University of Groningen
Absolute standards as a useful addition to the avian quantitative PCR telomere assay
Barrett, Emma L. B.; Boner, Winifred; Mulder, Geertje; Monaghan, Pat; Verhulst, Simon;
Richardson, David S.
Published in:
Journal of Avian Biology
DOI:
10.1111/j.1600-048X.2012.05787.x
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to
cite from it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date:
2012
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Barrett, E. L. B., Boner, W., Mulder, E., Monaghan, P., Verhulst, S., & Richardson, D. S. (2012). Absolute
standards as a useful addition to the avian quantitative PCR telomere assay. Journal of Avian Biology,
43(6), 571-576. DOI: 10.1111/j.1600-048X.2012.05787.x
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the
author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
Take-down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately
and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the
number of authors shown on this cover page is limited to 10 maximum.
Download date: 15-06-2017
Journal of Avian Biology 43: 571–576, 2012
doi: 10.1111/j.1600-048X.2012.05787.x
© 2012 The Authors. Journal of Avian Biology © 2012 Nordic Society Oikos
Subject Editor: Jan-Åke Nilsson. Accepted 24 August 2012
Absolute standards as a useful addition to the avian quantitative
PCR telomere assay
Emma L. B. Barrett, Winifred Boner, Ellis Mulder, Pat Monaghan, Simon Verhulst and
David S. Richardson
E. L. B. Barrett ([email protected]) and D. S. Richardson, School of Biological Sciences, Univ. of East Anglia, Norwich Research Park,
Norwich, Norfolk, NR4 7TJ, UK. DSR also at: Nature Seychelles, PO Box 1310, Mahe, Republic of Seychelles. – W. Boner and P. Monaghan,
College of Medical, Veterinary and Life Sciences, Graham Kerr Building, Univ. of Glasgow, Glasgow, Scotland, G12 8QQ, UK. – E. Mulder
and S. Verhulst, Behavioural Biology, Centre for Life Sciences, Univ. of Groningen, PO Box 11103, 9700 CC Groningen, the Netherlands­­.
Bird populations provide excellent systems to investigate variation in longevity in the wild since individuals can often
be monitored over their lifetime. A number of recent studies suggest that the dynamics of protective telomere chromosome caps (telomere length and rate of loss) are indicative of biological state and potentially useful as indicators of future
longevity. Currently, Terminal Restriction Fragment (TRF) analysis and relative quantitative PCR (qPCR) are used to
measure telomeres in birds, but with limitations. TRF analysis is time consuming, while relative qPCR gives a withinstudy relative value making it difficult to compare across experiments. Utilising an approach first developed in humans
of using synthetic oligomer telomeric (TTAGGG)n and normaliser gene standards of known length to calibrate qPCR
values, we describe a methodological adaptation to the avian qPCR telomere assay to make results comparable within,
and potentially between, bird species. We evaluate this absolute qPCR method in the Seychelles warbler Acrocephalus
sechellensis against relative qPCR measurements on the same samples. Telomere estimates from both methods showed an
age-related decline in telomere length, and were highly correlated (r  0.99). Absolute qPCR avian telomere analysis may
prove a useful means of estimating telomere lengths in a calibrated, sensitive, and efficient way using small amounts of
archived bird sample.
Telomeres are of interest to ecologists as possible indicators
of individual state, ageing trajectory, and potential life
expectancy (Monaghan and Haussmann 2006). To investigate these relationships, we need good life-history data
and methods by which we can reliably measure telomeric
variation. Bird populations are currently widely used in
such studies thanks to the quantity and quality of longterm data and archived samples, and the potential for
following marked individuals in the field (Bize et al. 2009,
Salomons et al. 2009). Hence, telomere assays originally
developed for medical research have been modified accordingly for use in birds (Haussmann and Mauck 2008,
Criscuolo et al. 2009).
The TRF length analysis method for measuring telo­
meres (Harley et al. 1990) was adapted for use in birds
by Haussmann et al. (Haussmann and Vleck 2002,
Haussmann and Mauck 2008), and more recently
Criscuolo et al. (2009) modified Cawthon’s (2002) qPCR
method for relative telomere measurement for use in birds.
Both methods are widely used; telomere measurement by
qPCR is the most time efficient and high-throughput
method current available and requires less DNA than the
TRF methods, but unlike the TRF method, the qPCR
protocol gives a relative measure of telomere length rather
than a length in kb, which makes it difficult to use in comparative studies.
