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ORIGINAL PAPER
Reference genes assessment for the seagrass Posidonia oceanicain different salinity, pH and light conditions
Ilia Anna Serra • Chiara Lauritano •
Emanuela Dattolo • Andrea Puoti • Silvia Nicastro •
Anna Maria Innocenti • Gabriele Procaccini
Received: 18 November 2011 / Accepted: 20 February 2012
� Springer-Verlag 2012
Abstract The stability on the expression level of putative
reference genes (RGs) for reverse transcriptase-quantitative
PCR (RT-qPCR) was tested in the seagrass Posidonia
oceanica for plants collected in different environmental
conditions (at different depths and in different salinity and
pH). Besides adult plants, seedlings were also used for pH
and salinity experiments. The putative RGs encompassed
the most frequently used in other species to normalize
RT-qPCR in a range of experimental conditions. Assign-
ment of the best RGs has been performed with three Excel-
based applets, BestKeeper, geNorm and NormFinder, and
the results showed that best RGs may change depending on
the experimental conditions tested, plant age and on the
software utilized. Preferred sets of RGs are proposed for
each of the conditions and a general procedure concerning
their selection is discussed. In addition, expression levels
of four genes involved in plant response to light, salinity
and pH variation have also been evaluated.
Introduction
Posidonia oceanica is an endemic Mediterranean seagrass,
widely distributed along the Mediterranean coasts where it
forms large monospecific beds across a wide bathymetric
gradient. P. oceanica meadows provide key ecological
services in the coastal area, including maintenance of
marine biodiversity, organic carbon production and export,
sediment stabilization and coastal erosion prevention
(Procaccini et al. 2003; Boudouresque et al. 2006; Marba
2009). P. oceanica has a monoecious mating system and is
capable of long-range dispersal by floating fruits (Serra
et al. 2010). Sexual reproduction occurs irregularly
throughout the whole basin, and very large clones can
be found within single meadows (Migliaccio et al. 2005).
P. oceanica meadows, like other seagrass ecosystems, are
extremely sensitive to both moderate and high levels of
disturbances often associated with highly human-impacted
coasts (Procaccini et al. 2003; Boudouresque et al. 2006;
Ralph et al. 2006; Marba 2009). At present, the likely
primary cause of seagrass loss is the increased reduction in
water clarity, caused by nutrient loading and turbidity, that
affects quality and quantity of the light reaching the leaves
(Duarte et al. 2004; Orth et al. 2006). Moreover, in the last
years, desalination plants became more widespread across
the Mediterranean (Fritzmann et al. 2007; Boye 2008;
Bashitialshaaer et al. 2011), and their hypersaline effluents
may represent a serious threat to nearby P. oceanica
meadows. Several experiments have already been under-
taken both in the laboratory and in the field, providing
evidence that increased salinity severely affects growth
rate, induces necrotic lesions and enhances meadow mor-
tality (Fernandez-Torquemada and Sanchez-Lizaso 2005;
Gacia et al. 2007; Ruiz et al. 2009). Lastly, global climate
change is predicted to have deleterious effects on
Ilia Anna Serra, Chiara Lauritano share equal responsibility.
Communicated by T. Reusch.
Electronic supplementary material The online version of thisarticle (doi:10.1007/s00227-012-1907-8) contains supplementarymaterial, which is available to authorized users.
I. A. Serra � S. Nicastro � A. M. Innocenti
Universita Della Calabria, Via P. Bucci,
Arcavacata di Rende, Cosenza, Italy
C. Lauritano � E. Dattolo � A. Puoti � G. Procaccini (&)
Stazione Zoologica Anton Dohrn,
Villa Comunale, 80121 Naples, Italy
e-mail: gpro@szn.it
123
Mar Biol
DOI 10.1007/s00227-012-1907-8
seagrasses (Waycott et al. 2009; Marba and Duarte 2010),
and projected changes in many environmental parameters
such as CO2 increase with resulting reduction of pH in sea
water can alter seagrass distribution, productivity and
community composition (Short and Neckles 1999; Bjork
et al. 2008), although it seems that reduction in pH does not
have a negative effect on the plant itself (Hall-Spencer
et al. 2008).
The speed at which environmental conditions are
changing is demanding high plasticity in the adaptive
response of species, especially when these cannot easily
move. Seagrass meadows will be able to persist and
propagate only if they will not be exposed to changes that
overcome the tolerance limit of the species. Recent studies
on Zostera marina showed that populations adapted to
different climatic conditions recover with different speed
from the exposure to heat waves, having different patterns
of gene expression among populations (Bergmann et al.
2010; Winters et al. 2011). Similarly, we could expect that
P. oceanica would exhibit differences in the adaptive
response and in the modulation of gene expression, among
populations present in different areas of the Mediterranean
Sea, where a strong genetic structure has been observed
(Arnaud-Haond et al. 2007; Serra et al. 2010).
Different approaches can be followed in order to study
variation in gene expression in response to specific envi-
ronmental conditions. ‘‘Whole’’ transcriptome sequencing
(e.g., RNA-seq and microarray studies) may show how
organisms respond to a particular condition and help in
identifying new genes affected/involved in that condition.
By contrast, a target gene approach is an useful way to
analyze specific enzymes/proteins that are known to be
involved in the tested condition (e.g., heat stress, drought,
pollutants). Reverse transcriptase-quantitative PCR (RT-
qPCR) studies are also growing in importance to validate
data from whole-genome oligonucleotide arrays and as a
primary source of expression data for smaller sets of genes.
In this contest, gene expression studies in P. oceanica may
help to clarify in which way this keystone species may
adapt/survive to various environmental conditions
(e.g., light, salinity and pH variations), and which is the
role played by the local adaptation along geographic
and environmental clines. The first RT-qPCR study in
P. oceanica has been performed to evaluate the effects of
cadmium (Cd) treatment on the expression level of a
member of the chromomethylase (CMT) family, a DNA
methyltransferase, using the ribosomal RNA 5.8S alone as
reference gene (RG) (Greco et al. 2011). A semiquantita-
tive RT-PCR analysis has also been performed in P. oce-
anica to study the geranylgeranyl reductase expression
patterns in response to light availability, and 18S expres-
sion levels were reported as a sort of internal control
(Bruno et al. 2010).
