adherent bacteria in heavy metal contaminated marine sediments

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Adherent bacteria in heavy metal contaminatedmarine sedimentsDavid C. Gillan a & Philippe Pernet aa Marine Biology Laboratory, Université Libre de Bruxelles, Brussels, BelgiumPublished online: 05 Apr 2007.

To cite this article: David C. Gillan & Philippe Pernet (2007) Adherent bacteria in heavy metal contaminated marinesediments, Biofouling: The Journal of Bioadhesion and Biofilm Research, 23:1, 1-13, DOI: 10.1080/08927010601108725

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Adherent bacteria in heavy metal contaminated marine sediments

DAVID C. GILLAN & PHILIPPE PERNET

Marine Biology Laboratory, Universite Libre de Bruxelles, Brussels, Belgium

(Received 14 July 2006; accepted 14 August 2006)

AbstractThe eubacterial communities adherent to sediment particles were studied in heavy metal contaminated coastal sediments.Six sampling sites on the Belgian continental plate and presenting various metal loads, granulometries, and organic mattercontent, were compared. The results indicated that the total microbial biomass (attachedþ free-living bacteria) wasnegatively correlated to HCl-extractable metal levels (p5 0.05) and that the percentage of cells adherent to sedimentparticles was close to 100% in every site even in highly contaminated sediments. Consequently, it seems that heavy metalcontamination does affect total bacterial biomass in marine sediments but that the ratio between attached and free livingmicroorganisms is not affected. The composition of the eubacterial communities adherent to the fine fraction of thesediments (5150 mm) was determined using fluorescent in situ hybridisation (FISH). The FISH results indicated that theproportion of g- and d-Proteobacteria, and Cytophaga-Flexibacter-Bacteroides (CFB) bacteria, was not related to the HClextractable metal levels. Most of the 79 complete 16S rRNA sequences obtained from the attached microbial communitieswere classified in the g- and d-Proteobacteria and in the CFB bacteria. A large proportion of the attached g-Proteobacterialsequences found in this study (56%) was included in the uncultivated GMS clades that are indigenous to marine sediments.

Keywords: Bacteria, sediment, heavy metal, North Sea, GMS clade

Introduction

As a consequence of human activities, many coastal

areas around the world are contaminated by heavy

metals, polychlorobiphenyls (PCBs) and polycyclic

aromatic hydrocarbons (PAHs) (Clark, 2001; Coteur

et al. 2003; Green et al. 2003; Danis et al. 2004).

These contaminants usually are accumulated in the

sediments where concentrations in pore-waters can

exceed those in overlying waters by several orders of

magnitude (Bryan & Langston, 1992; Calmano &

Forstner, 1996). Once contaminated, sediments may

constitute an important secondary source of con-

tamination for littoral ecosystems, even after the

primary source has disappeared (Heggie et al. 1987;

Rivera-Duarte & Flegal, 1994; Berelson et al. 2003;

Harrison et al. 2004). As the most abundant

organisms in sediments, microorganisms are known

to be involved in contaminant mobility and bioavail-

ability (Ford & Ryan, 1995; Ehrlich, 1997; Gadd,

2004).

It has long been recognised that aquatic micro-

organisms have a strong affinity for surfaces

(Cooksey & Wigglesworth-Cooksey, 1995) and that

the majority of benthic bacteria are not suspended in

pore-waters but are attached to sediment particles

(Epstein & Rossel, 1995). In contaminated marine

sediments, attached microorganisms might be more

exposed to contaminant stresses than their free-living

counterparts because, besides organic matter com-

plexation, metals and other contaminants also are

adsorbed to sediment particles such as clays and iron

oxyhydroxides (Spark et al. 1995; Calmano &

Forstner, 1996). As attached microorganisms are

able to increase the rate of mineral dissolution by

producing high- and low-molecular weight metabo-

lites at the mineral surface (Welch & Vandevivere,

1994), this microbial activity may lead to the release

of the adsorbed contaminants, which might, in turn,

become toxic and limit microbial activity and bio-

mass (Gadd, 2004). Although exopolymeric sub-

stances of biofilms are known to complex heavy

metals and other contaminants (Ehrlich, 1997),

growth in a biofilm does not provide resistance to

bacteria against killing by metal cations or oxyanions

(Harrison et al. 2004). Ultimately, then, the quan-

tity of microorganisms attached to sediment parti-

cles might be reduced in contaminated marine

sediments, especially in heavy metal contamina-

ted places, when compared to similar but less

Correspondence: David C. Gillan, Marine Biology Lab, CP160/15, Universite Libre de Bruxelles (ULB), 50 av F. Roosevelt, B-1050 Brussels, Belgium.

Fax: 32 2650 2796. E-mail: [email protected]

Biofouling, 2007; 23(1): 1 – 13

ISSN 0892-7014 print/ISSN 1029-2454 online � 2007 Taylor & Francis

DOI: 10.1080/08927010601108725

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contaminated sediments. To the authors’ knowl-

edge, this question has not been addressed in studies

of contaminated coastal marine sediments.

The aim of the present work was to compare the

biomass of the attached and the free-living micro-

organisms in six stations of the Belgian Continental

Plate (BCP) known to be contaminated by heavy

metals and PCBs (Danis et al. 2004). Biomasses

were determined using direct DAPI (40,60-diamidino-

2-phenylindole) counts and fluorescent in situ hybri-

disation (FISH). The phylogenetic position of some

attached eubacteria was determined using 16S ribo-

somal RNA sequencing.

Materials and methods

Sampling stations

The six sampling stations (stations 120, 130, 140,

700, 800, and ZG03) were disposed as represented

in Figure 1. Sediments were collected in April 2005

using a Reineck corer (diameter 15 cm) onboard the

research vessel ‘‘Le Belgica’’ (cruise number 2005/

10). The epibenthic macrofauna was principally

composed of echinoderms, bivalves, and flatfishes

(Danis et al. 2004). The top 2 cm layer of the

sediments was collected using acid-washed 200 ml

polyethylene (PET) vials. Four replicate samples

were collected at each site. Water depth, water

temperature, salinity, percentage of organic matter

(% OM), percentage of water in sediments, and

granulometry of the sediments (as determined by

dry-sieving and expressed in weight percentages)

were determined for all the stations.

Sampling

Each replicate sediment sample was subivided in two

portions. The first portion (called ‘total sediments’)

was used to study the whole microbial community

(attached cellsþ free-living cells in pore-waters). The

second portion (called ‘rinsed sediments’) was used

to study the attached microbial community. Rinsed

sediments were obtained as follows. Five ml of total

wet sediments and 45 ml of sterile seawater (auto-

claved 20 min at 1218C then filtered using a 0.2 mm

mesh size filter) were combined and placed into a

50 ml sterile tube. The tube was agitated manually

for 1 min then centrifuged for 5 min at 4000 rpm in

order to precipitate the smallest mineral particles.

