Marine Environmental Research - The Use of Artiﬁcial Crabs for Testing Predatory Behavior and H

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / m a r e n v r e v

Marine Environmental Research 101 (2014) 225e236

seagrass canopy and to epiphytic colonization of leaves (Dalla Via et al., 1998).

In pelagic algae, the single individual, often a single cell, can experience variations of light over a broad range of time and space (Rmiki et al., 1996), and can briskly pass from high to low light environments. Single individuals of benthic photosynthetic or-ganisms, instead, develop the whole life cycle at a stable depth, mainly experiencing light variability related to diurnal and seasonal cycles. Even within a single population, individuals growing at different depths may develop different photo-physiological adap-tive traits in order to cope with the different light properties of the speciﬁc environment (i.e. ecotypes;Carroll et al., 2007).

Acclimation and adaptation to light gradients occur in sea-grasses through a multilevel strategy, which includes changes in (i) meadow structure (Collier et al., 2007, 2009; Olesen et al., 2002;

Peralta et al., 2002), (ii) shoot morphology (Dalla Via et al., 1998;

Longstaff and Dennison, 1999; Olive et al., 2013), (iii) leaf-surface features (Enriquez et al., 1992; Durako, 2007), (iv) pigment and protein content (Casazza and Mazzella, 2002; Collier et al., 2008;

Mazzuca et al., 2009; Pirc, 1986; Sharon et al., 2009; Silva et al., 2013); (v) ratio between photosystem I and II units (Dattolo et al., 2013; Sharon et al., 2011) and (vi) photosynthetic parameters, such as photosynthetic efﬁciency (a), maximum electron transport rate (ETRmax), and saturating irradiance (Ek) (Campbell et al., 2003;

Collier et al., 2009; Larkum et al., 2006; Silva and Santos, 2003). In addition to these responses, seagrasses can alter resource allocation patterns to optimize carbon balance (Alcoverro et al., 2001), and can adapt their reproductive cycle along the bathymetric cline, as observed in Mediterranean species (Buia and Mazzella, 1991).

In seagrasses, the comprehension of the molecular and biochemical mechanisms for regulating light harvesting, carbon utilization and photosynthetic processes is still limited. Besides, there are no studies which relate photo-physiological features and genetic make-up of seagrasses populations, even if the phenotypic plasticity in photosynthetic acclimation is undoubtedly a funda-mental trait to take into consideration, for assessing resistance and resilience at population level (Crosbie and Pearce, 1982; Flood et al., 2011). The recent development of molecular resources for different seagrass species (e.g.Franssen et al., 2011; Wissler et al., 2009) has greatly facilitated the investigation of differentially expressed genes in response to environmental variations (Bergmann et al., 2010; Bruno et al., 2010; Gu et al., 2012; Massa et al., 2011;

Reusch et al., 2008; Serra et al., 2012; Winters et al., 2011), and ﬁrst attempts have been already carried out in Posidonia oceanica, to couple genomic and eco-physiological approaches (Dattolo et al., 2013; Mazzuca et al., 2013; Procaccini et al., 2012).

The seagrass Posidonia oceanica (L.) Delile is endemic of the Mediterranean sea, where it forms extensive monospeciﬁc meadows, extending from ~1.0 to 40e50 m depth (Duarte, 1991;

Pasqualini et al., 1998). This species supports a highly diverse associate community and is generally known to supply highly valuable ecosystem services as the protection of the coastline from erosion. However, its distribution is undergoing a regression, esti-mated at 10% over the last 100 years (Pergent et al., 2010), due to the several threats which disturb the coastal ecosystems (Marba and Duarte, 2010; Waycott et al., 2009).

P. oceanica is hermaphroditic, with sexual reproduction considered overall sporadic (Diaz-Almela et al., 2006). It grows according to a phalanx strategy (Migliaccio et al., 2005) and long living clonal lineages can persist for hundreds of years (Arnaud-Haond et al., 2012; Migliaccio et al., 2005; Procaccini et al., 2007).

These features make P. oceanica an ideal target for studying plas-ticity of genotypes in response to changes in environmental con-ditions (Buia and Mazzella, 1991; Lorenti et al., 2006; Mazzuca et al., 2009; Olesen et al., 2002; Reusch, 2014; Serrano et al., 2011).