Being aware that RGs can be specific for each given
experimental condition, here we suggest reference genes
for RT-qPCR assays in P. oceanica specimens, adult and/or
seedlings, living at different depth (i.e., variation in light
availability and temperature), pH and salinity conditions,
and a series of ‘‘universal’’ RGs for that conditions. We
screened a panel of 11 suitable candidate reference genes
(RGs) for normalization of P. oceanica messenger RNA
using three Excel-based applets, BestKeeper (Pfaffl et al.
2004), geNorm (Vandesompele et al. 2002) and Norm-
Finder (Andersen et al. 2004). Therefore, we analyzed
selected genes putatively involved in plant response to
variation of light, salinity and pH. To test for light-affected
variation, we selected two genes (a putative N(2),N(2)-
dimethylguanosine tRNA methyltransferase, Trm, and a
putative peptidase, Pep) coming from a preliminary anal-
ysis of a subtractive suppression hybridization (SSH)
library performed between plants growing above and below
the summer thermocline, which showed a remarkable dif-
ference in the plant transcriptome at different depths
(Procaccini et al. 2010). To test for pH- and salinity-
affected variation, we analyzed two aquaporin water
channels, PIP1 and PIP2. Plant aquaporins play essential
roles in the regulation of water exchanges and/or in the
transport of small neutral solutes or gases (Maurel 2007;
Maurel et al. 2008; Heinen et al. 2009; Danielson 2010).
PIP1 and PIP2 represent two distinct phylogenetic groups
of the PIP (plasma membrane intrinsic proteins) subfamily,
the largest and evolutionarily most conserved among the
plant’s aquaporins.
Finally, the general procedure concerning the choice of
RGs and the evaluation of RG expression stability in var-
ious experimental conditions for the accurate quantification
of P. oceanica target gene expression using RT-qPCR
assays is discussed.
Materials and methods
Plant materials
Different depth conditions
Shoots were collected by SCUBA diving in July 2010, after
the stabilization of the summer thermocline, from a mea-
dow located in Lacco Ameno, Ischia (Gulf of Naples, 40�450 5200 N; 13� 530 2900 E). Sampling site and depths were
the same utilized for the construction of the SSH library
(Procaccini et al. 2010). Sampling was performed at two
different depths, -5 m (shallow station) and -25 m (deep
station). Leaf tissue from 3 adult shoots for each depth was
cleaned from epiphytes and shock frozen in liquid nitrogen
on the research vessel soon after collection. When
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123
sampling was performed, conditions were the following: at
-5 m the temperature was 26.84�C, salinity 37.78 PSU
and PAR (photosynthetically active radiation) 703 lM/m2/s,
while at -22 m, the temperature was 18.99�C, salinity 37.78
PSU and PAR 100 lM/m2/s (Table 1).
Salt and pH treatment
Both mature plants collected from a natural meadow and
young leaves obtained from seedlings germinated in the lab
were used for this experiment. Cuttings were collected
from plagiotropic rhizomes at a depth of -5 m by SCUBA
diving in the Cirella meadow (Tyrrhenian coast, Southern
Italy, 39� 410 5000 N; 15� 480 0600 E) and transplanted into
two aquaria according to the procedures described by In-
nocenti et al. (2007). A teat of a Pasteur pipettes, filled with
autoclaved seawater, was used for closing the rhizome
surface exposed after sampling, in order to avoid bacterial
infection. No other substances stimulating growth were
utilized, to circumvent interference with salt and pH
treatments.
Seedlings were obtained from P. oceanica fruits col-
lected on a sandy beach adjacent to the meadow of Briatico
(Tyrrhenian coast, Southern Italy, 38� 430 2400 N; 16� 000
2400 E) after the occurrence of drift events. Fruits were
transferred to the laboratory, and seeds were carefully
extracted and placed in a folded filter paper inside semi-
transparent covered plastic containers, saturated with ster-
ilized natural seawater and placed in a germinating
chamber for 2 week, according to Belzunce et al. (2008).
Both cuttings and young seedlings were kept in the
natural seawater of the collection sites (37 PSU, pH 8.0)
and acclimated to laboratory conditions (21 shoots and 21
seedlings per closed circuit aquarium, constantly aerated,
containing 25 L of natural seawater, at natural photoperiod)
for a week. Hence, three cuttings and three juvenile seed-
lings were collected and frozen (control groups, T0),
whereas the other shoots and seedlings, kept in two dif-
ferent aquaria, were submitted to salt and pH treatments
respectively (salt treatment: 45 PSU; pH treatment:
pH = 6.0; Table 1). In either case, shoot and seedling
leaves were collected at different times (12, 24 and 48 h)
and stored at -80�C for RNA extraction. The hypersaline
water was obtained adding commercial aquarium salts to
deionized water as in Serra et al. (2011). The low pH was
obtained titrating seawater by adding HCl and quickly
stoppering in headspace-free chambers to prevent CO2
outgassing, following Invers et al. (2001).
RNA extraction
For each RNA extraction, approximately 200 mg wet
weigh of leaf tissues was utilized. In order to obtain this
amount of wet tissue, we had to pool leaves of three dif-
ferent specimens, from seedlings utilized for salt and pH
experiments. Accordingly, for the same experiments (salt
and pH), we pooled leaves of three different adult speci-
mens. Replicates were kept separated, instead, for the
depth-response experiments. For all samples, leaf tissue
was ground under liquid nitrogen adding 0.1 g PVP-40
(polyvinylpyrrolidone) directly to the mortar, and total
RNA was extracted following the procedure of Serra et al.