O’Callaghan et al. (2008) modified Cawthon’s relative
qPCR method to allow for an absolute telomere measurement in the human genome by the comparison of sample
amplification with that of synthesised telomere oligomers
of known length. We have, for the first time, modified this
method for its application to birds to provide kb units per
diploid genome for telomere estimation by qPCR. We
suggest this method could allow results to be compared
between experiments and laboratories within species, aid
the development of standard protocols, and may allow for
research into the evolution of telomere dynamics across
species. However, care should be taken in comparing
telomere estimates between species given the varying
prevalence of interstitial (TTAGGG)n sequence in birds
(Meyne et al. 1990, Venkatesan and Price 1998, Delany
et al. 2000, Nanda et al. 2002) included in the absolute
qPCR telomere estimate could be a confounding factor.
This protocol also potentially allows researchers to expand
their dataset even if the reference sample initially used is no
longer available. Here we compare telomere measurements
571
from the same samples using both qPCR protocols to establish that the comparison to an absolute standard does
not add errant variance to the relative measure, and discuss
the potential uses of an absolute qPCR method.
Methods
Study species and blood sampling
The Seychelles warbler is a 15 g passerine bird that can
live up to 17 yr in the wild (Brouwer et al. 2010). Since
1985 nestlings (ca 12 d old) have been captured on Cousin
Island and ringed with a unique combination of colour and
British Trust for Ornithology rings. Hence, the exact ages
are known for the majority of individuals ( 96% since
1997, Richardson et al. 2001). Every year blood samples
(ca 25 ml) are taken via brachial venipuncture and for
most birds’ repeated longitudinal blood samples have
been archived in 800 ml 100% ethanol at 2°C. Not all
sample storage mediums are suitable for subsequent telo­
mere analysis, and sub-optimal storage conditions may
jeopardise DNA integrity (Seutin et al. 1991, Freed and
Cann 2006). If ethanol is to be used, the concentration
must near 100% (Bainard et al. 2010), and it is good
practice to ensure ethanol volume is at least ten times
greater than the volume of the sample to be stored. To
maximize the range of telomere lengths investigated in
this preliminary sample we used whole blood taken from
adult birds of different ages (25 blood samples from 12
adult birds; 7 females and 5 males aged from 0 to 11 yr).
Three birds were measured three times (birds 2, 9 and 6),
seven birds twice (birds 1, 3, 7, 8, 10, 11 and 12), and
two birds once (birds 4 and 5; see Fig. 1 for specific ages).
Figure 1. Individual changes in mean absolute telomere quantity
(kb per diploid genome) measured by absolute qPCR with age.
Empty triangle and large dash line  bird 1; filled black circle and
large dash line  bird 2; empty square and double line  bird 3;
empty diamond  bird 4; star  bird 5; empty circle and large
dash-dot line  bird 6; black square with white cross and
small dash-dot  bird 7; grey filled diamond and grey solid
line  bird 8; filled black square and solid black line  bird 9;
small dash marker and large dash-double dot line  bird 10; filled
black triangle and small dash line  bird 11; filled black diamond
and dotted line  bird 12. Error bars represent the betweenplate standard error.
572
qPCR assay
It is vital with any telomere estimation technique that the
DNA is extracted by a means that ensures good purity
(Demeke and Jenkins 2010), and that samples are stored
in such a way as to ensure that DNA is of a high molecular
weight with no signs of degradation (Bustin et al. 2009).
Poor DNA quality can lead to biologically irrelevant
variance in telomere lengths. Using the DNeasy Blood and
Tissue Kit (Qiagen), we extracted genomic DNA from
~ 3 mm2 flake of whole blood stored in ethanol following
the manufacturer’s protocol, with the modification of the
overnight protein lysis at 37°C, and a final DNA elution
volume of 80 ml to obtain a concentrated solute. DNA
concentration and purity was quantified using a NanoDrop
8000 Spectrophotometer (ThermoScientific), and DNA inte­
grity was validated by electrophoresis verification on a 1.2%
agarose gel. Average total yield was 8.3 mg of DNA per
sample and DNA was of high molecular weight and purity.
