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SHOR T REPOR T Open Access
Reference gene validation for qPCR in rat carotid
body during postnatal development
Insook Kim
1
, Dongjin Yang
1
, Xinyu Tang
2
and John L Carroll
1*
Abstract
Background: The carotid bodies are the main arterial oxygen chemoreceptors in mammals. Afferent neural output
from the carotid bodies to brainstem respiratory and cardiovascular nuclei provides tonic input and mediates
important protective responses to acute and chronic hypoxia. It is widely accepted that the selection of reference
genes for mRNA normalization in quantitative real-time PCR must be validated for a given tissue and set of
conditions. This is particularly important for studies in carotid body during early postnatal maturation as the arterial
oxygen tension undergoes major changes from fetal to postnatal life, which may affect reference gene expression.
In order to determine the most stable and suitable reference genes for the study of rat carotid body during
development, six commonly used reference genes, b-actin, RPII (RNA polymerase II), PPIA (peptidyl-proyl-isomerase
A), TBP (TATA-box binding protein), GAPDH, and 18s rRNA, were evaluated in two age groups (P0-1 and P14-16)
under three environmental oxygen conditions (normoxia, chronic hypoxia and chronic hyperoxia) using the three
most commonly used software programs, geNorm, NormFinder and BestKeeper.
Findings: The three programs produced similar results but the reference gene rankings were not identical
between programs or experimental conditions. Overall, 18s rRNA was the least stable reference gene for carotid
body and, when hyperoxia and/or hypoxia conditions were included, actin was similarly unstable.
Conclusions: Reference or housekeeping gene expression for qPCR studies of carotid body during postnatal
development may vary with developmental stage and environmental conditions. Selection of the best reference
gene or combination of reference genes for carotid body development studies should take environmental
conditions into account. Two commonly used reference genes, 18s rRNA and actin, may be unsuitable for studies
of carotid body maturation, especially if the study design includes altered oxygen conditions.
Findings
Introduction
Mammals have evolved a complex oxygen sensing sys-
tem that links rapidly responding peripheral arterial PO
2
sensors, the carotid body (CB) chemoreceptors, with
central respiratory motor output and cardiovascular
reflexes. Afferent neural output from the carotid bodies
is transmitted via the carotid sinus nerves (CSN) to
brainstem respiratory and cardiovascular nuclei, which
control ventilatory, heart rate and blood pressure
responses to hypoxia [1] and mediate other important
defenses during hypoxic stress [2-6]. In all species
studied to date, CB responsiveness to hypoxia is low in
newborns and increases with age [7,8].
Glomus cells (or “type-1 cells”), the O
2
-sensing cells of
the CB, are arranged in clusters with apposed nerve
terminals of the carotid sinus nerve, whose cell bodies
are located in the petrosal ganglion (PG) [9]. Studies of
rat glomus cells from our laboratory demonstrate that
the magnitude of hypoxia-induced cell membrane depo-
larization, the intracellular calcium response ([Ca
2+
]
i
)to
hypoxiaandthemagnitudeofanO
2
-sensitive back-
ground K
+
current increase with age from birth (postna-
tal day 0: P0) to postnatal day 14-21 (P14-21) [10-12].
The mechanisms are unknown but may involve postna-
tal changes in glomus cell ion channel expression.
Therefore, studies of ion channel expression are crucial
to understanding the mechanisms of CB glomus cell O
2
sensing and its postnatal maturation.
* Correspondence: carrolljohnl@uams.edu
1
University of Arkansas for Medical Sciences, Department of Pediatrics,
Division of Pulmonary Medicine, Arkansas Children’s Hospital Research
Institute, Little Rock, Arkansas, USA
Full list of author information is available at the end of the article
Kim et al.BMC Research Notes 2011, 4:440
http://www.biomedcentral.com/1756-0500/4/440
© 2011 Carroll et al; licensee BioMed Centra l Ltd. This is an open access article distributed under the terms of the Creative Commons
Attribution License (http://creative commons.org/licenses/by/2.0), which permits unrestricted use, distribu tion, and reproduction in
any medium, pro vided the original work is properly cited.