(2007), with some modifications. The powder was trans-
ferred to an Eppendorf tube with 1 ml of extraction buffer
(2% CTAB, 2% b-mercaptoethanol, 1.4 M NaCl, 20 mM
EDTA, 200 mM Tris–HCl pH 7.5). After incubation at
60�C for 30 min, 800 ll chloroform-isoamyl alcohol (49:1
v/v) was added, the solution was centrifuged at 6,500 rpm
for 10 min and the supernatant was recovered and precip-
itated with 1 volume of ice-cold isopropanol. After cen-
trifugation at 13,500 rpm for 15 min, the acid nucleic
pellet was washed twice, first for 20 min with 100 ll of
Buffer A (76% ethanol, 200 mM sodium acetate) and then
for 2 min with 100 ll of Buffer B (76% ethanol, 10 mM
ammonium-acetate), air-dried and dissolved in RNase-free
water. Genomic DNA was removed by incubating at
37�C for 15 min with DNase I (Roche). In order to purify
RNA, sample was washed with one volume of phenol/
Table 1 Plant tissue utilized
for the different experimental
conditions tested in the analysis,
both in the field (different
depth) and in the laboratory
(salinity and pH treatments)
Experimental conditions,
number of samples and stage of
plant development are also
indicated
Plant material (tissue) Experimental conditions No. of
samples
Stage of plant
development
Shoots (leaves) Different depth conditions:
shallow station = -5 m;
PAR = 703 lM/m2/s;
Temp = 26.84�C
Deep station = -22 m; PAR = 100 lM/m2/s;
Temp = 18.99�C
3 (distinct)
per depth
Adult
Cuttings (leaves) Salinity = 37–45 psu
pH = pH 6.0–pH 8.0
3 (pooled) per
treatment
Adult
Seedlings (leaves) Salinity = 37–45 psu
pH = pH 6.0–pH 8.0
3 (pooled) per
treatment
Young
Mar Biol
123
chloroform/isoamyl alcohol (50:49:1 v/v/v). RNA was
recovered by overnight precipitation at -20�C with 2
volumes of 100% ethanol and 1/10 volume of sodium
acetate 3 M pH 5.2. The RNA pellet was washed with 70%
ethanol, air-dried and dissolved in RNase-free water.
RNA quantity was assessed by Nano-Drop (ND-1000
UV–Vis spectrophotometer; NanoDrop Technologies)
monitoring the absorbance at 260 nm; purity was deter-
mined by monitoring the 260/280 nm and 260/230 nm
ratios using the same instrument. RNA quality was also
evaluated by agarose gel electrophoresis.
cDNA synthesis
Of each RNA sample, 1 lg was retro-transcribed in com-
plementary DNA (cDNA) with the iScriptTM cDNA syn-
thesis kit (BIORAD), following the manufacturer’s
instructions, using the GeneAmp PCR System 9700 (Perkin
Elmer). The reaction was carried out in 20 ll final volume
with 4 ll 59 iScript reaction mix, 1 ll iScript reverse
transcriptase and H2O. The mix was first incubated 5 min
at 25�C, followed by 30 min at 42�C, and finally heated at
85�C for 5 min.
Selection of genes and primer design
Eleven candidate reference genes were identified among
the ones more commonly used in other species (Table 2),
both plants and animals. For example, ACT, EF1A and 18S
were the best RGs in chicory (Cichorium intybus) in leaf
and root samples (Maroufi et al. 2010), EF1A and UBI in
cucumber tissues treated with hormones and abiotic stress
(Wan et al. 2010), NTUBC2, L25 and EF1A in develop-
mentally distinct tissues or tissues exposed to abiotic stress
in tobacco (Nicotiana tobacum) (Schmidt and Delaney
2010) and TBP was between the best RGs in Zostera
marina after temperature treatments (Ransbotyn and
Reusch 2006). TUB and UBI were used as RGs for dif-
ferent Octopus vulgaris tissues (Sirakov et al. 2009), UBC,
GAPDH and ACT in Daphnia magna exposed to ibuprofen
(Heckmann et al. 2006), GAPDH in the copepod Calanus
helgolandicus exposed to a toxic diet (Lauritano et al.
2011), and ciclofillin was among the putative RGs for both
the seagrass Zostera marina after temperature treatments
(Ransbotyn and Reusch 2006), and the Cladoceran Daph-
nia magna exposed to ibuprofen (Heckmann et al. 2006).
In order to compare the stability of reference genes, four
genes of interest, two taken from the ones differentially
expressed at different depths (P. oceanica SSH library;
Procaccini et al. 2010) and two putatively involved in the
response to salinity and pH, were also selected (Table 2).
Primers for hypothetical reference genes (RGs) and genes
of interest (GOI) were designed considering sequences
from the seagrass EST database Dr. Zompo (Wissler et al.
2009) or from the generic online databases GenBank
(http://www.ncbi.nlm.nih.gov/genbank/; Table 2). Primers
were designed using the software Primer3 v. 0.4.0
(http://frodo.wi.mit.edu/primer3/). Primers for L25 were
designed aligning conserved regions of Zea mays and
Nicotiana tabacum (NCBI accession number AF061508.1
and L18908.1, respectively), while primers for TBP in
conserved regions of Zea mays, Oryza sativa and Zostera
marina (NCBI accession number NM_001111849.1,
AF464907.1 and AM768946.1, respectively). Alignments
were performed using the software Bioedit (V7.0.5.3,
http://www.mbio.ncsu.edu/BioEdit/bioedit.html). Gene
Runner v. 3.05 (Hasting Software) was used to predict primer
melting temperature (Tm) and check for primer quality.
