BACTERIAL COMMUNITY STRUCTURE OF SUPERFICIAL SEDIMENTS OF THE 1

BIZERTE LAGOON (TUNISIA), A SOUTHERN MEDITERRANEAN COASTAL 2

ANTHROPIZED LAGOON. 3

4

Olfa BEN SAID1, 2, 3

, Marisol GOÑI URRIZA1, Monia EL BOUR

2, Patricia AISSA3 5

& Robert DURAN1*

6

7

1Equipe Environnement et Microbiologie – UMR IPREM 5254 - IBEAS – Université de Pau et 8

des Pays de l'Adour - France. 9

2 Laboratoire de Bactériologie – Pathologie, Institut National des Sciences et Technologies de 10

la Mer INSTM– Salammbô - Tunisie. 11

3 Laboratoire de Biosurveillance de l’Environnement, Faculté des Sciences de Bizerte - 12

Tunisie. 13

14

Keywords: Hydrocarbon pollution, Heavy-metal pollution, T-RFLP, 16S rRNA gene library, 15

bacterial diversity 16

17

Running title: Bacterial communities in Bizerte lagoon polluted sediments 18

19

∗To whom all correspondence should be addressed: 20

Robert Duran 21

Equipe Environnement et Microbiologie – IPREM UMR 5254 - IBEAS - Université de Pau et 22

des Pays de l'Adour, Avenue de l'Université, BP 1155 F-64013 - Pau cedex France. 23

Tel : (33) 5 59407468 ; Fax : (33) 5 59407494 ; e-mail : robert.duran@univ-pau.fr 24

ABSTRACT 25

In order to estimate how pollution affects the bacterial community structure and composition of 26

sediments, chemical and molecular approaches were combined to investigate 8 stations around 27

the Bizerte lagoon. Terminal-Restriction Fragment Length Polymorphism (T-RFLP) analysis of 28

PCR-amplified 16S rRNA genes revealed that each station was characterized by a specific 29

bacterial community structure. The combination of this data with those of chemical analysis 30

showed a correlation between the bacterial fingerprint and the pollutant content, principally 31

with hydrocarbon pollution. The composition of the bacterial community of two contrasted 32

stations related to the pollution, revealed sequences affiliated to α, β, γ, δ, ε, subclass of the 33

Proteobacteria, Actinobacteria and Acidobacteria in both stations although in different extent. 34

Gamma and delta subclass of the Proteobacteria were dominant representing 75% of clones in 35

the heavy-metal-contaminated station while they were found to represent 55% in the 36

polyaromatic hydrocarbon (PAH)-contaminated station. Although sediments of Bizerte lagoon 37

contain bacterial communities well adapted to the presence of pollutants, especially 38

hydrocarbon compounds, most of the sequences were unaffiliated to cultured bacteria. Their 39

characterization will provide the basis for understanding the capacity of bacteria to cope with 40

pollutants.41

42

INTRODUCTION 43

Organic and inorganic pollution of coastal zones is a major ubiquitous environmental problem. 44

Once in the marine environment, contaminants accumulate in sediments (Ford & Ryan, 1995). 45

The fates of these contaminants are mainly drive by bacteria, since they are the most abundant 46

organisms in sediments (Ford & Ryan, 1995). Sediment microbial communities are exposed to 47

multiple stresses, as a result their structure and composition is most likely affected by many 48

different abiotic and biotic parameters (Yan et al., 2003). Recent studies have demonstrated 49

that natural attenuation and bioremediation of organic contaminants and heavy metals cannot 50

be effectively applied at many sites until we have a better understanding of the physiology, 51

ecology, and phylogeny of microbial communities at contaminated sites (Vrionis et al., 2005). 52

In the light of these data, we need to increase our understanding of how microbial communities 53

are affected by and interact with these compounds in contaminated sites. Recently, studies of 54

bacterial diversity in marine sediments by molecular approaches have given a fundamental 55

view of the capacities of these communities to cope with pollutants (Bordenave et al., 2004; 56

Inagaki et al., 2003; Jiang et al., 2006; Bordenave et al., 2007), but relatively little is known 57

about bacterial communities in contaminated sediments (Abulencia et al., 2006; Córdova-58

Kreylos et al., 2006; Edlund & Jansson, 2006; Hernández-Raquet et al., 2006). Moreover, 59

the combined effect of different pollutants such as polyaromatic hydrocarbons (PAH) and 60

heavy metals on microbial activities and community composition is still unclear since few 61

studies have addressed this point (Cao et al., 2006). 62

Costal urbanized and industrialized zones have often been shown to be characterized by the 63

concomitant presence of organic and inorganic pollutants (Córdova-Kreylos et al., 2006). 64

They provide a good study site for the investigation of microbial communities inhabiting mixed 65

contaminated sediments. The Bizerte lagoon (Tunisia) is an example of such ecosystem; it is 66

located in an urbanized and industrialized area subjected to the input of various pollutants. The 67

discharges of untreated domestic effluents and wastewater input from industrial companies 68

generate heavily polluted sediments characterized by the concomitant presence of PAHs, heavy 69

metals and drugs such as antibiotics. We previously characterized the aerobic PAH-degrading 70

bacteria isolated from sediments of different stations located around the Bizerte lagoon (Ben 71

Said et al., 2007). The present work combines chemical and microbial molecular approaches in 72

order to investigate the influence of PAHs and heavy metals concentrations on microbial 73

community structure and composition. First, bacterial T-RFLP fingerprints were correlated 74

with pollutant concentrations and, second, the bacterial composition of both the most PAH-75

polluted and the most heavy metal-polluted stations were determined by 16S rRNA gene 76

libraries analyses. 77

78

79

80

81

MATERIALS AND METHODS 82

Site description 83

Bizerte lagoon (Southern Mediterranean), a canalized lagoon system located in the Northern of 84