Developmental studies of gene expression typically
employ relative quantification by qPCR, where normali-
zation against an internal control gene is commonly
used. The use of internal control genes (reference
genes) assumes that their expression is invariant in the
cells or tissue under study and with experimental treat-
ments. Developmental studies, in addition, assume that
reference genes for qPCR do not change with stage of
development or ambient conditions. However, numer-
ous studies show that commonly used reference genes,
such as b-actin, GAPDH or 18s rRNA, are not constant
between different developmental stages and different
experimental conditions [13-20]. Normalization of the
data with unstable reference genes can result in mislead-
ing or false conclusions.
It has become widely accepted that the selection of
reference genes must be validated for a given tissue and
set of conditions [21] and the use of multiple reference
genesisviewedasamorerobust,accurateandreliable
approach to normalization [21-24]. This approach can
be facilitated by the use of tools such as geNorm [22],
NormFinder [25] and BestKeeper [26], which allow
selection of the most stable reference genes and deter-
mination of the best combination to use for normaliza-
tion for a given tissue and set of conditions.
Fetal arterial PO
2
in mammals is about 23-28 mmHg
and rises ~ 4 fold within the first two hours after birth
[27,28], raising the additional possibility that oxygen-
dependent gene expression may change during the first
days or weeks after birth [29,30]. Specifically, oxygen
tension may affect the expression of reference genes in a
tissue-specific manner [13,31], suggesting that candidate
reference genes for normalization should be validated
not only for a given tissue but also for oxygen condi-
tions that may affect expression and over the time
frame when such changes may occur.
In the present study, six candidate reference genes
were evaluated using three reference gene selection
tools in whole rat carotid bodies during a crucial devel-
opmental period (P0 to P16) and under conditions of
peri- and postnatal normoxia, hypoxia and hyperoxia.
Results indicate that actin and 18s rRNA, housekeeping
genes commonly used for qPCR normalization, were
among the least stable reference genes under most
conditions.
Methods
Overview of study design
Six commonly used reference genes, b-actin, RPII (RNA
polymerase II), PPIA (peptidyl-proyl-isomerase A), TBP
(TATA-box binding protein), GAPDH, and 18s rRNA,
were validated using geNorm [22], NormFinder [25] and
BestKeeper [26] for two age groups (P0-1 and P14-16)
under three environmental oxygen conditions
(normoxia, chronic hypoxia and chronic hyperoxia).
This yielded four groups as follows: age P0-1, normoxia
(N1), age P14-16, normoxia (N14), age P14-16, chronic
hyperoxia treated (Hyper14), and age P14-16, chronic
hypoxia treated (Hypo14). To reduce the inter-assay var-
iations, all six reference genes were run with qPCR with
all four sets of samples; N1, N14, Hyper14, and Hypo14.
In order to evaluate CB reference gene stability under
conditions relevant to postnatal development we per-
formed the analysis, with geNorm, NormFinder and
BestKeeper, using four combinations of the conditions,
as follows: N1+N14 (development), N14+Hyper14
(chronic hyperoxia during development), N14+Hypo14
(chronic hypoxia during development) and N1+N14
+Hyper14+Hypo14 (development + altered environmen-
tal condition). The procedures used in this study were
approved by the Institutional Animal Care and Use
Committee of the University of Arkansas for Medical
Sciences.
Chronic Hyperoxia or Hypoxia Treatment
For chronic hyperoxia or hypoxia treatment, timed-preg-
nant Sprague-Dawley rats were placed in a controlled
atmosphere chamber 1-2 days prior to expected delivery
andwereallowedtogivebirthinthechamber(OxyCy-
cler Model A84XOV, BioSpherix, Redfield, NY). The
system continuously monitors and maintains a preset
oxygen level, and O
2
/CO
2
tensions are recorded con-
tinuously (AnaWin software). The chambers employ a
controlled leak to the room environment in order to
limit CO
2
/humidity buildup. For chronic hyperoxia and
hypoxia treatment, rats were exposed to 0.60 FiO
2
or
0.12 FiO
2
, respectively. Pups and dams were maintained
in the chamber until use at P14-16.