Moreover, the clear genetic structure existing in P. oceanica pop-ulations along the depth gradient accounts for inter-genotype competition for colonizing environments characterized by sharp differences in environmental parameters, such as light (D'Esposito et al., 2012; Migliaccio et al., 2005).

This study aims to compare the photoacclimation properties of P. oceanica growing in two ecological niches (i.e. in high and low-light environments,5 and 25 m depth), characterized by dif-ferences in light quantity and quality, in order to assess the diver-gent acclimation mechanisms acting along the depth gradient within a single population. Regulation of metabolic pathways and photo-dependent processes were investigated, through the study of photo-physiological and molecular plant features.

Data were collected in two seasons (summer and autumn) of consecutive years (2010e2011) to investigate plant response in different combination of light and temperature regimes: in sum-mer, when maximum differences in light and temperature exist between the two sampled depths, and in autumn, when only light was different between the two depths, temperature being almost homogeneous along the water column. Together with gene expression analysis, we compared variations in photosynthetic parameters and pigment composition. The molecular information consisted in a large-scale gene expression screening of P. oceanica clones growing in the shallow and deep stand of the meadow using cDNA-microarray, in parallel with the assessment of the expression proﬁle of thirteen selected target genes, to speciﬁcally investigate the regulation of the molecular machinery of the photosynthesis and photoprotection systems.

2. Materials and methods 2.1. Study area

The study has been performed in the Posidonia oceanica meadow located in the Lacco Ameno Bay (Island of Ischia, Gulf of Naples, Italy; 4045⁰5⁰⁰ N, 1353⁰4⁰⁰ E), which extends from 1 to about 33 m depth (Buia et al., 1992; Zupo et al., 2006). The site is characterized by a thermal stratiﬁcation of the water column pre-sent from April to early October, with a summer maximal difference of up to 7C between5 and 25 m (Buia et al., 1992). In this meadow, the canopy growing at 5 m depth receives approximately 50% of surface photosynthetically active radiation (PAR), while plants growing at 25 m depth experience about 10% of surface PAR (Buia et al., 1992). The daily period of photosynthesis-saturating irradiance (Hsat) at25 m is about one half the duration of that experienced by plants at5 m (Lorenti et al., 1995). For both 5 and 25 m depth, light transmission inside the canopy is approximately 10% (Buia et al., 1992).

Samplings have been conducted in summer 2010 (July, 03th,

~12:00), and in early autumn 2011 (September, 29th, ~11:30).

Samples were collected from the shallow (5 m) and the deep portion of the meadow (25 m).

During the ﬁrst sampling (2010), temperature and photosyn-thetic photonﬂux density (PPFD) were measured using a multi-parameter instrument (SBE 16, Sea-Bird Electronics, Bellevue, WA, equipped with a quantum sensor LI-COR mod. LI-193SA) operated from the boat. During the second sampling (2011), PPFD was esti-mated using a portable quantameter (Biospherical, San Diego, CA, mod. QSI-140B) and temperature was measured by mean of HOBO loggers (Onset Computer Corp., USA), deployed at both depths.

2.2. Photosynthesis measurements and sampling strategy

In order to assess variations in photosynthetic performance of P. oceanica at different depths and between different seasons, E. Dattolo et al. / Marine Environmental Research 101 (2014) 225e236

226

photosynthetic parameters were obtained by pulse amplitude modulated (PAM)-ﬂuorometry using a Diving-PAM instrument (Walz, Effeltrich, Germany), operated by SCUBA divers. Measure-ments of chlorophyllﬂuorescence were taken in situ on the mid portion of the youngest fully mature leaf in the shoot (usually the second-rank leaf). Its photosynthetic performance is relatively high and stable (Modigh et al., 1998) and the production pattern is representative of the whole shoot (Zupo et al., 1997).

Rapid light curves (RLCs) of irradiance vs. ETR (electron trans-port rate past PSII) were obtained by exposing selected leaf spots to a range of irradiances between ~ 10 and 800m^mol/m²^/s¹^, pro-duced by the Diving-PAM lamp and lasting 10 s each.

