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Antonie van LeeuwenhoekJournal of Microbiology ISSN 0003-6072Volume 99Number 2 Antonie van Leeuwenhoek(2010) 99:341-354DOI 10.1007/s10482-010-9499-0

Bacterial communities reflect the spatialvariation in pollutant levels in Brazilianmangrove sediment

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ORIGINAL PAPER

Bacterial communities reflect the spatial variationin pollutant levels in Brazilian mangrove sediment

R. Peixoto • G. M. Chaer • F. L. Carmo •

F. V. Araujo • J. E. Paes • A. Volpon •

G. A. Santiago • A. S. Rosado

Received: 9 May 2010 / Accepted: 9 August 2010 / Published online: 29 August 2010

� Springer Science+Business Media B.V. 2010

Abstract The majority of oil from oceanic oil spills

converges on coastal ecosystems such as mangrove

forests. A major challenge to mangrove bioremedi-

ation is defining the mangrove’s pollution levels and

measuring its recuperation from pollution. Bioindi-

cators can provide a welcome tool for defining such

recovery. To determine if the microbial profiles

reflected variation in the pollutants, samples from

different locations within a single mangrove with a

history of exposure to oil were chemically character-

ised, and the microbial populations were evaluated by

a comprehensive range of conventional and molec-

ular methods. Multivariate ordination of denaturing

gradient gel electrophoresis (DGGE) microbial com-

munity fingerprints revealed a pronounced separation

between the sediment and rhizosphere samples for all

analysed bacterial communities (Bacteria, Betapro-

teobacteria, Alphaproteobacteria, Actinobacteria and

Pseudomonas). A Mantel test revealed significant

relationships between the sediment chemical fertility

and oil-derived pollutants, most of the bacterial

community fingerprints from sediment samples, and

the counts by different cultivation strategies. The

level of total petroleum hydrocarbons was signifi-

cantly associated with the Bacteria and Betaproteo-

bacteria fingerprints, whereas anthracene and the

total level of polycyclic aromatic hydrocarbons were

associated with the Actinobacteria. These results

show that microbial communities from the studied

mangrove reflect the spatial variation of the chemi-

cals in the sediment, demonstrating the specific

influences of oil-derived pollutants.

Keywords Mangrove � Microbial diversity � Oil �Bioindicators � NMS

Introduction

Mangroves grow in inter-tidal estuarine wetlands

along the coastlines of tropical and sub-tropical

regions; they are productive ecosystems (Kathiresan

and Binghan 2001) that provide various natural

functions of great ecological and economic

Electronic supplementary material The online version ofthis article (doi:10.1007/s10482-010-9499-0) containssupplementary material, which is available to authorized users.

R. Peixoto (&) � F. L. Carmo � A. S. Rosado

Laboratorio de Ecologia Microbiana Molecular, Instituto

de Microbiologia Paulo de Goes, Universidade Federal do

Rio de Janeiro, Rio de Janeiro 21941902, Brazil

e-mail: raquelpeixoto@micro.ufrj.br

G. M. Chaer

Embrapa Agrobiologia, Seropedica, Rio de Janeiro, Brazil

F. V. Araujo

FFP-Faculdade de Formacao de Professores –

Universidade Estadual do Rio de Janeiro, Rio de Janeiro

24435000, Brazil

J. E. Paes � A. Volpon � G. A. Santiago

Petrobas, Rio de Janeiro 21941915, Brazil

123

Antonie van Leeuwenhoek (2011) 99:341–354

DOI 10.1007/s10482-010-9499-0

Author's personal copy

importance. Some of these functions include being an

important nursery for fish, crustaceans, molluscs,

reptiles and mammals, a habitat for bird nesting, a

site of carbon and nutrient accumulation, a location of

marine biomass renovation and a buffer that offers

protection against coastal erosion (Alongi 2002).

Generally, these unique environments are situated

in areas of high anthropogenic influence, and they are

often exposed to pollutants, such as those released by

oil spills (Burns et al. 1993; Li et al. 2007; Barbier et al.

2008). Thus, mangroves are at risk for extinction (Lee

et al. 2006; Duke et al. 2007), indicating an urgent need

to conserve, protect and restore these ecosystems

(Duke et al. 2007; Barbier et al. 2008). When properly

understood and managed, microorganisms provide a

wide range of ecosystem services, such as bioremedi-

ation, and are a promising alternative for the recovery

of impacted mangroves (Duke et al. 2000; Burns et al.

2000; Xu et al. 2003, 2005; Yun et al. 2008).

A major challenge for mangrove remediation is

defining the pollution levels in a mangrove and

measuring its recuperation. Typically, the chemical

parameters of the pollution levels, such as the level of

polycyclic aromatic hydrocarbons (PAH), are used to

determine the level of pollution; however, field

measurements of these parameters are extremely

variable (Santos et al. 2010a). Therefore, meaningful

mangrove monitoring strategies must be developed.

The microbial community structure likely serves as

an early indicator of ecosystem degradation or

improvement (Peixoto et al. 2006; Aboim et al.