Tunisia (Fig. 1), has been exploited in fishing activities since several centuries and in mussel 85

farming since 1964. This area extending to approximately 150 km2 represent a receptor of 86

several industrial sewages, aquaculture wastes, fertilizers and pesticides through run-off and 87

soil erosion, wastewaters from towns implanted around. At least, five different contamination 88

sources can be identified in the Bizerte lagoon: Menzel Bourguiba-Tinja and Menzel 89

Bourguiba landfill, Bizerte, Menzel Abderahmen, Menzel Jmil industrial zone, and 90

Southeastern agricultural zone (Yoshida et al., 2002). Furthermore, this lagoon connected to 91

Mediterranean Sea by way of a straight channel is submitted to intensive maritime traffic and 92

indirectly to several pollutants coming from oil and steel factories. 93

94

Sediment collection and field measurements 95

Surface sediments (0-3cm depth) were collected with a Van Veen Grab at 15 stations located 96

around the Bizerte lagoon and the bay (Fig. 1) as previously described (Ben Said et al., 2007). 97

Water column temperature, pH and salinity were determined in the field with a handheld multi-98

parameter system. The water temperature and level of dissolved oxygen were determined with 99

a multiparameter probe (YSI GRANT 3800). Chemical analyses, namely, ammonium (NH4), 100

nitrates (NO3), nitrites (NO2), orthophosphate (PO4), and total phosphorus (Pt), were performed 101

using standard methods (Murphy & Riley, 1962). Chlorophyll a concentrations were obtained 102

by spectrophotometry (Richards & Thompson, 1952). 103

104

105

106

Chemical analyses 107

Metal analyses (cadmium, cobalt, copper, iron, lead, manganese, nickel, zinc, chromium) were 108

performed by atomic emission spectrometry with inductively coupled plasma according to NF 109

EN ISO 11885 (March-1998) standard. 110

Polycyclic Aromatic Hydrocarbon composition of sediments was conducted by an automated 111

extraction with ASE 200 (Accelerated Solvent Extractor- Dionex), extract purification and gaz 112

chromatography-mass spectrometry (GC/MS) analysis. More details are available in 113

Bordenave et al. (2004). 114

115

T-RFLP Analysis 116

Mixed community DNA was extracted directly from sediments samples using an UltraClean 117

soil DNA isolation Kit (MoBio Laboratoires, California) by following the manufacturer’s 118

protocol with minor modifications as previously described (Precigou et al., 2001). Genes 119

encoding 16S rRNA were PCR amplified from extracted samples using fluorescently labelled 120

bacterial primers 8F HEX (5-Hexa-chloro-fluorescein; 5’-AGAGTTTGATCCTGGCTCAG-3’) 121

and 1489R TET (5-Tetrachloro-fluorescein; 5’-TACCTTGTTACGACTTCA-3’) as previously 122

described (Bordenave et al., 2007). The PCR amplification mixture contained 12.5 µl Hot 123

Start Taq polymerase master mix (Qiagen), 0.5 µl of each primer (20 µM) and 10 ng of DNA 124

template. A final volume of 50 µl was adjusted with distilled water. 16S rRNA gene 125

amplification reactions were cycled in a PTC200 thermocycler (MJ Research) with a hot start 126

step at 94°C for 15 min, followed by 35 cycles of 94°C for 1 min, 52°C for 1.5 min, and 72°C 127

for 1 min, with a final extension step at 72°C for 10 min. 128

PCR products were purified with the GFX PCR DNA purification kit (Amersham-Pharmacia). 129

Purified PCR products (600 to 700 ng) were digested with 3 units of enzyme HaeIII (New 130

England Biolabs). The length of terminal fluorescently labelled fragments (TRFs) from the 131

digested PCR products was determined by capillary electrophoresis on ABI prism 310, 132

(Applied Biosystems) as previously described (Fourçans et al., 2006). Briefly, about 50 ng of 133

the digested DNA from each sample was mixed with 18.5 µl of deionized formamide and 0.5 134

µl of TAMRA size standard (Applied Biosystems) and then denatured at 94°C for 2 min and 135

immediately chilled on ice prior to electrophoresis. After an injection step of 10 s, 136

electrophoresis was carried out for up to 30 min applying a voltage of 15 KV. 137

The T-RFLP profiles (T-RFs) were analyzed using Gene Scan Software version 3.1 (Applied 138

Biosystem). Dominant T-RFs were selected by comparison of numerical values and 139

electropherograms. For analysis, only T-RF representing more than 1% of the total 140

fluorescence were considered (Hewson Fuhrman 2004). 141

T-RFLP profiles were compared by Canonical Correspondence Analysis (CCA) according to 142

Fromin et al. (2002). This test is based on the linear correlation between community data 143

(abundance of each T-RF) and environmental parameters in the sediments. The CCA were 144

performed with MVSP v3. 13d software. 145

146

16S rRNA gene libraries analyses, 16S rRNA gene sequencing and sequences analysis 147

Bacterial composition of the stations 1’ and 2 was further analyzed by cloning PCR amplified 148

16S rRNA genes in E. coli. PCR amplifications from these samples were carried out with 149

unlabelled 8F and 1489R primers as described before (Bordenave et al., 2007). PCR products 150

were purified with the GFX PCR DNA purification kit (Amersham-Pharmacia) then cloned in 151

E. coli TOP10 using the Topo-TA cloning kit (Invitrogen). Almost 100 clones from each 152

library were selected randomly and insert were amplified using the primers M13F (5’-153

CTGGCCGTCGTTTTAC-3’) and M13R (5’-GGTCATAAGCTGTTTCCTG-3’). 154

Partial sequences of the 16S rRNA gene were determined by the dideoxy nucleotide chain-155

termination method using the BigDye cycle sequencing kit (Applied Biosystems) on an ABI 156

PRISM 310 205 Genetic analyzer (Applied Biosystems) at the Génotypage - Séquençage de 157