Photosynthetic parameters (relative maximum electron trans-port rate, rETRmax, initial slope of the curve,aand the saturating irradiance, E_k) were calculated by ﬁtting empirical data to an exponential function (Ralph and Gademann, 2005).

In 2010, light-adapted RLCs were performed on P. oceanica leaves (n¼ 8 for each depth) without subjecting them to a dark adaptation period. In 2011, the RLCs were performed for leaves after a 10 min-dark-adaptation period (n¼ 5 for each depth). Thanks to this procedure, in autumn, we also estimated the Fv/Fm ratio (maximum photosynthetic efficiency of PSII), and the maximum capacity of non-photochemical quenching (NPQmax) developed along the RLC (Dimier et al., 2009). NPQ corresponds to the thermal dissipation of excess of energy capacity and was quantified by the Stern-Volmer expression: NPQ ¼ (FmFm')/Fm' (Bilger and Bj€orkman, 1994), where Fm and Fm' are the maximal PSII fluores-cence yield, for dark adapted and illuminated leaves, respectively.

Average values estimated at the two depths were compared for each sampling season using the Student's t test. Signiﬁcance was determined at p< 0.05.

In both seasons (July 2010 and September 2011), the youngest fully mature leaves of individual shoots were collected for molec-ular and biochemical assays among those utilized for photosyn-thetic measurements. This allowed a better correlation between photosynthetic performance and biochemical/molecular features.

All samples were collected around midday (between 11:30 and 12:00 h) and placed in black containers to avoid overexposure to full sunlight. On the research vessel, leaf material was rapidly scraped free of epiphytes, towel-dried and shock-frozen in liquid nitrogen (within 15e20 min after collection), prior to storage at

80C in the laboratory. This relatively shortﬁxing-time and the comparable time of the day among the different seasons and depths support a direct comparability of the obtained data. The list of all performed analyses, divided per season, is reported in Table 1S.

2.3. cDNA-microarray

Asﬁrst, we used a cDNA microarray analysis to characterize the whole transcriptome expression of P. oceanica, and select pathways more affected by light variation along the depth cline.

2.3.1. Microarray design

The microarray was built using clone's sequences taken from the two ESTs P. oceanica libraries presently available and stored in the database Dr. Zompo (http://drzompo.uni-muenster.de/). The whole set of unigenes (495) belonging to the SSH-EST library (“Pooc_B” in Dr. Zompo), which are differentially expressed along the bathy-metric gradient (Dattolo et al., 2013), and another set of unigenes (501), selected from a P. oceanica non-subtracted EST-library (“Pooc_A” in Dr. Zompo), were included in the microarray. For each unigene, a single EST was used as probe (selected from P. oceanica ESTs, Genbank Accession Numbers: GO34959 to GO349047;

JZ354020 to JZ356595).

2.3.2. Target preparation

Total RNA was extracted from youngest fully mature leaves (n¼ 3 for each depth), collected in 2010, using Cetyl Trimethyl Ammonium Bromide (CTAB) method (Chang et al., 1993), with some modiﬁcations (seeDattolo et al., 2013).

RNA quantity was assessed by Nano-Drop (ND-1000 UVeVis spectrophotometer; NanoDrop Technologies), monitoring the absorbance at 260 nm; purity was determined by monitoring the 260/280 nm and 260/230 nm ratios, integrity was tested measuring the RIN number with Agilent RNA 6000 Nano Kit (Agilent 2100 Bioanalyzer) and with 1% denaturing gel electrophoresis. All sam-ples resulted free from proteins and organic solvents used during RNA extraction. Complementary DNA (cDNA) synthesis incorpo-rating dUTP-Cy3 or dUTP-Cy5 was carried out employing the CyScribeﬁrst-strand cDNA labeling kit (GE Healthcare, Amersham Biosciences) following the manufacturer's instructions. Equal quantities of labeled cDNA were hybridized using the hybridization solution.

