Bacterial communities in polluted seabed sediments: a molecular biology assay in Leghorn harbor

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Volume 2013, Article ID 165706,13pages http://dx.doi.org/10.1155/2013/165706

Research Article

Bacterial Communities in Polluted Seabed Sediments:

A Molecular Biology Assay in Leghorn Harbor

Carolina Chiellini,

1

Renato Iannelli,

2

Franco Verni,

1

and Giulio Petroni

1 1_{Department of Biology, Unit of Protistology-Zoology, University of Pisa, via Luca Ghini 13, 56126 Pisa, Italy} 2_{Department of Engineering for Energy, Systems, Territory and Constructions, University of Pisa,}

via Carlo Francesco Gabba 22, 56122 Pisa, Italy

Correspondence should be addressed to Giulio Petroni; gpetroni@biologia.unipi.it Received 26 July 2013; Accepted 22 August 2013

Academic Editors: Y. Nishita, S. A. Stephenson, and Y. Xi

Copyright © 2013 Carolina Chiellini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Seabed sediments of commercial ports are often characterized by high pollution levels. Differences in number and distribution of bacteria in such areas can be related to distribution of pollutants in the port and to sediment conditions. In this study, the bacterial communities of five sites from Leghorn Harbor seabed were characterized, and the main bacterial groups were identified. T-RFLP was used for all samples; two 16S rRNA libraries and in silico digestion of clones were used to identify fingerprint profiles. Library data, phylogenetic analysis, and T-RFLP coupled with in silico digestion of the obtained sequences evidenced the dominance of

Proteobacteria and the high percentage of Bacteroidetes in all sites. The approach highlighted similar bacterial communities between

samples coming from the five sites, suggesting a modest differentiation among bacterial communities of different harbor seabed sediments and hence the capacity of bacterial communities to adapt to different levels and types of pollution.

1. Introduction

Sea stretches of commercial ports are often characterized by high levels of pollution in sediments, low oxygen concentra-tions in the water column, and low biodiversity of benthic

communities [1] that may cause the decrease of fauna in their

seabed [2]. Further reasons of impact on harbor sea stretches

often come from high concentrations of organic matter due to eutrophication, time variability of sediment deposition,

allochthonous inputs, and low hydrodynamism [3,4]. These

reasons demonstrate that the activities conducted in ports lead to critical conditions in the environments, and the organisms develop several ways to adapt to the alterations of

water and sediments parameters [5]. A crucial characteristic

of harbor sites is the removal of organic matter. Consumption capacity of organic matter is mainly due to biotic uptake and degradation performed by microorganisms. This process is generally insufficient to maintain the equilibrium of ecosys-tems; in most cases, the presence of detrital organic matter (which increases anoxic conditions), heavy metals,

polychlo-rinated biphenyls (PCBs, [6]), and other toxic compounds

plays also a crucial role in negatively impacting the seabed

biocenosis [7, 8]. Although the origin and concentration

of pollutants in seabed harbor sediments can be different, many studies demonstrate that, in most cases, pollution is mainly due to the presence of hydrocarbons, heavy metals

[9], and organic matter. Possible methods for hindering the

problem of contaminated aquatic sediments have been widely

discussed [10]. Polluted seabed sediments can be sanitised via

in situ actions, which always require an accurate knowledge

of the local biocenosis [11]. Moreover, it was demonstrated

that bacterial communities have great potential to be used as sensitive indicators of contamination in aquatic sediments

[12]. New and accurate methods to study the composition

of microbial communities in harbor seabed sediments are necessary for these reasons. Differences in number and distribution of bacteria, in such a large area as a harbor, can be related to pollutant distribution and general sediment state. Today, Leghorn’s Harbor is one of the most important ports in the Mediterranean Sea, linked with more than 300 ports worldwide. It is a multipurpose port that can cater for all kinds of vessels and handle all kinds of goods,

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as well as passenger traffic. It sports a marine area of 1.6 km2

and a useable land area of 2.6 km2. The Harbor offers 11 km of

quays with more than 90 berths, with up to 13 m draught. The

total extension covers one km2surface outdoor and 70000 m2

indoor areas.

In the present research, the bacterial community compo-sition of Leghorn Harbor seabed sediments was studied with a molecular fingerprint approach and with the construction of libraries of the 16S rRNA gene. The Terminal Restriction Fragment Length Polymorphism (T-RFLP) approach was used for all samples to identify the community structure. in

silico digestion of clones retrieved from two clone libraries

(one for a less polluted sample and one for a more polluted sample) was used for the identification of bacterial Opera-tional Taxonomic Units (OTUs) in the T-RFLP fingerprint profiles. The aim of the study was to highlight how the different conditions of port seabed influence the composition of microbial communities and provide hints of dynamics for nutrient removal.

2. Materials and Methods

2.1. Description of the Study Area: Collection and Storage of Samples. Figure1shows Leghorn’s Harbor area. The five sampling sites are located with tagged marks and briefly described within the figure. In each site, three samples were collected for a total of fifteen samples. According to the site

assessment performed by the port authority [13] and the

chemical parameters reported in Table1, site one, which is

located in the passenger terminal, represents the less polluted spot, while site two, which is located inside the “Darsena Calafatari” ship repairing section and has never been dredged for 60 years, has the highest concentrations of heavy metals and hydrocarbons. In this work, the samples named 2.1 (site 2, sample 1) and 1.2 (site 1, sample 2) were used. They were collected on the November 3, 2009, by a scuba diver and immediately brought to the laboratory for the extraction of the total DNA.