As the telomere sequence (TTAGGG)n is ubiquitous
among vertebrates (Meyne et al. 1989), the telomere
primers designed (Cawthon 2002) and modified (Roos
et al. 2008) for use in humans can be used in all birds
(Supplementary material Appendix 1). In addition, an
endogenous reference sequence that is numerically invariable between individuals must be amplified and measured
as an internal control to normalise the amount of telomere
sequence compared to the amount of DNA in the reaction
(Cawthon 2002). We used the primers designed to
amplify part of the glyceraldehyde-3-phosphate dehydro­
genase (GAPDH or G3PDH ) gene in the zebra finch
Supplementary material Appendix 1; Criscuolo et al.
2009) that also work in the alpine swift (Criscuolo et al.
2009), and the Seychelles warbler. Unlike mammalian
GAPDH, which has multiple pseudogenes, avian GAPDH
is both autosomal and found in single copy in the bird
species for which the genome is known (chromosome 1;
zebra finch: genbank accession no: AF255390 and
chicken: NW_001471525). Moreover, GAPDH is often
used to construct avian phylogenies as it shows no unexpected stop codons, indels, or distinct multiple peaks in
chromatograms that would indicate the presence of pseudogenes (Alström et al. 2011).
For the qPCR analysis, we manually loaded MicroAmp
Fast Optical 96-Well Reaction Plates (Applied Biosystems),
each biological sample and oligomer standard had 3
technical replicates within each plate for both telomere
and GAPDH primers to examine within-plate variability.
This set up was then, itself, repeated over 3 separate plates
to test for between-plate repeatability. We ran a total of
21 plates (7 plates run 3 times). Each sample reaction
contained 20 ng of DNA. Telomere and GAPDH primers
were run on the same plate for each sample; this differs
from most previous telomere qPCR assays where primer
sets are run on different plates (Cawthon 2002, Criscuolo
et al. 2009). All primers were used at a concentration of
300 nM in a final volume of 25 ml containing 12.5 ml of
Absolute BLUE QPCR SYBR Green Low ROX Mix
(Abgene). Plates were sealed using MicroAmp Optical
Adhesive Film (Applied Biosystems, Cat no. 4311971).
Note that different qPCR platforms and brands of SYBR
Green mixes may require optimisation. Running conditions differ to those of Criscuolo et al. (2009), in the
present study the qPCR assays were performed using an
Applied BioSystems 7500 instrument (15 min at 95°C
followed by 40 cycles of 15 s at 95°C, 30 s annealing at
58°C and 30 s extending at 72°C; data were collected
during the extension phase). The only substantial difference was that the annealing temperature was set at an
intermediate 58°C so both amplicons could be run on the
same plate. At this intermediate annealing temperature
there was high specificity for the telomere and GAPDH
amplicons, which had very similar melt temperatures
(Supplementary material Appendix 2). We used the
open-source software LinRegPCR 12.7.0.0 to correct for
baseline fluorescence; many programmes that accompany
standard qPCR platforms fail to account for this which
can lead to incorrect estimations of efficiencies (Ruijter
et al. 2009). We also used LinRegPCR 12.7.0.0 to
determine the window of linearity per amplicon, and
calculate individual well efficiencies (Ruijter et al. 2009).
Threshold values (Nq) were set in the centre of the window
of linearity per amplicon (Telomere, Nq  0.664; GAPDH,
Nq  0.531 cycles). Threshold cycle (Cq) values were
obtained per sample for each amplicon and represent the
cycle at which the amplification plot (fluorescence level)
crosses the threshold (Nq). The number of cycles it takes
for this to occur is proportional to the quantity of template
DNA. We used the programme GenEx 5.0.1. (MultiD
analyses, Sweden), to calibrate Cq values per amplicon
across different plates using synthetic standard A of
Telomere and GAPDH, and the ‘golden sample’ reference
from the relative qPCR assay as between-plate calibrators.
We used multiple between-plate calibrators as they give
more precise results with a smaller error (Hellemans
et al. 2007). Using the same programme we averaged the
technical qPCR repeats.
The standard curves of both relative and absolute qPCR
methods had coefficient of determination  0.99.
Amplification efficiencies were calculated using the formula
 1
 
e  10 m   1  100 , where e is the percentage amplification


efficiency and m is the slope of the log of the standard
curve (Pfaffl 2001). Amplification efficiencies were different
for the different primer sets, and between the oligomer
standards and avian sample (Supplementary material
Appendix 3; Telomere primers: Oligo standard curve
86%, sample standard curve 83%; GAPDH primers:
Oligo standard curve 92%, sample standard curve 94%).
