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

Mar Biol

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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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

Mar Biol

123

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

123

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

Mar Biol

123

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)

Mar Biol

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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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