2.2. DNA Extraction, Construction of 16S rRNA Gene Libraries, Sequencing, T-RFLP Analysis, and In Silico Digestion of Sequenced Clones. Total DNA extraction was performed

on all of the 15 samples using a soil master DNA extraction kit (Epicentre Biotechnologies, WI, USA). The library was built with samples 1.2 and 2.1 using the procedure described by

Amann et al. [14]: 16S rRNA genes were directly amplified

from extracted DNA using universal bacterial primers, 8F

(5󸀠-AGA GTT TGA T(CT)(AC) TGG CTC AG-3󸀠) and

reverse 1492R (5󸀠-GG(AGCT)(AT)AC CTT GTT ACG ACT

T-3󸀠) [15]; the amplification products were cloned in a

plasmid vector (pCRs2.1-TOPOs, TOPO TA Cloning Kit, Invitrogen, UK) and inserted in chemically competent cells (OneShot TOP10, Invitrogen, UK). The inserted fragments from a representative number of clones were then amplified by control PCR with M13F and M13R universal primers. The inserted genes showing a size similar to the 16S rRNA gene size were directly sequenced with primers M13F and M13R by the Macrogen Inc. sequencing service (Republic of Korea).

For T-RFLP analysis, the amplification of the 16S rRNA gene was performed with the same procedures of the library construction, and primer 8F was labeled with FAM fluo-rochrome (Applied Biosystem CA, USA). The template was digested with two different restriction endonucleases: BsuRI

(GG∧CC, 0.2 u/𝜇L, Fermentas, Canada) and RsaI (GT∧AC,

0.2 u/𝜇L, Fermentas). Digested DNA was precipitated with

cold ethanol 100% to eliminate salts at 4∘C and 10,000 RCF.

For each reaction, a mix was prepared with 1.2𝜇L of

loading buffer (GeneScan 600 LIZ, Applied Biosystem), a

maximum of 5.5𝜇L of sample (calculated after cold ethanol

precipitation on the bases of its final concentration), and

13.3𝜇L of deionized formamide (Applichem, Germany).

Capillary electrophoresis was performed with Abi Prism 310 Genetic Analyzer (Applied Biosystem); T-RFLP profiles were analyzed using GeneScan analysis software (Applied Biosystem), and the data matrix was transformed for statistics

as described in Iannelli et al. [13]. Nonmetric

Multidimen-sional Scaling (NMDS) was performed on the whole T-RFLP dataset with the Bray-Curtis coefficient and the Shannon diversity index was also calculated. All statistical analyses were performed using PAST software v.2.15 (Paleontological

Statistic, [16]). Further details on the applied techniques are

reported in Chiellini et al. [17]. All sequences obtained from

the construction of both libraries were digested in silico by searching the restriction site on Arb database, as recognized by restriction enzymes BsuRI and RsaI in order to know the size of the terminal restriction fragments representative of the OTUs. With the in silico digestion of retrieved clones, we could correlate the peaks on T-RFLP fingerprints of the 15 samples from Leghorn Harbor seabed (3 replicates in 5 different sites) to specific OTUs. The coverage percentages of the peaks corresponding to the recognized OTUs and corresponding bacterial phyla and classes were calculated for each electropherogram.

2.3. Detection of Chimeric Sequences and Phylogenetic Anal-ysis. All the retrieved sequences were scanned with the

Bellerophon server [18] to identify chimeras. We decided to

analyze chimeric sequences by “cutting” them in proximity of the recombination site identified by Bellerophon software and considering the obtained fragments as independent sequences belonging to different organisms. Fragments were treated in the same way as complete sequences from other screened clones; they were included in 16S rRNA library anal-ysis, but they were not included in the construction of phylo-genetic trees, due to their short length. NCBI BLAST analysis

[19] was used to determine a preliminary affiliation of clone

sequences. After BLAST analysis, sequences were inserted in

SILVA 104 database [20] and aligned using the appropriate

tool from the ARB program package [21]. A deeper

phyloge-netic analysis was performed on the Proteobacteria phylum, because (i) it included the most represented 15 T-RFLP pro-files in the two libraries, and (ii) it was one of the main groups

previously retrieved in marine sediments (e.g., [37, 38]).

Two phylogenetic reconstructions on different subclasses of

Proteobacteria were performed independently. The analysis

focuses in particular on two groups of sequences: the first one including bacteria belonging to the Deltaproteobacteria

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Site 3 Site 4 Site 5 Site 2 Site 1 0 W S N E 0.5 1 (km)

Figure 1: Representation of the Leghorn’s Harbor area. The tagged marks represent the sampling sites in the seabed of the area, which are briefly described as follows. Site 1: ferry boat departure area (sandy loam; water depth 11 m). Site 2: seldom dredged shipbuilding area (loam; water depth 4 m; last dredging 60 years ago). Site 3: container terminal (silt loam; water depth 13 m; located by the open sea mouth of the Navicelli canal). Site 4: cargo ferry transit (loam; water depth 6 m). Site 5: chemical processing and oil refinery terminal (silty clay loam; water depth 9 m; located by the mouth of Ugione stream, which crosses the inland industrial area and collects some spare wastewater discharges).

Table 1: Chemical parameters of the five sampling sites [13].