Each plate contained three ‘no template’ controls (ntc) for
each primer set.
Relative qPCR method
The standard for the relative qPCR method was a two-fold
serial dilution in Buffer AE (Qiagen) of an arbitrary
Seychelles warbler sample (resulting in 40, 20, 10, 5 and
2.5 ng of DNA per well). Cq values of the serial dilutions
were plotted against the log concentration to generate a
linear reference trend line used to quantify the amplifying
efficiency of the qPCR for telomere and GAPDH primers.
The Cq value of the 20 ng sample was also used as the
reference, or ‘golden sample’ (Criscuolo et al. 2009), to
which all other samples were compared for the relative qPCR
calculations. As amplification efficiencies of telomere and
GAPDH primers were different we determined the amount
of relative template in the sample for the telomere (XOTEL)
and GAPDH (XOGAP) amplification using the equation XO 5 10[(Cq2b)/m] (Gallup and Ackermann 2008); where Cq is
the cycle at which the amplification plot of the focal sample
crosses the threshold (Nq), while b and m are respectively the
intercept and slope of the log of the standard curve. To
normalise the amount of telomere to the DNA quality
(given by the GAPDH amplification) per sample (XOsample)
we used the equation XOsample 5 XOTEL/XOGAP. To obtain
the relative telomere estimate relative to the golden sample
we divided XOsample/XOGDS, where XOGDS 5 XOTEL/XOGAP for
the ‘golden’ reference sample amplification.
Absolute qPCR method
We used absolute qPCR to measure the average quantity
of telomere per diploid genome. This differs from TRF
analysis, which measures the average telomere length per
chromosome end. We followed the method of O’Callaghan
et al. (2008), which we modified for use in birds by using
GAPDH rather than 36B4 as the housekeeping reference
gene. Standard curves were generated by running a tenfold dilution of known quantities of synthesised oligo­
nucleotides of both telomere and GAPDH sequences in
buffer AE (Supplementary material Appendix 1, 3). Telo­
mere standards were calculated as in O’Callaghan et al.
(2008) with modifications to the magnitude of the original
standard to account for the larger abundance of avian
telomere as compared to that of humans. The number of
diploid genomes was calculated using GAPDH.
Empty plasmid DNA (pcDNA3.2, Invitrogen) was
added to each standard after dilution to give 20 ng of
total DNA per reaction well. This was done to provide
equal competition for primer binding sites as in the focal
samples (as in O’Callaghan et al. 2008). This differs from
the relative standard, which has a declining gradient of
DNA concentration over dilutions. We mathematically
corrected for differences in amplification efficiency between
sample and synthesised amplicons. We calculated how
much absolute template (Xo; kb for telomere amplification,
number of diploid genomes for GAPDH amplification)
was present in each sample as follows, where subscript s and
a respectively denote sample and absolute target template
values. We compared the sample Cqs values with the oligomer
standard curves, reconciling the efficiency differences
between the sample and exogenous oligomer reference mathematically using the equation Xo 5 EAMPsbalogEAMPs(EAMPa) 2 Cqs
(Gallup 2011), where EAMP is the exponential amplification
 1
 
value for the target reaction, calculated as E AMP  10 m  ,
and b and m are the intercept and slope of the standard
curve. We divided the quantity of telomere by the number of
diploid genome copies to obtain the amount of telomere per
diploid genome.
573
Statistical analyses
Analyses were performed in JMP 7 (SAS Inst., Marlow,
UK). Mean Cq values and standard deviations were
calculated for each of the technical triplicates. Using the
standard deviations for each amplicon we then calculated
the within-plate variability as the median standard deviation ( inter-quartile range; Bustin et al. 2009) of oligomer
and sample amplification for each set of plate replicates.
To investigate between-plate comparisons it is advised that
repeatability should be given as the variation in the final
concentration or copy number variance (Bustin et al. 2009),
and so our between-plate repeatability comparisons used
final telomere estimates. Between-plate repeatability was
given both as the co-efficient of variation and repeatabilityvalue of the telomere estimate. The co-efficient of variation
is the standard deviation expressed as a percentage of the
mean. The repeatability-value uses the means of squares
values from anova tests of samples over plates (Lessells
and Boag 1987). The repeatability-value ranges from 0 to 1,
respectively indicating low to high within-sample repeatability. The repeatability-value is superior to the co-efficient
of variation as it compares variance within samples relative
to the total variance within and across all samples. To
avoid pseudo-replication both between-plate repeatability
measurements were analysed using one random sample per
bird (n  12).