2008; Santos et al. 2010b) because the soil/sediment

quality is strongly influenced by microbe-mediated

processes and can also have a strong influence on

microbial function and diversity.

Todos os Santos Bay is the largest Brazilian bay; it

is located in Bahia (BA) State (coordinates between

12�39’40’’S-13�S and 38�30’W-38�43’30’’W) and is

the second largest bay in the world. This bay is

affected by the presence of a large metropolitan area

and industrial activity, including an oil refinery

(Amado-Filho et al. 2008). The growth of urban

and industrial areas is responsible for the loss of

mangroves and other coastal wetlands due to the

impact of chemical pollutants, resulting in changes to

the hydrological and sedimentation regimes as well

as nutrient flow in these environments (Lee et al.

2006). Currently, only small regions with original

vegetation remain on Todos os Santos Bay; these are

in different stages of regeneration (Amado-Filho

et al. 2008).

Despite the economic and ecological importance of

the mangroves in Todos os Santos Bay, there are few

ecological reports about the impact of urbanisation on

this area (Venturini and Tommasi 2004; Martins et al.

2005; Amado-Filho et al. 2008). Furthermore, there

are no available studies on the microbial diversity of

Todos os Santos Bay mangroves that incorporate both

molecular and conventional methods, and there are

also no reports regarding the potential value of these

mangrove microbial communities for biotechnology

and bioremediation. Existing strategies for the mon-

itoring and recovery of this area are also poor.

We believe that different microbial population

profiles can indicate the impacts of different oil-

derived pollutants on mangroves. These microbial

markers could be used as indicators of environmental

changes in these ecosystems. The main goal of this

study was to determine whether there is a significant

correlation between mangrove microbial diversity

(using a comprehensive range of both dependent and

independent methods) and nutrients and oil-derived

pollutants and, additionally, to determine which

parameters influenced each bacterial group most

strongly. These results can be applied to identify

useful microbial groups for the biomonitoring of

petroleum-impacted mangroves.

Materials and methods

Sampling and physico-chemical analysis

of sediment

The studied mangrove is located on Todos os Santos

Bay (Bahia State, Brazil) at the coordinates described

in the supplementary data. This mangrove is mainly

colonised by specimens of Laguncularia racemosa

and presented some areas with exposed sediment

without plants. A nearby mangrove area, ‘‘Caboto,’’

was used as a reference and is considered a pristine

environment, exhibiting no areas without plant spec-

imens. The coordinates of this area are also available in

the supplementary data. Three transects perpendicular

to the coastline and situated adjacent to a petrochem-

ical plant were selected in the mangrove area with an

oil spill history (Fig. 1). Along each transect, three

points that represented variations in both height and

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vegetation density within the mangrove were chosen

for the collection of sediment and rhizosphere samples

in March of 2006. A total of nine points were sampled.

From each point, Laguncularia racemosa rhizosphere

samples and approximately 50 g of surface (0–5 cm)

sediment were collected in sterile plastic bags and

Falcon tubes, respectively, for microbial analysis.

Each sediment sample was composed of six sub-

samples collected near each corresponding rhizo-

sphere (within about 20 cm). Samples were stored at

–20�C. For PAH and THP measurement, 500 g of

collected sediment was transferred to a clean alumin-

ium container. For physico-chemical analysis, 500 g

of sediment was kept in a plastic bag and submitted to

an analysis of the sediment texture, pH, organic C and

macro- and micronutrients according to published

methodologies (Embrapa 1997). A global positioning

system (GPS) was used to determine the sampling

positions (see Table S1).

Sediment and rhizosphere samples from one

sampling point of the Caboto mangrove, which is

close to the Todos os Santos Bay but is located

farther (7 km) from the industrial plant, were col-

lected in triplicate and mixed to form a uniform

sample to serve as the outlier group in the molecular

and conventional analyses.

Sediment evaluation of total petroleum

hydrocarbon (TPH) and polycyclic aromatic

hydrocarbon (PAH) content

A 10-g aliquot of humid sediment (approximately 5 g

dry) from each sample was used for extraction with a

dichloromethane:acetone mixture (1:1) in a Soxhlet

extractor. Prior to the extraction, 100 ng of the

standard p-terfenil-d14 was added to the sample for

the aromatic fraction. The volume of the raw extract

was reduced in an evaporator via rotary flow of N2 to

yield a volume of 1 ml. Fraction separation was

accomplished by chromatography performed with

silica/alumina using a glass column. The aliphatic

hydrocarbon fraction (F1) (n-alkanes and isoprenoids)

was separated by elution with hexane. The fraction

containing PAH (F2) was eluted soon after the

addition of hexane:dichloromethane (1:1). The ali-

phatic fraction extract volume was reduced in a

rotating evaporator with a light flow of N2 and was

then adjusted to a volume of 1 ml. The internal

standard n-C24d was used for quantification. The US

EPA-8270D methodology (1998) was used for PAH

determination, with some modifications. The quanti-

fication was based on internal standardisation using a

mixture containing naphthalene-d8, acenaphthene-d10,

phenanthrene-d10, chrysene-d12 and perilene-d12.