Samples Cd Ni Pb Cr Cu Zn TPH TOC TN TP NO3 − _Cl− _SO 42− mg/kgdw mg/kgdw mg/kgdw mg/kgdw mg/kgdw mg/kgdw mg/kgdw mg/kgdw mg/kgdw mg/kgdw mg/kgfw g/kgfw mg/kgfw 1.1 1.06 37.7 33.2 25.2 75.0 313 1433 6102 197 917 831 10 3587 1.2 0.63 25.2 271.0 34.7 73.5 353 1066 8938 225 1024 975 12 4211 1.3 0.71 39.6 104.0 15.8 268.0 407 1030 7720 304 1321 903 11 3899 2.1 1.16 41.0 249.0 48.8 80.5 308 1915 25460 2170 696 1161 17 3192 2.2 1.42 75.4 221.0 35.4 375.0 854 2031 33540 1580 759 1363 19 3747 2.3 1.31 94.9 171.0 27.7 485.0 884 1798 27500 1890 722 1262 18 3469 3.1 1.22 101.0 47.1 32.1 74.4 257 900 19664 1019 568 708 16 3417 3.2 1.44 98.4 36.5 35.6 71.5 336 766 25736 1325 651 832 19 4012 3.3 1.20 118.0 37.8 29.2 71.4 228 633 21200 1631 455 770 17 3714 4.1 1.09 63.1 58.4 16.9 123.0 568 1765 19572 730 616 1929 17 3848 4.2 1.20 67.0 54.3 18.7 91.3 277 1563 21100 878 504 2265 20 4517 4.3 1.11 67.4 90.0 25.0 86.4 247 1666 16628 602 570 2097 18 4183 5.1 1.36 100.0 62.0 34.6 119.0 500 1266 17148 1993 864 491 17 2807 5.2 1.59 103.0 65.6 39.4 127.0 476 1331 26652 1660 724 576 20 3295 5.3 1.48 135.0 67.7 42.6 135.0 368 1032 23900 1327 844 534 19 3051

TPH: total petroleum hydrocarbons; TOC: total organic carbon; TN: total N; TP: total P; dw: dry weight; fw: fresh sample weight. All parameters are means of three replicates.

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subclass using Epsilonproteobacteria as an outgroup, and the second one focusing on the Gammaproteobacteria group with some sequences of Alphaproteobacteria used as an outgroup. Both trees were constructed using maximum likelihood algorithm, 100 bootstraps, and a filter specifically designed for the selection of sequences in each tree, considering only positions conserved in at least 10% of sequences. A distance matrix, constructed using the neighbor joining algorithm, was also calculated and examined for clone sequences of the two samples that clustered together with described and cultivated bacterial species, in order to understand which clades represented the species-level or the genus-level groups. In the construction and interpretation of similarity matrices, we used the 95% limit to represent the threshold for genus definition and the limit of 97% to represent the threshold for

OTUs [22].

2.4. Statistical Analysis of Clone Libraries. OTUs were

identi-fied in each library using MOTHUR software [23]. Richness

and alpha diversity indices, including the Chao 1 estimator

[24] and the Shannon index, were calculated for each library

at different cutoff levels (0.01, 0.03, and 0.05).

The Chao 1 estimator evaluates the richness of a total species as Chao1 = Sobs + 𝑛 2 1 2𝑛2, (1)

where Sobs is the number of observed species, 𝑛₁ is the

number of singletons (species captured once), and𝑛₂is the

number of doubletons (species captured twice).

Shannon’s diversity index [25] was obtained as

𝐻 = −Σ𝑝𝑖ln𝑝𝑖, (2)

where𝑝_𝑖is the population of each species𝑖, and the resulting

product is summed up across species and multiplied by−1

[26].

The MOTHUR software was also applied to calculate the number of OTUs shared among the two different libraries at

different cutoff levels. The LIBSHUFF software [27], based on

the Jukes-Cantor pairwise distance matrix, was also applied to find out similarities in the two libraries, as described by

Zhang et al. [28].

3. Results

3.1. Construction of 16S rRNA Gene Libraries and Detection of Chimeric Sequences. Two libraries were constructed on

samples 1.2 and 2.1. A total of 194 clones were sequenced, 101 of which taken from sample 1.2 and 93 from sample 2.1. All obtained sequences were submitted online on the DDBJ/EMBL/GenBank databases. They are available under the accession numbers from HE803828 to HE804037. The percentages of detected chimeric sequences were 11.9% in sample 1.2 (12 sequences) and 4.3% in sample 2.1 (4 sequences). All chimeric sequences were composed of par-tial sequences coming from only two different organisms. Considering fragments constituting chimeras as independent

sequences belonging to different organisms, the library from sample 1.2 was composed of a total of 115 sequences and the library from sample 2.1 of 95 sequences. The affiliation of nucleotide sequences was determined by BLAST analysis. In both libraries, the highest percentage of screened clones emerged as belonging to the Proteobacteria phylum (74%

to library 1.2 and 73% to library 2.1, Figure2(a)). Within

this group, bacteria belonging to Alpha-, Gamma-, Delta-, and Epsilonproteobacteria classes were present in all libraries, while bacteria belonging to the Betaproteobacteria class were detected only in library 2.1 with low percent values (1%). In particular, the Gammaproteobacteria subclass was the most represented one in both 1.2 and 2.1 libraries (35% and 38%, resp.), followed by the Deltaproteobacteria subclass (resp., 24% and 20%). The second most represented bacterial taxon detected in both libraries was the Bacteroidetes phylum, represented by 12% of the total sequences in library 1.2, and by 6% of the sequences in library 2.1. There are some differences in the distribution of bacterial phyla detected in the two libraries, especially for minor groups such as the

Lentisphaerae, Nitrospirae, and Thermotogae phyla, that were

present only in sample 1.2 with percent values lower than 1%, and the Chlorobi, Verrucomicrobia, Deferribacteres, and

Gemmatimonadetes phyla, that were detected in sample 2.1

with fractions ranging from 1% to 2%.