2.3.3. Probe preparation

cDNA clones, stored in phage libraries, were ampliﬁed with universal primers T7 and M13 reverse (for primers sequences:

pCR2.1-TOPO vector manufacture's protocols. Invitrogen). PCR products were puriﬁed and spotted in triplicates on slides. Glass spotting was carried out with the Microarray Spotarray24 (Perkin Elmer) using Micro Spotting Pins (Telechem Corporation, Sunny-vale, CA, United States) on GAPS II Coated Slides. Probe's spotted sequences and their relative P. oceanica unigenes are reported in Table 2S (Supplementary material).

2.3.4. Hybridization

Prior to hybridization, microarrays were incubated at 42C for 60 min in a pre-hybridization buffer (50% Formamide, 5X SSC, 0.1%

SDS and 0.1 mg/ml BSA). Hybridization was performed for 16 h in hybridization buffer (50% Formamide, 5X SSC, 0.1% SDS and 0.1 mg/

ml Herring Sperm) at 42C, using equal quantities of labeled cDNA.

Pre-hybridization and hybridization buffers were removed by several washing with Wash solution 1 (2X SSCþ 0.1% SDS in MilliQ H2O) and Wash solution 2 (0.1X SSCþ 0.1% SDS in MilliQ H2O) and with a last wash in MilliQ H2O. For both shallow (i.e. high-light condition ¼ S) and deep stand (i.e. low-light condition ¼ D), microarray has been hybridized with three biological replicates (S1-S3; D1eD3), each of them replicated at least four times (i.e.

technical replicates), in order to assess the technical variability of each microarray hybridization output (Lee et al., 2000).

2.3.5. Scanning and data analysis

Acquisition and quantification of array images were performed with a Perkin Elmer Scanarray Express confocal microarray scan-ner. The software package Imagene (Biodiscovery) was used to screen spots and quantify spot signals asfluorescence intensities for each dye channel. According to the software Imagene (Bio-discovery), spots with poor quality and artefacts were excluded from further analysis and only high quality signals were selected to compare the expression profiles among replicates. After dis-carding low quality replicates, the number of independent tech-nical replicates used for the analysis was the following: S1¼ 4;

S2¼ 5; S3 ¼ 3; D1 ¼ 4; D2 ¼ 5; D3 ¼ 4. To detect differentially regulated genes among shallow and deep samples, differences in the number of replicates were taken into account and signiﬁcance was assessed using an Unpaired t test. A Benjamini and Hochberg false discovery rate (FDR) correction for multiple testing method was applied and only genes with p-values< 0.05 were considered signiﬁcant. In order to illustrate the degree of similarity between depths and among biological replicates, a Principal Component

E. Dattolo et al. / Marine Environmental Research 101 (2014) 225e236 227

Analysis (PCA) was performed and data were plotted in a tri-dimensional graph.

A gene ontology (GO) term enrichment analysis was performed to identify biological processes, cellular components and molec-ular functions involved in acclimation to the different conditions.

For each GO class, we compared the proportion of associated genes in the group of significantly differently expressed genes with the same proportion relative to the entire set of genes spotted on the microarray. Proportions were compared using the Fisher's exact test and the p-values corrected using the Benjamini and Hochberg method. We considered as significant all the classes showing a significantly higher proportion of genes in the differentially expressed genes group with a corrected p-values smaller or equal to 0.1.

2.4. RT-qPCR

A reverse transcriptione quantitative Polymerase Chain Reac-tion (RT-qPCR) analysis has been performed in order to further investigate differences in gene expression of genes belonging to differentially expressed pathways, as resulting from the microarray analysis.

For the summer sampling, RNA from three biological repli-cates, independent from the ones used for microarrays, was pooled. For the autumn sampling, experiments were conducted on three independent biological replicates per depth, collected from the same shoot replicates on whichﬂuorometric measures were conducted.

2.4.1. RNA extraction and cDNA synthesis

Total RNA was extracted as described inMazzuca et al. (2013).

RNA quantity and purity was assessed by Nanodrop (ND-1000 UVeVis spectrophotometer; NanoDrop Technologies), RNA quality was evaluated by 1% agarose gel electrophoresis. Total RNA (500 ng) was reverse-transcribed in complementary DNA (cDNA) with the iScript™ cDNA Synthesis Kit (Bio-Rad), following the

manufacturer's instructions, using the GeneAmp PCR System 9700 (Perkin Elmer).

