiplex PCR. Primers were designed using
PrimerSuite [67] and were divided into two PCR
reaction pools prior to barcoding (Supplementary Table
3). Three primer pairs were unable to be optimised as
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part of the overall multiplex PCR assay and were
removed from the analysis. The remaining 26 CpG sites
were remodelled using the RRBS methylation data by
applying the ridge model component in the glmnet
function (α-parameter = 0) resulting in alternative
weights for each site (Supplementary Table 3). A
generalised linear model was applied to the raw
prediction values from the elastic net regression model
(sum of the coefficient weights multiplied by the DNA
methylation beta values). The final model to estimate
age in zebrafish is:
ln( ) 1.008age x
Where x is the sum of the raw summed methylation beta
values for each sample.
Samples were run in triplicate to determine reproducibility
of the method. The final 50μL PCR reaction contained
1x Green GoTaq Flexi Buffer (Promega), 0.025 U/μL of
GoTaq Hot Start Polymerase (Promega), 4.5mM MgCl2
(Promega), 0.5x Combinatorial Enhancer Solution (CES)
(Refer to [68]), 200μM of each dNTP (Fisher Biotec),
15mM Tetramethylammonium chloride (TMAC)
(Sigma-Aldrich), primers (both forward and reverse)
were used at 200nM and finally the bisulfite treated DNA
(2ng/μL). Cycling conditions were 94° C/5mins; 12
cycles of 97° C/15 seconds and 45° C/30 seconds,
72° C/120 seconds; 1 cycle of 72° C/120 seconds and
6° C/hold. An Eppendorf ProS 384 thermocycler was
used for amplification. Primers were designed using
PrimerSuite [67] and primer sequences are provided in
Supplementary Table 3.
Barcoding
Oligonucleotides with attached MiSeq adaptors and
barcodes were used for the barcoding reaction (Fluidigm
PN FLD-100-3771). Barcoding was performed using 1x
Green GoTaq Flexi Buffer, 0.05 U/μL of GoTaq Hot
Start Polymerase, 0.25x CES, 4.5mM MgCl2, 200μM of
each dNTP, 25μL of the pooled template after Sera-Mag
Magnetic SpeedBeads (GE Healthcare Life Sciences)
clean up. Cycling conditions for barcoding were as
follows 94° C/5mins; 9 cycles of 97° C/15 seconds,
60° C/30 seconds and 72° C/2mins; 72° C/2mins;
6° C/5mins. Barcoding was performed using an
Eppendorf ProS 96 or 384 thermocycler. Sequencing
was performed on an Illumina MiSeq using the MiSeq
Reagent Kit v2 (300 cycle; PN MS-102-2002).
Multiplex PCR followed by sequencing data analysis
Sequencing data was hard clipped by 15bp at both 5′ and
3′ ends to remove adaptor sequences by SeqKit v 1.2
[69]. Reads were aligned to a reduced representation of
the genome focusing on a 500bp upstream and
downstream of the zebrafish clock sites. Reads were
aligned using Bismark v 0.20.0 with the following
options: --bowtie2 -N 1 -L 15 --bam -p 2 --score L,-0.6,-
0.6 --non_directional and methylation calling was
performed using bismark_methylation_extractor [70].
Methylation sensitive PCR
msPCR primers were designed using MethPrimer v2.0
[71] which produces two pairs of primers for when the
DNA is methylated and unmethylated. msPCR was
optimised using the protocol detailed previously [44]
with the final cycling conditions: Initialisation step
95° C/15 mins, denaturation step 95° C/30 seconds,
annealing 55° C/40 seconds and extension 72° C/40
seconds, for 40 cycles. msPCR was performed using an
AllTaq Mastermix (Qiagen) with 1 x SYBR Green
(Thermo Fisher) in a Bio-Rad CFX96. The ΔCt values
for each primer pair was used as a quantitative method
for methylation. A leave-one-out cross validation
approach was used to determine the level of precision
for using msPCR to estimate age [60, 72].
AUTHOR CONTRIBUTIONS
B.M. designed, carried out the study, analysed and
interpreted the data and wrote the manuscript. D.K.
designed, analysed, carried out the multiplex PCR and
provided discussion and intellectual input into the
manuscript. L.K and B.E. were involved in the zebrafish
maintenance, study design and provided discussion and
intellectual input into the manuscript. O.B. and S.J.
were involved in the study design, provided discussion
and intellectual input into the manuscript. All authors
read and approved the final version of the manuscript.
ACKNOWLEDGMENTS
The authors would like to thank Bioplatforms Australia
for their assistance and funding for the reduced
representation bisulfite sequencing that was presented in
this paper. We would also like to thank Mark
Bravington, Pierre Feutry, Yi Jin Liew, and Jason Ross
for their suggestions towards our manuscript. We would
also like to thank Wendy Hopper and Dion Mellows for
their assistance in zebrafish maintenance and handling.
We would also like to thank the reviewers for their
constructive comments towards our manuscript. This
project was funded by the CSIRO Environomics Future
Science Platform and is supported by the North West
Shelf Flatback Turtle Conservation Program.
CONFLICTS OF INTEREST
The authors have no conflicts of interest to declare.