Bordeaux (France) using the primers M13F and M13R. 158

DNA sequences were compared to those present in the databank via the NCBI server 159

(http://www.ncbi.nlm.nih.gov) using the Basic Local Alignment Search Tool (BLAST) 160

(Altschul et al., 1997). Sequence data were checked using the CHECK CHIMERA program 161

(http://rdp8.cme.msu.edu/html/) to determine the presence of hybrid sequences (Maidak et al., 162

2001). Nucleotide sequences were initially aligned with the Ribosomal Database Project (RDP) 163

database (Maidak et al., 2001) by means of the automatic alignment function of the RDP 164

phylogeny inference package (PHYLIP) interface, after sequences were manually aligned using 165

ClustalW (Thompson et al., 1997). The phylogenetic tree was constructed with the MEGA 166

software version 3.0 (Kumar et al., 2004) using Neighbour-Joining method (Saitou & Nei, 167

1987); the distance was calculated on the basis of Kimura’s 2-parameter algorithm (Kimura, 168

1980); 100 bootstrap resamplings were performed to estimate the reproductibility of the tree. 169

PAST (PAleontological Statistics v1.60) software from http://folk.uio.no/ohammer/past/ 170

website was used to perform rarefaction analysis and calculate diversity indices for each clone 171

library with clone phenotype similarity defined at 97% (Hammer et al., 2001). In order to 172

determine the significance of differences between the clone libraries, LIBSHUFF method was 173

applied (Singleton et al., 2001). 174

Nucleotide sequence accession numbers 175

The sequences determined in this study have been submitted to the GenBank database and 176

assigned Accession Nos. AM889144 to AM889146, AM889150, AM889157 to AM889198, 177

AM889201 to AM889204, FM211753 to FM211808 and FM211815 to FM211816. 178

179

180

181

RESULTS 182

Physico-chemical parameters, Metals and PAH concentrations in Bizerte lagoon’s 183

sediments 184

The physical and chemical parameters observed in the sampling stations are summarized in 185

Table 1. The channel stations (1, 1’ and 2) and, in less extent station 6, are submitted to the 186

marine influence with lower temperature and higher salinities. Channel station showed lower 187

O2 concentrations. 188

Total metal contents varied from 1864.44 (station 1') to 180274.93 µg.g-1 of dry wt sediment 189

(station 10) (Table 1). Station 2, located in the channel showed the second high score for metals 190

contents (41020.76 µg.g-1 of dry wt sediment). Iron is by far the most abundant metal in the 191

superficial sediments of all stations surveyed. Station 2 showed maximum concentrations of 192

cobalt, copper, nickel and chrome (respectively, 13.68, 34.9, 47.7, 162.6 µg.g-1 of dry wt 193

sediment) and station 10 of lead, manganese and zinc (respectively, 97.4, 993.2, 512.5 µg.g-1 of 194

dry wt sediment,) 195

Total PAH concentrations in the coastal sediments ranged from 23.9 (station 8, Southeast zone) 196

to 876.6 (station 1’, channel) ng.g-1 dry wt sediment (Fig. 2A and Table 1). Two different PAH 197

concentration profiles were distinguished: stations 1’, 2 and 6 were more contaminated by high 198

molecular weight PAHs (mainly fluoranthene, pyrene and benzo(a)pyrene for stations 1' and 2 199

and indeno(1,2,3-cd)pyrene for station 6) whereas in station 10, 8, 9 and 1 the main 200

hydrocarbon was naphthalene (data not shown). The unexpected high [Low PAH]/[High PAH] 201

ratio found in station 8 is due to a hardly absence of PAHs of high molecular weight. 202

203

Bacterial community structures 204

The T-RFLP profiles contained 25 ± 7.5 T-RFs defined as operational taxonomic units (OTUs). 205

The lowest OTU number (14) was observed in station 1 which is located in the connexion of 206

the channel with the Mediterranean Sea subjected to both marine and lagunar influences. In 207

contrast the highest OTU number (37) was recorded in station 12 which contained low level of 208

PAHs. Except for station 1, the general trend is that the diversity of the bacterial community 209

decreased when the concentration of PAHs in the superficial sediments increased (Fig. 2A). 210

The T-RFs of 73, 192, 204, 206 bp were detected in all the stations (Fig. 2B). Whereas most of 211

these T-RFs showed a low relative abundance, the T-RF 204 bp is the more abundant T-RF in 212

all the stations except for station 1'. It represents up to 60% of total fluorescence in station 9. In 213

contrast, some T-RFs were station-specific such as T-RFs of 36-37 bp (station 1’), 49 bp 214

(station 2), 56 bp (station 1), 114-120-127-186-209 bp (station 8), 110-111-215-259-388-399 215

bp (station 12), 227-290-291 bp (station 10). Except for T-RF 36 bp in station 1' which is 216

dominant, all these T-RFs are low abundant. Some OTUs (such as T-RF 204, 256 and others) 217

could be related to sequences of previously isolated strains and affiliated to the Pseudomonas 218

or Acinetobacter genera (Ben Said et al, 2007). 219

The T-RFLP fingerprints correlated by Canonical Correspondence Analysis (CCA) with the 220

PAH contents (Fig. 3) showed that the sediment bacterial community of station 1’ was clearly 221

influenced by all the PAHs analysed (Fig. 3A, insert). To observe the influence of the PAH 222

content on the bacterial community structure of the other stations, data concerning the station 223