The water phase containing the free-living cells (and

supposedly some loosely fixed cells) was then

eliminated and the whole procedure was repeated

four more times. Simple hand shaking of sediments

with water is supposed to leave more than 99% of the

attached microbial cells fixed to the mineral particles

(Meyer-Reil et al. 1978; Epstein & Rossel, 1995).

Samples of total sediments and rinsed sediments

then were fixed or frozen for subsequent analyses

(see below).

HCl extractable metals in total sediments

Total sediment subsamples (+1 ml) were frozen

immediately in liquid nitrogen and stored at 7808C.

Frozen sediment samples (n¼ 4) subsequently were

oven-dried (48 h, 608C). Aliquots of dried sediments

(500+ 3 mg) were placed in acid-washed Teflon

vials with 10 ml of 0.5 M Suprapur HCl (Merck)

(Sutherland, 2002; Snape et al. 2004). The sedi-

ments then were agitated at 200 rpm for 1 h at

ambient temperature using an orbital-shaker. After

centrifugation (10 min, 700 g), the supernatant was

separated and the metal content was determined

directly by Atomic Absorption Spectrometry (AAS)

as described elsewhere (Coteur et al. 2003). Such an

extraction method was used because it targets labile

phases and helps liberate metals from Fe and Mn

Figure 1. Location and coordinates of the six sampling stations in the Southern North Sea.

2 D. C. Gillan & P. Pernet

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oxides, which can be a major sink for heavy metals in

oxic sediments (Spark et al. 1995; Sutherland, 2002;

Snape et al. 2004). It is also efficient at decomposing

labile organic phases (Snape et al. 2004) and was

used in other studies, allowing direct comparisons

(Gillan et al. 2005). A contamination index (CI) was

calculated for each site using the HCl extractable

metals. This index is a measure of contamination

relative to the metal content of the sediment at the

least contaminated site included in the study (Station

800) (Feris et al. 2003). The CI was calculated for

each site as follows:

CIn ¼ Cdn=Cd800 þCun=Cu800

þ Pbn=Pb800 þ Znn=Zn800;

where n represents one of the six sites.

Organic matter (OM) determinations in total sediments

Frozen sediment samples (7208C) were oven-dried

for 48 h at 608C and sieved in order to remove large

particles (mesh size: 4 mm). Aliquots of the dried

sediments (ca. 50 g, n¼ 4) then were weighed using

an analytical balance, transferred into a Carbolite

CWF 11/23 furnace and carbonised at 4508C for

4 h. After cooling to ambient temperature, samples

were reweighed and the organic matter content was

determined as the ash-free dry weight (Gillan et al.

2005).

Direct DAPI counts

Direct counts were determined both in total and

rinsed sediments. Sediment samples (n¼ 4) were

fixed using 2% (wt/vol) paraformaldehyde for 48 h

at 48C, centrifuged, rinsed in filtered seawater (FS;

0.2 mm mesh size filter), centrifuged and stored in a

1:1 mixture of FS and 100% ethanol at 7208C. On

the day the analyses were performed, the tubes were

vortexed and a 75 ml aliquot of the suspension was

sampled. This protocol allows for the selection a

single size fraction of the sediments (a grain size

between 5 and 150 mm, as checked by microscopy).

The aliquot then was diluted 10 times in FS-ethanol

and treated by sonication (30 sec using pulse mode)

with a sterilised sonic probe (VibraCell 375 ultra-

sonic processor; output control: 3; 5 mm microtip;

duty cycle: 20%) in order to detach bacteria from the

particles (Epstein & Rossel, 1995). After sonication,

samples were left untreated for 3 min in order to

settle large sediment particles that could otherwise

darken the microscopic field. A volume of 100 ml of

sonicated bacteria then was combined with 10 ml of

FS and filtered using an Isopore membrane filter

(0.2 mm, Millipore, Cat. No. GTTP02500) placed on

a 0.45 mm filter (Millipore, Cat. No. HAWP02500).

Filters were stained with DAPI (1 mg ml71) for 3 min,

rinsed briefly with distilled water and 70% ethanol, air

dried and mounted in Vectashield (Vector Lab.,

Burlingame, USA). Filters were observed under a

Leitz Diaplan microscope fitted for epifluorescence

microscopy with a 50 W mercury high pressure bulb

and a filter set for DAPI. A total of 14 pictures

(153662048 pixels) was acquired for each filter at

6100 objective magnification with a Peltier-cooled

high resolution CCD camera (QImaging MicroPub-

lisher, 3.3 Mpixels) controlled by the QCapture

software version 1.1.8 (exposure time, 5 sec). The

14 pictures were taken randomly along two transects at

right angles to each other, which crossed in the centre

of the filter (the filter edges and filter center were

included in the countings) (Austin, 1989). Each

picture then was counted manually. Counts from the

14 pictures (total area observed¼ 5.39 1078 m2) were

summed and compared to the effective filtration area

(1.77 1074 m2). Four filters were counted for each site

(n¼ 4) and the mean number of bacteria per field was

31+14. Such a counting scheme guarantees the

lowest error in environments with great spatial

heterogeneity (Kirchman et al. 1982; Montagna,

1982). Total numbers of bacteria were expressed per

g (dw) of sediment (the weight of particles in the

aliquot of 75 ml was evaluated by centrifugation and

drying of a 750 ml aliquot; values then were divided

by 10).

Fluorescent in situ hybridisation (FISH) of rinsed

sediments

The biomass of the three main eubacterial lineages

found in marine sediments (i.e. g- and d-Proteobac-

teria and CFB bacteria) (Llobet-Brossa et al. 1998;

Ravenschlag et al. 1999; 2001; Bowman et al. 2003)

was determined using the FISH approach. The

tested oligonucleotide probes were EUB338 for eub-

acteria (Amann et al. 1990), GAM42a for g-Proteo-

bacteria (Manz et al. 1992), CF319a for the CFB

bacteria (Manz et al. 1996), and DSS658 for

d-Proteobacteria of the Desulfosarcina-Desulfococcus

group (Manz et al. 1998). NON338 (Wallner et al.