Isolation of Rat CB and tRNA Extraction
Carotid bodies (CB) were isolated from rats age P0-1
(N1), P14-16 (N14), P14-16 male and female rats
exposed continuously to hyperoxia (60% O
2
)frombirth
(Hyper14), and P14-16 rats exposed to hypoxia (12%
O
2
) from birth (Hypo14) as described previously [12].
For CB isolation, rats were anesthetized with isoflurane
and decapitated under deep surgical anesthesia. The car-
otid bifurcations were dissected and placed in ice-cold
phosphatebufferedsaline(PBS).Thecarotidbodies
were then removed from the bifurcations and placed in
cold sterile PBS. Isolated CBs were placed into a 1.5 ml
centrifuge tube, washed with ice-cold PBS, and stored in
RNAlater stabilization reagent (Qiagen) at -80°F. Frozen
collected CBs were processed to extract the Total RNA
(tRNA) using AquaPure RNA isolation kit (Bio-Rad).
tRNA was extracted from ~ 50 frozen CBs and the
tRNA pellet was reconstituted in 10-16 μlofhydration
solution depending on size of isolated CB. The
Kim et al.BMC Research Notes 2011, 4:440
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concentration of extracted tRNA was measured by spec-
trophotometer (SmartSpec Plus, Bio-Rad) at 260 nm.
Purity of tRNA was determined as the 260 nm/280 nm
ratio with expected values between 1.8 and 2.
Extracted tRNA were treated with RQ1 RNA-free
DNase (Promega) and estimated 1 μgofpuretRNAran
for cDNA sysnthesis (20 μl) by using iScript cDNA
synthesis kit (Bio-Rad). cDNA at 5X dilution were run
using qPCR in triplicate. The six putative reference
genes, b-actin, RPII (RNA polymerase II), PPIA (pepti-
dyl-proyl-isomerase A), TBP (TATA-box binding pro-
tein), GAPDH, and 18s rRNA, were evaluated using
qPCR. All six reference genes were tested with cDNA
synthesized from N1, N14, Hyper14, and Hypo14 on the
same plate to reduce inter-assay variations.
Quantitative Real Time RT-PCR (qPCR)
Primers for reference genes were designed by using Bea-
con Designer 2.0; PPIA, TBP, RPII, b-actin, GAPDH,
18s rRNA, TASK-1, TASK-2 and TASK-3 (Table 1).
Designed primers were ordered from Integrated DNA
Technologies (IDT, Coraville, IA) and tested on cDNA
from positive control tissues prior to testing on cDNA
of CB. The PCR efficiency of each primer pair was
tested with the serial dilution of cDNA prepared with
N14 CB in triplicate (Table 1). The real-time PCR effi-
ciency rate (E) in the exponential phase was calculated
according to the following equation [32]:
E=10
[
−1
/
slope
]
−
1
To exclude genomic DNA contamination, tRNA was
treated with RQ1 RNase-free DNase (Promega) as
described above (tRNA extraction methods) and cDNA
was synthesized using iScript cDNA synthesis kit (Bio-
Rad). To check for DNA contamination on newly made
tRNA, cDNA without reverse transcriptase (-RT) was
synthesized and tested prior to use. To prevent false
detection with the qPCR, a no-template-control (NTC)
was tested on every run for each primer set and the
melting curve was checked.
Real time PCR using SYBR green technology was per-
formed on an iCycler iQ real-time detection system in
96-well microtitre plates using a final volume of 20 μl
(Bio-Rad). SYBR Green Supermix (Bio-Rad and Applied
Biosystems) and 0.1 μM of primers were mixed with
DNAse and RNase-free water to make the 9/10
th
of the
total reaction volume and 1/10
th
of cDNA was added
into the mixture. The following amplification program
was used: after 5 min of denaturation at 95°C, 50 cycles
of real time PCR with 2-step amplification were per-
formed consisting of 15s at 95°C for denaturation, 45s
at 60°C for annealing and 1min at 95°C for polymerase
elongation. In each qPCR run, all six reference genes
were run on all four conditions, N1, N14, Hyper 14, and
Hypo 14, in one 96 well PCR plate simultaneously. All
samples were amplified in triplicate and the mean was
obtained for further calculation. C
T
values of 50 were
excluded from further mathematical calculation, because
50 represents no quantitative information of the RNA
amount, but only the end of the PCR run.
