3. MATERIALS AND METHODS
3.1. Sampling
3.1.1. Hediste diversicolor
Individuals of Hediste diversicolor were collected at nine sites (Table 3.1), located from western Mediterranean to European Atlantic coasts (Fig. 3.1). For each locality, at least twelve individuals were sampled, labelled, and preserved in absolute ethanol at 4 °C until genetic analyses were perform ed. For some of the analysis, samples were divided into two regions: Atlantic Ocean (FUR and ROS) and Mediterranean Sea (CAL, FIG, MIG, FMO, COL, CAB and CAP).
Table 3.1. Sampling localities for Hediste diversicolor, relative coordinates and number of individuals (N).
Sampling localitiy Abbreviation Latitude Longitude N
Calich pond (Sardinia) CAL 40°36'24"N 8°18'28"E 25 Figari bay (Corsica) FIG 41°26'30"N 9°07'21"E 22 Migliacciaru canal (Corsica) MIG 42°59'35"N 9°26'40 "E 22 Fiume Morto canal (Tuscany) FMO 43°44'03"N 10°17'02 "E 22 Coltano canal (Tuscany) COL 43°38'29"N 10°24'49"E 2 2 Cabras pond (Sardinia) CAB 39°54'41"N 8°30'57"E 21 Cala Petralana canal (Sardinia) CAP 41°11'10"N 9°20 '20"E 13 Furadouro estuary (Portugal) FUR 40°51'16"N 8°39'32 "W 22 Roscoff bay (France) ROS 48°42'46"N 4°00'03"W 12
20 km 4
5
100 km 1
2 3
6
7
200 km 9
8
20 km 4
5
20 km 4
5
100 km 1
2 3
6
7
100 km 1
2 2 3 3
6
7 7
200 km 9
8
200 km 9
8
Fig. 3.1. Sampling localities for H. diversicolor. 1) Calich pond; 2) Figari bay; 3) Migliacciaru canal; 4) Fiume Morto canal; 5) Coltano canal; 6) Cabras pond; 7) Cala Petralana canal; 8) Furadouro estuary;
9) Roscoff bay
3.1.2. Mytilaster minimus
Samples of Mytilaster minimus were collected from three brackish-water and two marine sites (Table 3.2), located in different localities in north-western Mediterranean and Adriatic Sea (Fig. 3.2). Collected samples of mussels were dissected, soft tissues preserved in absolute ethanol and stored at -20 °C until DNA extraction.
Table 3.2. Sampling localities for Mytilaster minimus, relative coordinates and number of individuals (N).
Sampling localitiy Abbreviation Latitude Longitude N
S’Ena Arrubia (Sardinia) SEN 39°49'48"N 8°33'9"E 9
Calich pond(Sardinia) CAL 40°35'44"N 8°17'15"E 2
Lesina Lagoon (Pulia) LES 41°53'39"N 15°29'42"E 9
Bocca d’Arno (Tuscany) BDA 43°40'52"N 10°16'47"E 5
Venice Lagoon (Veneto) VEN 45°27'29"N 12°20'40"E 1
100 km 1
2
3 4
5
100 km 100 km 100 km 1
2
3 4
5
Fig. 3.2. Sampling localities for M. minimus. 1) S’Ena Arrubia; 2) Calich pond; 3) Lesina lagoon; 4) Bocca d’Arno; 5) Venice lagoon
3.1.3. Xenostrobus securis
Samples of X. securis were collected from hard surfaces and soft bottoms at five localities (Table 3.3), located in North-western Mediterranean, Venice Lagoon and the gulf of Olbia (Fig. 3.3). Collected samples of pygmy mussels were washed from sediments and soft tissues preserved in absolute ethanol and stored at -20 °C until DNA extraction.
Table 3.3. Sampling localities for Xenostrobus securis, relative coordinates and number of sequenced individuals (N).
Sampling localitiy Abbreviation Latitude Longitude N
Scolmatore canal (Tuscany) SCO 43°36'3"N 10°21'25"E 14
Arno river (Tuscany) ARN 43°41'1"N 10°20'7"E 7
Fiume Morto canal (Tuscany) MOR 43°44'1"N 10°17'45" E 1
Venice Lagoon (Veneto) VEN 45°27'29"N 12°20'40"E 14
Gulf of Olbia (Sardinia) OLB 40°55'46"N 9°30'26"E 3
100 km 1
2 3
4
5
100 km 100 km 100 km 1
2 3
4
5
Fig. 3.3. Sampling localities for X. securis. 1) Scolmatore canal; 2) Arno river; 3) Fiume Morto canal; 4) Venice lagoon; 5) Olbia bay
3.2. DNA extraction
Genomic DNA of H. diversicolor was extracted from 4-6 parapodia per individual using QIAGEN® DNeasy Tissue kit (QIAGEN Inc., Valencia, California, USA) according to the manufacturer’s instructions. We analysed only specimens bright- or dark-green coloured, in order to minimise the presence of individuals belonging to different age classes in order to exclude the temporal component from sampling design (Dales & Kennedy 1954). For M. minimus and X. securis genomic DNA was extracted from the foot tissue with the GenElute Mammalian Genomic DNA Miniprep Kit (Sigma-Aldrich, St. Louis, MO, U.S.A.) according to the manufacturer's instructions. Once extracted, DNA was stored in solution at -20 °C.