The equipment was calibrated using nine solutions

(2, 5, 10, 20, 50, 100, 200, 400 and 1,000 ng/ml)

containing 16 PAH and internal standards in duplicate.

The PAH sum (16 compounds) and specific PAHs

(phenanthrene, naphthalene and anthracene) were

measured. For TPH determination, 200 ml of the

extract was collected from each fraction (aromatic and

aliphatic) into a single vial for injection into a gas

chromatography-mass spectrometry (GC–MS). TPH

quantification was accomplished by chromatography

of the gaseous phase with an ionisation detector. The

quantification limit for each PAH, when the extracted

dry sediment mass of approximately 5 g was consid-

ered, was approximately 4 ng/g.

Plating and counts

The sediment and rhizosphere samples were resus-

pended in 0.85% NaCl (1:10), shaken at 120 rpm for

60 min, and diluted from 10-1 to 10-7 in 0.85%

NaCl. The dilutions were inoculated on different

culture media (malt extract (Difco #211320), TSA

(Difco #236950) and marine agar (Difco #279110),

all from Difco, NJ, USA; King’s B prepared as

described by King et al. (1954); casein–starch

prepared as described by (Kuster and Williams

1964) and TY prepared as described by Holm and

Falsen (1967)). These media are semi-selective for

Sampling point 1

Composite sample C

Composite sample B

Composite sample A

Sea

Sampling point 2 Sampling point 3

Petrochemical Plant

Fig. 1 Sampling scheme showing the layout of the transects

in the mangrove adjacent to petrochemical plant

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different microbial groups, including Actinomycetes,

yeasts, heterotrophics, bacteria that are salt tolerant

and Pseudomonas. Samples were added to TY

medium both before and after the pasteurisation

process (80�C for 30 min). Pasteurisation of the

samples was performed to promote the selection of

spore-forming strains. The plates were incubated at

28�C, and the CFU counts were performed after 2 and

7 days. Colonies with different morphologies were

selected, isolated and then inoculated in solid Bush-

nell-Hass medium (BH medium [Difco #257820]

plus ultrapure agar; BBL, Difco, NJ, USA) with 1%

crude oil added to test the morphotype’s ability to

grow with oil as the only carbon source.

Hydrocarbon-degrading bacteria prepared in BH

medium were also analysed by the most probable

number (MPN) method. Microtiter plates were

prepared using 175 ll of BH medium and 100 ll of

each diluted sample per well in triplicate, followed by

the addition of 25 ll of oil. The growth results were

obtained by monitoring the last three dilutions that

presented positive growth and by searching in the

MPN table for a positive combination of tubes

according to the turbidity and degradation levels.

This value was corrected by the dilution factor and

expressed as MPN/100 ml. MPN analyses of anaer-

obic microorganisms were also performed, as

described by Drake et al. (1996).

We chose these media and strategies to include

many different bacterial groups, which is an impor-

tant aspect of obtaining a comprehensive picture of

the microbial diversity of this mangrove.

Total DNA extraction from environmental

samples

Samples of 0.5 g sediment and rhizosphere cell

pellets (obtained as described by Bakken and Lindahl

1995) were subjected to a direct DNA extraction

method using the BIO 101 DNA extraction kit (Q

Biogene, Carlsbad, USA) according to the manufac-

turer’s instructions.

Clone libraries

All nine individual samples of sediment from the

mangrove with an oil spill history were mixed to

make one unique and uniform sample to indicate the

general microbial diversity of this environment.

Bacterial 16S rRNA genes were amplified using the

U968-L1401 set of primers (13). The PCR mixture

(25 ll) contained 10.2 mM Tris buffer, 2.3 mM

MgCl2, 50 mM KCl, 2% DMSO, 0.05 lg BSA,

0.2 mM of each dNTP, 0.2 lM of each primer, 0.5 U

of Taq DNA polymerase and 20 ng DNA. PCR was

performed in the PerkinElmer GeneAmp PCR Sys-

tem 9700 (PerkinElmer Applied Biosystems, Nieu-

werkerk a/d IJsel, the Netherlands) using the

following program: 95�C for 5 min; 35 cycles of

94�C for 1 min, 57.5�C for 30 s, 72�C for 4 min and

a final elongation step of 72�C for 7 min.

The PCR product was inserted into the pGEM-T

easy vector (Promega, Madison, USA) according to

the manufacturer’s protocol. Electro-competent

Escherichia coli DH5-a cells were transformed by

this vector, and 400 clones were selected randomly.

The clones were sequenced after performing a

Miniprep procedure. Sequencing reactions were per-

formed on the plasmid material according to the PE

ABI Prism protocol (Applied Biosystems, Foster

City, USA), using primer 1401 to avoid vector

contamination. Sequence runs were performed on a

MEGABACE DNA sequencer (Applied Biosystems,

Foster City, USA).