To ensure that comparison to absolute standards did not
add variance to the measure obtained by relative qPCR we
compared the two methods of telomere measurement.
We performed pair-wise correlations on relative and absolute telomere measures from the one randomly chosen
sample taken from each individual. To examine the relationship between telomere length and age we performed REML
mixed models for each method of measurement with bird
identity as a random effect, sex was also included as an independent variable as telomere lengths are known to vary
according to sex in some organisms (Barrett and Richardson
2011). The interaction between age and sex was analysed
in a separate model as the significance of main effects cannot be easily interpreted if there are interactions in the
model that include the focal main effect (Engqvist 2005).
Results
The total telomere estimate from the absolute qPCR method
ranged from 1172 to 4737 kb per diploid genome and
telomere length significantly declined with age but did not
differ with regard to sex (Table 1, Fig. 1). Within-plate
Cq variability varied little for sample or oligomers across
plate replicates (Table 2). The between-plate co-efficient of
variation for the final calculated values obtained by qPCR
methods was 9.73% for the absolute method and 13.64%
for the relative method. The between-plate repeatabilityvalue was 0.94  0.03 SE for the absolute method, and
0.90  0.06 SE for the relative method (Lessells and Boag
1987). We found a strong correlation between the relative
and absolute methods of qPCR (r  0.99, p  0.0001,
n  12; Fig. 2).
Given that telomere and GAPDH primers were run on
the same plate for each sample, and that this differs from
most previous telomere qPCR assays where primer sets
are run on different plates (Cawthon 2002, Criscuolo et al.
2009), we compared the standard deviations of the mean
ΔCq (Cq TEL2Cq GAPDH) when Cq TEL and Cq GAPDH were
measured on the same plate to the standard deviations of
the mean ΔCq when Cq GAPDH was measured on one of
the other two replicate plates. Variability was significantly
lower when both sets of primers were run on the same
plate; the distribution of standard deviations was onetailed and the median ( inter-quartile range) of standard
deviations of within-plate comparisons was 0.27 (0.18 to
0.35), while the standard deviations of between-plate
normalisation of Cq TEL of plate 1 with Cq GAPDH on plate 2
was 0.40 (0.20 to 0.58), and the between-plate normali­
sation of Cq TEL of plate 1 with Cq GAPDH on plate 3 was
0.33 (0.23 to 0.51). Using Wilcoxon sign-rank
matched pair non-parametric tests we found within-plate
normalisation to be significantly less variable than when
the normaliser was run on a separate plate (within-plate
normalisation compared to between-plate normalisation of
Cq TEL of plate 1 with Cq GAPDH on plate 2; T  119,
p  0.0001, n  25, and plate 3; T  72, p  0.01, n  25).
There was no significant difference in the variability
between the two between-plate normalisations (T  29.00,
p  0.60, n  25). This result was similar when only
one sample per individual was compared, although the
comparison of the within-plate comparison and the
between-plate normalisation with plate 3 was short of
significance (within-plate normalisation compared to
between-plate normalisation of Cq TEL of plate 1 with
Cq GAPDH on plate 2; T  36.5, p  0.008, n  12, and
plate 3; T  21.5, p  0.06, n  12).
Discussion
By using synthesised oligomer standards of known length
within a qPCR framework, we were able to measure
repeatable absolute telomere estimates that can be used for
comparisons within and potentially between species from
Table 1. Telomere shortening in Seychelles warblers measured by absolute qPCR and relative qPCR analysis. Results are from general linear
mixed models where bird identity was included as a random effect. Significance of the main effects was derived from models where the
associated interaction terms were removed. 25 samples measured from 12 birds.
Absolute qPCR
Age
Sex (female)
Age  Sex
Relative qPCR
Estimate
SE
DF
t
p
Estimate
SE
DF
t
p
2147.60
263.45
259.29
39.83
164.59
36.87
1,15.9
1,7.88
1,14.54
23.71
20.39
21.61
0.002
0.71
0.13
20.08
20.04
20.03
0.02
0.10
0.02
1,14.79
1,7.21
1,14.03
23.57
20.36
21.54
0.003
0.73
0.15
­% residual variance explained by identity, absolute method 29.92%, relative method 33.66%.