1993) was used as a negative control. Rinsed sedi-

ments were fixed and cells were placed on filters as

described above (direct counts section). Filter sect-

ions were placed into 0.2 ml tubes (one filter section

per tube) with 150 ml of hybridisation solution

(0.9 M NaCl; 20 mM Tris/HCl pH 7.5; 0.01%

SDS; 750 ng of probe; formamide: 10% for probes

EUB338 and NON338; 35% for probes GAM42a

and CF319a; and 60% for probe DSS658) (Manz

et al. 1992). The competitor probes cBET42a was

used for hybridisation with probe GAM42a (Manz

et al. 1992). Tubes were incubated for 1.5 h at 468Cin a water bath. Probes were purchased from Qiagen

Adherent bacteria in marine sediments 3

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(HPLC purified oligonucleotides labelled with Cy3

at the 50 end). Filters then were placed in 5 ml of

washing solution at 488C for 20 min. The washing

solution consisted of 20 mM Tris/HCl pH 7.5;

0.01% SDS; 450 mM NaCl (EUB338 and

NON338), 70 mM (GAM42a, CF319a), or 4 mM

(DSS658); and 5 mM EDTA (no EDTA added for

probes EUB338 and NON338) (Manz et al. 1992).

Filter sections then were rinsed briefly with distilled

water, air dried, stained with DAPI, placed under an

epifluorescence microscope, and photographed as

described in the direct counts section (a filter set for

Cy3 was added). Image pairs (one with DAPI, one

with Cy3), acquired at 6100 objective magnifica-

tion, were superimposed using Photoshop 7.0 soft-

ware. DAPI and Cy3-stained cells were manually

enumerated. All the Cy3-stained cells that were

counted presented a DAPI signal. Four filters were

counted for each site (n¼ 4). The number of DAPI-

stained cells that were counted for each probe at each

site was 41200. The signal obtained with probe

NON338 at each sampling site was subtracted from

all the counts (this signal was 50.5% of the DAPI-

counts for each station).

Cloning and sequencing of 16S rRNA genes in rinsed

sediments

DNA was extracted from 500 mg of rinsed sedi-

ments (n¼ 4) with the in situ lysis method described

earlier (Schauer et al. 2003; Gillan et al. 2005).

Briefly, this method uses lysozyme, proteinase K,

SDS, and heat shocks to lyse the cells. Proteins then

are removed using phenol-chloroform-isoamyl alco-

hol and DNA is precipitated using ethanol. DNA

samples were purified using a DNA cleaning kit

(QIAquick, Qiagen, Germany) and eluted in 50 ml of

PCR water. Relative amounts of DNA were esti-

mated visually after agarose electrophoresis, by

comparison with a molecular mass ladder (Gibco-

BRL). Yield was estimated as 10 mg DNA g71 of wet

sediments (except for station 800 where the DNA

yield was ten times lower). DNA was diluted 20

times before the PCR.

The complete 16 rRNA gene was amplified using

the bacterial primers 8F and 1492R (Buchholz-

Cleven et al. 1997). The DNA from the four

replicate samples was first combined before the

PCR. The PCR amplification procedure was per-

formed with an Eppendorf Mastercycler using the

PCR kit Red’y’StarMix (Eurogentec). A 50 ml

reaction tube of Red’y’StarMix contains HotGold-

Star DNA polymerase, 200 mM of each dNTP,

1.5 mM of MgCl2, Tris-HCl (pH 8.0 at 258C),

KCl and red dye loading buffer. The final concen-

tration of primers was 1 mM. Three ml of DNA were

used in the PCR. Tubes first were incubated 10 min

at 958C to activate the HotGoldStar DNA polymer-

ase. The PCR was performed using 30 cycles, each

cycle consisting of denaturation (948C, 1 min),

annealing (468C, 1 min), and primer extension

(728C, 1 min). Annealing at 468C (instead of 40.08C[Buchholz-Cleven et al. 1997]) was chosen to ensure a

higher specificity. For the last extension step, tubes

were incubated for 10 min at 728C.

PCR products were analysed and quantified by

agarose gel electrophoresis, then purified with

QIAquick columns, and cloned into TOP10 chemi-

cally competent E. coli cells using the TOPO TA

Cloning Kit (Invitrogen). Clones containing the

complete 16S rRNA gene, as revealed by PCR with

primers M13, were selected for plasmid isolation

with the QIA prep spin miniprep kit (Qiagen).

Plasmids were sequenced completely on an ABI

Prism 3100 genetic analyzer using vector pri-

mers M13F and M13R as well as eubacterial

primers (Buchholz-Cleven et al. 1997; Gillan et al.

2005).

Phylogenetic analysis

PCR-generated chimeric sequences first were de-

tected and eliminated using three different softwares:

CHIMERA_CHECK version 2.7 of the Ribosomal

Database Project II (Cole et al. 2003), Bellerophon

(Huber et al. 2004), and Pintail version 0.33

(Ashelford et al. 2005). Sequences then were

submitted to BLAST version 2.2.12 to identify the

closest relatives and download their 16S rRNA

sequence (Madden et al. 1996). All sequences were

manually aligned and analysed using Se-Al version

2.0a11 (Rambaut, 1996). Distance and maximum-

likelihood trees were generated with the Phylip

program package, version 3.6 (Felsenstein, 2002).

Distance trees were generated with ‘‘Dnadist’’ using

Jukes-Cantor distances and Neighbour-Joining.

Maximum-likelihood trees were generated with

‘‘Dnaml’’ (Ti/Tv¼ 2.0; empirical base frequencies,

one category of sites with a constant rate of

variation). The statistical significance of the phylo-

genetic groups within the trees was tested by

using bootstrap analysis with the Phylip programs

‘‘Seqboot’’ and ‘‘Consense’’ (100 bootstrap repli-

cates). Trees were created using the program Tree-

view (version 1.6.6.).

Statistical analyses

For HCl extractable metals, direct counts and FISH

data, the four sampling sites were compared by a

one-way ANOVA (a¼ 0.05). Significant differences

were determined by the Tukey’s HSD test (Systat

9.0). The arcsine transformation was used for per-

centages (x0 ¼ arcsin �x). The Pearson correlation


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coefficient was used for correlations (Systat 9.0).

The Bartlett chi-square test was used to test the

significance of all the correlations, then the Bonfer-

roni-adjusted probabilities associated with each cor-

relation coefficient were determined.

Nucleotide sequence accession numbers

The 79 sequences obtained in this study have been

assigned in the GenBank database under accession

no. DQ351738 to DQ351816.

Results

The general characteristics of the sediments in each

sampling station, including water depth, salinity,

percentage of organic matter, and granulometry, are

presented in Table I.