Primers were synthesized commercially by Primm Labs
(MA, Boston). All cDNA amplicons ranged from 100 to
200 bp in size, in order to facilitate cross-comparison of
assays and assure equal PCR efficiencies. PCR conditions
were optimized on a GeneAmp PCR System 9700 (Perkin
Elmer). Reactions were carried out in 20 ll, with 2 ll of 109
PCR rbuffer Roche, 2 ll of 0.1% BSA, 2 ll of 109 2 mM
dNTP, 0.8 ll of 5U/ll Taq Roche, 1 ll of 20 pmol/ll for
each oligo, template cDNA and nuclease-free water to final
volume. The PCR program consisted of a denaturation step at
95�C for 3 min, 40 cycles at 95�C for 30 s, 60�C for 1 min
and 72�C for 30 s, and a final extension step at 72�C for
7 min. Amplified PCR products were analyzed by 1.5%
agarose gel electrophoresis in TBE buffer. In order to verify
the correct assignment of amplicons to target genes, the
resulting bands were excised from the gel, extracted
according to the QIAquick Gel Extraction Kit protocol
(QIAGEN) and the sequence analyzed. Sequence reactions
were obtained by BigDye Terminator Cycle Sequencing
technology (Applied Biosystems) and purified in automation
by the robotic station Biomek FX (Beckman Coulter), using
the Agencourt CleanSEQ Dye terminator removal Kit
(Agencourt Bioscience Corporation). Products were ana-
lyzed on the Automated Capillary Electrophoresis Sequen-
cer 3730 DNA Analyzer (Applied Biosystems). The identity
of each sequence was confirmed using the bioinformatic tool
BLAST (Basic local alignment search tool).
Reverse transcriptase-quantitative polymerase chain
reaction (RT-qPCR)
RT-qPCR experiments were performed in a Chromo4 TM
Real-time Detector (Biorad) thermal cycler, whereas fluo-
rescence was measured using the Opticon Monitor 3.1
(Biorad). PCR volume for each sample was 25 ll, with 19
of Fast Start SYBR Green Master Mix (Roche), 2 ll of
cDNA template and 0.7 pmol/ll for each oligo. The RT-
qPCR thermal profile was obtained using the following
Mar Biol
123
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Mar Biol
123
procedure: 95�C for 10 min, 40 cycles of 95�C for 15 s and
60�C for 1 min, followed by 5 min at 72�C. The program
was set to reveal the melting curve of each amplicon from
60�C to 95�C and read every 0.5�C. Only a single peak in
the melting-curve analyses of all genes was identified,
confirming a gene-specific amplification and the absence of
primer-dimers. All RT-qPCR were carried out in triplicate
to capture intra-assay variability. Each assay included three
no-template controls (NTC) for each primer pair. Effi-
ciencies were calculated generating for each oligonucleo-
tide pair standard curves with at least five dilution points by
using the cycle threshold (Ct) value versus the logarithm of
each dilution factor and using the equation E = 10(-1/slope).
Primer efficiencies (E) ranged from 89 to 100% (Table 2).
A 1:100 template dilution was used for the experiments,
which allowed almost all gene amplifications to fit in the
optimal read window (from 15 to 25 cycles).
The expression of the six RGs for which the PCR con-
ditions were fully optimized and of the four GOI was
evaluated on three replicates per condition for the samples
coming from the two different depths, while the leaf
material of the three replicates was pooled before PCR for
the salinity/pH experiment. Three different algorithms were
utilized to identify the best reference genes in our experi-
mental design: BestKeeper (Pfaffl et al. 2004), geNorm
(Vandesompele et al. 2002) and NormFinder (Andersen
et al. 2004). Raw data coming from all the different
experimental conditions were analyzed together for
searching the ‘‘universal RGs,’’ while raw data coming from
each individual experimental condition, or a combination of
conditions, were used to find condition-specific RGs.
To study the expression of each GOI, we used REST
tool (Relative expression software tool) (Pfaffl et al. 2002)
normalizing the RT-qPCR data with (i) universal RGs, (ii)
condition-specific RGs or (iii) the best two condition-spe-
cific RGs obtained with NormFinder. REST tool used a
mathematical model based on the PCR efficiencies and the
mean crossing point deviation between the sample and the
control group (Pfaffl et al. 2002). Being aware that REST
tool normalize the RT-qPCR data using RGs for each
conditions, when statistical analyses were not possible due
to pooled replicates, ±1 cycle expression variation was
considered possible technical error and considered not
significant. Statistical analysis was performed using Stu-
dent’s t-test and GraphPad Prism statistic software, V4.00
(GraphPad Software).
Results
Validation of best reference genes for RT-qPCR
After initial PCR amplifications, ACT, TUB and CICL
were discarded from the analyses because they consistently
gave double amplifications, while L25 and TBP oligo gave
no reliable amplification products. Hence, software analy-
ses have been performed on the remaining six genes, that
is, UBI, GAPDH, L23, NTUBC, 18S and EF1A (see
Table 2 for the complete name of genes).
Raw Ct data of potential RGs are reported in Fig. 1. High
Ct variability was observed in particular for seedlings
exposed to both pH and salinity variations and for adults
collected in shallow water. The three software BestKeeper,
NormFinder and geNorm were used to assess reference
genes (i) that have stable expression levels over the various
experimental conditions tested in our analysis (‘‘universal
101112131415161718192021222324252627282930
S1 S2 S3 D1 D2 D3 12h 24h 48h 12h 24h 48h A c 12h 24h 48h 12h 24h 48h Y c
L23 NTUBC2 EF1A UBI GAPDH 18S
Shallow Deep A Sal. A pH Y Sal. Y pH
Raw
Ct v
alue
s
Fig. 1 Ct values obtained for
all candidate reference genes
during all samplings. Each linerepresents the degree of stability
of Ct values for each RG for the
tested conditions (S1, S2, S3 are
samples collected in shallow
water, D1, D2, D3 in deep
water, A means adult, Y means
young plants, Sal. both salinity
and pH conditions are reported
for 12, 24 and 48 h time of
exposure, Ac and Yc are the
control at 37 PSU and pH 8, for
adults and young
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RGs’’) and (ii) that are stable for each of the different con-
ditions (adult specimens exposed to pH or salinity variations,
young specimens exposed to pH or salinity variations, and
adults collected at different depths) or a combination of them
(adult and young exposed to salinity, adult and young
exposed to pH, adult exposed to pH, salinity and different
depths, and young exposed to pH and salinity).