3.2. T-RFLP Analysis. NMDS analysis on T-RFLP data matrix

highlights some groups of samples that correspond to the

five sampling sites (Figure3). The grouping of the triplicates

is mainly evident for sites 1 (samples 1.1, 1.2, and 1.3) and 4 (samples 4.1, 4.2, and 4.3). Triplicates of sites 2 and 3 clustered together in the central part of the plot, and the triplicates from site 5 (samples 5.1, 5.2, and 5.3) were all located in the second quadrant of the plot, isolated from all the other samples.

The Shannon diversity index calculated from the T-RFLP

data matrix ranged from 3.17 to 3.75. Figure4presents some

of the results of the attribution of the peaks obtained by in

silico clone digestion, in comparison with the peaks detected

in the microbial communities of the five sampling sites. The coverage percentages of the bacterial groups in T-RFLP

profiles are shown in Figure2(b). Not all the bacterial OTUs

detected with the rDNA clone library could be retrieved in the T-RFLP electropherograms of the 15 samples. All OTUs retrieved in the T-RFLP electropherograms through in silico digestion belong to subclasses of the Proteobacteria phylum, with the only exception of the Betaproteobacteria class, which

has not been detected. Figure2(b)highlights a dominance

of Gammaproteobacteria in sites 1 and 2 and a dominance of Deltaproteobacteria organisms in sites 3, 4, and 5. The less represented group of Proteobacteria was the

Epsilonpro-teobacteria in all BsuRI samples. When considering the RsaI

restriction endonuclease, the Alphaproteobacteria subclass emerged as the less represented group in each case.

3.3. Phylogenetic Analysis. In order to assess the relative

abundance of Proteobacteria related sequences, we focused on the phylogenetic analysis of this group of organisms. In par-ticular, two phylogenetic trees were built: the first one com-prised all the retrieved Delta- and Epsilonproteobacteria clone

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1% 1% 1%1% 1% 1% 1% 1% 3% 2% 8% 6% 73% 38% 7% 6% 20% 2.1 Spirochaetes Bacteroidetes Chlorobi Verrucomicrobia Lentisphaerae Others Chloroflexi Nitrospirae Gemmatimonadetes Acidobacteria Cyanobacteria Thermotogae Gammaproteobacteria Alphaproteobacteria Epsilonproteobacteria Deltaproteobacteria Planctomycetes Betaproteobacteria Deferribacteres 74% 1% 12% 1% 1% 1%1% 1% 3%3% 2% 35% 10% 5% 24% 1.2 Spirochaetes Bacteroidetes Chlorobi Verrucomicrobia Lentisphaerae Others Chloroflexi Nitrospirae Gemmatimonadetes Acidobacteria Cyanobacteria Thermotogae Gammaproteobacteria Alphaproteobacteria Epsilonproteobacteria Deltaproteobacteria Planctomycetes Betaproteobacteria Deferribacteres (a) 30 25 20 15 10 5 0 1 2 3 4 5 BsuRI Gamma (%) Delta (%) Epsilon (%) RsaI 30 25 20 15 10 5 0 1 2 3 4 5 Delta (%) Alpha (%) Epsilon/Bacteroidetes (b)

Figure 2: (a) Comparison among main bacteria phyla detected in the two clone libraries; the larger pink sectors represent Proteobacteria. (b) Coverage percentages of the main bacterial groups attributed through in silico digestion of the 15 T-RFLP electropherograms. Each of the five sites is represented by the means of three replicates; the black bars represent the standard deviations.

5.1 5.2 5.3 4.3 4.2 4.1 _3.22.2 3.1 3.3 2.3 1.1 1.3 1.2 −0.32 −0.24 −0.24 −0.16 −0.16 −0.08 −0.08 0.08 0.08 0.16 0.16 0.24 0.24 0.32 Coordinate 1 Co or di n at e 2 2.1

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3000 3500 4000 4500 5000 5500 6000 6500 7000 450 400 350 300 250 200 150 100 50 0 Sample 1.1 Sample 1.1 (a) 700 600 500 400 300 200 100 0 3000 3500 4000 4500 5000 5500 6000 6500 7000 Sample 2.3 Sample 2.3 (b) 2500 2250 2000 1750 1500 1250 1000 750 500 250 0 3000 3500 4000 4500 5000 5500 6000 6500 7000 Sample 3.1 Sample 3.1 (c) 3000 3500 4000 4500 5000 5500 6000 6500 7000 450 400 350 300 250 200 150 100 50 0 Sample 4.3 (d) 1800 1600 1400 1200 1000 800 600 400 200 0 3000 3500 4000 4500 5000 5500 6000 6500 7000 Enzyme BsuRI Enzyme RsaI Sample 5.3 Sample 5.3 (e)

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sequences and their closer relatives (Figure5); the second one included all the retrieved Gamma- and Alphaproteobacteria

clone sequences and their closer relatives (Figure6). The trees

were constructed using PHYML with 100 bootstraps [29]

from the ARB package. The majority of sequences in the trees are derived from studies about marine sediment samples. Some of our clones are closely related to symbiotic bacteria

found in marine metazoan bodies [30,31]. These sequences

belong to the Gammaproteobacteria subclass; clone 147b from library 2.1 shows a 99% similarity with the already

mentioned Spongiispira norvegica (AM117931, [31]), while

the OTU composed of clones 228, 143, 162, 61, and 160, all coming from library 1.2, shows a 98% similarity with

Endozoicomonas elysicola (AB196667, [30]). All the clones related to the Deltaproteobacteria subclass cluster together with species having a metabolism involving the presence of sulphur (most sulphate reducing microorganisms). The majority of published sequences that are closely related to our clone sequences belong to uncultured bacteria, with the exception of some Deltaproteobacteria, which belong to

Desulfobulbus, Desulfosarcina, and Desulfonema genera, all

providing the sulfate reduction in the marine environment. The similarity matrix evidenced that within the

Deltapro-teobacteria subclass our clone 188b from library 2.1 showed

a 95% similarity with the described Desulfobulbus japonicus

species (AB110549, [32]), while the 52b clone from library 2.1

showed a 96% similarity with the described Desulfosarcina

ovata (Y17286, [33]). The 124b and 130b sequences from library 2.1 and the sequence 184 from library 1.2 are all parts of the same OTU. They cluster together with the described

Epsilonproteobacteria species Arcobacter nitrofigilis (L14627,

[34]), symbiotic bacteria characterized by a nitrogen fixing

metabolism.