All chromatograms were analysed for sequence

quality using Bioedit. Sequences containing ambigu-

ities were not analysed further. Sequences were

screened for vector contamination using NCBI’s

VecScreen (http://www.ncbi.nlm.nih.gov), and con-

taminated sequences were eliminated. The remaining

sequences were then classified using BlastN (http://

blast.ncbi.nlm.nih.gov/Blast.cgi).

PCR and denaturing gradient gel electrophoresis

(DGGE)

PCR for DGGE evaluation was performed with a

universal set of bacterial primers, U968f-GC1-L1401,

as previously described by Heuer and Smalla (1997),

and group-specific primers described by Costa and

colleagues (2006). DNA from each sample was

quantified using the Qubit fluorometer (Invitrogen)

and the same amount of DNA (20 ng) was added to

each PCR mixture.

DGGE of the amplified gene sequences was

performed using a DCode System (universal mutation

detection system; Bio-Rad). The GC-clamped ampli-

cons were applied to a polyacrylamide gel containing

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6% acrylamide with a gradient of 45–70% denaturant

(urea and formamide). All gels were loaded with

DNA markers in the first and last lanes, surrounding

the lanes with samples, to allow gel standardisation

according to the manufacturer’s instructions. The run

was performed in 1X Tris–acetate-EDTA buffer at

60�C at a constant voltage of 70 V for 17 h. The

DGGE gels were stained with Sybr Green and

visualised using a Storm 860 Imaging System (GE

Healthcare). The dendrograms were constructed after

the image capture and analysis by Pearson’s corre-

lation coefficients (r) and cluster analysis was

performed by the unweighted pair group method

with average linkages (UPGMA) using the BioNum-

erics software (Applied Maths, Ghent, Belgium).

Each band was identified and its intensity was

measured. This band intensity was then expressed

as a proportion of the total intensity of all bands

comprising a particular community profile.

Statistical analyses

Multivariate analyses were performed using the PC-

ORD statistical package V5 (B. McCune and M.J.

Mefford, PC-ORD for Windows: multivariate analy-

sis of ecological data, MjM Software, Gleneden

Beach, OR). A Mantel test (Douglas and Endler

1982), which evaluates the null hypothesis of no

relationship between two distance matrices, was used

to evaluate the association between the matrix of the

sediment chemical properties and the matrix of the

CFU counts from the different cultivation media. The

same strategy was used to evaluate the association

between the DGGE fingerprints from the sediment

and rhizosphere samples and the matrices of the

chemical properties and the CFU counts. All Mantel

analyses were run using Mantel’s asymptotic approx-

imation method and the Sørensen distance (Bray and

Curtis 1957).

The matrices were generated by Bionumerics

software (Applied Maths) according to the manufac-

turer’s instructions using bands with a quantitative

value (surface). Then, the matrices of the sediment

chemical properties and of the microbial community

fingerprints that were obtained by the DGGE analyses

of the sediment and rhizosphere samples were

ordinated using non-metric multidimensional scaling

(NMS) analysis (Kruskal 1964; Mather 1976) run

with the Sørensen distance matrices. NMS was

performed using a random starting configuration,

and the data matrix was analysed using 50 runs with

real data and compared to a Monte Carlo test with 50

runs on randomised data. The NMS solution was

constrained to two dimensions to simplify the data

analysis (stability criterion = 0.00001; iterations to

evaluate stability = 15; maximum number of itera-

tions = 250). The stability of the reduced-dimen-

sional ordination pattern was assessed by plotting the

values of stress versus the iteration number. For

ordinations, a secondary matrix was used to overlay

the major gradients of oil-derived pollutants, thus

allowing the direct assessment of the relationship

between each of these environmental variables and

the variations in the microbial profiles. All resulting

bi-dimensional ordinations were graphically dis-

played after being rotated to maximise the correlation

of the organic pollutants with axis 1.

Richness and diversity indices were calculated

based on the DGGE results using PC-ORD. Richness

(R) was defined as the total number of bands detected in

the DGGE for a given sample. The Shannon index (H’)

(Shannon and Weaver 1949) was calculated according

to the following equation: H’ = - RP log P, where Pis the proportion represented by each DGGE band

relative to the total number of bands. These indices

were also overlaid in the NMS ordinations.

Results

Physico-chemical characterisation of sediment

samples

The sediment texture of both the studied mangrove

and the reference mangrove was classified as silty

clay loam. Different concentrations of nutrients and

oil-derived chemical pollutants were found in the

nine transects and in the reference mangrove sedi-

ment sampling points (Table S1). The bi-dimensional

NMS ordination showed that the samples were

grouped according to their chemical properties

(Fig. 2). Ordination axis 1 (80% of the total data

variance) formed three groups, from left to right: (1)

Caboto mangrove samples, (2) transect 2 (S2A, S2B

and S2C) samples, and (3) transect 1 (S1A, S1B and

S1C) and 3 (S3A, S3B and S3C) samples. Transects 1

and 3 were characterised by higher levels of P, Zn

and most of the oil-derived pollutants evaluated. In

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contrast, the Caboto and transect 2 samples had lower

levels of these chemicals, as well as higher levels of

Cu and Mn. Transect 1 differed from transect 3 along

axis 2 (16% of the total variance) because transect 1

had lower levels of Na and higher levels of THP,

organic C, total N and P.