Universal RGs
Results for the assessment of the best universal RGs given
by the three approaches utilized are reported in Table 3
(BestKeeper) and in Fig. 2 (NormFinder and geNorm).
According to the mathematical approach of BestKeeper,
18S, which had the lowest standard deviation (SD), was the
most stable gene, followed by NTUBC and EF1A
(Table 3). The geNorm analysis did not confirm the Best-
Keeper results, detecting as the two most stable genes (i.e.,
with the lowest expression stability, M), NTUBC and UBI
(Fig. 2a). Pairwise variation was subsequently calculated to
evaluate the effect of adding another RG to those already
analyzed. The addition of other RGs is considered as not
required when the value is below the cutoff of 0.15.
According to that, only the two best RGs were enough for
the normalization of GOI expression levels (Fig. 2b).
According to the third statistical approach utilized, the one
implemented in NormFinder, the best candidate reference
genes were EF1A, 18S and L23, which show the lowest
stability values (Fig. 2c). In summary, the stability rank of
the genes analyzed was not the same for the three software.
Considering the best RGs assigned by each software, 18S,
EF1A, NTUBC and UBI should be selected as ‘‘universal’’
RGs for the experimental conditions considered.
Specific RGs
Results of the same analysis performed for each of the
experimental conditions analyzed in this paper are not
always consistent between adult and young leaves and
among the three software utilized. BestKeeper, geNorm
and NormFinder results for each conditions are reported in
online resource and summarized in Table 4. The best RGs
to use for leaves of adult plants exposed to salinity varia-
tions are 18S, UBI, L23 and NTUBC. 18S, L23 and UBI
were the most stable RGs in leaves of young seedlings
exposed to salinity variations, while pooling adult and
young leaves exposed to different salinity, the best RGs
were 18S, UBI and EF1A. For adult leaves of plants cul-
tivated at different pH, the most stable genes were 18S,
NTUBC and GAPDH; for leaves of seedlings exposed to
the same treatments, the best RGs were 18S, NTUBC, L23
and UBI, while, for both adult and seedlings, the best RGs
were 18S, NTUBC, UBI and EF1A. L23, EF1A and
NTUBC were assessed as best and most stable RGs for
plants collected at different depths. Finally, considering all
the adults (salinity, pH and depths), the best RGs were
EF1A, UBI and NTUBC, while for all seedlings (pH and
salinity) best RGs were 18S, NTUBC, EF1A and UBI.
Regarding the geNorm pairwise variation analysis, the
addition of a third gene to the best RG couple is considered
as not required, because the values were below the cutoff of
0.15, for the following conditions and samples: adults and
young exposed to salinity, young exposed to salinity, adults
exposed to salinity, adults exposed to pH and adults
exposed to different depths (Table 4 and online resource).
The addition of a third RG was instead suggested for adults
and young exposed to pH and young exposed to salinity and
Table 3 Results of the BestKeeper analysis on the six putative reference genes tested
L23 NTUBC EF1A UBI GAPDH 18S
N 20 20 20 20 20 20
GM 26.63 27.20 25.84 26.16 26.00 12.70
AM 26.66 27.21 25.86 26.18 26.04 12.74
Min 25.07 25.91 24.52 24.38 23.57 10.68
Max 29.71 29.17 28.10 28.23 29.17 15.41
SD 0.89 0.76 0.79 0.88 1.09 0.74
CV 3.35 2.78 3.06 3.36 4.20 5.81
min [x-fold] -2.95 -2.35 -2.50 -3.40 -4.97 -4.05
max [x-fold] 8.41 3.74 4.79 4.14 8.00 6.58
SD [± x-fold] 1.86 1.69 1.73 1.84 2.13 1.67
Lowest standard deviation (SD) values are underlined
n Number of samples, GM geometric mean of Ct, AM arithmetic mean of Ct, Min and Max extreme values of Ct, SD standard deviation of the Ct,
CV coefficient of variance, expressed as a percentage on the Ct level, extreme values of expression levels, expressed as an absolute x-fold over-
or under-regulation coefficient (Min [x-fold] and Max [x-fold]), and standard deviation of the absolute regulation coefficients (SD [± x-fold]) are
given. According to BestKeeper, the most stable RG was18S, which had the lowest a standard deviation (SD), followed by NTUBC and EF1A
Mar Biol
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pH, while considering adults plants in all the tested condi-
tions the software suggests the use of all the six RGs. In
addition, due to the high Ct variability in P. oceanica
seedlings, pairwise variation analysis proposed the addition
of NTUBC, EF1A and 18S to the best RG couple of geNorm
in young exposed to pH (Table 4 and online resource).
Genes of interest (GOI) expression levels using
the specific RGs for each condition
We selected and analyzed specific genes of interest puta-
tively affected by our experimental conditions. We also
compared the results obtained using all the best RGs for
each specific tested condition with the ones obtained using
the ‘‘universal’’ RGs or only the best two RGs obtained
with NormFinder, as suggested by Andersen et al. (2004).
Variation in depth
Using samples (n = 3) collected at -5 m as control and
normalizing the data with the best RGs in this experimental
condition (L23, EF1A and NTUBC), our results for the two
genes (Trm and Pep), that were differentially expressed at
the two different depths in the SSH library, were consistent
with the expectations (Procaccini et al. 2010; Dattolo et al.
in preparation). Trm was down-regulated (p \ 0.001),
while Pep expression levels increased of about twofold,
even though variability between the replicates was high
(p \ 0.05) in samples collected at -25 m (Fig. 3a). Simi-
lar results were obtained using as RGs the best two RGs
obtained with NormFinder (data not shown) and the four
‘‘universal’’ RGs (EF1A, NTUBC, 18S and UBI; Fig. 3b)
(p \ 0.05 for Trm, p [ 0.05 for Pep). Standard deviations
increased in the last two cases (from SD = 0.11 when
using specific RGs to SD = 0.17 for Trm and from
SD = 0.79 to SD = 1.10 for Pep, using the best two RGs
given by NormFinder; from SD = 0.11 to SD=0.28 for
Trm and from SD = 0.79 to SD=1.92 for Pep, using uni-
versal RGs) with respect to the use of specific RGs,
reducing data significance.