Table 1 Information on the primers used for real time PCR
Gene Accession # Primers Product Size (bp) Efficiency Rate (E)
PPIA NM_017101 F GTCAACCCCACCGTGTTCTTC 133 1.93
R ATCCTTTCTCCCCAGTGCTCAG
TBP NM_001004198 F ACCGTGAATCTTGGCTGTAAAC 123 2.06
R CGCAGTTGTTCGTGGCTCTC
RPII AB017711 F GGCTCTCCAGATTGCGATGTG 124 1.93
R CAGGTAACGGCGAATGATGATG
b-actin NM_031144 F CAGGGTGTGATGGTGGGTATGG 115 2.03
R AGTTGGTGACAATGCCGTGTTC
GAPDH NM_017008 F CAAGTTCAACGGCACAGTCAAG 123 1.91
R ACATACTCAGCACCAGCATCAC
18s X01117 F CACGGGTGACGGGGAATCAG 105 2.03
R CGGGTCGGGAGTGGGTAATTTG
TASK-1 NM_033376 F GCAGAAGCCGCAGGAGTTG 126 2
R GCCCGCACAGTTGGAGATTTAG
TASK-2 AM229406 F ACGCCCTCTACCGCTACTTTG 129 2.12
R GCCGCCTCCTCCTCTTCTTG
TASK-3 NM_053405 F CGGTGCCTTCCTCAATCTTGTG 144 1.8
R TGGTGCCTCTTGCGACTCTG
Nucleotide sequences for the primers, size of PCR products, and PCR amplification efficiency rate of each primer set. All the genes are from rat origin. The real-
time PCR efficiency rate (E) in exponential phase was calculated according to the equation: E = 10[-1/slope] [32].
Kim et al.BMC Research Notes 2011, 4:440
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Data analysis
Reference gene ranking and selection of best combination
of multiple reference genes
Three popular software programs, geNorm [22], Norm-
Finder [25] and BestKeeper [26], were used to evaluate
or validate the best reference genes for use during CB
development. In general, they are based on the principle
that the expression of reference genes should be the
same under all experimental conditions and cell types
studied. geNorm measures the variation between any
two candidate control genes as the standard deviation of
(log-transformed) control gene ratios. Each candidate
referencegeneisassignedastabilitymeasure(M)by
comparing it (in a pairwise fashion) with each other
reference gene candidate. The candidate genes with the
most stable expression have the lowest M. The genes
are then ranked, using stepwise exclusion of the least
stable genes. NormFinder [25] uses a model-based
approach to estimate overall reference gene stability but
also considers variations between sample subgroups.
BestKeeper [26] uses repeated pair-wise correlation ana-
lysis to determine the optimal reference genes. For rela-
tive gene quantification, REST 384 or REST2009 were
used [33].
Statistical Method for Rank Aggregation
These three programs, geNorm, NormFinder and Best-
Keeper, have been previously reported to yield different
rankings of reference genes [34]. As they use different
approaches to evaluating reference gene stability [34],
they would not be expected to yield identical results.
Therefore, weighted rank aggregation was performed to
combine the ordered lists of genes produced by geN-
orm, NormFinder, and BestKeeper to a consensus rank
of genes. The M-values obtained from geNorm, variabil-
ity measurements from NormFinder, and the coefficients
of correlation from BestKeeper were used as weights in
the aggregation process. Brute force method was used to
enumerate all possible candidate lists and find the one
with the minimum Spearman footrule distance using the
BruteAggreg function [35]. Although it is recommended
that the Cross-Entropy Monte Carlo algorithm should
be used when the size of the ranking list is relatively lar-
ger than 10, it was used additionally to validate the con-
sensus rank of genes resulting from the brute force
approach. The two methods yielded consistent ranking
lists, demonstrating that the consensus ranks of genes
were robust to the methods used. The rank aggregations
were conducted with R software version 2.11.1 (R Devel-
opment Core Team, 2009).