3.4. Statistical Analysis of Clone Libraries. Table2shows the results concerning richness and alpha diversity indices in each of the two constructed libraries at three different cutoff levels. The Shannon diversity index shows similar values in both sampling sites, at all cutoff levels. The richness index (Chao1 estimator) emerged as being higher in sample 1.2 at 0.01 cutoff level and lower in sample 1.2 at 0.03 and 0.05 cutoff values. The number of detected OTUs emerged as being very similar in both sites. The OTUs at a 0.03 cutoff value were represented by the same number in both cases; library 2.1 at 0.01 and 0.05 cutoff values shows less detected OTUs than

library 1.2 at the same cutoff levels (Table2).

Table 3 presents the MOTHUR analysis of OTUs that

are shared between the two different libraries. At 0.01 cutoff (specie level), there are 7 OTUs shared between sites 1.2 and 2.1; at the 0.03 cutoff there are 12 shared OTUs; at 0.05 cutoff, the number of shared OTUs, which usually represents the genus level, rises up to 17.

Figure7presents the results of the LIBSHUFF analysis.

The two lines, representing the homologous and heterologous curves, are almost totally overlapping, thus indicating that the

samples are similar to each other, as confirmed by the high𝑃

value (𝑃 > 0.025 with Bonferroni correction, as described in

[27]).

Table 2: Alpha diversity indices for the two samples at different cutoff values.

Sample cutoff Observed

richness (OTUs)

Chao 1

estimator Shannon index

1.2 0.01 82 667,2 4,36 0.03 75 258,3 4,24 0.05 71 193,8 4,17 2.1 0.01 80 620,2 4,33 0.03 75 306,1 4,24 0.05 68 239,1 4,07

Table 3: OTUs shared between site 1.2 and 2.1 calculated with MOTHUR software.

Cutoff Observed richness shared between 1.2 and 2.1

0.01 7

0.03 12

0.05 17

4. Discussion

The molecular approach is commonly used in the recent literature for the study of bacterial communities in marine

sediments [35–38] and to study the influence of bacterial

communities in removing pollutants from marine sediments

[39]. The two libraries highlighted a substantial similarity

concerning the abundance and diversity of the sequences rep-resented in the two different sites, even though the chemical

composition was different [13]. The similarity between the

two clone libraries is shown by the alpha diversity indices

calculated with MOTHUR software (Table2). The Shannon

diversity index calculated for both libraries at different cutoff levels, on the base of the DNA sequences obtained after the library construction, ranged from 4.07 to 4.36. The same Shannon diversity index calculated on the base of T-RFLP profiles of the 15 samples ranged from 3.17 to 3.75. This difference can be explained because the diversity in a sample is underestimated when only T-RFLP profiles are used. This approach does not register rare OTUs. The Shannon diversity index used in this work should provide more reliable values, although an increase of the sample size of the sequenced clones of each library could probably provide even more accurate values.

The construction of the two libraries and the screening of clones coupled with T-RFLP peak identification highlighted the dominance of Proteobacteria related microorganisms in all the five sites. There are no significant differences among the coverage percentages of different subclasses of the

Proteobacteria phylum among the two libraries and the 5 sites;

these data are in agreement with previously published studies, where PCR-based techniques on marine sediment samples revealed a dominance of bacteria related to Proteobacteria

phylum [35, 37] or to Firmicutes, Delta-, and

Gammapro-teobacteria [40,41]. In other papers, the analysis of marine

sediment showed a dominance of Actinobacteria [38]. In this

study, no significant differences in the coverage percentage of different subclasses of the Proteobacteria phylum were

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99% 100% 100% 100% 99% 100% 100% 88% 100% 95% 91% 79% 90% 94% 100% 100% 100% 100% 100% 97% 100% 98% 78% 100% 100% 100% 97% 100% 100% 97% 100% 100% 100% 94% 100% 76% 99% 80% 84% 98% 94% 100% 71% 100% 96% 99% 98% 100% 100% 100% 99% 83% 97% 90% 100% 100% 100% 71% 100% 100% 100% 98% 95% 73% 100% 90% 99% 100% 100% 95% Uncultured bacterium, FJ748774 Uncultured bacterium, EU617844 Uncultured bacterium, EF999371 Uncultured bacterium, FM165238 Uncultured bacterium, DQ833497

Uncultured bacterium, FJ516915 Uncultured bacterium, DQ395007 Uncultured bacterium, EU385735 Uncultured bacterium, DQ267496 Uncultured bacterium, FJ517013 Uncultured bacterium, AM997597 Uncultured bacterium, GU291346

Uncultured bacterium, FJ545548 Pelobacter sp. A3b3, AJ271656 Uncultured bacterium, GQ249592