Variation in salinity
For the response of adult plants to salinity conditions
(specific RGs: 18S, UBI, L23, and NTUBC), PIP2 showed
increased expression levels at 12, 24 and 48 h of treatment,
compared to the control (37 PSU), while PIP1 was sig-
nificantly up-regulated only after 24 h (Fig. 4). PIP2 was
approximately twofold up-regulated at any checked time of
exposure, while PIP1 showed approx twofold increase only
at 24 h treatment. On the contrary, in young P. oceanica
leaves, salinity variations did not induce significant chan-
ges in both PIP1 and PIP2 expression level (specific RGs:
18S, L23 and UBI) (Fig. 4). Similar results were observed
when using ‘‘universal’’ RGs and the best two of Norm-
Finder (data not shown).
Variation in pH
Variations in pH induced significant expression level
changes for PIP1 and PIP2 in both adult and young
P. oceanica leaves (Fig. 5). In adult leaves (specific RGs:
18S, NTUBC and GAPDH), PIP1 was twofold up-regu-
lated after both 24 and 48 h of exposure at pH 6, while
PIP2 was approx threefold up-regulated at 24 h and two-
fold increased at 12 and 48 h of treatment. In young leaves,
PIP2 was almost twofold up-regulated at any time of
0.013
0.011 0.011
0.009
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
V2/3 V3/4 V4/5 V5/6
Pai
rwis
e V
aria
tions
Sta
bilit
y V
alue
(a)
(b)
(c)
<::::: Least stable genes Most stable genes ::::>
Ave
rage
exp
ress
ion
stab
ility
M
0.000
0.200
0.400
0.600
0.800
1.000
EF1A 18S L23 NTUBC UBI GAPDH
0
0.01
0.02
0.03
0.04
0.05
0.06
18S GAPDH L23 EF1A UBINTUBC
Fig. 2 Ranking of the best universal reference genes (RGs) obtained
with geNorm and NormFinder. a The stepwise exclusion of genes that
are more variable among samples using the geNorm program. More
stable genes are indicated by the arrow; b Pairwise variation (V) to
evaluate the effect of adding another RG to the best couple already
analyzed using geNorm (e.g., adding a third gene V2/3, a fourth V3/4,
etc.). The inclusion of additional RGs was not required below the
cutoff value of 0.15; c The NormFinder algorithm ranks the candidate
RGs according to their expression stability. Lower stability values
indicate more stable genes
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exposure to pH 6 (Fig. 5), while PIP1 expression levels
increased significantly only after 12 and 24 h (specific
RGs: 18S, NTUBC, L23 and UBI). Plants maintained at pH
8 were used as control. Also in this condition, similar
results were observed when using ‘‘universal’’ RGs and the
best two of NormFinder (data not shown).
Table 4 Best reference genes as given by BestKeeper, NormFinder
and Genorm analyses, for each of the experimental condition and for
each plant age: adult specimens exposed to salinity variations, adult
exposed to pH variations, young specimens exposed to salinity
variations, young exposed to pH variations, adult and young exposed
to salinity or pH, adults collected at different depths, young exposed
to both salinity and pH variations and adults exposed to salinity, pH
and depth variations
Ranking Adult Young Adult and
young
Adult Young Adult and
young
Adult Adult Young
Salinity Salinity Salinity pH pH pH Depth Salinity, pH
and depth
Salinity
and pH
BestKeeper
1 18S 18S 18S 18S 18S 18S L23 EF1A 18S
2 UBI EF1A EF1A EF1A EF1A EF1A 18S L23 NTUBC
3 EF1A GAPDH GAPDH GAPDH UBI GAPDH NTUBC NTUBC EF1A
4 GAPDH NTUBC NTUBC L23 NTUBC NTUBC UBI 18S UBI
5 NTUBC UBI UBI NTUBC GAPDH L23 EF1A UBI L23
6 L23 L23 L23 UBI L23 UBI GAPDH GAPDH GAPDH
NormFinder
1 UBI L23 UBI NTUBC NTUBC NTUBC EF1A EF1A NTUBC
2 18S UBI EF1A EF1A UBI EF1A NTUBC 18S UBI
3 EF1A EF1A L23 UBI 18S UBI L23 UBI EF1A
4 NTUBC NTUBC 18S GAPDH EF1A GAPDH 18S L23 L23
5 L23 GAPDH NTUBC L23 L23 18S GAPDH GAPDH GAPDH
6 GAPDH 18S GAPDH 18S GAPDH L23 UBI NTUBC 18S
GeNorm
1 L23 L23 UBI NTUBC L23 UBI EF1A UBI EF1A
2 NTUBC UBI EF1A GAPDH UBI EF1A NTUBC NTUBC UBI
3 UBI EF1A L23 EF1A NTUBC* L23* L23 EF1A* NTUBC*
4 18S NTUBC NTUBC L23 EF1A* NTUBC 18S 18S* L23
5 EF1A GAPDH GAPDH UBI 18S* GAPDH GAPDH L23* 18S
6 GAPDH 18S 18S 18S GAPDH 18S UBI GAPDH* GAPDH
Best RGs assigned by each software for each experimental condition/condition combination are shown in bold. Genes to be added to the best
couple in order to normalize the RT-qPCR data, as suggested by GeNorm, are shown with an asterisk
(a)
Log
2 x-
fold
exp
ress
ion
ratio
-1
0
1
2
3
4
DEEP
Trm Pep
-1
0
1
2
3
4
DEEP
(b)
*
*** *
Trm PepFig. 3 Expression levels of
Trm and Pep (y-axis,
Mean ± SD) in Posidoniaoceanica specimens collected at
-25 m. Specimens collected at
-5 m were used as control and
the expression level of GOI in
the control is represented in the
figure by the x-axis. RT-qPCR
data were normalized (a) with
the best RGs in this
experimental condition (EF1A,
L23, NTUBC), b using
‘‘universal’’ RGs (EF1A,
NTUBC, 18S and UBI)
(*p \ 0.05, ***p \ 0.001)
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Discussion
In this study, we searched for reference genes (RGs) in the
seagrass Posidonia oceanica sampled in different envi-
ronmental conditions, and we suggest the best sets of RGs
for that conditions reporting examples of expression level
analyses of selected genes of interest (GOI) and comparing
results obtained utilizing treatment-specific RGs or more
‘‘universal’’ RGs. Assignment of the best RGs has been
performed with three Excel-based applets, BestKeeper,
geNorm and NormFinder, and results showed that best RGs
may change depending on the experimental conditions
tested, plant life stage and on the software utilized. Con-
sidering the best candidate RGs selected by each software,
preferred sets of RGs are proposed for each condition.