Results
All six reference genes, PPIA, TBP, RPII, b-actin,
GAPDH, and 18s rRNA, were detected from all tested
CB samples (Figure 1). The median expression levels
(C
T
value) for each validated reference gene are shown
in Figure 1. For 18s rRNA, cDNA was diluted 125x to
fit its C
T
value into a reasonable C
T
range. TBP showed
the lowest expression level among 6 reference genes,
but its C
T
values were at a reasonable detection level for
SYBR green real time PCR.
Reference genes during CB development in normoxia,
chronic hyperoxia and chronic hypoxia treatment were
evaluated with extracted RNA in four combinations as
follows: N1+N14, N14+Hyper14, N14+Hypo14, or N1
+N14+Hyper14+Hypo14. geNorm found PPIA and TBP
or RPII and TBP to be the most stable reference gene
combinations (Table 2). 18s rRNA was the least stable
in all groups according to geNorm (Table 2). geNorm
calculates an expression stability value, termed “M”,
which is highest for the least stable gene and lowest for
the most stable (Figure 2). The PPIA and TBP combina-
tion was by far the most stable and 18s rRNA was the
least stable reference gene (Figure 2).
For the combination of all four conditions, N1+N14
+Hyper14+Hypo14, NormFinder also selected PPIA and
Figure 1 The distribution of gene expression levels of tested
reference genes in pooled C
T
values. Data obtained from
immature (N1), mature (N14), chronic hyperoxia (Hyper14), and
chronic hypoxia (Hypo14) treated rat CB. Top panel: PCR amplicons
from all tested reference genes were detected on 1.5% agarose gel
as single band and correct size of PCR product. Bottom panel: C
T
values for each reference gene are shown as medians (line in box),
means (asterisks), 25 to 75 percentile (boxes), and 1 to 99 percentile
ranges (whiskers).
Kim et al.BMC Research Notes 2011, 4:440
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TBP as the best two reference genes (Table 2) and Best-
Keeper ranked PPIA and TBP as the top two most
stable reference genes (Table 2). Rankings for other
combinations of conditions varied more with NormFin-
der and BestKeeper, compared to geNorm (Table 2).
Similar to the 18s rankings produced by geNorm, 18s
rRNA tended to rank near the bottom of stability rank-
ings using NormFinder and BestKeeper (Table 2).
As the three programs produced different results, we
attempted to determine consensus rankings using
weighted rank aggregation. The most consistent result
of consensus rankings was that 18s rRNA ranked at or
near the bottom of the rankings under all conditions
(Table 3). For development alone (N1+N14) PPIA and
GAPDH ranked as the top two reference genes by con-
sensus analysis (Table 3). For hyperoxia during develop-
ment (N14+Hyper14) RPII and TBP were ranked as the
top two genes by consensus (Table 3). In both groups
involving hypoxia during development (N14+Hypo14
and N1+N14+Hyper14+Hypo14) TBP and PPIA were
Table 2 Ranking of the 6 selected reference genes in rat whole carotid bodies by geNorm, NormFinder and
BestKeeper
geNorm N1+N14 (n = 16) N14+Hyper14 (n = 14) N14+Hypo14 (n = 14) N1+N14+Hyper14+ Hypo14 (n = 28)
Rank Gene M-value Gene M-value Gene M-value Gene M-value
Best two genes RPII TBP 0.26 PPIA TBP 0.20 PPIA TBP 0.29 PPIA TBP 0.26
3 PPIA 0.27 RPII 0.35 RPII 0.36 RPII 0.38
4 actin 0.34 GAPDH 0.43 GAPDH 0.45 GAPDH 0.44
5 GAPDH 0.38 actin 0.50 actin 0.50 actin 0.49
6 18s 0.40 18s 0.57 18s 0.52 18s 0.53