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SUPPLEMENTARY MATERIALS
Supplementary Figures
Supplementary Figure 1. Genomic distribution of CpG sites which were captured in reduced representation bisulfite
sequencing.
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Supplementary Figure 2. Principle component analysis displaying no separation of sample sex.
www.aging-us.com 24831 AGING
Supplementary Figure 3. Correlation between the chronological and predicted age in zebrafish by multiplex PCR. Samples
were run in triplicate.
Supplementary Figure 4. Absolute error rate of samples by multiplex PCR over increasing age.
www.aging-us.com 24832 AGING
Supplementary Figure 5. Methylation-sensitive PCR to estimate age in zebrafish. (A) Correlation between the chronological and
predicted age and (B) the absolute error rate in age estimation.
www.aging-us.com 24833 AGING
Supplementary Tables
Please browse Full Text version to see the data of Supplementary Tables 1, 3, 4.
Supplementary Table 1. Phenotype and sequencing data of the 96 caudal fin tissue from zebrafish used for RRBS.
Supplementary Table 2. Locations of the zebrafish clock sites used to estimate age using RRBS data and the closest
genomic feature.
CpG site
Association with age
Closest feature
chr
position
strand
Weight
Correlation
p-value
Gene
feature
start
end
strand
Intercept
NA
NA
3.261736
NA
NA
NA
NA
NA
NA
NA
chr12
21540399
+
-0.06868
-0.456308633
3.80E-06
mrpl27
exon
21563072
21563114
+
chr12
35432443
+
0.041155
0.425636828
1.90E-05
chmp6b
exon
35487001
35487160
+
chr13
31180246
+
0.422877
0.49406794
4.18E-07
mettl18
exon
31259863
31260037
+
chr13
38582448
+
0.287827
0.518416373
8.70E-08
zgc:153049
exon
38688631
38688754
+
chr14
38455793
-
-0.34896
-0.404759937
5.20E-05
csnk1a1
exon
38442661
38443287
-
chr14
45387151
+
-0.22242
-0.432519711
1.34E-05
sncb
exon
45619305
45619341
+
chr17
52836692
+
0.089225
0.44142766
8.45E-06
meis2a
exon
52833657
52835083
+
chr18
38107080
+
-0.40695
-0.407236996
4.63E-05
nucb2b
exon
38210387
38210462
+
chr18
50792250
+
-0.3449
-0.434582509
1.21E-05
reln
CDS
50795737
50795848
+
chr19
20077224
+
0.013643
0.428542532
1.64E-05
hibadha
CDS
20079490
20079646
+
chr1
23386154
+
0.267495
0.462650352
2.67E-06
mab21l2
CDS
23385795
23386871
+
chr1
43259461
+
-0.28726
-0.419849369
2.53E-05
cabp2a
exon
43425989
43426036
+
chr20
16578711
-
0.007467
0.459510595
3.18E-06
ches1
CDS
16578582
16579053
-
chr20
21624045
+
0.310809
0.383202718
0.000138
jag2b
exon
21573904
21575945
+
chr20
26523373
+
0.436491
0.468930078
1.87E-06
zbtb2b
exon
26504936
26508142
+
chr20
28928268
+
0.050606
0.492313214
4.66E-07
fntb
exon
28924424
28924877
+
chr21
23231786
+
-0.09242
-0.406502544
4.79E-05
alg8
exon
22864361
22864801
+
chr21
25150743
+
-0.33385
-0.541055377
1.80E-08
sycn.2
exon
25189953
25190586
+
chr24
19868851
+
-0.22858
-0.559326016
4.64E-09
LOC100334155
exon
20073262
20073368
+
chr24
4215673
+
0.06477
0.410284892
4.01E-05
wdr37
exon
3494784
3495510
+
chr25
14631230
+
0.217506
0.420374681
2.46E-05
mpped2
CDS
14637373
14637488
+
chr25
16313450
+
0.307822
0.482149108
8.63E-07
tead1a
CDS
16315617
16315681
+
chr25
36872756
+
-0.17805
-0.360931599
0.000352
chmp1a
exon
36871083
36871567
+
chr25
6461988
+
0.453596
0.408996487
4.26E-05
snx33
exon
6351734
6353787
+
chr2
8207957
+
0.258846
0.462933479
2.63E-06
chst2a
exon
8314444
8316603
+
chr3
23616782
+
-0.27465
-0.451308167
4.99E-06
hoxb3a
exon
23616752
23617534
+
chr4
17690807
+
-0.26411
-0.603855727
1.17E-10
gnptab
exon
17690788
17690922
+
chr4
18675145
+
-0.20748
-0.461522112
2.84E-06
slc26a4
CDS
18793563
18793599
+
chr5
51679905
+
0.034253
0.386666431
0.000118
slc14a2
exon
51529758
51531231
+
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Supplementary Table 3. Primer sequences targeting the zebrafish clock sites for multiplex PCR and weights for
age-estimation.
Supplementary Table 4. Primer sequences targeting the zebrafish clock sites by methylation sensitive PCR.
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Supplementary File
Please browse Full Text version to see the data of Supplementary File 1.
Supplementary File 1. Details on how the zebrafish were maintained.