Plating and CFU counts

The CFU counts varied in accordance with the

culture medium, ranging from 106 to 108 cells/g of

sediment or rhizosphere (Fig. 3a, b). The highest

counts, approximately 105–106 cells/g, were obtained

in marine agar media and in the MPN of anaerobic

microorganisms from uniform sediment samples. The

lowest counts were detected in the TY medium that

was inoculated with samples after the pasteurisation

process and in the King’s B medium, which is semi-

selective for Pseudomonas spp. A Mantel test anal-

ysis revealed a significant association between the

distance matrix based on the CFU counts in the nine

culture strategies and the distance matrix based on the

sediment chemical properties, as shown in Table 1

(r = 0.32, P \ 0.01).

To determine the biotechnological potential of the

obtained isolates, a total of 364 different morpholog-

ical isolates (47 from King’s B, 36 from TY, 49 from

amido caseine, 60 from malt extract, 73 from marine

agar, and 99 from TSA) were tested for their

S cab

S1AS1B

S1C

S2A

S2B

S2C

S3A

S3B

S3C

TPH

16 PAH

Phe

Ant

Nap

P

C org

Zn

Cu

Mn

N

Na

Axis 1 (80%)

Axi

s 2

(16%

)

Sampling Point 2

2A, 2B, 2C

Sampling Point 1

1A, 1B, 1C

Sampling Point 3

3A, 3B, 3C

Fig. 2 NMS ordination of the sediment samples from the

Todos os Santos Bay mangroves in the chemical properties

space. The studied mangrove is represented by nine sampling

points that were collected on three transects perpendicular to

the coastline (1A, 1B and 1C; 2A, 2B and 2C; 3A, 3B and 3C),

and a reference mangrove is represented by ‘‘cab’’. The

fraction of the total variance that was accounted for by each

axis is indicated in parentheses. Statistically significant

gradients of soil chemical properties are overlaid (correlation

coefficient with axes 1 or 2 [ 0.63). TPH total petroleum

hydrocarbon, 16 PAH petroleum aromatic hydrocarbon, Phephenanthrene, Ant anthracene, Nap naphthalene, P available

phosphorous, N total nitrogen, C org organic carbon, Zn zinc,

Cu copper, Mn manganese

0 2 4 6 8

cells/g

0 2 4 6 8

cells/g

A

B

Fig. 3 CFU counts using different culture media (King’s B,

TY [samples were plated before and after the pasteurisation

process], malt extract, casein starch, TSA and marine agar) and

MPN results used to evaluate the microbial rates in individual

rhizosphere samples (a) or the composed (Comp) sediment

samples (b) of the studied and reference mangroves in Todos

os Santos Bay

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oil-degrading capabilities. A total of 70% of these

isolates could grow with oil as the only carbon source

(Table 2), demonstrating the great biotechnological

potential of these microorganisms.

16S rRNA Clone libraries

The Proteobacteria phylum was found to be the

dominant bacterial group in the studied mangrove,

accounting for 53% of the total clones analysed,

followed by uncultured bacteria, which accounted for

20%. Representatives of Bacterioidetes, Chloroflexi,

Actinobacteria, Firmicutes, Gemmatimonadetes,

Planctomycetes, Spirochaetes, Verrucomicrobia, Aci-

dobacteria and Deinococcus were also detected

(Fig. 4a) (accession numbers CQ 482983-CQ 483296).

Among the Proteobacteria representatives, the predom-

inant group observed was Gammaproteobacteria (60%

of the total Proteobacteria), followed by Deltaprote-

obacteria (24%), Betaproteobacteria (9%) and Alpha-

proteobacteria (7%) (Fig. 4b).

Denaturing gradient gel electrophoresis

DGGE analysis was performed to evaluate the

fingerprints of the total bacterial communities (Bac-

teria) and of three specific bacterial groups, Pseudo-

monas, Betaproteobacteria and Alphaproteobacteria,

which represent the dominant Proteobacteria phylum

detected by the clone library in the Todos os Santos

mangrove, and Actinobacteria, a non-dominant group

of the clone library. DGGE gels revealed a high

diversity of bands in the rhizosphere and sediment

samples (Fig. 5a–e).