1’ was excluded. In this condition, the first two axes of the CCA described 48.2% of the 224

variation (Fig. 3; axis 1, 25.5% and axis 2, 22.7%). The bacterial community structures of 225

stations 6 and 9 were mostly influenced by fluorene, dibenzoanthracene and benzoperylene 226

while those of stations 1, 2 and 10 by fluoranthene and pyrene (Fig. 3A). In contrast, bacterial 227

community structures of stations 8 and 12 were not influenced by PAH contents (Fig. 3A). As 228

presented in figure 3B some OTUs were correlated with the presence of some PAHs, such as 229

the OTU of 56 bp which is found only in station 1 mostly influenced by fluoranthene and 230

pyrene. Also, the OTU of 256 bp, detected in stations 6 and 9, was mostly influenced by 231

fluorene, dibenzoanthracene and benzoperylene. 232

The impact of metals in the bacterial community structure could be observed in station 12 that 233

was mainly influenciated by Cr, and in stations 8 and 10, influenciated by Mn and Fe (data not 234

shown). Despite the high concentrations of metals in station 2, no clear influence of a specific 235

metal on the community structure of this station could be characterized. 236

237

Bacterial community composition of sediments from stations 1’ and 2 238

The bacterial community composition of the channel stations 1’ and 2 were determined in order 239

to evaluate the impact of pollutants on the bacterial diversity. These stations were chosen 240

because i) they are both channel stations and their sediments have almost similar physico-241

chemical parameters (Table1); ii) they are under the influence of PAH contents at different 242

levels, station 1' beeing much more contaminated (Fig. 2A) and iii) the station 2 is the more 243

metal contaminated channel station (41020.8 and 1864.4 µg.g-1 of dry wt sediment in station 2 244

and 1’, respectively, Table 1). 16S rRNA gene library analysis of the stations 1’ and 2 was 245

performed in order to determine the composition of the bacterial communities. From the 98 and 246

97 clone sequences analysed, 78 (64 singletons) and 82 (73 singletons) phylotypes were found 247

in library of station 1’ and station 2 respectively. Although the rarefaction curves did not attain 248

the saturation (data not shown), the coverage values (0.95) indicated that a number sufficient of 249

clones was analysed for each station. Shannon index was almost similar for both libraries (4.22 250

and 4.28 for library 1’ and 2 respectively) and equitability was identical (0.97) indicating a 251

high bacterial diversity in both stations. No monophyletic groups (cut off level of < 97% 252

similarity) were found to be dominant as indicated by low Dominance indexes (1.73% for 253

library 1’ and 1.68% for library 2). A comparison of the libraries by the LIBSHUFF method 254

revealed that they were composed by significantly different phylotypes (XY p-value=0.001; 255

YX p-value=0.004, with a confidence of 99% (p = 0.01)). These observations are consistent 256

with the T-RFLP analyses that showed major differences in the bacterial populations between 257

the two stations with high diversity in bacterial community inhabiting superficial sediment of 258

station 2. 259

The phylogenetic analysis of the clone sequences revealed their distribution within six major 260

taxonomic groups of prokaryotic organisms (Fig. 4). Sequences affiliated to α, β, γ, δ, ε, 261

subclass of the Proteobacteria, to the phylum of Actinobacteria and Acidobacteria were 262

detected in both libraries, though in different extent. Sequences affiliated to the phyla of 263

Nitrospirae and Verrucomicrobia were detected only in library 2. Nevertheless, about 80% 264

(library 1’) and 90% (library 2), of the total sequences could not be closely related to cultured 265

organisms suggesting that they may constitute new taxa (Fig. 5). Surprisingly, about 15% of 266

the total sequences in library 1’and 6% of the total sequences in library 2 were closely related 267

to 16S rRNA sequences of unknown bacteria. The library 1’ was dominated by sequences 268

related to γ-Proteobacteria (31% of the sequences) and 3% of these sequences were related to 269

16S rRNA sequences of identified species such as Pseudomonas sp., Pseudomonas lanceolata, 270

Legionella pneumophila, Acinetobacter sp. (Fig. 5). Library 2 was characterized by the co-271

dominance of δ- and γ-Proteobacteria (38 and 37%, respectively) (Fig. 4). Numerous 272

sequences in the libraries were related to sequences previously found in organic and inorganic 273

contaminated sediment. Indeed, main phylotype, representing 6.1% and 12.4% of library 1' and 274

2 respectively, is affiliated within the γ-Proteobacteria and related to uncultured clones 275

obtained from polluted environments, heavy metal-rich and/or organic-rich, originated from 276

harbor sediments, marine sediments or fish farm sediments (Bissett et al., 2006; Gillan & 277

Pernet 2007; Zhang et al., 2008). 278

Several 16S rRNA gene sequences could be related to T-RFLP’s OTUs by predictive 279

digestions (data not shown). Both molecular methods revealed the same dominant populations, 280

i.e. clones sequences with an in silico digestion size of 204 bp, which is the domonant T-RF in 281

T-RFLP analysis. 282

283

DISCUSSION 284

The impact of environmental and pollutant variables on sediment microbial communities was 285

studied at 8 different stations located in a Southern Mediterranean lagoon. Considering 286

hydrological and trophic conditions, this ecosystem was divided into two zones: (i) the channel 287

(stations 1, 1’and 2) and (ii) the lagoon itself (stations 6, 8, 9, 10, and 12). This is the 288

consequence of the contribution of inflow streams into the Bizerte lagoon as previously 289

described (Yoshida et al., 2003), the Tinja wadi (Fig. 1) being the main contributor. The same 290

division could also be observed considering the PAH contents in sediments (from 23.9 ng.g-291

1dry wt to 876.6 ng.g-1 dry wt), the channel stations being the most contaminated. T-RFLP 292

analysis revealed differences between the microbial community structures. The lowest bacterial 293

diversity was observed in the closer Mediterranean Sea station (14 OTUs, station 1) subjected 294

to both marine and lagoonar influences and showing highest salinity. In contrast the highest 295

bacterial diversity was recorded in station 12 (37 OTUs) that shows lowest salinity, low PAH-296

levels and that receives Tinja wadi effluant in wet season, when the samples were taken. 297

Variations of the microbial diversity have been shown along environmental gradients such as 298

salinity (Goñi Urriza et al., 2007), pollution (Hunter et al., 2006) and other parameters 299