574
Table 2. Within-plate variability measured as the median and inter-quartile range of the standard deviations calculated around the individual
means of all technical triplicates per sample and oligomer per set of replicates (7 plates in each set).
Set 1
Telomere
GAPDH
Set 2
Set 3
Samples
Oligomers
Samples
Oligomers
Samples
Oligomers
0.08 (0.05 to 0.13)
0.09 (0.03 to 0.16)
0.08 (0.03 to 0.16)
0.06 (0.03 to 0.08)
0.12 (0.08 to 0.20)
0.11 (0.08 to 0.16)
0.11 (0.05 to 0.23)
0.06 (0.04 to 0.11)
0.09 (0.06 to 0.16)
0.07 (0.03 to 0.14)
0.12 (0.08 to 0.17)
0.05 (0.03 to 0.09)
small quantities of Seychelles warbler blood stored in ethanol. Telomere estimates from both methods detected an
age related decline. Researchers often prefer to use a qPCR
method as a quicker alternative to the TRF assay, and
this absolute qPCR method could provide a comparative
scale to qPCR analysis.
While the synthetic standards give a standard scale to
the telomere estimates, we do not advocate the sole use of
synthetic standards. Our method uses an avian betweenplate calibrator (‘golden sample’) on each plate to correct for
variation between runs, and then a synthetic oligomer standard to give the measurements a scale. Combinations of
standards are known to make qPCR analyses more repro­
ducible (Hellemans et al. 2007), and within studies will
reduce non-biological variation. This combination of standards should retain the sensitivity of the relative qPCR
method, while providing an absolute scale to the samples
and the between-plate calibrator, and may therefore allow
for further investigations where between-plate calibrators
(‘golden samples’) have run out, become contaminated,
or degrade. Likewise, a comparative scale may be useful
for researchers interested in the evolution of variation in
telomere sequence between avian genomes. However,
care must be taken when comparing telomere estimates
between individuals (Kipling and Cooke 1990, Delany et al.
2000) and species (Meyne et al. 1990, Nanda et al. 2002)
as a proportion of the difference in genomic telomere
Figure 2. Scatter-plot of the relationship between telomere estimates by absolute and relative qPCR of Seychelles warbler
whole blood samples. For graphical purposes all telomere estimates
from all samples are plotted, however for the pair-wise analyses,
one random sample was used per individual to avoid pseudore­
plication (filled circles). Empty circles were not part of the formal
analyses, although their inclusion did not change the correlation
coefficient. Elliptical lines represent 95% bivariate normal density
ellipse for analysed points only.
sequence profiles may be due to non-telomeric interstitial
and sub-telomeric repeats. This will be true when telomeres
are measured using any method that includes more than
just the telomeric end repeats (Nakagawa et al. 2004).
A major concern with interstitial repeats is that their
variation could swamp the signal from the telomeres,
which could reduce our ability to detect age-related changes
in telomere lengths within individuals. Moreover, even
if variation could be detected within individuals, if interstitial repeats were variable between individuals of the same
species; age-related telomere loss may not be able to be
detected between individuals. This is not an issue in the
Seychelles warbler, where age-related telomere loss is
detected within and between individuals. However, the
Seychelles warbler population experienced a severe bottleneck (Brouwer et al. 2007), which may have reduced interstitial repeat diversity (sensu Delany et al. 2000).
The problem of interstitial repeats is no greater in qPCR
approaches than it is when using results from Southernblot TRF methods, which also include interstitial and
sub-telomeric sequences yet have a similar common scale
that is used with caution for hypotheses, predictions,
and experimentation. Methods that allow for absolute
measurement of telomere sequence without incorporation
of interstitial and sub-telomeric sequence {e.g. FISH and to
some extent in-gel hybridisation TRF and STELA PCR;
Nakagawa et al. 2004} are not practical for field researchers
who wish to work with archived samples as they require
precise storage, and technically require very high standard
of technical experience.
The number of chromosomes in the Seychelles warbler is
currently unknown, so our values are given per diploid
genome rather than per chromosome end as is usual
when using the TRF method. Further work is required
on species of birds where the number of chromosomes is
known to compare the values generated by both methods.
Comparisons of absolute telomere values between methods
are potentially hindered by differences in the range of
TTAGGGn sequence included within the measurement
(Nakagawa et al. 2004), and hence provide results that
although related, would vary. Hence, caution is to be
heeded when comparing absolute telomere estimates
between different measuring methods, due to differences in
the classes of telomeres that are measured. However, even
if results cannot be compared between qPCR and TRF
approaches, the absolute qPCR technique would still make
results more comparable between different qPCR studies,
while being as reliable as the relative qPCR technique.