NMS ordinations of the DGGE data revealed a

clear distinction between the microbial community

compositions from the sediment and rhizosphere

samples, which were generally clustered into two

distinct groups (Fig. 6a–e). In addition to differences

in composition, the overlay of the richness (R) and

Shannon (H’) diversity indices indicated a higher

bacterial diversity of Alphaproteobacteria, Betapro-

teobacteria and Pseudomonas in sediment as com-

pared to rhizosphere (Fig. 6b–d). The ordinations also

revealed a distinct microbial community composition

in the Caboto mangrove, especially for Bacteria and

Actinobacteria in the sediment samples and Bacteria,

Pseudomonas, Alpha- and Betaproteobacteria in the

rhizosphere samples. Interestingly, the DGGE-based

diversity indices also suggested a lower bacterial

diversity in both the sediment and rhizosphere sam-

ples from the Caboto mangrove than was found in the

samples from the three transects (Fig. 6a).

Overlays of oil-derived pollutants in NMS ordina-

tions suggested that some compounds had a significant

Table 1 Standardized Mantel statistic of the relationship between the DGGE fingerprints and the CFU counts, and between DGGE

fingerprints and sediment inorganic chemical properties and oil derived pollutants

DGGE profile CFU counts Chemical properties Oil derived pollutants

Sediment Rhizosphere Sediment Rhizosphere

Bacteria 0.46*** 0.60* 0.79*** 0.50* 0.59*

Alphaproteobacteria ns ns 0.50** 0.49** ns

Betaproteobacteria 0.25** 0.29? 0.69*** 0.46** ns

Pseudomonas ns 0.57*** ns 0.45* 0.27?

Actinobacteria ns 0.57** ns 0.37? ns

*** P \ 0.001; ** P \ 0.01; * P \ 0.05; ? P \ 0.10; ns non-significant

Table 2 Percentage of oil degrading bacteria from Todos os

Santos Bay mangrove

Medium Number of

isolated

morphotypes

Number of oil

degrading

isolates

Percentage

of oil

degrading

King’s B 47 45 95.7

TY 36 14 38.8

Amido caseine 49 32 653

Malt extract 60 38 63.3

Marine agar 73 45 61.6

TSA 99 80 80.8

Total 364 254 69.8

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influence on the compositions of the bacterial groups

(Fig. 6a–c, e). For instance, TPH was positively

associated with axis 1 in the Bacteria and Betaprote-

obacteria fingerprints, reflecting the higher TPH levels

found in transect 1 samples and the lower levels found

in transect 3 and Caboto samples (Fig. 2). Similar

associations were found between phenanthrene and

Alphaproteobacteria composition and between anthra-

cene and total PAH and Actinobacteria composition.

A Mantel test was applied to verify the correla-

tions of the bacterial profiles in the sediment and

rhizosphere samples with each inorganic chemical

property and each oil-derived pollutant matrix. The

results revealed a significant relationship between

most of the bacterial community fingerprints from the

sediment samples and both the sediment chemical

fertility and the oil-derived pollutants (Table 1).

A Mantel test also showed a significant relationship

Fig. 4 a Representation of

the bacterial diversity

detected by a clone library

of 16S rRNA gene

fragments from sediment

samples of the studied

mangrove at Todos os

Santos Bay and b the

distribution of the sub-

groups of the proteobacteria

representatives of the 16S

rRNA clone library from

sediment samples of the

studied mangrove at Todos

os Santos Bay

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Fig. 5 DGGE fingerprints and sampling site comparison of

16S rRNA gene fragments amplified from sediment and

rhizosphere samples from the studied Todos os Santos Bay

mangrove for Bacteria (a), Alphaproteobacteria (b), Betapro-teobacteria (c), Pseudomonas (d) and Actinobacteria(e) groups. The studied mangrove is represented by nine

sampling points (1A, 1B, 1C, 2A, 2B, 2C, 3A, 3B and 3C), and

the reference mangrove is represented by ‘‘cab’’. ‘‘S’’ and ‘‘R’’

denote sediment and rhizosphere, respectively. Dendrograms

were generated by Bionumerics/Gel compare software cluster

analysis using Pearson’s coefficient index and UPGMA

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R cab

R1A

R1B

R1CR2A

R2B

R2C

R3AR3B

R3C

S cab

S1A

S1BS1C

S2AS2B

S2CS3A

S3B

S3C

R H

TPH

Axis 1 (44%)

Axi

s 2

(35%

)

R cab

R1A

R1B

R1C

R2AR2B

R2C

R3AR3BR3C

S cab

S1A

S1B

S1C

S2A

S2BS2C

S3AS3B

S3CRH

Phe

Axis 1 (36%)

Axi

s 2

(37%

)

R cab

R1A

R1BR1C

R2A

R2B

R2C

R3A

R3B

R3C

S cab

S1A

S1B

S1C

S2A

S2B

S2CS3A

S3B

S3C

RH

TPH

Axis 1 (31%)

Axi

s 2

(48%

)

R cab

R1A

R1B

R1C

R2A

R2B

R2C

R3A

R3B

R3C

S cab

S1A

S1B

S1C

S2A

S2BS2C S3A

S3BS3C

R

H

Axis 1 (15%)

Axi

s 2

(34%

)

Bacteria Alphaproteobacteria

Betaproteobacteria Pseudomonas

R cab

R1A

R1B

R1C

R2A

R2B

R2C

R3A

R3B

R3C

S cab

S1A

S1BS1CS2A

S2B

S2C

S3A

S3B

S3C

16 PAH

Ant

Axis 1 (32%)

Axi

s 2

(24%

)

Actinobacteria

A B

C D

E

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between the Bacteria and Betaproteobacteria DGGE

profiles and the CFU count profile that was obtained

in nine different cultivation strategies (Table 1).