HCl-extractable metals in total sediments

The six sampling stations may be subdivided into

three groups: high metal concentrations (stations 130

and 700), moderate metal concentrations (stations

120, 140, and ZG03), and low metal concentrations

in the sediments (station 800) (Table II). Heavy

metals in stations 130 and 700 were always signi-

ficantly higher than those determined in stations 120,

140, and ZG03 (about 5-fold for Cd, 16-fold for

Cu, 6-fold for Pb, and 4-fold for Zn). Heavy metal

concentrations did not differ significantly in these

three stations and were always significantly higher

than those determined in the weakly contaminated

station 800 (about 5-fold for Cd, 2-fold for Cu, 4-fold

for Pb, and 10-fold for Zn). Station 130 presented the

highest CI value and station 800 the lowest.

Direct DAPI counts

The numbers of cells observed in the selected sedi-

ment fraction (�150 mm) are shown in Table III.

Numbers ranged from 0.63 – 5.946109 cells g71

(DW). There were no significant differences between

total sediments and rinsed sediments except for

stations 130 and ZG03 where cell numbers declined

by 22% and 37% after rinsing, respectively.

Table I. Characteristics of the sampling stations investigated.

Characteristics 120 130 140 700 800 ZG03

Sampling date 25-04-05 25-04-05 27-04-05 25-04-05 27-04-05 26-04-05

Water depth (m) 11.3 12.9 8.1 6.0 37.2 17.3

Water temperature (8C) 10.2 9.6 10.4 ND 8.4 10.0

Salinity (%) 33.9 31.4 31.4 ND 34.6 34.0

Granulometry (weight percentages+SD)a:

% with granulometry of �63 mm 0.6+0.2 100.0 2.0+ 1.4 100.0 0 2.5+ 1.2

% with granulometry of 63 – 125 mm 2.2+0.3 0 2.4+ 0.7 0 0.1+0.1 2.4+ 0.9



% with granulometry of 500 – 1,000 mm 4.1+1.0 0 0.4+ 0.0 0 9.9+6.4 0.4+ 0.1

% with granulometry of �1,000 mm 3.6+1.0 0 0.4+ 0.3 0 1.6+1.0 0.3+ 0.2

Water %+SDb 20.0+1.3 66.8+3.6 25.6+ 4.4 55.2+2.7 17.7+0.4 26.3+ 2.2

% organic matter+SDc 0.52+0.40 3.88+0.32 0.35+ 0.10 1.99+ 0.41 0.23+0.20 0.45+ 0.11

S6PCB in 1998d 4.79+0.56 10.6+1.01 0.83+ 1.03 1.93+ 0.74 ND 1.36+ 0.99

aFour samples of ca. 150 g of total sediments (dry weight) were passed through five sieves with decreasing pore sizes; bfour samples of ca.

10 g of total sediments (wet weight) were used; cfour samples of ca. 25 g were used, values are percentages of total weight+SD. dPCB

concentrations (mean+SD; mg g71 DW, n¼ 6) in the bulk fraction of the sediments, six PCB congeners were summed, namely, #52, #101,

#118, #138, #153, and #180. (Data from Danis et al. 2004.) ND¼not determined.

Table II. HCl-extractable metals and CI of the sedimentsa.

Site Cd Cu Pb Zn CI

130 0.25+ 0.01 a 7.74+ 0.85 a 29.51+2.04 a 53.35+1.99 a 155.6+ 7.7 a700 0.27+ 0.03 a 5.17+ 1.41 b 20.05+2.17 b 43.46+5.71 b 121.9+ 18.3 b140 0.05+ 0.01 b 0.37+ 0.15 g 4.91+1.08 g 11.56+1.72 g 24.7+ 4.7 gZG03 0.05+ 0.01 b 0.54+ 0.14 g 5.94+1.07 g 7.68+1.27 g 22.7+ 3.4 g120 0.06+ 0.00 b 0.33+ 0.04 g 3.15+2.12 g 7.63+0.29 g 19.4+ 2.1 g800 0.01+ 0.00 g 0.18+ 0.04 g 0.87+0.13 d 0.95+0.84 d 4.0+ 0.9 d

aValues are mean concentrations (mg g71 DW+SD [n¼ 6]); a, b, g, and d refer to comparisons between sites; different symbols indicate

significant differences (Tukey’s test, a¼ 0.05); CI¼ contamination index.


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FISH of rinsed sediments

Results of the FISH analyses are presented in

Table IV. Eubacteria represented 47.5 – 76.8% of

the DAPI counts, g-Proteobacteria 7.6 – 17.1%,

d-Proteobacteria 5.0 – 9.7%, and the CFB bacteria

5.4 – 19.2%. Significant differences were found be-

tween sites except for the d-Proteobacteria.

Data correlations

All HCl-extractable metals were negatively corre-

lated with the total DAPI counts and positively

correlated with organic-matter levels (Table V). The

correlation coefficients of these two variables were

always significant (Bonferroni probabilities5 0.05)

and above 0.722. The g-Proteobacteria and the

CFB bacteria were negatively correlated to all HCl-

extractable metals (coefficients: 70.399 to 70.555),

but the correlation coefficients were not significant

(Bonferroni probabilities4 0.05), except for the

CFB bacteria and HCl-extractable Cd. The percen-

tages of eubacteria and d-Proteobacteria were not

correlated to the metal levels.

The methods used in this study to determine

DAPI counts and HCl-extractable metals in 6

stations of the Belgian continental plate were iden-

tical to the methods used earlier to study 4 sampling

sites in the heavy-metal polluted Sørfjord (sites S1-

S4) (Gillan et al. 2005). In addition, the fraction of

the sediments used for DAPI counts was identical in

both studies (�150 mm). Consequently, direct com-

parisons are allowed between these 10 stations, and a

graph expressing DAPI counts in total sediments

against the contamination index (CI) may be drawn

(Figure 2). It can be seen that highly contaminated

stations (S1, S2, S3, 130, 700) always show DAPI

counts5 1.109 cells g71 (DW) whereas moderately

contaminated and weakly contaminated stations

show variable DAPI counts, up to 6.109 cells g71

(DW).

Phylogenetic analysis

Four 16S rRNA clone libraries were constructed

using rinsed sediments. PCR amplifications were not

successful for stations 700 and 800, which may be

related to the presence of PCR inhibiting substances

in the DNA extracts of station 700, and to the very

low DNA extraction yield of station 800. Approxi-

mately 20 clones were sequenced completely in each

DNA library (ca. 1500 bp). After discarding seq-

uences that were suspected of being chimeric or

of poor quality, 79 sequences remained in total

(Table VI). Four sequences could not be related to

any of the known bacterial lineages.