(Fourçans et al., 2004; Córdova-Kreylos et al,. 2006). The bacterial community structure was 300

correlated with the PAH content allowing the identification of specific OTUs. For example 301

OTU of 256 bp was found specifically in fluorene, dibenzoanthracene and benzoperylene 302

contaminated station. Interestingly, this OTU could correspond to Acinetobacter sp. and strains 303

belonging to this genus (and having a predictive restriction size of 256 bp) have been 304

previously isolated from Bizerte lagoon sediment for their capacity to degrade PAHs (Ben Said 305

et al., 2007). 306

The stations 1’ and 2 showed similar physico-chemical parameters and different PAH and 307

metal contamination levels. The comparison of their composition would reveal the effect of 308

contaminants on the bacterial community structure. Comparison of 16S rRNA gene library 309

analysis of these stations revealed clear differences between the bacterial community 310

compositions. Considerable number of singletons in the composition of the two rRNA gene 311

libraries was observed and the majority of phylotypes detected were not closely related to any 312

cultivated representatives. These results are in accordance with previous observations reported 313

in marine environmental sequence analysis (Hunter et al., 2006). We observed mainly 314

phylotypes affiliated with α, β, γ, δ, ε- Proteobacteria. In addition phylotypes related to the 315

phyla of Actinobacteria and Acidobacteria were also detected. Members of these phyla are 316

commonly found in less permeable sediments of Seas and estuaries (Lopez-Garcia et al., 317

2003). Numerous sequences in the libraries are related to sequences of bacteria that were 318

previously found in heavy metal and hydrocarbon contaminated sediments (Bowman & 319

McCuaig, 2003; Heijs et al., 2005; Bissett et al., 2006; Hunter et al., 2006). The libraries 320

were dominated by sequences related to γ- and δ-Proteobacteria, α-Proteobacteria related 321

sequences being less abundant. This is typically observed in marine microbial communities 322

(Brown & Bowman, 2001). Although γ-Proteobacteria related sequences were detected in 323

sediments of both PAH and metal contaminated stations, they dominated the community of the 324

most PAH-contaminated sediments (station 1’). Previous research showed that the microbial 325

community structure in a long-term mixed waste contaminated site might reflect both metal 326

and aromatic hydrocarbon concentrations (Nakatsu et al., 2005). The dominance of γ-327

Proteobacteria related bacteria in PAH-contaminated sediments is not unexpected since rapid 328

and strong selection for γ-Proteobacteria have been reported in oil-treated microcosms 329

(Bordenave et al., 2007), in oil-contaminated marine sediments (Hernandez-Raquet et al., 330

2006; Paisse et al., 2008) and after oil spill accident (Kasai et al., 2001). Moreover, 75% of 331

PAHs degrading strains isolated from the Bizerte lagoon in a previous study were γ-332

Proteobacteria (Ben Said et al., 2007). By cloning and sequencing, we detected clones 333

affiliated to Acinetobacter sp.. Interestingly, we have isolated strains affiliated to the same 334

genus capable of fluoranthene and pyrene mineralization (Ben Said et al., 2007). Their relative 335

abundance associated with their metabolic capacities indicates the possible role of strains of 336

this genus in in situ biodegradation of these pollutants. In the previous study we also isolated 337

other hydrocarbonoclastic strains, mainly affiliated to the Pseudomonas genus (Ben Said et al., 338

2007) that we could not detected in this study by library analysis but related T-RFs were detect 339

by T-RFLP analysis. 340

Sediments of the most metal contaminated station (station 2) were dominated by δ-341

Proteobacteria affiliated sequences followed by γ-Proteobacteria. Numerous δ-Proteobacteria 342

phylotypes were related to both aerobic sulfur oxidizers and anaerobic sulfate reducers 343

suggesting an active sulfur cycle in the sediments as usually found in sea sediments (Hunter et 344

al., 2006; Paisse et al., 2008). Interestingly, δ-Proteobacteria related sequences have been 345

observed as predominant in sediments contaminated with multiple pollutants (Hg, PAHs and 346

PCBs, Edund & Jansson, 2006). Nevertheless, Cordova-Kreylos et al. (20006) found that 347

metals had a greater effect on microbial community composition than organic pollutants. 348

Sequences affiliated to β-Proteobacteria were detected in both stations in low abundance (4 349

and 1 %) as reported in other studies of marine sediments (Bowman et al., 2003; Mills et al., 350

2003; Asami et al., 2005). Since β-Proteobacteria-related phylotypes are known to play a 351

critical role in ecosystem function, Hunter et al. (2006) hypothesised that the lack of β-352

Proteobacteria-related phylotypes is believed to be the result of the lowest detection limits of 353

these taxa rather than their absences. 354

355

In conclusion, our results revealed bacterial community structure differences along Bizerte 356

lagoonar sediments. We showed the influence of pollutants in bacterial community structure 357

that is clearly observed by the quantity and the type of PAHs and to a lesser extent by some 358

metals. Strong relationships were observed between individual-PAH concentrations and some 359

OTUs. Clone libraries analysis for two most contaminated PAHs and metals sediment stations 360

have demonstrated significant differences in bacterial community compositions, gamma 361

proteobacterial phylotypes dominated the most PAH-polluted station whereas delta and gamma 362

proteobacterial phylotypes dominated the most metal-polluted station. However, both libraries 363

were dominated by sequences affiliated with uncultured bacteria. Future research might focus 364

on in situ activity level of the γ-Proteobacteria, δ-Proteobacteria in order to determine if these 365

groups play a role in the biotransformation of the pollutants. 366

367

ACKNOWLEDGMENTS 368

This research was supported by the Tunisian “Ministère de la Recherche Scientifique, de la 369

Technologie et du Developpement des Competences” (MRSTDC). We acknowledge the 370

financial support by the Conseil Régional d’Aquitaine and the Conseil Général des Pyrénées 371

Atlantiques. Sequencing experiments presented in the present publication were performed at 372

the Genotyping and Sequencing facility of Bordeaux (grants from the Aquitaine Regional 373

Government Council n° 20030304002FA and 20040305003FA and from the European Union, 374

FEDER n° 2003227). 375

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496

Table 1. Physico-chemical parameters of the stations located around the Bizerte lagoon. Temperature (T), salinity (S), suspended matter (SM), dissolved

oxygen (O2), nitrites (NO2), nitrates (NO3), ammonium (NH4), total nitrogen (Nt), orthophosphate (PO4), total phosphorus (Pt), chlorophyll (Chla), total

PAH (PAHt) and total metals (Metalst) were determined in 8 sites of Bizerte lagoon (May 2004).