3.3. DNA amplification
All PCR reaction where carried out in a One-Advanced (EuroClone, Milano, Italy) thermal cycler using EuroTaq (EuroClone) TaqDNA Polymerase. For H.
diversicolor, the universal primers LCO1490 and HCO2198 (Folmer et al., 1994) (Table 3.4) were used to amplify a portion of the mitochondrial gene COI. PCR reactions were performed in a total volume of 20 µl containing up to 30 ng of genomic DNA, PCR buffer 1x, 2.5 mM MgCl2, 0.2 mM nucleotides, 0.4 µM primers and 2.5 units of TaqDNA Polymerase. Uno che legge non trova informazioni: o quantità
assolute o concentrazioni e volumi The thermal profile for the COI gene was 2 min at 94 °C, 35 cycles of 1 min at 94 °C, 1 min at 54 °C and 1 min and 30 sec at 72 °C, followed by 5 min at 72 °C.
For M. minimus, the universal primers 16sbr-H and 16sar-L (Kocher et al., 1989; Palumbi et al., 1991;) (Table 3.4) were used to amplify a portion of the mitochondrial gene 16S. PCR reactions were performed in a total volume of 20 µl containing up to 30 ng of genomic DNA, PCR buffer 1x, 2.5 mM MgCl2, 0.2 mM nucleotides, 0.2 µM primers and 1 unit of Taq Polymerase. PCR profiles for the 16S rDNA gene was 2 min at 94 °C, 30 cycles of 30 sec a t 94 °C, 1 min at 56 °C, and 1 min at 72 °C, followed by 2 min of final extension at 72 °C.
For X. securis, the universal primers LCO1490 and HCO2198 (Folmer et al., 1994) (Table 3.4) were used to amplify a portion of the mitochondrial gene COI. PCR reactions were performed in a total volume of 20 µl containing up to 30 ng of genomic DNA, PCR buffer 1x, 2.5 mM MgCl2, 0.2 mM nucleotides, 0.1 µM primers and 1 unit of Taq polymerase. PCR profiles for the COI gene was 2 min at 94 °C, 30 cycles of 30 s at 94 °C, 1 min at 54 °C and 1 min at 72 °C, f ollowed by 2 min at 72 °C.
For each reaction a negative control was included. All PCR products were run in a 1.2 % agarose gel for 50 min at 80 V. Gels were stained in ethidium bromide for 10 min and size and presence of the bands relative to PCR products were scored on a ECX-20M UV transilluminator (CelBio).
table 3.4. Primer names, sequences and references.
Primer 5’ – sequence – 3’ author
16sbr-H CCGGTCTGAACTCAATCACG Palumbi et al., 1996
16sar-L CGCCTGTTTAACAAAAACAT Kocher et al., 1989
LCO1490 GGTCAACAAATCATAAAGATATTGG Folmer et al., 1994
HCO2198 TAAACTTCAGGGTGACCAAAAAATCA Folmer et al., 1994
For each reaction a negative control was included. All PCR products were run in a 1.2 % agarose gel for 50 min at 80 V. Gels were stained in ethidium bromide for 10 min and size and presence of the bands relative to PCR products were scored on a ECX-20M UV transilluminator (CelBio, Milano, Italy).
3.4. PCR products purification and sequencing
PCR products were purified with the following protocol:
• Add 0.1 volumes of sodium acetate and 3 volumes of absolute ethanol
• Mix well the solution by inversion
• Incubate for 15 min at room temperature
• Centrifuge at 3000 × g for 30 min
• Empty tubes and tip on tissue paper
• Add three volumes of 70 % ethanol
• Centrifuge at 1650 × g for 5 min
• Empty tubes and tip on tissue paper
• Repeat last three steps
• Dry samples
Once purified, all samples were send by mail to Macrogen Inc (Korea) for sequencing reactions.