Four main clusters of g-Proteobacteria were

detected in this study and contained 76% of the

g-proteobacterial sequences. These clusters were the

GMS 2, the GMS 3, the GMS 4, and the KM23

groups (Figure 3). Three of these clusters (the GMS

groups) were exclusively composed of clones

Table III. DAPI counts (mean+SD; 6109 cells g71 DW)a.

Station 120 130 140 700 800 ZG03

Total sediments 4.67+1.79 0.81+ 0.28 4.01+ 2.21 0.86+0.30 3.76+ 1.80 5.94+ 1.19

Rinsed sediments 4.44+1.63 0.63+ 0.24 4.58+ 1.30 0.77+0.30 3.79+ 2.28 3.72+ 0.80

aFor each treatment, 4 filters were counted; the fraction considered for DAPI counts was the�150 mm fraction; bold face values¼ significant

differences between treatments (paired Student t-test, a¼0.05).

Table IV. Results of the FISH analysesa.

EUB338 GAM42a DSS658 CF319a

120 76.8+ 4.2 a 15.2+ 2.6 ag 9.7+2.0 a 19.2+ 4.6 a130 60.7+ 5.3 b 10.2+ 2.4 ab 5.0+1.8 a 7.3+ 0.5 bg140 47.5+ 8.2 g 15.7+ 0.7 g 6.3+2.7 a 6.4+ 2.0 bg700 54.4+ 1.7 bg 7.6+ 2.1 b 6.7+1.4 a 5.4+ 1.7 g800 48.8+ 2.0 bg 10.5+ 2.9 b 9.2+7.7 a 10.5+ 3.1 bgZG03 55.1+ 8.4 bg 17.1+ 1.9 g ND 12.2+ 2.2 ab

aData are given as percentages of DAPI stained cells (mean+SD;

n¼ 4). Probes used were EUB338 for Eubacteria, GAM42a for

g-Proteobacteria, DSS658 for d-Proteobacteria, and CF319a for

CFB-bacteria. Values sharing at least one symbol (a, b, g) did not

differ significantly (Tukey’s HSD test; a¼0.05). ND¼not

determined.

Table V. Correlation coefficients between HCl-extractable metals

and microbial characteristics.

Characteristic

Correlationa

Cd Cu Pb Zn

Total DAPI counts 70.731 70.728 70.722 70.739

% OM 0.907 0.951 0.965 0.948

EUB338 0.064 0.025 0.064 0.016

GAM42a 70.555 70.545 70.460 70.498

DSS658 70.195 70.232 70.260 70.233

CF319a 70.449 70.399 70.419 70.476

aZG03 values are not included for CF319a data. Significant

correlations are in boldface (p50.05).


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obtained from marine sediments (Bowman et al.

2005). The last cluster (the KM23 group) also

contained clones from the bacterioplankton and sea

ice. Stations 140 and ZG03 featured sequences that

were almost identical; such sequences were observed

in several groups: in the GMS 3 group (sequences

140 – 22 and ZG-17 were 99.8% similar), in the

GMS 4 group (sequences 140 – 7 and ZG-4 were

99.8% similar), and in the KM23 group (sequences

140 – 17 and ZG-25 were 99.9% similar) (Figure 3).

The highest similarity observed between a g-proteo-

bacterial sequence of this study and a clone from

another marine environment was between sequence

120 – 8 and clone 13aFS found in Mediterranean

sediments (similarity: 99.7%) (Figure 3).

Two main clusters were detected in the

d-Proteobacteria (Figure 4). The first cluster was

the Desulfosarcina-Desulfonema group which included

4 sequences. The second cluster, the Desulfobulbus-

Desulfotalea group, included 7 sequences. Four

d-proteobacterial sequences of the present study

were highly similar to other clones found in an

intertidal mudflat in the German Wadden Sea

(Mußmann et al. 2005). These sequences were

140 – 2 (99.5% similar to clone SB2), sequences

140 – 9 and 140 – 18 (99.8 – 99.9% similar to clones

SK41, SK28, SD33 and SP33), and sequence

130 – 17 (99.8% similar to clone SK11).

Eight sequences of the present study grouped in

the CFB bacteria (Figure 5). The highest similarities

observed were between sequence 140 – 15 and clone

C319a-R8C-C8 found in estuarine sediments in

Massachusetts (similarity: 99.5%). Thirteen se-

quences were affiliated to 8 other groups such as

Planctomycetales and Acidobacteria (Figure 6). The

greatest similarities observed in these groups were

between sequence 130 – 26 in the Verrucomicro-

biales and Fucophilus fucoidanolyticus (similarity:

98.0%), between sequence 140 – 14 in the Acido-

bacteriales and clone Sva0450 from Arctic Ocean

sediments (similarity: 97.0%) (Ravenschlag et al.

1999), and between sequence ZG-15 in the Nitros-

pirales and clone B58 from deep-sea sediments in the

Pacific (Zeng et al. 2005). Four sequences could not

be affiliated with any eubacterial group (sequences

120 – 13, 130 – 2, 130 – 33 and ZG-3).

Figure 2. Relationship between DAPI counts and the contamination index (CI) of the sediments. Four sediment samples (S1-S4) from the

Sørfjord in Norway were used (Gillan et al. 2005) as well as the six sediment samples from the Belgian continental plate obtained in this

study. The 95% confidence band of the best-fit curve is shown (dotted line).

Table VI. Number of complete 16S rRNA sequences obtained in

each sitea.

Site 120 130 140 ZG03 Total %

g-Proteobacteria 9 10 16 8 43 54.4

d-Proteobacteria 1 4 4 2 11 13.9

CFB bacteria 1 3 1 3 8 10.1

Unknown 1 2 0 1 4 5.1

Acidobacteria 0 0 1 2 3 3.8

Planctomycetes 0 0 0 2 2 2.5

Verrucomicrobia 1 1 0 0 2 2.5

a-Proteobacteria 0 1 1 0 2 2.5

Nitrospira 0 0 0 1 1 1.3

Actinobacteria 0 0 0 1 1 1.3

e-Proteobacteria 1 0 0 0 1 1.3

Chloroflexi 0 1 0 0 1 1.3

Total 14 22 23 20 79 100

aData for rinsed sediments; no sequences were obtained for sites

700 and 800.


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Discussion

Metal analyses have confirmed the contamination of

the selected BCP stations. High concentrations of

HCl extractable metals were found in stations 130

and 700, and low concentrations were found in

station 800. This distribution may be easily explained

by the fact that stations 130 and 700 are located

53 km from the coast, near heavily industrialised

areas that also possess port installations and sewage

outlets to the sea. Station 800, located 465 km

from the coast, is consequently less contaminated.