While there are errors involved in obtaining a singular
absolute value with all current methods (Horn et al. 2010),
absolute values give a scale by which standard protocols can
be developed and tested to generate results that are useful
to the research community both now and in the future.­­­
575
Acknowledgements – This work was supported by a standard Natural
Environment Research Council grant to DSR (NE/F02083X/1)
on which ELBB is a PDRA and SV and PM are project partners.
Thanks to Johan Nilsson and Jan-Åke Nilsson for their constructive
comments. Thanks to Mark Haussmann and Els Atema for useful
discussions. Nature Seychelles and the Dept of Environment and
Seychelles Bureau of Standards kindly permitted work on Cousin
Island.
References
Alström, P., Fregin, S., Norman, J. A., Ericson, P. G. P., Christidis,
L. and Olsson, U. 2011. Multilocus analysis of a taxonomically
densely sampled dataset reveal extensive non-monophyly
in the avian family Locustellidae. – Mol. Phylogenet. Evol.
58: 513–526.
Bainard, L. D., Klironomos, J. N. and Hart, M. M. 2010.
Differential effect of sample preservation methods on plant
and arbuscular mycorrhizal fungal DNA. – J. Microbiol.
Methods 82: 124–130.
Barrett, E. L. B. and Richardson, D. S. 2011. Sex differences in
telomeres and lifespan. – Aging Cell 10: 913–921.
Bize, P., Criscuolo, F., Metcalfe, N. B., Nasir, L. and Monaghan,
P. 2009. Telomere dynamics rather than age predict life
expectancy in the wild. – Proc. R. Soc. B 276: 1679–1683.
Brouwer, L., Komdeur, J. and Richardson, D. S. 2007.
Heterozygosity-fitness correlations in a bottlenecked island
species: a case study on the Seychelles warbler. – Mol. Ecol.
16: 3134–3144.
Brouwer, L., Barr, I., van de Pol, M., Burke, T., Komdeur, J.
and Richardson, D. S. 2010. MHC-dependent survival in a
wild population: evidence for hidden genetic benefits gained
through extra-pair fertilisations. – Mol. Ecol. 19: 344–345.
Bustin, S. A., Benes, V., Garson, J. A., Hellemans, J., Huggett, J.,
Kubista, M., Mueller, R., Nolan, T., Pfaffl, M. W. and
Shipley, G. L. 2009. The MIQE guidelines: minimum
information for publication of quantitative real-time PCR
experiments. – Clin. Chem. 55: 611.
Cawthon, R. M. 2002. Telomere measurements by quantitative
PCR. – Nucleic Acids Res. 30: e47.
Criscuolo, F., Bize, P., Nasir, L., Metcalfe, N. B., Foote, C. G.,
Griffiths, K., Gault, E. A. and Monaghan, P. 2009. Real-time
quantitative PCR assay for measurement of avian telomeres.
– J. Avian Biol. 40: 342–347.
Delany, M. E., Krupkin, A. B. and Miller, M. M. 2000. Organization of telomere sequences in birds: evidence for arrays of
extreme length and for in vivo shortening. – Cytogenet.
Cell Genet. 90: 139–145.
Demeke, T. and Jenkins, G. R. 2010. Influence of DNA extraction
methods, PCR inhibitors and quantification methods on
real-time PCR assay of biotechnology-derived traits. – Anal.
Bioanal. Chem. 396: 1977–1990.
Engqvist, L. 2005. The mistreatment of covariate interaction
terms in linear model analyses of behavioural and evolutionary
ecology studies. – Anim. Behav. 70: 967–971.
Freed, L. A. and Cann, R. L. 2006. DNA quality and accuracy of avian
malaria PCR diagnostics: a review. – Condor 108: 459–473.
Gallup, J. M. 2011. qPCR inhibition and amplification of difficult
templates. – In: Kennedy, S. and Oswald, N. (eds), PCR
troubleshooting and optimization: the essential guide, Vol. 1.
Caister Academic Press, p. 59.
Gallup, J. M. and Ackermann, M. R. 2008. The ‘PREXCEL-Q
method’ for qPCR. – Int. J. Biomed. Sci. 4: 273.
Supplementary material (Appendix JAB5787 at , www.
oikosoffice.lu.se/appendix .). Appendix 1–3.