2.4.2. Selection of target genes and primer design

For RT-qPCR analysis, we selected thirteen genes encoding for the photosynthetic and the photoprotection molecular machineries (Table 1): (i) four genes from the Lhc gene family: Chlorophyll a-b binding protein 6A (CAB-6A), Chlorophyll a-b binding protein 151 (CAB-151), Chlorophyll a-b binding protein 4 (LHCA4) and Chloro-phyll a-b binding protein CP29.2 (LHCB4.2); (ii) three genes belonging to Photosystem II: PSII 22 kDa protein (PSBS), PSII protein D1 (psbA) and PSII protein D2 (psbD); (iii) two genes belonging to Photosystem I: PSI reaction center subunit IX (psaJ) and PSI reaction center subunit V (PSAG); (iv) the ferredoxin (SEND33), and the small subunit of RuBisCO (SSU5B) and (v) two genes involved in the photoprotective cycles (due to xanthophyll and tocopherols respectively): Zeaxanthin epoxidase (ZEP) and a Homogentisate phytyltransferase 1 (HPT1). Twelve genes were already described in Mazzuca et al. (2013).

For two genes (psbA and psbD), primers were designed on conserved regions of the seagrass Zostera marina and other related species. For the other eleven genes, primers were designed on P. oceanica ESTs sequences present in Genbank (Table 1); Swiss-Prot and TAIR (The Arabidopsis Information Resource) best scoring hits obtained through BLASTX searches are reported inTable 3S. For all target genes, primers were designed using the software Primer3 v.

0.4.0 (http://frodo.wi.mit.edu/primer3/). Gene Runner v. 3.05 (Hasting Software) was used to predict primer melting temperature (Tm) and secondary structures. All cDNA amplicons ranged from 100 to 200 bp in size in order to ensure similar PCR efﬁciency.

PCR conditions were optimized on a GeneAmp PCR System 9700 (Perkin Elmer). For a detailed description, seeSerra et al. (2012).

Ampliﬁcation efﬁciency (E) for all primer pairs has been calculated from the slopes of standard curves of the threshold cycle (Ct) vs.

cDNA concentration, with the equation E¼ 10^1/slope1. All E > 92%

and R²> 0.96.

Table 1

List of target genes selected for RT-qPCR experiment. Gene and protein names, biological process, GenBank Accession Number, primer sequences, amplicon size (S, base pair), percent efﬁciency (E), and correlation coefﬁcient (R²) are shown.

Gene Protein Biological process GenBank Primer sequence 5⁰/3⁰ S E R²

psbA Photosystem II protein D1 Photosynthesis KC954695 F:GACTGCAATTTTAGAGAGACGC

R:CAGAAGTTGCAGTCAATAAGGTAG

137 92% 0.99

psbD Photosystem II protein D2 Photosynthesis KC954696 F:CCGCTTTTGGTCACAAATCT

R:CGGATTTCCTGCGAAACGAA

162 100% 0.98

PSBS Photosystem II 22 kDa protein, chloroplastic

Photosynthesis GO346095.1 F:CCGCTCCTGTTGTTCTTCAT R:GGACCTCCTTCCTTGAGACC

158 100% 0.99

psaJ Photosystem I reaction center subunit IX Photosynthesis GO346974.1 F:GGTTTGGGTCTTTAGCAGGTC R:GAATGGGTGGGAGGAGAAAT

160 98% 0.99

PSAG^a Photosystem I reaction center subunit V, chloroplastic Photosynthesis GO348645.1 F:CTATGTGCTTGCTACGTCCAG R:TCAAACAAACCACCAGCATC

187 100% 0.99

LHCB4.2 Chlorophyll a-b binding protein CP29.2, chloroplastic Photosynthesis GO346860.1 F:TCGAACACTTGACGGTGGTA R:ACGCTTCAGTTGGCTGAGAT

195 100% 0.98

CAB-151^a Chlorophyll a-b binding protein 151, chloroplastic Photosynthesis GO347467.1 F:AAGCCCATTAGCACAACCTG R:GGGCAATGCTTGGTACTCTC