Station Location Depth (m)

T (°C)

S (psu)

MS (g.m-3)

O2 (g.m-3)

NO2 (mg.m-3)

NO3 (mg.m-3)

NH4 (mg.m-3)

Nt (mg.m-3)

PO4 (mg.m-3)

Pt (mg.m-3)

Chla (mg.m-3)

PAHt (ng.g-1)

Metalst (µg.g-1)

1 3715452 N 951821 E

11 21.6 36 20 5.5 11.69 30 8 180 2 35 4.4 30 17698.65

1’ 3714600 N 950420 E

11 21.8 36.3 18 5.5 13.13 26.96 11.94 208.36 11.19 47.21 4.12 876.6 1864.44

2 3714000 N 949400 E

11 21.9 36 11.89 5.5 3.43 45.34 14.01 219.73 18.84 41.57 5.00 340.2 41020.76

6 3712460 N 955790 E

7.5 22.3 35.7 20 8.3 5.81 37.33 6.77 178.01 1.05 35.83 4.56 90.9 16941.01

8 3711040 N 955790 E

1.5 23.3 34.2 80 9.1 7.35 13.67 9.42 227.31 12 41.85 4.95 23.9 17103.40

9 3709770 N 954893 E

3.9 23 34.5 80 7 13.5 35 9 135 12 30 5.1 37.7 6927.65

10 3708480 N 949400 E

3 23.1 34.6 65 6.5 10 40 18 135 12 37 5.4 110 80274.93

12 3711042 N 947313 E

3 22.2 33.7 18 7.1 6.21 4.49 8.31 200.77 12.4 41.57 5.23 53.7 19974.23

500

Figure captions 501 502

Figure 1. Map of the Bizerte lagoon indicating the location of the sampling stations (•). Areas 503

of populated zones (dashed circles) and industrial zones (solid circles) are indicated. The 504

arrows indicate streams. 505

506

Figure 2. Bacterial community structure analysis of Bizerte lagoon sediments. A: number of 507

operational taxonomic units (OTUs) in each station, total PAH contents (ng.g-1 dry wt) and low 508

molecular weight PAHs/high molecular weight PAHs ratio (LPAHs/HPAHs) are also 509

indicated. B: relative abundance of T-RFs for each station from T-RFLP patterns obtained by 510

HaeIII digestion of 16S rRNA amplified fragment 511

512

Figure 3. Canonical Correspondance Analysis (CCA) between sediment bacterial communities 513

characterized by T-RFLP fingerprints and individual PAH concentrations. A: CCA of the 514

stations except station 1’, insert shows the CCA graph with all stations. B: CCA presented in A 515

showing the corresponding variables (i.e. T-RFs), the length of T-RF are indicated in base pair. 516

517

Figure 4. Repartition of representative phylogenetic groups detected in 16S rRNA gene 518

libraries of station 2 and 1’ of Bizerte lagoon sediment. 519

520

Figure 5. 16S rRNA-based phylogenetic reconstruction showing the affiliation of Bizerte 521

lagoon sequences (in bold) with selected reference sequences. The scale bar represents 5% 522

estimated sequence divergence. 523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

FIG. 1. 548

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte LagoonTinja

MenzelBourguiba

MediterraneanSea

01

2

34

6

7

8

9

10

11

12

13

14

5

1’

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte Lagoon

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte LagoonTinja

MenzelBourguiba

MediterraneanSea

01

2

34

6

7

8

9

10

11

12

13

1414

5

1’

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte Lagoon

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte LagoonTinja

MenzelBourguiba

MediterraneanSea

01

2

34

6

7

8

9

10

11

12

13

1414

5

1’

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte Lagoon

Bizerte

Bizerte LagoonTinja

MenzelBourguiba

MediterraneanSea

01

2

3 46

7

8

9

10

11

12

13

1414

5

1’

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte Lagoon

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte LagoonTinja

MenzelBourguiba

MediterraneanSea

01

2

34

6

7

8

9

10

11

12

13

1414

5

1’

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte Lagoon

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte LagoonTinja

MenzelBourguiba

MediterraneanSea

01

2

34

6

7

8

9

10

11

12

13

1414

5

1’

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte Lagoon

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte LagoonTinja

MenzelBourguiba

MediterraneanSea

01

2

34

6

7

8

9

10

11

12

13

1414

5

1’

Menzel Abderahmen

Menzel Jmil

Jarzouna

Bizerte

Bizerte Lagoon

Bizerte

Bizerte LagoonTinja

MenzelBourguiba

MediterraneanSea

01

2

3 46

7

8

9

10

11

12

13

1414

5

1’

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

FIG. 2. 574

Stat

ions

Cha

nnel

Lago

on

1

1’

2

6

8

9

12

10

T-RF (bp)

Outdistance Mediterranean Sea

36 37 56 63 64 65 66 71 72 73 74 113

192

200

201

202

204

231

215

207

206

216

399

340

339

323

322

321

291

271

270

269

259

258

256

255

254

236

228

234

235

422

421

420

406

405

404

403

112

20 - 30 %

10 - 20 %

2 - 5 %

5 - 10 %

>60%

40 - 50 %

30 - 40 %Stat

ions

Cha

nnel

Lago

on

1

1’