Discussion

In this study, the microbial diversity at different

points of a Brazilian mangrove was extensively

explored and related to pollutants and nutrient

distribution. As microorganisms play an essential

role in biogeochemical processes and overall man-

grove productivity, the maintenance and restoration

of the mangrove microbial communities should be

a priority, as should increasing our knowledge of

them (Holguin et al. 2001; Yun et al. 2008).

Cultivation-dependent and -independent methods of

analysing microorganisms have been proposed to

increase our understanding of environmental micro-

bial communities and their oil-degrading capacity,

allowing for the critical development of strategies for

bioremediation and the monitoring of these environ-

ments (Santos et al. 2010a).

Initially, we evaluated the cultivated diversity that

was recovered with the media used in this work. The

highest bacterial counts were obtained using marine

agar medium plates and anaerobic MPN estimation.

These results are most likely due to the higher salinity

of the marine agar media as compared to the other

culture media and as a result of the anaerobic

conditions of mangrove sediments. Even the upper

layers of mangrove sediments are primarily anaero-

bic, with only a thin superficial aerobic layer

(Holguin et al. 2001). The CFU count profile (matrix

of the CFU counts in the nine culture strategies) was

significantly correlated with the sediment chemical/

pollutant conditions. This correlation indicates that

the metabolic potential of mangrove microorgan-

isms varies as a function of the general chemical

environmental conditions in the mangrove sediment.

This result was expected and corroborates the

potential use of microbial populations in biomonitor-

ing approaches.

A high diversity of different microbial morpho-

logical isolates was recovered. To our knowledge,

there have been no previous reports regarding

mangrove-cultivated microbial diversity in which an

array of culture media was used and oil-degradation

potential was tested. We observed that 70% of the

364 isolated morphotypes were able to grow using oil

as the sole carbon source. A higher growth potential

was observed using King’s B medium, semi-selective

for Pseudomonas spp., in which 95% of the isolates

were oil-degrading bacteria. The TY medium pro-

vided the lowest percentage (38%) of degrading

bacteria; however, this also represents a reasonable

recovery level. Mangroves are areas of high organic

matter concentration and the highly diverse microbial

community found within these environments is

essential for the productivity of this ecosystem

(Holguin et al. 2001). This high diversity likely

results in a diversified metabolic potential that allows

the bacterial isolates to grow under different envi-

ronmental conditions.

The DGGE profiles indicated that the greatest

difference in Bacteria diversity was observed when

samples from the Todos os Santos transects were

compared to those from the reference Caboto man-

grove. Gomes et al. (2008) also evaluated microbial

community DGGE profiles from the sediments of

three different mangroves of Guanabara Bay, located

in the Rio de Janeiro State of Brazil. Their results

demonstrated that different mangroves exhibited

distinct profiles when specific or total bacterial

groups were analysed, whereas no differences were

found within replicate samples collected from the

same mangrove.

Generally, our analyses of the DGGE profiles also

demonstrated variations in the structure of the

detected bacterial community within the studied

mangrove that were clearly associated with nutrient

and pollutant gradients (Fig. 5a, Table 1). This result

suggests a dominant, well-adapted bacterial commu-

nity distributed within the studied mangrove that

varies with the chemical characteristics of the

Fig. 6 NMS ordination of the DGGE fingerprints of 16S rRNA

gene fragments amplified from sediment (‘‘S’’) and rhizosphere

(‘‘R’’) samples from a Todos os Santos Bay mangrove for

Bacteria (a), Pseudomonas (b), Actinobacteria (c), Betaprote-obacteria (d) and Alphaproteobacteria (e) groups. The studied

mangrove is represented by nine sampling points collected along

three transects perpendicular to the coastal line (1A, 1B and 1C;

2A, 2B and 2C; 3A, and 3B and 3C), and a reference mangrove is

represented by ‘‘cab’’. The fraction of the total variance

accounted for by each axis is indicated in parentheses. Major

gradients of oil-derived pollutants and DGGE diversity indices

are overlaid. TPH total petroleum hydrocarbon, PAH petroleum

aromatic hydrocarbon, Phe phenanthrene, Ant anthracene, Napnaphthalene, Ft phytane, R richness index, H’ Shannon index)

b

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sediment. For instance, Bacteria profiles in the

rhizosphere and sediment from transect 1 were most

influenced by the expressive presence of TPH,

followed by samples from transects 2 and 3, and

finally samples from the Caboto mangrove. This

sequence corresponds to differences in the average

levels of TPH found in the sediment from the transect

and Caboto sampling points.