Although other contaminants have not been deter-

mined in this study, the presence of other anthropic

substances such as PCBs and PAHs may be sus-

pected (Danis et al. 2004).

No relationships between the percentage of at-

tached bacteria and the heavy metal contamination

level were found in this study. In the most heavy-

metal-contaminated site (station 700), as well as in the

least contaminated one (station 800), 100% of the

cells were attached to the sediment fraction con-

sidered (�150 mm). In stations 140 and ZG03,

which presented similar HCl extractable metal

Figure 3. Maximum-likelihood tree showing the relationships among the 16S rRNA clones obtained in this study (in boldface) and other

g-Proteobacteria. Thiobacillus denitrificans (b-Proteobacteria) served as the outgroup. A matrix of 1331 nucleotides was used (E. coli

100 – 1392). GenBank accession numbers are listed for the close relatives of the clones. Bootstrap values of 450% (obtained with 100

resamplings and the distance method) are shown. Bar¼ the expected numbers of substitutions per 10 nucleotides; aaccording to Bowman

et al. 2005; btype of environment and location (when available).


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concentrations and similar sediment characteristics

such as granulometry and organic matter content, the

percentages of attached bacteria differred significantly

(100% and 62%, respectively). It can be concluded, at

least for the 6 stations investigated, that the ratio

between attached and free living microorganisms is

not affected by heavy metal contamination.

In the present research, total bacterial abundance

in the marine sediments investigated was negatively

correlated to HCl extractable metal levels. A negative

relationship also was evident when data from the

present study were combined with data obtained in

the heavy-metal-contaminated Sørfjord (Figure 2).

Although many environmental factors other

than metals may influence biomass (e.g. viruses

[Weinbauer & Rassoulzadegan, 2004], protists [Van

Hannen et al. 1999], or nematodes [De Mesel et al.

2004]), negative correlations between metals and

bacterial biomass in marine sediments have been

reported in other studies (Fabiano et al. 1994; Gillan

et al. 2005). In the soil and freshwater environment,

total microbial biomass was found to be relatively


d-Proteobacteria. Escherichia coli (g-Proteobacteria) served as the outgroup. A matrix of 1323 nucleotides was used (E. coli 102 – 1381). See

the legend to Figure 3 for other information.


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insensitive to the total heavy metal load (Frostegard

et al. 1993; Knight et al. 1997; Baath et al. 1998; Shi

et al. 2002; Feris et al. 2003). However, this is not

always the case and negative correlations were found

in some soil environments (Brookes & McGrath,

1984; McGrath et al. 1995; Aoyama & Nagumo,

1997). Other marine sediments should, of course, be

investigated using the same HCl extraction method

and a similar counting scheme to obtain a better

picture of the situation.

HCl extractable metals in the sediments investi-

gated were not correlated with the abundance of the

main bacterial lineages as determined by FISH. This

result contrasts with the research conducted in

the marine sediments of the Sørfjord where HCl

extractable Cu, Pb, and Zn were negatively corre-

lated with the abundance of g-Proteobacteria and

CFB bacteria (Gillan et al. 2005). This result also

contrasts with a freshwater study, where g-Proteo-

bacteria were positively correlated to total metal


CFB-bacteria. Escherichia coli (g-Proteobacteria) served as the outgroup. A matrix of 1323 nucleotides was used (E. coli 94 – 1391). See the

legend to Figure 3 for other information.


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levels (Feris et al. 2003). Such differences might be

explained by environmental differences (freshwater

vs. saltwater), methodological differences (the use of

rinsed or total sediments), and/or contamination

levels (metal concentrations were higher in the

polluted Sørfjord [Gillan et al. 2005]). But in the

final analysis, the search for correlations might, itself,

also be questionned. Why would g- or d-Proteobac-

teria be correlated to metal levels in marine

sediments? These bacterial lineages, especially the

g-Proteobacteria, feature many different species and

many types of metabolisms (Ravenschlag et al. 1999;

2000; 2001; Powell et al. 2003; Bowman et al. 2003;

2005; Buhring et al. 2005; Gillan et al. 2005;

Mußmann et al. 2005). Consequently, correlations

of bacterial abundances with metal levels probably

are not to be expected in future studies, at least for

the main bacterial lineages.

The limited screening of the 16S rDNA libraries

in the present work (+20 clones per library) has

indicated that three groups of bacteria are dominant

in the attached microbial communities, namely, the

g- and d-Proteobacteria and the CFB bacteria. Other

studies of continental shelf sediments, in which more


bacterial groups (a-Proteobacteria, E-Proteobacteria, Verrucomicrobiales, Acidobacteriales, Planctomycetales, Actino-bacteria, Chloroflexi,

and Nitrospirales). Escherichia coli (g-Proteobacteria) served as the outgroup. A matrix of 1103 nucleotides was used (variable regions V2, V3,

V6 and V7 were omitted). See the legend to Figure 3 for other information.


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extensive screening of the DNA libraries was carried

out, have shown the same result for the whole

microbial community (attached and free-living);

g- and d-Proteobacteria, as well as CFB bacteria,

seem to be the most abundant eubacteria in coastal

marine sediments (Llobet-Brossa et al. 1998;

Ravenschlag et al. 1999; 2001; Bowman et al. 2003;

Buhring et al. 2005), even in heavy metal contami-

nated sites (Powell et al. 2003; Gillan et al. 2005).

A large proportion of the attached g-proteobacterial

sequences found in this study (56%) was included in

the four uncultivated GMS clades described pre-

viously (Bowman et al. 2005). These clades, which

appear only indigenous to marine sediments and so

far have an unknown functionality, may constitute a

significant part of microbial communities in sedi-

ments (Bowman et al. 2005). Results from the pre-

sent research suggest that all GMS bacteria are able to

attach to sediment particles. Interestingly, it seems

that GMS clade 1 bacteria are not affected by high

levels of HCl extractable heavy metals, as it is the only

GMS clade that was found both in contaminated

sediments of the Belgian continental plate (this study)

and the contaminated Sørfjord sediments (7 se-

quences in the OB3_49 group corresponding to

GMS clade 1 [Gillan et al. 2005]).

Understanding the functioning of sediment-asso-

ciated microbial communities in contaminated en-

vironments is important for geochemists’ evaluation

of the impact of microorganisms on important

sedimentary processes such as metal remobilisation

and benthic fluxes of contaminants (Calmano &

Forstner, 1996). Future studies should focus on

other marine sediments and on the in situ abundance

of GMS clades in g-Proteobacteria. For that, high-

resolution techniques such as the combination of

FISH with catalysed reporter deposition (CARD-

FISH) (Ishii et al. 2004) and/or real-time PCR

analysis using clade-specific primers are absolutely

needed (Bowman et al. 2005).