Uncultured bacterium, EU700147 Uncultured bacterium, GU180167

Uncultured bacterium, GQ246309 Uncultured bacterium, EU652627

Uncultured bacterium, EU592455 Uncultured bacterium, EU488292

Uncultured bacterium, FJ202384 Uncultured bacterium, EU617864

Uncultured Arcobacter sp., AB262371 Arcobacter nitrofigilis, L14627 Arcobacter butzleri, AY621116

Uncultured bacterium, FJ717179 Sulfurimonas autotrophica, AB088431

Uncultured bacterium, AB193949 Uncultured Campylobacterales bacterium, DQ234141

Uncultured bacterium, AY218555

Uncultured bacterium, FJ717140

Uncultured bacterium, FM179902, Uncultured bacterium, FN396770, Uncultured bacterium, AM040136

Uncultured bacterium, FN421322 Uncultured bacterium, AB530217 Uncultured bacterium, EU617853 Uncultured bacterium, GU230394 Uncultured bacterium, FN396678 Uncultured bacterium, AY907763 Uncultured bacterium, AJ620500

Uncultured bacterium, DQ394998 Uncultured bacterium, GQ259292

Uncultured bacterium, EU592471 Uncultured bacterium, FM242403 Uncultured bacterium, DQ351771 Desulfosarcina ovata, Y17286 Desulfonema magnum, U45989

Uncultured bacterium, AY799901 Uncultured bacterium, FN429813 Uncultured bacterium, FJ748772 Uncultured bacterium, AM746084

Uncultured bacterium, GQ246438 Uncultured bacterium, DQ395025 Uncultured bacterium, EU386051 Uncultured bacterium, GU302477

Uncultured bacterium, Y771932 Uncultured bacterium, DQ351762 Uncultured bacterium, AY771964 Uncultured Desulfocapsa sp., DQ831542 Desulfobulbus sp., U85473

Desulfobulbus japonicus, AB110549 Uncultured bacterium, DQ831547 Uncultured bacterium, DQ831555 Uncultured bacterium, GQ246300 Uncultured bacterium, GQ246285 Uncultured bacterium, DQ831548 Uncultured bacterium, FJ416070 Uncultured bacterium, AB530214

Uncultured bacterium, FJ202627 Uncultured bacterium, EF123510 Desulfovibrio sp. r02, AB470955 164 Agriport 1b 16 Agriport 1b 181b Agriport 2a 7 Agriport 1b 230 Agriport 1b 194 Agriport 1b 190b Agriport 2a 158 Agriport 1b 52b Agriport 2a 184b Agriport 2a 159b Agriport 2a 61b Agriport 2a 173b Agriport 2a 221 Agriport 1b 179 Agriport 1b 188b Agriport 2a 63 Agriport 1b 51 Agriport 1b 59b Agriport 2a 191b Agriport 2a 188 Agriport 1b 141 Agriport 1b 74 Agriport 1b 199 Agriport 1b

31a Agriport 2a,

163b Agriport 2a 6 Agriport 1b 192b Agriport 2a 136a Agriport 2a 117b Agriport 2a 34 Agriport 1b 5 Agriport 1b 146 Agriport 1b 79a Agriport 2a 178 Agriport 1b 2 Agriport 1b 124b Agriport 2a 130b Agriport 2a 184 Agriport 1b 12 Agriport 1b 157 Agriport 1b 38a Agriport 2a 137 Agriport 1b 158b Agriport 2a 183 Agriport 1b 0.10 19b Agriport 2a

Figure 5: Maximum likelihood phylogenetic tree made with PHYML and 100 bootstrap pseudoreplicates. The tree represents the phylogenetic position of characterized Delta- and Epsilonproteobacteria clone sequences together with closely related sequences present in the database. The characterized sequences are in bold.

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100% 100% 80% 99% 89% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 76% 99% 75% 97% 99% 85% 91% 97% 95% 95% 75% 79% 93% 73% 70% 94% 92% 80% 99% 70% 84% 97% 82% 98% 99% 78% 98% 71% 90% 90% 82% 82% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 93% 88% 97% 98%98% 97% 87% 90% 86% 77% 81% 76% 70% 76% 74% 94% 80% 76% 99% 88% 79% 84% 89% 97% 88% 100% Uncultured bacterium, EU617749

Uncultured bacterium, AB294972 Uncultured bacterium, FJ712598 Uncultured bacterium, DQ351764

Uncultured bacterium, FJ717240 Uncultured bacterium, FJ717244

Uncultured bacterium, AM882548 Uncultured bacterium, GU119320

Uncultured bacterium, DQ394975

Uncultured bacterium, AY225632 Uncultured bacterium, FJ497588 Uncultured bacterium, GU369896

Uncultured bacterium, FJ717231 Uncultured bacterium, AF223299 Uncultured bacterium, AY193138

Uncultured bacterium, FJ358865 Uncultured bacterium, DQ351795 Uncultured bacterium, DQ395021 Uncultured bacterium, GU061255

Uncultured bacterium, GQ346961 Uncultured bacterium, GQ337082 Uncultured bacterium, EU488031 Uncultured bacterium, GQ246299 Uncultured bacterium, FJ545438 Uncultured bacterium, GU230336 Uncultured Pseudomonas sp., DQ395002 Uncultured bacterium, GQ246314 Uncultured bacterium, GQ249568 Uncultured bacterium, GU230337 Uncultured bacterium, GQ246429 Uncultured bacterium, FJ628326

Uncultured bacterium, GU369889 Uncultured bacterium, AB294964 Thiohalomonas nitratireducens, DQ836238 Uncultured bacterium, EU652539 Uncultured bacterium, GU230360

Uncultured bacterium, EU491143 Uncultured bacterium, EU491145 Uncultured bacterium, FJ497645