3.5. Data treatment
3.5.1. Alignment of sequences
Obtained sequences were aligned using CLUSTALX 2.0 (Blackshields et al., 2007); subsequently, aligned sequences were checked and edited in BioEdit 7.0 (Hall, 1999). The program jModelTest 0.1.1 (Posada, 2008), based on the hierarchical likelihood ratio test was used to assess the best model of evolution for the sequences under the Bayesian Information Criterion (BIC) (Schwarz, 1978). The BIC was developed as an approximation to the log marginal likelihood of a model, and therefore, the difference between to BIC estimates may be a good approximation to the natural log of the Bayes factor (Kass & Wasserman, 1995). Given equal priors for all competing models, choosing the model with the smallest BIC is equivalent to selecting the model with the maximum posterior probability. In this way, BIC weights can be seen as approximate model posterior probabilities (Wasserman, 2000).
The program DnaSP v5 (Librado & Rozas, 2009) was used to obtain aminoacidic sequences using Platynereis dumerilii and Mytilus edulis genetic code, which are equal to Drosophila genetic code (Hoffman et al., 1992; Boore, 2001) for H.
diversicolor and X. securis respectively.
3.5.2. Within-population genetic diversity
The program DnaSP was used to estimate values of haplotype diversity (h) and nucleotide diversity (π). Haplotype diversity (h) is an analogue of the expected heterozygosity for diploid data, representing the probability that two randomly chosen haplotypes in a dataset are different:
Here, n is the number of copies of a given haplotype in the dataset, k is the number of haplotypes, and pi is the frequency of the i-th haplotype. If all the haplotypes of the dataset are equal, then h = 0, if all the haplotypes are different h reaches 1, which is its maximum value. Nucleotide diversity (π) is the probability that two randomly chosen homologous nucleotides are different:
Here dij is an estimate of the mutation separating haplotypes i e j, k is the number of haplotypes, pj is the frequency of the i-th haplotype and L is the length of the considered region expressed in bp.
For H. diversicolor parameters where calculated for each sampling locality, as well as for the total dataset. For M. minimus parameters were calculated for each sampling locality with the exception of the Venice lagoon (because sample size = 1), as well as for the pooled data of Sardinian samples (hereafter denominated Sardinia = SEN + CAL). For X. securis parameters were calculated for each sampling locality with the exception of the Morto river (because sample size = 1), as well as for the pooled data of northern Tuscany samples (hereafter denominated Pisa = SCO + ARN + MOR).
3.5.3. Among-population genetic diversity
Nonmetric multidimensional scaling (MDS), as implemented in STATISTICA 6 (StatSoft.it), was carried out on the matrix of pairwise Tamura & Nei (1993) genetic distances between haplotypes, calculated using ARLEQUIN 3.5 (Excoffier et al., 2010). The Tamura & Nei (1993) distance with the gamma model corrects for multiple hits, taking into account the different rates of substitution between nucleotides and the inequality of nucleotide frequencies. In this distance, evolutionary rates among sites are modeled using the gamma distribution. MDS allowed to ordinate the observations – haplotypes in this case – in a two-dimensional space, maximizing the consistency of represented distances on the graph and actual genetic distances between observations. The level of concordance between represented distances and genetic distances is expressed by the stress coefficient. In case of total concordance stress is equal to zero (Clarke, 1993). Reliability of MDS was estimated evaluating stress values according to Sturrock & Rocha (2000).
Estimates of genetic divergence among populations of H. diversicolor were obtained with an exact test based on the MonteCarlo Markov Chain (MCMC) algorithm. This test evaluates the statistical significance of genetic heterogeneity among all populations as a complex. Estimates of genetic divergence between each population pair were obtained by the fixation index Φst (Excoffier et al., 1992) an analogous Wright’s Fst (1951) as implemented in Arlequin.
For H. diversicolor hierarchical analysis of molecular variance (AMOVA;
Excoffier et al., 1992) was used to partition genetic variance in the: 1) within-sample;
2) among-sample within-region; and 3) among-region components, based on the matrix of inter-haplotypic Euclidean squared distances. For M. minimus and X.
securis AMOVA was performed at two levels: 1) within-samples; 2) among-samples.
The significance of variance components and Φ-statistics was assessed by permutation tests with 10000 replicates.
We used BAPS 5.4 (Bayesian Analysis of Population Structure; Corander &
Marttinen, 2006; Corander & Tang, 2007; Corander et al., 2008), to provide deeper insight into H. diversicolor genetic structure by clustering sampled individuals into panmictic groups. BAPS adopts a Bayesian approach with a stochastic optimization algorithm for analysing models of population structure, which greatly improves the speed of the analysis compared to traditional Markov chain MonteCarlo-based algorithms (Corander et al., 2003; 2008). When testing for population haplogroups,
where k is the maximum number of haplogroups, we ran five replicates for every value of k, up to k = 12. In addition, we used 500 reference individuals and repeated the admixture analysis 500 times per individual.