Acknowledgements

D.C.G. is a Senior Research Assistant of the Belgian

National Fund for Scientific Research (NSFR). The

research was supported by a Belgian federal research

program (SSTC, contract EV/11/23A) and by con-

tributions from the Centre Interuniversitaire de

Biologie Marine (CIBIM) and from the National

Bank of Belgium (BNB).

References

Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R,

Stahl DA. 1990. Combination of 16S rRNA-targeted oligonu-

cleotide probes with flow cytometry for analyzing mixed

microbial populations. Appl Environ Microbiol 56:1919 – 1925.

Aoyama M, Nagumo T. 1997. Effects of heavy metal accumula-

tion in apple orchard soils on microbial biomass and microbial

activities. Soil Sci Plant Nutr 43:601 – 612.

Ashelford KE, Chuzhanova N, Fry JC, Jones AJ, Weightman AJ.

2005. At least one in twenty 16S rRNA sequence records

currently held in public repositories estimated to contain

substantial anomalies. Appl Environ Microbiol 71:7724 – 7736.

Austin B. 1989. Methods in aquatic bacteriology. Chichester, UK:

John Wiley & Sons.

Baath E, Diaz-Ravina M, Frostegard A, Campbell CD. 1998.

Effect of metal-rich sludge amendments on the soil microbial

community. Appl Environ Microbiol 64:238 – 245.

Berelson W, McManus J, Coale K, Johnson K, Burdige D,

Kilgore T, Colodner D, Chavez F, Kudela R, Boucher J. 2003.

A time series of benthic flux measurements from Monterey Bay,

CA. Cont Shelf Res 23:457 – 481.

Bowman JP, McCammon SA, Dann AL. 2005. Biogeographic and

quantitative analyses of abundant uncultivated g-Proteobacter-

ial clades from marine sediment. Microb Ecol 49:451 – 460.

Bowman JP, McCammon SA, Gibson JAE, Robertson L,

Nichols PD. 2003. Prokaryotic metabolic activity and commu-

nity structure in Antarctic continental shelf sediments. Appl

Environ Microbiol 69:2448 – 2462.

Brookes PC, McGrath SP. 1984. Effects of metal toxicity on the

size of the soil microbial biomass. J Soil Sci 35:341 – 346.

Bryan GW, Langston WJ. 1992. Bioavailability, accumulation and

effects of heavy metals in sediments with special reference

to United Kingdom estuaries: a review. Environ Pollut 76:

89 – 131.

Buchholz-Cleven BEE, Rattunde B, Straub KL. 1997. Screening

for genetic diversity of isolates of anaerobic Fe(II)-oxidizing

bacteria using DGGE and whole-cell hybridization. Syst Appl

Microbiol 20:301 – 309.

Buhring SI, Evert M, Witte U. 2005. The microbial community

structure of different permeable sandy sediments characterized

by the investigation of bacterial fatty acids and fluorescence

in situ hybridization. Environ Microbiol 7:281 – 293.

Calmano W, Forstner U. 1996. Sediments and toxic substances.

Berlin, Germany: Springer-Verlag.

Clark RB. 2001. Marine pollution, 5th ed. Oxford, UK: Oxford

University Press.

Cole JR, Chai B, Marsh TL, Farris RJ, Wang Q, Kulam SA,

Chandra S, McGarrell DM, Schmidt TM, Garrity GM,

Tiedje JM. 2003. The Ribosomal Database Project (RDP-II):

previewing a new autoaligner that allows regular updates and

the new prokaryotic taxonomy. Nucleic Acids Res 31:442 – 443.

Cooksey KE, Wigglesworth-Cooksey B. 1995. Adhesion of bact-

eria and diatoms to surfaces in the sea: a review. Aquat Microb

Ecol 9:87 – 96.

Coteur G, Gosselin P, Wantier P, Chambost-Manciet Y, Danis B,

Pernet Ph, Warnau M, Dubois P. 2003. Echinoderms as bio-

indicators, bioassays, and impact assessment tools of sediment-

associated metals and PCBs in the North Sea. Arch Environ

Contam Toxicol 45:190 – 202.

Danis B, Wantier P, Flammang R, Dutrieux S, Dubois Ph,

Warnau M. 2004. Contaminant levels in sediments and

asteroids (Asterias rubens L., Echinodermata) from the Belgian

coast and Scheldt estuary: polychlorinated biphenyls and heavy

metals. Sci Tot Environ 333:149 – 165.

De Mesel I, Derycke S, Moens T, Van der Gucht K, Vincx M,

Swings J. 2004. Top-down impact of bacterivorous nematodes

on the bacterial community structure: a microcosm study.


Ehrlich HL. 1997. Microbes and metals. Appl Microbiol

Biotechnol 48:687 – 692.

Epstein SS, Rossel J. 1995. Enumeration of sandy sediment

bacteria: search for optimal protocol. Mar Ecol Prog Ser 117:

289 – 298.


Dow

nloa

ded

by [

Uni

vers

ity o

f H

aifa

Lib

rary

] at

01:

35 0

1 A

ugus

t 201

3

Fabiano M, Danovaro R, Magi E, Mazzucotelli A. 1994. Effects of

heavy metals on benthic bacteria in coastal marine sediments: a

field result. Mar Pollut Bull 28:18 – 23.

Felsenstein J. 2002. Phylip, phylogeny inference package, version

3.6a3, July 2002. University of Washington, Seattle: Depart-

ment of Genome Sciences.

Feris K, Ramsey P, Frazar C, Moore JN, Gannon JE, Holben WE.

2003. Differences in hyporheic-zone microbial community

structure along a heavy-metal contamination gradient. Appl


Ford T, Ryan D. 1995. Toxic metals in aquatic ecosystems: a

microbiological perspective. Environ Health Perspect 103

(Suppl.1):25 – 28.

Frostegard A, Tunlid A, Baath E. 1993. Phospholipid fatty acid

composition, biomass, and activity of microbial communities

from two soil types experimentally exposed to different heavy

metals. Appl Environ Microbiol 59:3605 – 3617.

Gadd GM. 2004. Microbial influence on metal mobility and

application for bioremediation. Geoderma 122:109 – 119.

Gillan DC, Danis B, Pernet P, Joly G, Dubois P. 2005. Structure

of sediment-associated microbial communities along a heavy-

metal contamination gradient in the marine environment. Appl


Green N, Bjerkeng B, Hylland K, Ruus A, Rygg B. 2003. Hazardous

substances in the European marine environment: trends in

metals and persistent organic pollutants. Topic report 2/2003.