576
Harley, C. B., Futcher, A. B. and Greider, C. W. 1990. Telomeres
shorten during ageing of human fibroblasts. – Nature
345: 458–460.
Haussmann, M. F. and Vleck, C. M. 2002. Telomere length
provides a new technique for aging animals. – Oecologia
130: 325–328.
Haussmann, M. F. and Mauck, R. A. 2008. New strategies for
telomere-based age estimation. – Mol. Ecol. Res. 8: 264–274.
Hellemans, J., Mortier, G., De Paepe, A., Speleman, F. and
Vandesompele, J. 2007. qBase relative quantification framework and software for management and automated analysis
of real-time quantitative PCR data. – Genome Biol. 8: R19.
Horn, T., Robertson, B. C. and Gemmell, N. J. 2010. The use
of telomere length in ecology and evolutionary biology.
– Heredity 105: 497–506.
Kipling, D. and Cooke, H. J. 1990. Hypervariable ultra-long
telomeres in mice. – Nature 347: 400–402.
Lessells, C. M. and Boag, P. T. 1987. Unrepeatable repeatabilities:
a common mistake. – Auk 104: 116–121.
Meyne, J., Ratliff, R. L. and Moyzis, R. K. 1989. Conservation
of the human telomere sequence (TTAGGG)n among
vertebrates. – Proc. Natl Acad. Sci. USA 86: 7049–7053.
Meyne, J., Baker, R. J., Hobart, H. H., Hsu, T. C., Ryder, O. A.,
Ward, O. G., Wiley, J. E., Wurster-Hill, D. H., Yates, T. L.
and Moykis, R. K. 1990. Distribution of non-telomeric
sites of the (TTAGGG)n telomeric sequence in vertebrate
chromosomes. – Chromosoma 99: 3–10.
Monaghan, P. and Haussmann, M. F. 2006. Do telomere dynamics
link lifestyle and lifespan? – Trends Ecol. Evol. 21: 47–53.
Nakagawa, S., Gemmel, N. J. and Burke, T. 2004. Measuring
vertebrate telomeres: applications and limitations. – Mol.
Ecol. 13: 2523–2533.
Nanda, I., Schrama, D., Feichtinger, W., Haaf, T., Schartl, M. and
Schmid, M. 2002. Distribution of telomeric (TTAGGG)n
sequences in avian chromosomes. – Chromosoma 111:
215–227.
O’Callaghan, N. J., Dhillon, V. S., Thomas, P. and Fenech, M.
2008. A quantitative real-time PCR method for absolute
telomere length. – BioTechniques 44: 807–809.
Pfaffl, M. W. 2001. A new mathematical model for relative quantification in real-time RT–PCR. – Nucleic Acids Res. 29: e45.
Richardson, D. S., Jury, F. L., Blaakmeer, K., Komdeur, J.
and Burke, T. 2001. Parentage assignment and extra-group
paternity in a cooperative breeder: the Seychelles warbler
(Acrocephalus sechellensis). – Mol. Ecol. 10: 2263–2273.
Roos, G., Kröber, A., Grabowski, P., Kienle, D., Bühler, A.,
Döhner, H., Rosenquist, R. and Stilgenbauer, S. 2008. Short
telomeres are associated with genetic complexity, highrisk genomic aberrations, and short survival in chronic
lymphocytic leukemia. – Blood 111: 2246–2252.
Ruijter, J. M., Ramakers, C., Hoogaars, W. M. H., Karlen, Y.,
Bakker, O., van den Hoff, M. J. B. and Moorman, A. F. M.
2009. Amplification efficiency: linking baseline and bias in the
analysis of quantitative PCR data. – Nucleic Acids Res. 37: e45.
Salomons, H. M., Mulder, G. A., de Zande, L., Haussmann,
M. F., Linskens, M. H. K. and Verhulst, S. 2009. Telomere
shortening and survival in free-living corvids. – Proc. R.
Soc. B 276: 3157–3165.
Seutin, G., White, B. N. and Boag, P. T. 1991. Preservation of
avian blood and tissue samples for DNA analyses. – Can. J.
Zool. 69: 82–90.
Venkatesan, R. N. and Price, C. 1998. Telomerase expression
in chickens: constitutive activity in somatic tissues and
down-regulation in culture. – Proc. Natl Sci. Acad. Sci. USA
95: 14763–14768.