Norm Finder N1+N14 (n = 16) N14+Hyper14 (n = 14) N14+Hypo14 (n = 14) N1+ N14+Hyper14+ Hypo14 (n = 28)
Rank Gene Variability Gene Variability Gene Variability Gene Variability
Best two genes TBP Actin 0.009 RPII GAPDH 0.007 RPII GAPDH 0.008 PPIA TBP 0.011
1 GAPDH 0.01 RPII 0.013 TBP 0.007 TBP 0.015
2 PPIA 0.011 GAPDH 0.013 PPIA 0.015 GAPDH 0.017
3 actin 0.014 TBP 0.014 GAPDH 0.018 18s 0.019
4 TBP 0.015 actin 0.019 RPII 0.018 PPIA 0.020
5 RPII 0.016 PPIA 0.021 18s 0.020 RPII 0.021
6 18s 0.018 18s 0.021 actin 0.021 actin 0.023
Best Keeper N1+N14 (n = 16) N14+ yper14 (n = 14) N14+Hypo14 (n = 14) N1 + N14 + Hyper14+ Hypo14 (n = 28)
Rank Gene Coeff. of corr.[r] Gene Coeff. of corr.[r] Gene Coeff. of corr.[r] Gene Coeff. of corr.[r]
1 PPIA 0.89 RPII 0.97 RPII 0.86 PPIA 0.97
2 actin 0.84 PPIA 0.96 PPIA 0.85 TBP 0.95
3 GAPDH 0.83 GAPDH 0.96 TBP 0.85 GAPDH 0.95
4 18s 0.73 TBP 0.95 actin 0.75 RPII 0.93
5 RPII 0.72 actin 0.93 GAPDH 0.69 actin 0.92
6 TBP 0.70 18s 0.87 18s 0.56 18s 0.89
The reference genes are ranked in several groups of CB as follows: N1+N14 = immature and mature CB, reared in normoxia (n = 16). N14+Hyper14 = mature
normoxia and hyperoxia treated CB (n = 14). N14+Hypo14 = mature normoxia and hypoxia treated CB (n = 14). N1+N14+Hyper14+Hypo14 = immature,
normoxia (N1), mature, normoxia (N14), mature, hyperoxia (Hyper14), and mature, hypoxia treated (Hypo14) CB (n = 28). Genes are ranked on their stability value
calculated by each respective program: geNorm, M-value; NormFinder, Variability; and BestKe eper, Coefficient of correlation [r]. (n) = number of independent
determinations.
Figure 2 geNorm analysis of the 6 candidate reference genes
in all pooled data from N1, N14, Hyper14, and Hypo14 rat CB
(n = 28). Average expression stability of candidate reference gene
was determined by stepwise exclusion of least stable genes. Lower
M values indicate greater stability.
Kim et al.BMC Research Notes 2011, 4:440
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ranked, by consensus, as the top two most stable refer-
ence genes (Table 3). When the group included hyper-
oxia or hypoxia during development, actin ranked at or
near the bottom of stability rankings (Table 3).
Another potential source of variability is the source of
the qPCR master mixture. Therefore, the six reference
genes were tested with iQ SYBR green supermix from
Bio-Rad and compared with PCR master mix from
Applied Biosystems, using geNorm, NormFinder and
BestKeeper (Table 4). Overall, the Applied Biosystems
SYBR green mastermix provided more consistent rank-
ings across the three software programs. However, in
spite of the greater variability in rankings in the Bio-Rad
group, the consensus rankings were nearly identical for
the two different vendors (Table 4).
The impact of reference gene choice was evaluated by
determining the relative gene expression ratios of three
relevant targets during CB development, genes for
TASK-1, TASK-2 and TASK-3 potassium channels.
Their relative expression during development was evalu-
ated with REST2009 (http://www.gene-quantification.de/
rest-2009.html) using the stable combination PPIA and
TBP, as determined by geNorm, vs. the least stable
reference gene, 18s rRNA. As shown in table 5, TASK-2
expression was found to be significantly down-regulated
during development when PPIA+TBP were the refer-
ence genes, but not when 18s rRNA was used as a refer-
ence gene.