Copenhagen, Denmark: European Environment Agency.

Harrison JJ, Ceri H, Stremick CA, Turner RJ. 2004. Biofilm sus-

ceptibility to metal toxicity. Environ Microbiol 6:1220 – 1227.

Heggie D, Klinkhammer G, Cullen D. 1987. Manganese and

copper fluxes from continental margin sediments. Geochim

Cosmochim Acta 51:1059 – 1070.

Huber T, Faulkner G, Hugenholtz P. 2004. Bellerophon; a pro-

gram to detect chimeric sequences in multiple sequence align-

ments. Bioinformatics 20:2317 – 2319.

Ishii K, Mußmann M, MacGregor B, Amann R. 2004. An im-

proved fluorescence in situ hybridization protocol for the

identification of bacteria and archaea in marine sediments.

FEMS Microbiol Ecol 50:203 – 212.

Kirchman D, Sigda J, Kapuscinski R, Mitchell R. 1982. Statistical

analysis of the direct count method for enumerating bacteria.

Appl Environ Microbiol 44:376 – 382.

Knight BP, McGrath SP, Chaudri AM. 1997. Biomass carbon

measurements and substrate utilization patterns of microbial

populations from soils amended with cadmium, copper, or zinc.


Llobet-Brossa E, Rossello-Mora R, Amann R. 1998. Microbial

community composition of Wadden Sea sediments as revealed

by fluorescence in situ hybridization. Appl Environ Microbiol

64:2691 – 2696.

Madden TL, Tatusov RL, Zhang J. 1996. Applications of network

BLAST server. Methods Enzymol 266:131 – 141.

Manz W, Eisenbrecher M, Neu TR, Szewzyk U. 1998. Abundance

and spatial organization of Gram negative sulfate-reducing

bacteria in activated sludge investigated by in situ probing with

specific 16S rRNA targeted oligonucleotides. FEMS Microbiol

Ecol 25:43 – 61.

Manz W, Amann R, Ludwig W, Wagner M, Schleifer KH. 1992.

Phylogenetic oligodeoxynucleotide probes for the major sub-

classes of Proteobacteria: problems and solutions. System Appl

Microbiol 15:593 – 600.

Manz W, Amann R, Ludwig W, Vancanneyt M, Schleifer KH.

1996. Application of a suite of 16S rRNA-specific oligonucleo-

tide probes designed to investigate bacteria of the phylum

Cytophaga-Flavobacter-Bacteroides in the natural environment.

Microbiology 142:1097 – 1106.

McGrath SP, Chaudri AM, Giller KE. 1995. Long-term effects of

land application of sewage sludge: soils, microorganisms and

plants. J Ind Microbiol 14:94 – 104.

Meyer-Reil LA, Dawson R, Liebezeit G, Tiedge H. 1978.

Fluctuations and interactions of bacterial activity in sandy

beach sediments and overlying waters. Mar Biol 48:161 – 171.

Montagna PA. 1982. Sampling design and enumeration statistics

for bacteria extracted from marine sediments. Appl Environ

Microbiol 43:1366 – 1372.

Mußmann M, Ishii K, Rabus R, Amann R. 2005. Diversity and

vertical distribution of cultured and uncultured Deltaproteobac-

teria in an intertidal mud flat of the Wadden Sea. Environ

Microbiol 7:405 – 418.

Powell SA, Bowman JP, Snape I, Stark JS. 2003. Microbial

community variation in pristine and polluted nearshore

Antarctic sediments. FEMS Microbiol Ecol 45:135 – 145.

Rambaut A. 1996. Se-Al: Sequence Alignment Editor. [Online].

http://evolve.zoo.ox.ac.uk/.

Ravenschlag K, Sahm K, Amann R. 2001. Quantitative molecular

analysis of the microbial community in marine Arctic sediments

(Svalbard). Appl Environ Microbiol 67:387 – 395.

Ravenschlag K, Sahm K, Pernthaler J, Amann R. 1999. High

bacterial diversity in permanently cold marine sediments. Appl


Ravenschlag K, Sahm K, Knoblauch C, Jørgensen BB, Amann R.

2000. Community structure, cellular rRNA content, and

activity of sulfate-reducing bacteria in marine Arctic sediments.


Rivera-Duarte I, Flegal AR. 1994. Benthic lead fluxes in San

Francisco Bay, California, USA. Geochim Cosmochim Acta

58:3307 – 3313.

Schauer M, Balague V, Pedros-Alio C, Massana R. 2003. Seasonal

changes in the taxonomic composition of bacterioplankton in a

coastal oligotrophic system. Aquat Microb Ecol 31:163 – 174.

Shi W, Becker J, Bischoff M, Turco RF, Konopka AE. 2002.

Association of microbial community composition and activity

with lead, chromium, and hydrocarbon contamination. Appl


Snape I, Scouller RC, Stark SC, Stark J, Riddle MJ, Gore DB.

2004. Characterization of the dilute HCl extraction method for

the identification of metal contamination in Antarctic marine

sediments. Chemosphere 57:491 – 504.

Spark KM, Johnson BB, Wells JD. 1995. Characterizing heavy-

metal adsorption on oxides and oxyhydroxides. Eur J Soil Sci

46:621 – 631.

Sutherland RA. 2002. Comparison between non-residual Al,

Co, Cu, Fe, Mn, Ni, Pb and Zn released by a three-step

sequential extraction procedure and a dilute hydrochloric acid

leach for soil and road deposited sediment. Appl Geochem 17:

353 – 365.

Van Hannen EJ, Veninga M, Bloem J, Gons HJ, Laanbroek HJ.

1999. Genetic changes in the bacterial community structure

associated with protistan grazers. Arch Hydrobiol 145:25 – 38.

Wallner G, Amann R, Beisker W. 1993. Optimizing fluorescent in

situ-hybridization with rRNA-targeted oligonucleotide probes

for flow cytometric identification of microorganisms. Cytome-

try 14:136 – 143.

Weinbauer MG, Rassoulzadegan F. 2004. Are viruses driving

microbial diversification and diversity? Environ Microbiol

6:1 – 11.

Welch SA, Vandevivere P. 1994. Effect of microbial and other

naturally occurring polymers on mineral dissolution. Geomi-

crobiol J 12:227 – 238.

Zeng R, Zhao J, Zhang R, Lin N. 2005. Bacterial community in

sediment from the Western Pacific Warm Pool and its relation-

ship to environment. China Environ Sci 48:282 – 290.


Dow

nloa

ded

by [

Uni

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ity o

f H

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rary

] at

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adherent bacteria in heavy metal contaminated marine sediments

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