2

6

8

9

12

10

T-RF (bp)

Outdistance Mediterranean Sea

36 37 56 63 64 65 66 71 72 73 74 113

192

200

201

202

204

231

215

207

206

216

399

340

339

323

322

321

291

271

270

269

259

258

256

255

254

236

228

234

235

422

421

420

406

405

404

403

112

20 - 30 %

10 - 20 %

2 - 5 %

5 - 10 %

>60%

40 - 50 %

30 - 40 %

20 - 30 %

10 - 20 %

2 - 5 %

5 - 10 %

>60%

40 - 50 %

30 - 40 %

0

200

400

600

800

1000

10 9 12 8 6 2 1' 1

Tota

l PA

Hs(

ng.g

-1dr

y w

eigh

t)

L PA

Hs/

HPA

Hs

05

10152025303540

OTU

snumber

Outdistance Mediterranean SeaChannelLagoon

L.PAHs/H.PAHs Total PAHs OTU numbers

89

00,5

11,5

22,5

3

0

200

400

600

800

1000

10 9 12 8 6 2 1' 1

Tota

l PA

Hs(

ng.g

-1dr

y w

eigh

t)

L PA

Hs/

HPA

Hs

05

10152025303540

OTU

snumber

Outdistance Mediterranean SeaChannelLagoon

L.PAHs/H.PAHs Total PAHs OTU numbersL.PAHs/H.PAHs Total PAHs OTU numbers

8989

00,5

11,5

22,5

3

00,5

11,5

22,5

3

A

B

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

FIG. 3. 599

600

A

B

12 8

6 9

1

-1.1

1.9

N

ANY

ANA

F

P

A

FLPY

CBA

BBF+BkF BAPIN

DBA

BPE

25.5 %

22.7 %

-1.1 1.9

2

10

12 8

6 9

1

-1.1

1.9

N

ANY

ANA

F

P

A

FLPY

CBA

BBF+BkF BAPIN

DBA

BPE

25.5 %

22.7 %

-1.1 1.9

2

10

22.7%

25.5 %

49

6365

71

72

7374

77

110

111112

113114

115

117

120

122

127186

190

192200

202

204206 207

209

215

216

227

228

229

231234

235

236

254

255

256

258259 269

270

273

290291

321

322

323

338

339

340

388

399402

404405

406

419

420421

422

-0.80

-2.41

0.80

4.02

-2.41

N

ANY

F

P

A

FL

PY

CBA

BBF + BkF BAP

IN

DBA

BPE

4.02

56

ANA

22.7%

25.5 %

49

6365

71

72

7374

77

110

111112

113114

115

117

120

122

127186

190

192200

202

204206 207

209

215

216

227

228

229

231234

235

236

254

255

256

258259 269

270

273

290291

321

322

323

338

339

340

388

399402

404405

406

419

420421

422

-0.80

-2.41

0.80

4.02

-2.41

N

ANY

F

P

A

FL

PY

CBA

BBF + BkF BAP

IN

DBA

BPE

4.02

56

ANA

2

12

81’

6

10

9

1

-1.06

5.31

-1.06 5.31

N

ANYF

PAFLPYC

BABBF+BkFBAP

INDBABPE

34.7 %

17.7 %

ANA

2

12

81’

6

10

9

1

-1.06

5.31

-1.06 5.31

N

ANYF

PAFLPYC

BABBF+BkFBAP

INDBABPE

34.7 %

17.7 %

ANA

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

FIG. 4. 617

618

619

620

621

622

623

624

625

31%

24%4%

10%

3%

15%

5%

6%

37%

1%

38 %

6%

2%3%

4%6%

1%1%

Unknown Eubacteria

γ−Proteobacteria δ−Proteobacteria β-Proteobacteria ε− Proteobacteria

Actinobacteria Acidobacteria Nitrospirae Verrucomicribia

α-Proteobacteria

Library 1’ Library 2

31%

24%4%

10%

3%

15%

5%

6%

31%

24%4%

10%

3%

15%

5%

6%

37%

1%

38 %

6%

2%3%

4%6%

1%1%

37%

1%

38 %

6%

2%3%

4%6%

1%1%

Unknown Eubacteria

γ−Proteobacteria δ−Proteobacteria β-Proteobacteria ε− Proteobacteria

Actinobacteria Acidobacteria Nitrospirae Verrucomicribia

α-Proteobacteria

Unknown Eubacteria

γ−Proteobacteria δ−Proteobacteria β-Proteobacteria ε− Proteobacteria

Actinobacteria Acidobacteria Nitrospirae Verrucomicribia

α-Proteobacteria

Library 1’ Library 2

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

Fig 5 652

Sulfurimonas denitrificans (L40808)100100

100

87

100

100

100

92

100

51

100

61

90

99

100

100

67

89

83

99

58

76

89

53

77

77

66

100

100

96

70

98

100

52

53

58

57

99

89

82

100

99

85

90

73

86

83

53

50

59

51

0.02

1’ (1%); 2 (1%)Comamonas testosteroni (M11224)

Iodobacter fluviatilis (M22511)Achromobacter group

1’(1%)Burkholderia group

Nitrosomonas oligotropha (AF272422)Gallionella ferruginea (L07897)

Stenotrophomonas acidaminiphila (AF273080)1‘ (2%); 2 (3.1%)

2 (1%)Beggiatoa alba (AF110274)

1‘ (6.1%); 2 (12.4%) 1’ (1%)

Pseudomonas groupEnterobacter group

Alteromonadales groupsMarinobacter group

Marinomonas mediterranea (AF063027)1’ (1%)

Acinetobacter group2 (2.1%)

Oceanospirillum beijerinckii (AB006760)1’ (1%); 2 (1%)