Uncultured bacterium, GU061260 Uncultured bacterium, AB294952

Uncultured bacterium, AM882516 Uncultured bacterium, AM882511 Uncultured bacterium, AM882517 Uncultured bacterium, FJ497360

Uncultured bacterium, GU108533 Uncultured bacterium, FM958463 Spongiispira norvegica, AM117931

Uncultured bacterium, FJ626892 Uncultured bacterium, FJ626896 Thalassomonas actiniarum, AB369380

Glaciecola sp. HTCC2999, ABST01000050 Uncultured bacterium, FN435248

Glaciecola punicea, U85853 Uncultured bacterium, EF379837

Amphritea japonica, AB330881

Uncultured bacterium, FJ744816 Uncultured bacterium, EU050812

Uncultured bacterium, GU118490 Endozoicomonas elysicola, AB196667 Uncultured bacterium, FN435240 Uncultured bacterium, EU287400 Uncultured bacterium, FM242245 Uncultured bacterium, BD411343

Uncultured bacterium, FM242466 Uncultured bacterium, EU799754

Uncultured bacterium, AM997787 Uncultured bacterium, GU230343 Uncultured bacterium, EU488035 Uncultured bacterium, EU488024

Uncultured bacterium, EU328058 Uncultured bacterium, GU118250 Uncultured bacterium, FJ717220 Uncultured bacterium, GU118337 Uncultured bacterium, EU287169

Uncultured bacterium, FM242243 Uncultured bacterium, DQ351782 Uncultured bacterium, AB271120

Uncultured bacterium AB271123 Uncultured bacterium, AB271124

Uncultured bacterium, FJ712548 Uncultured bacterium, AB015250

Uncultured bacterium, FJ787300 Uncultured bacterium, FM242244

Uncultured bacterium, AB530202 Uncultured bacterium, EU707297 Uncultured bacterium, EU652547

Uncultured bacterium, FJ717249 Roseobacter sp., AY258102 Loktanella maricola, EF202613

Uncultured bacterium, EU050750

Uncultured bacterium, AY162058 Ochrobactrum anthropi, U70978

Uncultured bacterium, F62377A9 Uncultured bacterium, FJ497610 Uncultured bacterium, AY588951 Uncultured bacterium, FJ497661 Uncultured bacterium, EU652596 Uncultured bacterium, GQ259326 Erythrobacter longus, L01786 Uncultured bacterium, FJ545435

Uncultured bacterium, AM889165 Beggiatoa sp., AF110276 99 Agriport 1.2 206b Agriport 2.1 94b Agriport 2.1 5 Agriport 1.2 227 Agriport 1.2 39b Agriport 2.1 60b Agriport 2.1 100b Agriport 2.1 73 Agriport 1.2 21 Agriport 1.2 225 Agriport 1.2 8 Agriport 1.2 82a Agriport 2.1 219 Agriport 1.2 56b Agriport 2.1 120b Agriport 2.1 173 Agriport 1.2 80b Agriport 2.1 22b Agriport 2.1 13b Agriport 2.1 86b Agriport 2.1 48a Agriport 2.1 62b Agriport 2.1 147 Agriport 1.2 223 Agriport 1.2 193 Agriport 1.2 72a Agriport 2.1 139b Agriport 2.1 169 Agriport 1.2 3a Agriport 2.1 77b Agriport 2.1 46a Agriport 2.1 70 Agriport 1.2 171 Agriport 1.2 147b Agriport 2.1 8b Agriport 2.1 59 Agriport 1.2 65b Agriport 2.1 39a Agriport 2.1 228 Agriport 1.2 143 Agriport 1.2 162 Agriport 1.2 61 Agriport 1.2 160 Agriport 1.2 93 Agriport 1.2 85b Agriport 2.1 76b Agriport 2.1 41b Agriport 2.1 211 Agriport 1.2 213 Agriport 1.2 144 Agriport 1.2 15b Agriport 2.1 153 Agriport 1.2 155b Agriport 2.1 182b Agriport 2.1 220 Agriport 1.2 119b Agriport 2.1 52a Agriport 2.1 55a Agriport 2.1 222 Agriport 1.2 206 Agriport 1.2 161b Agriport 2.1 58b Agriport 2.1 23b Agriport 2.1 205 Agriport 1.2 170 Agriport 1.2 4 Agriport 1.2 91 Agriport 1.2 148 Agriport 1.2 20 Agriport 1.2 103a Agriport 2.1 75 Agriport 1.2 2a Agriport 2.1 0.10 45a Agriport 2.1 140 Agriport 1.2

Figure 6: Maximum likelihood phylogenetic tree made with PHYML and 100 bootstrap pseudoreplicates. The tree represents the phylogenetic position of characterized Alpha- and Gammaproteobacteria clone sequences together with closely related sequences present in the database. The characterized sequences are in bold.

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1 0.9 0.8 0.7 0.6 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1b 2a P = 0.1341

Figure 7: Results of LIBSHUFF comparison between the 1.2 and 2.1 libraries. The solid line indicates the homologous coverage curve𝐶_𝑋(𝐷),

and the broken line indicates the heterologous coverage curve𝐶_𝑋𝑌(𝐷). The 𝑃 value is 𝑃 > 0.025, thus indicating that the two libraries are

similar to each other in OTU composition. The𝑦-axis represents the coverage (𝐶) while the 𝑥-axis represents the evolutionary distance (𝐷).

observed among the five sites; this agrees with other studies in which different libraries of marine sediments collected

in different sites showed similar bacterial compositions [35].