A significant correlation was also observed

between the Bacteria DGGE profile and the CFU

count profile (Table 1), indicating a correspondence

between the genetic structure and the metabolic

potential of the mangrove microorganisms. For

specific bacterial groups, with the exception of

Betaproteobacteria, no significant correlations were

detected. This result indicates a high relative amount

of Betaproteobacteria among the cultivated isolates.

With the intention to detect the dominant and non-

dominant groups observed within the studied man-

grove, a clone library was constructed. Sequencing

the DNA of the recovered clones revealed a high

prevalence of the Proteobacteria phylum (57% of

analysed sequences), with Gammaproteobacteria

being the most abundant group, followed by Delta-

proteobacteria, Betaproteobacteria and Alphaprote-

obacteria. Liao and colleagues (2007) also found a

dominance of Proteobacteria 16S rDNA sequences

in a clone library analysis of a Taiwanese estuary.

The clone library also demonstrated that 20% of the

community was comprised of unclassified represen-

tatives, which was expected because any environ-

mental sample can provide a site for uncultivated

bacteria. Additionally, mangroves are highly produc-

tive ecosystems (Holguin et al. 2001) and are

potential sources of previously uncultivated bacteria

for biotechnological exploration.

Based on our clone library results, and to elucidate

whether the distributions of other specific bacterial

groups were similar to the total Bacteria distribution,

the fingerprint profiles of both dominant, and likely

well-adapted, groups in this environment (Proteobac-

teria phylum) and less abundant groups (Actinobac-

teria phylum) were analysed. NMS ordination of the

DGGE profiles indicated that in all bacterial group

profiles, the influence of the rhizosphere was stronger

than that of the geographical location of samples, as

microbial communities from the rhizosphere and

sediment formed distinct groups in most of the

situations evaluated (Fig. 4a–d). The exception was

the Actinobacteria profile, in which samples were

more randomly ordinated. Because Actinobacteria

was not classified as a dominant bacterial represen-

tative in this environment by our clone library

analyses, this group could be more sensitive to small

variations in different niches that were not evaluated.

Interestingly, the richness (R) and Shannon (H’)

indices did not differ between the rhizosphere and

sediment samples for Actinobacteria, but they did

differ among Alphaproteobacteria, Betaproteobacte-

ria and Pseudomonas (Fig. 4b–e).

The rhizosphere is the portion of the soil that

forms the complex habitat of plant roots, and its

composition can be altered by root activity. An

important component of the rhizosphere is the

actively growing microbial population that feeds on

the organic nutrients in the root exudates (Bashan and

Holguin 1997, 2002). We hypothesise that the higher

levels of microbial diversity of some of the analysed

bacterial groups in the sediment as compared to the

rhizosphere might be the result of a stress state of the

mangrove plants in transects of the mangrove with an

oil spill history. Therefore, these stressed plants

might not be contributing a high amount or diversity

of exudates and consequently might not enrich the

rhizosphere soil with nutrients. This hypothesis is

corroborated by the higher microbial diversity that is

observed in the Caboto mangrove rhizosphere sam-

ples when compared to transect rhizospheres.

The detected differences in microbial diversity,

especially between sediment samples, indicate that

even on a small scale, when compared to variations

with other mangrove sites, abiotic factors or pollutant

distributions can generate niche variations that influ-

ence bacterial profile clusters within the same man-

grove area. Mangroves are complex and heterogeneous

environments that are influenced by plant diversity,

plant abundance, tidal variations and sediment niches,

which are factors that encompass variations in pollu-

tants and nutrient levels. The determination of the

microbial heterogeneity within a mangrove is critical

because microorganisms can be used as early indica-

tors of environmental changes (Peixoto et al. 2006;

Aboim et al. 2008). The association of molecular tools

with statistical tools could provide such information, as

uncultured organisms comprise the majority of the

microbial world (Nocker et al. 2007; Roesch et al.

2007). For instance, phenanthrene was more associated

with the composition of the Alphaproteobacteria taxa,

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whereas 16 PAH and specific anthracene PAH were

more associated with the Actinobacteria group. These

data are important for guiding the choice of potential

groups for further bioremediation or for approaches to

monitoring each type of pollutant in mangrove

ecosystems.

The conservation of mangroves and other related

ecosystems depends on studies that determine to what

extent pollutants can affect the different forms of life in

these areas and on the definitions of groups that can be

used in monitoring programs. We believe that the

results presented in this study represent a small but

important step in this direction, confirming the hypoth-

esis that microbial diversity profiles can be used as

indicators of the pollutant gradient in mangroves and

also indicating the specific oil-derived compounds

(phenanthrene or total 16 PAH and anthracene) that

most influenced certain bacterial groups (Alphaprote-

obacteria and Actinobacteria, respectively).

Acknowledgments This study was supported by a grant from

Petrobras. A.S. Rosado and R.S. Peixoto also greatly appreciate

the support of CNPq and FAPERJ. The authors would like to

thank RG Taketani, S Paim, EA dos Reis and B Fonseca for

technical assistance.

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