Normalization is required to correct sample-to-sample
variability in order to reveal gene-specific variation among
experimental conditions. Our experiments showed that the
expression of several putative RGs vary among different
conditions (light, salinity and pH variations) and/or age of
plants (adult and seedlings) and, selecting the best RGs
given by the three software, four genes may be necessary in
some cases for gene expression normalization. In the
existing literature, the number of RGs used in RT-qPCR
analysis varied among organisms, tissues and studies (e.g.,
one RG in Greco et al. 2011 for P. oceanica, three RGs in
Lauritano et al. 2011 for the copepod Calanus helgo-
landicus, four RGs in Li et al. 2011 for the tree Hevea
brasiliensis), and the existence of an optimal number was
recently debated (Bustin et al. 2009; Bustin et al. 2010).
Bustin and co-authors (2010) suggested three as the mini-
mum number of RGs for RT-qPCR, while other studies
suggest that the optimal number and the choice of reference
genes should be experimentally determined and the method
reported (Pfaffl et al. 2002; Vandesompele et al. 2002;
Andersen et al. 2004).
In our study, the use of ‘‘universal’’ RGs (EF1A,
NTUBC, 18S and UBI) leads to similar results in com-
parison with the use of condition-specific RGs as regard as
the gene expression level of the GOI. The important dif-
ference to stress is in the significance level of the gene
expression assessment. The standard deviation, in fact,
increased remarkably using the universal RGs (i.e., Pep),
affecting the reliability of the gene expression estimates.
-1
0
1
2
3
4
PIP1 PIP2
Log
2 x-
fold
exp
ress
ion
ratio
A 12 h A 24 h A 48 h Y 12 h Y 24 h Y 48 h
Fig. 4 Expression levels of the two aquaporins, PIP1 and PIP2, in
Posidonia oceanica adult leaves (A) and young leaves (Y), exposed to
45 PSU for 12, 24 and 48 h. Control plants were maintained at 37
PSU, and the expression levels of PIP1 and PIP2 in the control
condition are represented in the figure by x-axis. RT-qPCR data were
normalized with 18S, UBI, L23 and NTUBC as RGs for adult plants
and 18S, L23 and UBI for seedlings
-1
0
1
2
3
PIP1 PIP2
Log
2 x-
fold
exp
ress
ion
ratio
A 12 h A 24 h A 48 h Y 12 h Y 24 h Y 48 h
Fig. 5 Expression levels of the two aquaporins, PIP1 and PIP2, in
Posidonia oceanica adult leaves (A) and young leaves (Y), exposed to
pH 6 for 12, 24 and 48 h. Control plants were maintained at pH 8, and
the expression levels of PIP1 and PIP2 in the control condition are
represented in the figure by x-axis. RT-qPCR data were normalized
with 18S, NTUBC and GAPDH as RGs for adult plants and 18S,
NTUBC, L23 and UBI for seedlings
Mar Biol
123
In the example shown in Fig. 3, in fact, the significance
levels of the two GOI reported, were markedly lower when
using the universal RG with respect to the specific ones.
Our results confirmed that RGs may change depending
on the species life stage and on the experimental condition
utilized and supported the need of a preliminary study for
selecting specific RGs in order to ensure accurate quanti-
tative expression analysis for each specific analysis. Gly-
ceraldehyde-3-phosphate dehydrogenase (GAPDH) is
widely used in gene expression studies (Spinsanti et al.
2006; Toegel et al. 2007). For example, it was the most
stable RG in tomato leaf exposed to light stress (geNorm
analyses; Lovdal and Lillo 2009). In our analysis, GAPDH
was not considered a good RG, except for adult plants
exposed to pH variation (geNorm analyses). Bustin (2000)
also reported that GAPDH was up- or down-regulated in
various conditions.
Ribosomal RNA genes (i.e., 18S) were not included as
potential RG candidates in various studies because they
may be related to the physiological and/or metabolic status
of the analyzed organism (Sirakov et al. 2009) or because
they are not polyadenylated upon transcription and cannot
be utilized when using polydT priming in the reverse-
transcription reaction (Ransbotyn and Reusch 2006). In our
analyses, retro-transcription kit with both polydT and
random primers has been used, and 18S was assigned as
best RG by BestKeeper in all the tested conditions (also for
the universal RG assessment analyses), except for
depth variations. Ribosomal RNA 18S was also used as
internal control in a semiquantitative RT-PCR analysis in
P. oceanica to study the geranylgeranyl reductase expres-
sion patterns in response to light availability (Bruno et al.
2010), and the 5.8S was used as RG in the same species to
study the effects of Cd treatment on the expression levels
of a DNA methyltransferase by RT-qPCR (Greco et al.
2011), suggesting that in P. oceanica expression of ribo-
somal genes seems to be stable in different experimental
conditions.
EF1A and UBI were the most stable RGs for nitrogen
stress condition and EF1A for cold stress condition in
tomato leaves (geNorm analyses; Lovdal and Lillo 2009).
In the present study, UBI, EF1A, NTUBC and L23
expression levels were more or less stable depending on the
condition analyzed and the software used (as summarized
in Table 4).