Discussion
We investigated the expression stability in rat whole
carotid body of multiple commonly used qPCR refer-
ence genes, during early postnatal development when
rat CB O
2
-sensing maturation takes place [10-12,36,37],
using three popular software programs for reference
gene selection as well as qPCR reagents from different
vendors. Although the three programs produced similar
results, the rankings were not identical and, in some
cases, were substantially different. With respect to
agreement between the three programs, for the combi-
nation of all conditions (N1+N14+Hyper14+Hypo14)
geNorm and NormFinder selected PPIA+TBP as the
best combination of multiple reference genes and Best-
Keeper selected PPIA and TBP as the two highest
ranked (more stable) reference genes. The results indi-
cated that 18s rRNA was the least stable reference gene
for CB overall and, when hyperoxia and/or hypoxia con-
ditions are included, actin was similarly unstable. The
use of reagents from different vendors may substantially
impact reference gene stability rankings.
geNorm produced the most consistent results across
all developmental/oxygen conditions, selecting PPIA
+TBP as the best multiple reference gene combination
inthreeoffourgroups(Table2).TheM-valueforthe
multiple reference gene combination PRII+TBP, selected
by geNorm for N1+N14, is very close to that for PPIA,
suggesting that PPIA+TBP would be a good choice of
reference genes for all groups (Table 2).
Although our study was not designed to measure the
effect of altered oxygen environment on individual refer-
ence gene expression, it appears that hyperoxia and
hypoxia affect the stability rankings of specific genes.
For example, NormFinder ranked actin as one of the
best two reference genes for N1+N14, while actin was
ranked as the least stable reference gene for the two
groups that included chronic hypoxia (Table 2). Simi-
larly, BestKeeper ranked actin as one of the most stable
reference genes for development (N1+N14), while actin
was ranked among the least stable in the groups that
Table 3 Consensus ranking
Gene ranking for group N1+N14 (n = 16)
Rank Position geNorm NormFinder BestKeeper Consensus
1 RPII GAPDH PPIA PPIA
2 TBP PPIA Actin GAPDH
3 PPIA Actin GAPDH Actin
4 Actin TBP 18s RPII
5 GAPDH RPII RPII TBP
6 18s 18s TBP 18s
Gene ranking for group N14+Hyper14 (n = 14)
Rank Position geNorm NormFinder BestKeeper Consensus
1 PPIA RPII RPII RPII
2 TBP GAPDH PPIA TBP
3 RPII TBP GAPDH GAPDH
4 GAPDH Actin TBP PPIA
5 Actin PPIA Actin Actin
6 18s 18s 18s 18s
Gene ranking for group N14+Hypo14 (n = 14)
Rank Position geNorm NormFinder BestKeeper Consensus
1 PPIA TBP RPII TBP
2 TBP PPIA PPIA PPIA
3 RPII GAPDH TBP RPII
4 GAPDH RPII Actin GAPDH
5 Actin 18s GAPDH Actin
6 18s Actin 18s 18s
Gene ranking for group N1+N14+Hyper14+Hypo14 (n = 28)
Rank Position geNorm NormFinder BestKeeper Consensus
1 PPIA TBP PPIA TBP
2 TBP GAPDH TBP PPIA
3 RPII 18s GAPDH GAPDH
4 GAPDH PPIA RPII RPII
5 Actin RPII Actin 18s
6 18s Actin 18s Actin
Weighted rank aggregation was performed to combine the ordered lists of
genes produced by geNorm, NormFinder, and BestKeeper to a consensus rank
of genes.
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included chronic hyperoxia or hypoxia (Table 2). Thus,
theeffectofhypoxiaonhousekeepinggeneexpression
may vary with experimental conditions and should be
tested for a given tissue and set of conditions. We did
not investigate the effects of chronic intermittent
hypoxia (CIH) on reference gene stability because there
are many different CIH exposure paradigms and such a
large undertaking was beyond the scope of the present
study.
An obvious potential limitation of this study is that
better combinations of reference genes may exist, for
the study of rat CB development, beyond the ones cho-
sen. The choice of the six candidate reference genes stu-
died was based on their common use and evidence that
they are stable so-called “housekeeping genes”in other
tissues. Never-the-less, others may exist that will turn
out to be equally or more stable and suitable for devel-
opmental carotid body studies.