The Bacteroidetes phylum is the only further phylum with an appreciable presence in all sites, and the percentages of abundance are comparable to those found by other authors

[35,38]. This result can mean that bacteria belonging to these

groups play the main role in nutrient recycling in the harbor seabed ecosystem.

Considering the digestion profiles obtained with the

BsuRI restriction endonuclease, Gammaproteobacteria

emerged as being dominant in sites 1 and 2, while the sed-iments were dominated by Deltaproteobacteria organisms in sites 3, 4, and 5. Considering the RsaI restriction endo-nuclease, sites 2, 4, and 5 emerged as being dominated by

Deltaproteobacteria microorganisms, while sites 1 and 3

were dominated by Epsilonproteobacteria and Bacteroidetes, which are groups of bacteria sharing the same peak interval. Some groups of bacteria could be detected only with one of the two adopted restriction enzymes. For instance,

Alphaproteobacteria were not detected with BsuRI and Gammaproteobacteria were not detected with RsaI. This

circumstance can be explained by the fact that the first restriction site in the gene sequences of those bacteria is located outside the interval 50–500 bp, which is the interval covered by our T-RFLP analysis. In fact, the use of more than one restriction enzyme is a recommended practice in this technique, in order to facilitate the resolution of bacterial

populations [42, 43]. The fact that other OTUs identified

through clone library construction were not identified in T-RFLP profiles can be in part explained by their poor representation in the sediment samples, causing the failure of their T-RFLP quantification.

Overall, our results are in agreement with results of other authors that examined samples of port sediments.

In the study performed by Wu et al. [44], three different

samples of marine sediments were investigated in order to characterize their bacterial communities. In all samples, the dominant phylum was Proteobacteria with a presence of

82%, 42%, and 42% in the three samples. Zhang et al. [28]

analyzed four sites characterized by different pollution levels in Victoria Harbor (Hong Kong). In all sites, they found the dominance of bacteria belonging to the Proteobacteria phylum and, especially, to the same subclasses detected in our work. The dominance of Proteobacteria was also found

in many other studies (e.g., [37,45,46]). The diversity index

calculated by Zhang et al. [28] did not highlight significant

differences among the four analyzed sites; this result is similar to the finding of this work, where the two ana-lyzed libraries highlighted similar Shannon diversity values. Bacteria belonging to the Alphaproteobacteria subclass are

important for hydrocarbon degradation [47]. The presence of

these bacterial taxa in all five sites confirms a contamination

of hydrocarbons in Leghorn seabed sediments [13].

A large portion of the sequences detected in our clone libraries belong to the Deltaproteobacteria subclass. With a deep phylogenetic analysis, we discovered that all these clones are closely related to bacteria characterized by a metabolism involved in the removal of sulphur. This observation is in agreement with other two works performed on marine port

sediments [28, 48]. The percentage of Deltaproteobacteria

detected in the two different samples is not significantly different (24% library 1.2 and 20% library 2.1), and T-RFLP results suggest that their abundance should be roughly comparable among all the five sites. This result does not agree with the chemical results published in Iannelli et al.

[13], which evidenced that the concentration of SO₄2−

in site 2.1 (4399 mg/(kg dw)) was double that in site 1.2 (2321 mg/(kg dw)).

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An interesting finding concerns the

Gammaproteobac-teria subclass, in particular the sequences 228, 143, 162,

61, and 160, from library 1.2, and the sequence 147b from library 2.1. The first group of sequences, all representing the same OTU, shows a 98% similarity with Endozoicomonas

elysicola, which is a typical symbiont of the Elysia ornata

slug. Conversely, the sequence 147b is 99% similar to the sequence of Spongiispira norvegica, a typical symbiont of sponges. This observation possibly suggests that site 1.2 is richer in marine metazoans characterized by a symbiotic association with bacteria than site 2.1. This difference is probably due to the chemical characteristics of the sediments in Leghorn seabed area. Site 1.2 in fact, emerged as being less polluted than site 2.1, and probably more suitable for being colonized by marine metazoans harboring bacterial symbionts.

5. Conclusions

In the present study, bacterial communities from five sites of Leghorn Harbor seabed were analyzed and identified through T-RFLP analysis, 16S rRNA library construction, and in silico digestion of retrieved clones. Some considerations emerged about organisms involved in the recycling of organic matter.

Both the diversity indices and the phylogenetic affiliation of bacterial sequences obtained from the molecular screening highlighted a substantial similarity in the composition of the bacterial communities. This finding contrasts with the chemical characterization of an earlier study, where the same sites evidenced significant differences in the presence of nutrients and pollutants, and the T-RFLP analysis evidenced a heterogeneity among the different sites of the harbor.

Retrieved sequences from the two libraries were more than sufficient to provide generic information on metabolism present in all of the 15 seabed sediment samples and to compare them with previous similar studies. Only a sig-nificantly higher sequencing coverage would have probably allowed to distinguish between the samples that looked rather similar in the presented analysis. The T-RFLP approach proved to be more efficient in highlighting the bacterial community structure in the whole harbor area, whereas the present work helped in clarifying the role of specific bacteria present in the studied samples and their related metabolisms.

Acknowledgments

The authors acknowledge the Livorno Port Authority for their support in supplying information, historical data, the database of the ongoing environmental site assessment, and technical assistance for samplings. S. Gabrielli is gratefully acknowledged for his help with photographic artwork. Dr. Maria Nella Dell’Orbo is gratefully acknowledged for her help with the English revision. This study was performed within the CIP Eco-Innovation Project ECO/08/239065-AGRIPORT, “Agricultural Reuse of Polluted Dredged Sedi-ments.”

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Bacterial communities in polluted seabed sediments: a molecular biology assay in Leghorn harbor

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