Not surprisingly in the light of their diverging mathe-
matical approaches, BestKeeper, NormFinder and geNorm
differed in the rank of the most stably expressed RG can-
didates. According to the mathematical approach of Best-
Keeper, the first assessment of RG expression stability was
obtained considering the standard deviation of the Ct val-
ues. Hence, the RGs were ranked from the most stably
expressed with the lowest SD, to the least stable with the
highest SD. BestKeeper has been criticized by Rasbotyn
and Reusch (2006), since it does not take into account the
differences in RNA input or reverse-transcription effi-
ciencies, not correcting to sample input. By contrast, the
other two software do correct for intersample variation.
NormFinder enables estimation of the variation not
only between candidate RGs but also between sample
subgroups, comparing intra- and intergroup variation.
geNORM uses pairwise comparisons and geometric aver-
aging across a matrix of reference genes and biological
samples to determine the best reference genes for a given
set of samples. In addition, it also allows the determination
of the optimal number of RGs to normalize the RT-qPCR
by calculating the pairwise variation (V), although it may
favor co-regulated genes. Furthermore, in comparing the
NormFinder and geNorm approaches, Andersen et al.
(2004) showed that NormFinder may be more sensible than
geNorm in detecting unstable gene expression (see also
Ransbotyn and Reusch 2006) and recommended using the
best pair of RGs given by NormFinder. This is why we also
analyzed the GOI expression levels using the two best RGs
by NormFinder to normalize the RT-qPCR, although also
geNorm alone has been used for suitable RG assessment in
other studies (Nicot et al. 2005; Lovdal and Lillo 2009; Gu
et al. 2011). Giving the complementarity in the information
obtained with the three different software, and the lack in
the literature of a general consensus on the best approach to
use, we suggest to use all of the three software in order to
have a more accurate RG assessment.
In order to test the use of the identified RGs to address
biological questions, the expression levels of target genes,
putatively involved in the plant response to the tested
experimental conditions, were analyzed. Trm, which cor-
responds to a putative N(2),N(2)-dimethylguanosine tRNA
methyltransferase, showed lower expression levels in
plants collected at -25 m compared to plants collected at
-5 m, showing a higher tRNA processing rate in shallow
plants. Pep, which corresponds to a putative peptidase,
showed increased expression levels in deeper plants, sug-
gesting a higher activity of protein catabolism. Further
analyses, using RT-qPCR with the specific RGs selected
here and other transcriptomic approaches, such as micro-
arrays, will allow a comprehensive overview of gene
expression patterns in plants collected at different depths.
Regarding the two aquaporins, plant PIP2s seem to be
highly efficient water transporters, whereas PIP1s are
almost impermeable to water (Biela et al. 1999; Chaumont
et al. 2000; Moshelion et al. 2002). However, because in
plants aquaporins tend to form tetramers, it was hypothe-
sized that the formation of heterotetramers (containing both
PIP1 and PIP2 monomers) could have consequences on
their membrane water transport activity. Co-expression of
PIP1 s and PIP2 s in heterologous expression systems, in
Mar Biol
123
fact, dramatically increased water permeability. Experi-
ments using various membrane PIP1/PIP2 isoforms have
given comparable results and also showed that co-expres-
sion not only affects water transport but also pH sensitivity
and CO2 transport (Fetter et al. 2004; Alleva et al. 2010;
Bellati et al. 2010; Otto et al. 2010). Our data support the
involvement of both aquaporin subgroups in response to
both pH and salinity changes, with greater PIP2 over-
expression mostly in response to salt variation. Our results,
in fact, showed that expression levels of PIP1 and PIP2
isoforms increased in P. oceanica leaves exposed to salt
stress and low pH, with a similar time course pattern. Only
in leaves of seedlings kept in hypersaline water, the
expression levels of PIPs did not change significantly,
suggesting the inability of the seedlings to deal with this
stressful condition. The over-expression of PIP1 and PIP2
isoforms analyzed confirmed at the gene level the results of
previous studies that showed an increment of both PIP1
transcripts and peptides in P. oceanica leaf tissue following
salt stress (Maestrini et al. 2004; Cozza and Pangaro 2009;
Serra et al. 2011), strongly suggesting that both these
aquaporins are involved in osmotic balance maintenance in
seagrasses. In addition, after 48 h of exposure, PIP1 and
PIP2 expression levels were lower than at 24 h, except for
PIP2 in seedlings exposed to pH variation. This could
suggest that after 48 h, plants are already adapted to the
levels of salinity and pH used in the experiments.
The experimental conditions tested fell in the range of
the real values experienced by meadows at the extremes of
the environmental tolerance of the species as regards
salinity. P. oceanica in fact tolerates salinity up to 48 PSU
in costal lagoon of Marsala during summer months
(Tomasello et al. 2009). Posidonia shoots, instead, are not
found at pH values lower than 7.6 in a natural CO2 sites of
Castello Aragonese (Ischia, Gulf of Naples) (Hall-Spencer
et al. 2008), although the lower tolerance limit of the
species is not known. Further experiments, both in labo-
ratory and in field, using RGs tested in different pH and
salinity conditions, may help to clarify the response in the
expression levels of genes of interest in the seagrass
P. oceanica to variation of these environmental conditions,
which also expected as consequence of global climatic
changes or water pollution.
Our study confirmed the need of RG evaluation under
different experimental conditions, proving that the relative
quantification of the GOI varied according to the RG and
the number of RGs used, thus highlighting the importance
of the choice of RGs in such experiments. The evaluation
of the expression stability of P. oceanica RGs in various
experimental conditions will be useful in further studies in
this species for the accurate quantification of target gene
expression using RT-qPCR assay.
Acknowledgments The authors thank the staff of the Molecular
Biology Service of Stazione Zoologica Anton Dohrn for their tech-
nical support in laboratory experiments, M. Lorenti and V. Rando for
their help in collecting environmental data, M.C. Buia for providing
support in the Lacco Ameno sampling site and the anonymous
reviewers for their constructive suggestions.
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