Our results add to a growing body of literature show-
ing that reference or housekeeping gene expression for
qPCR may vary with developmental stage and environ-
mental conditions, and the specific genes, pattern and
timing of variation may be tissue-specific [38]. It is also
important to consider that our results are likely to be
species-specific and may be developmental time-frame
specific; studies of carotid body maturation in other spe-
cies should validate reference genes for each species, O
2
conditions and developmental time-frame.
Acknowledgements
This study was supported by NIH grant 5R01HL054621.
Author details
1
University of Arkansas for Medical Sciences, Department of Pediatrics,
Division of Pulmonary Medicine, Arkansas Children’s Hospital Research
Institute, Little Rock, Arkansas, USA.
2
University of Arkansas for Medical
Sciences, Department of Pediatrics, Division of Biostatistics, Arkansas
Children’s Hospital Research Institute, Little Rock, Arkansas, USA.
Authors’contributions
IK participated in study concept and design, carried out the qPCR studies
and participated in drafting the manuscript. DY helped with the qPCR
studies. XT performed rank aggregation statistical analysis and participated in
writing the manuscript. JC participated in study concept, design and
drafting the manuscript. All authors read and approved the final manuscript.
Competing interests
Statement for all authors:
Table 4 Gene ranking and consensus ranking using SYBR green super mixtures from two different vendors, Bio-Rad
and Applied Biosystems
Gene and consensus ranking for group N1+N14 (n = 16) using Bio-Rad SYBR
Bio-Rad SYBR
Rank Position geNorm M-value NormFinder Variability BestKeeper Coeff. of corr. [r] Consensus
1 TBP 0.26 GAPDH 0.01 PPIA 0.89 PPIA
2 RPII 0.26 PPIA 0.011 Actin 0.84 GAPDH
3 PPIA 0.27 Actin 0.014 GAPDH 0.83 Actin
4 Actin 0.34 TBP 0.015 18s 0.73 TBP
5 GAPDH 0.38 RPII 0.016 RPII 0.72 RPII
6 18s 0.40 18s 0.018 TBP 0.70 18s
Gene and consensus ranking for group N1 + N14 (n = 16) using Applied Biosystems SYBR
Applied Biosystems SYBR
Rank Position geNorm M-value NormFinder Variability BestKeeper Coeff. of corr. [r] Consensus
1 PPIA 0.21 PPIA 0.008 PPIA 0.99 PPIA
2 GAPDH 0.21 GAPDH 0.013 GAPDH 0.99 GAPDH
3 Actin 0.25 Actin 0.013 Actin 0.99 Actin
4 RPII 0.32 RPII 0.014 RPII 0.98 RPII
5 TBP 0.34 TBP 0.016 TBP 0.98 TBP
6 18s 0.39 18s 0.024 18s 0.97 18s
The reference genes are ranked for N1+N14 group by geNorm, Normfinder and BestKeeper and the consensus ranking determined by rank aggregation. (n) =
number of independent determinations.
Table 5 Relative gene expression ratios of three interest
TASK channel genes, TASK-1, TASK-2, and TASK-3, were
compared by using one least stable reference gene (18s
rRNA) and two best stable reference genes
(PPIA and TBP)
Reference Gene TASK-1
(pvalue, n)
TASK-2
(pvalue, n)
TASK-3
(pvalue, n)
18s rRNA 1.172
(0.756, n = 11)
0.682
(0.442, n = 8)
1.285
(0.661, n = 12)
PPIA & TBP 0.69
(0.183, n = 13)
0.29
(0.000, n = 11) Down
0.925
(0.545, n = 13)
The ratios and statistical pvalues of three interest genes in groups N1+N14
were analyzed by REST2009 software. (n) = number of independent
determinations.
Kim et al.BMC Research Notes 2011, 4:440
http://www.biomedcentral.com/1756-0500/4/440
Page 7 of 8
The authors declare that they have no competing interests.
Received: 29 June 2010 Accepted: 24 October 2011
Published: 24 October 2011
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doi:10.1186/1756-0500-4-440
Cite this article as: Kim et al.: Reference gene validation for qPCR in rat
carotid body during postnatal development. BMC Research Notes 2011
4:440.
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