For the three species, relationships between haplotypes were analyzed by constructing a median-joining network (Bandelt et al., 2000), using the software NETWORK 4.5 (freeware available at www.fluxus-engeneering.com).
For H. diversicolor, Bayesian trees were obtained by MrBayes 3.1 (Huelsenbeck & Ronquist, 2001). A tree was constructed by adding the previously published sequences by Audzijonyte et al. (2008) (FJ030956–FJ030994) and Virgilio et al. (2009) (EU300637-EU300786) to our dataset. A sequence of Hediste japonica (D38032) was used as outgroup. Sequences were aligned in MEGA 4 (Tamura et al., 2007) and collapsed into haplotypes using DnaSP. Calculation employed a cold chain and three incrementally heated chains with T = 0.1. Starting trees for each chain were random and the default values of MrBayes were chosen for all settings (including prior distributions). Each Metropolis coupled MonteCarlo Markov chain (MCMC) was run for ten million generations, with trees sampled every 1000 generations, and discarded the first five million generations (5000 trees). By this time the chains had always converged to stable likelihood values lower than 0.01. Posterior probabilities (PP) were used to assess clade supports. Analyses were run using the evolutionary models selected for each gene fragment by the Bayesian information criterion of jModelTest (Posada, 2008).
Nucleotide sequences of M. minimus and X. securis were used to construct one neighbour-joining tree (Saitou & Nei, 1987) for each species using the software MEGA 4. A sequence of Brachidontes variabilis (GenBank accession number:
DQ836016) was used as outgroup for the former species, whereas sequences from Xenostrobus atratus and X. pulex (GenBank accession numbers: AB2985998 and DQ917582, respectively) were downloaded from GenBank and used as outgroup for the latter target species. Statistical support for each node in the trees was evaluated by bootstrapping (Felsenstein, 1985). Bootstrapping is a technique of statistical resampling that allows the estimation confidence intervals for a certain parameter when classical statistics is not applicable. Suppose that you had data points x1, x2, ...,
when T is so complex that its standard error is difficult to compute. Resampling n points with replacement from the data we can have fictional sets of data. For each fictional set of data we compute the estimate t* = T (x1*, x2*, ..., xn*). Repeating the process r times we have r estimates of the parameter t and the distribution of this estimates approximates the distribution of the actual estimate t. The variance of t can be inferred by computing the variance of this collection of t* values, and the confidence limits on the parameter can be approximated by using the appropriate upper and lower percentiles of the observed distribution of the t* values (Felsenstein, 1985).
3.5.4. Inferences on demographic history and neutrality tests
Demographic history of H. diversicolor was inferred by the analysis of the distribution of the number of site differences between pairs of sequences (mismatch distribution), under the sudden expansion model by Rogers & Harpending (1992).
This model is suitable for non-recombinant markers and consider a female population of initial effective size N0, at the equilibrium between genetic drift and mutation. It is supposed that in a time of t generation, the initial population will expand (or shrink) to the final effective size N1. This model can approximate the historical demography of a population using the three parameters N0, N1 and t, and each parameter is expressed as a function of µ, the mutation rate of the chosen marker. Hence, there is: θ0 = 2µN0, θ1 = 2µN1, and τ = 2µt (Rogers, 1995) and female effective population size is expressed in 1/2µ individuals, and time is expressed in 1/2µ generations. Mismatch analysis, as implemented in ARLEQUIN, was carried out on each population.
Expected values for a model of suddenly expanding population were calculated and plotted against the observed values. Populations that have experienced a rapid demographic growth in the recent past are expected to show unimodal distributions, resembling the shape of a wave, whereas those at demographic equilibrium present multimodal, or ragged, distributions (Rogers & Harpending, 1992). Harpending’s (1994) raggedness index (r, quantifying the smoothness of the mismatch distributions and distinguishing between population expansion and stability) and the sum of squared deviations (SSD), as implemented in ARLEQUIN, were used to evaluate the Rogers’ (1995) sudden expansion model, which is consistent with a unimodal mismatch distribution (Rogers & Harpending, 1992). To test for neutral evolution of
the mtDNA sequences Tajima’s D was calculated for each population (Tajima, 1989).
To test for population expansion, we computed two other tests: Fu’s (1997) FS test by using ARLEQUIN; and Ramos-Onsins & Rozas’ (2002) R2 test by means of DnaSP.
R2 test measures the excess of recent mutations deriving from demographic expansion on the external branches of a genealogy (i.e. a star phylogeny) using the formula:
Here n is sample size, S is the number of segregating sites, k is the average number of nucleotide differences among sequences and Ui is the number of exclusive mutations in the i-th sequence. Statistical tests and confidence intervals for FS were based on a coalescent simulation algorithm and for R2 on parametric bootstrapping with coalescence simulations.