Halomonas nitritophilus (AJ309564)Alcanivorax borkumensis (Y12579)

1‘ (1%); 2 (1%)Methylomicrobium pelagicum (X72775)

1‘ (1%); 2 (4.1%)

Thiothrix nivea (L40993)1‘ (2%)

1’ (1%)Legionella pneumophila (M59157)

1‘ (1%); 2 (2.1%)1’ (1%)

Rhodospirillum rubrum (D30778)Magnetite containing magnetic vibrio (L06455)

Rickettsia prowazekii (M21789)Sphingomonas group

1‘ (2%)Bradyrhizobium elkanii (U35000)

1‘ (1%); 2 (2.1%)1‘ (1%); 2 (2.1%)Agrobacterium tumefaciens (DQ468100)

1‘ (2%)2 (1%)Rhizobiales groups

1‘ (2%); 2 (1%)

1‘ (2%); 2 (3.1%)Desulfonatronum lacustre (AF418171)

Desulfovibrio halophilus (U48243)1‘ (1%); 2 (2.1%)

Geobacter metallireducens (L07834)2 (1%)Pelobacter carbinolicus (X79413)

1‘ (3.1%); 2 (1%)Desulfosarcina variabilis (M26632)

1‘ (2%)2 (1%)

Desulfococcus multivorans (AF418173)2 (1%)

Desulfobacterium anilini (AJ237601)2 (2.1%)

Desulfonema ishimotoei (U45992)Desulfobacca acetoxidans (AF002671)

1‘ (5.1%); 2 (4.1%)Syntrophobacter sp. (X94911)

2 (2.1%)Desulfobulbus elongatus (X95180)

Desulfotalea arctica (AF099061)Desulfocapsa thiozymogenes (X95181)

Desulforhopalus vacuolatus (L42613)1‘ (3.1%); 2 (8.2%)

1‘ (4.1%); 2 (2.1%)Helicobacter baculiformis (EF070342)

Helicobacter nemestrinae (AF348617)(1‘ (2%)

Campylobacter fetus subsp. fetus (DQ174128)2 (1%)

Candidatus Arcobacter sulfidicus (AY035822)Arcobacter cryaerophilus (L14624)

1’ (1%)

Psychrobacter marincola (AY292940)

δ-Proteobacteria

ε-Proteobacteria

α-Proteobacteria

γ-Proteobacteria

β-Proteobacteria

Desulfobacter group

Sulfurimonas denitrificans (L40808)100100

100

87

100

100

100

92

100

51

100

61

90

99

100

100

67

89

83

99

58

76

89

53

77

77

66

100

100

96

70

98

100

52

53

58

57

99

89

82

100

99

85

90

73

86

83

53

50

59

51

0.02

1’ (1%); 2 (1%)Comamonas testosteroni (M11224)

Iodobacter fluviatilis (M22511)Achromobacter group

1’(1%)Burkholderia group

Nitrosomonas oligotropha (AF272422)Gallionella ferruginea (L07897)

Stenotrophomonas acidaminiphila (AF273080)1‘ (2%); 2 (3.1%)

2 (1%)Beggiatoa alba (AF110274)

1‘ (6.1%); 2 (12.4%) 1’ (1%)

Pseudomonas groupEnterobacter group

Alteromonadales groupsMarinobacter group

Marinomonas mediterranea (AF063027)1’ (1%)

Acinetobacter group2 (2.1%)

Oceanospirillum beijerinckii (AB006760)1’ (1%); 2 (1%)

Halomonas nitritophilus (AJ309564)Alcanivorax borkumensis (Y12579)

1‘ (1%); 2 (1%)Methylomicrobium pelagicum (X72775)

1‘ (1%); 2 (4.1%)

Thiothrix nivea (L40993)1‘ (2%)

1’ (1%)Legionella pneumophila (M59157)

1‘ (1%); 2 (2.1%)1’ (1%)

Rhodospirillum rubrum (D30778)Magnetite containing magnetic vibrio (L06455)

Rickettsia prowazekii (M21789)Sphingomonas group

1‘ (2%)Bradyrhizobium elkanii (U35000)

1‘ (1%); 2 (2.1%)1‘ (1%); 2 (2.1%)Agrobacterium tumefaciens (DQ468100)

1‘ (2%)2 (1%)Rhizobiales groups

1‘ (2%); 2 (1%)

1‘ (2%); 2 (3.1%)Desulfonatronum lacustre (AF418171)

Desulfovibrio halophilus (U48243)1‘ (1%); 2 (2.1%)

Geobacter metallireducens (L07834)2 (1%)Pelobacter carbinolicus (X79413)

1‘ (3.1%); 2 (1%)Desulfosarcina variabilis (M26632)

1‘ (2%)2 (1%)

Desulfococcus multivorans (AF418173)2 (1%)

Desulfobacterium anilini (AJ237601)2 (2.1%)

Desulfonema ishimotoei (U45992)Desulfobacca acetoxidans (AF002671)

1‘ (5.1%); 2 (4.1%)Syntrophobacter sp. (X94911)

2 (2.1%)Desulfobulbus elongatus (X95180)

Desulfotalea arctica (AF099061)Desulfocapsa thiozymogenes (X95181)

Desulforhopalus vacuolatus (L42613)1‘ (3.1%); 2 (8.2%)

1‘ (4.1%); 2 (2.1%)Helicobacter baculiformis (EF070342)

Helicobacter nemestrinae (AF348617)(1‘ (2%)

Campylobacter fetus subsp. fetus (DQ174128)2 (1%)

Candidatus Arcobacter sulfidicus (AY035822)Arcobacter cryaerophilus (L14624)

1’ (1%)

Psychrobacter marincola (AY292940)

δ-Proteobacteria

ε-Proteobacteria

α-Proteobacteria

γ-Proteobacteria

β-Proteobacteria

Desulfobacter group