199 93% 0.99

CAB-6A^a Chlorophyll a-b binding protein 6A, chloroplastic Photosynthesis GO346691.1 F:CGACCGTTCTTGATCTCCTT R:AGTTCATCACCATCGCCTTC

154 96% 0.99

LHCA4^a Chlorophyll a-b binding protein 4, chloroplastic Photosynthesis GO347781.1 F:GGTCCAACACAACGTGACAG R:GACCTCCCTTGGAACCTTTC

200 100% 0.98

SEND33^a Ferredoxin, chloroplastic Electron transport GO348399.1 F:TCAGACTGGGGGTAAGCAAC

R:TCTACATCCTCGACCACTGC

187 100% 0.98

SSU5B RuBisCO small subunit Carbon dioxideﬁxation GO346679.1 F:AGCATGGTAGCACCCTTCAC

R:GGGGGAGGTATGAGAAGGTC

169 100% 0.99

ZEP Zeaxanthin epoxidase, chloroplastic Xanthophyll cycle GO348250.1 F:TGCTCCAGAGAAAGCCAGTT R:TGGCATCCCCAAATGTTATA

197 100% 0.96

HPT1 Putative Homogentisate phytyltransferase 1 Lipid metabolic process JZ356529 F:CCACTAGCTTTGTCGCCTTC R:ATGGTGTCTGGGGGAGGTAT

185 100% 0.99

aEST reverse/complement. Gene names according to Swiss-prot best hit.

E. Dattolo et al. / Marine Environmental Research 101 (2014) 225e236 228

2.4.3. RT-qPCR

RT-qPCR was performed as described inMazzuca et al. (2013).

All reactions were conducted in triplicate to capture intra-assay variability and each assay included three no-template negative controls (NTC) for each primer's pair.

Relative expression of each target gene was calculated using the Relative Expression Software Tool REST 2009, version 2.0.13 (Pfafﬂ et al., 2002), which uses the hypothesis test P(H1) to determine signiﬁcant differences between controls and targets. This software provides proper error propagation and robust statistical analysis by using a random reallocation algorithm with 10,000 iterations.

Relative gene expression in plants collected in the shallow stand was analyzed using samples collected in the deep stand as control.

L23 (GenBank: GO347779), EF1A (GenBank: GO346663) and NTUBC2 (GenBank: GO347619) were used as reference genes (RGs) for the relative quantiﬁcation because they have been previously identiﬁed as the most stable RGs in P. oceanica in the same exper-imental conditions (Serra et al., 2012).

2.5. Pigment composition and short-term incubation in high light

In order to characterize the pigment pool of plants under different light regimes, during the second sampling campaign (September, 2011) four replicate shoots were collected at each depth, selected among those used also for photosynthetic measurements.

Additionally, a short high-light incubation experiment was conducted on three leaves from each batch of shallow and deep shoots collected for the other analysis. Leaf fragments were incu-bated on board in seawater to full sunlight (z1200mmol photons m²sec¹) for 20 min before freezing, in order to investigate the photoprotective xanthophyll cycle activation and compare the fast pigment reactions to high-light stress (pigments were extracted after pooling the three leaf replicates from each depth).

For bothfield and high-light exposure, photosynthetic pigment identification and quantification were accomplished using high-performance liquid chromatography (HPLC). Samples were stored in complete darkness at80C until the chromatographic analysis.

Pigments from frozen leaf pieces of approximately 2.5e4 cm were extracted with a mini potter in 3 ml 100% methanol in dim light.

300ml of ammonium acetate (1M) was added to 1 ml ofﬁltered extract. After 5 min at 4C, the extract was injected in a Hewlett Packard Series 1100 HPLC. A 3-m^{m C}8BDS column (100 4.6 mm) was used and the mobile phase was composed of two solvent mixtures: A (methanol: aqueous ammonium acetate, 70:30) and B (methanol). The gradient between the solvents was the same as in Vidussi et al. (1996). Pigments were detected at 440 nm using an HP photodiode array detector Model DAD Series 1100, which gives the 400e700 nm spectrum for each detected pigment. Single pigments

Nel documento The Use of Artiﬁcial Crabs for Testing Predatory Behavior and Health in the Octopus (pagine 113-125)