Rapid Impact of Phenanthrene and Arsenic on Bacterial Community Structure and Activities in Sand Batches

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ENVIRONMENTAL MICROBIOLOGY

Rapid Impact of Phenanthrene and Arsenic on BacterialCommunity Structure and Activities in Sand Batches

A. Cébron & F. Arsène-Ploetze & P. Bauda & P. N. Bertin & P. Billard &

C. Carapito & S. Devin & F. Goulhen-Chollet & J. Poirel & C. Leyval

Received: 27 February 2013 /Accepted: 10 October 2013# Springer Science+Business Media New York 2013

Abstract The impact of both organic and inorganic pollutionon the structure of soil microbial communities is poorlydocumented. A short-time batch experiment (6 days) wasconducted to study the impact of both types of pollutants onthe taxonomic, metabolic and functional diversity of soilbacteria. For this purpose sand spiked with phenanthrene(500 mg kg!1 sand) or arsenic (arsenite 0.66 mM and arsenate12.5 mM) was supplemented with artificial root exudates andwas inoculated with bacteria originated from an aged PAH andheavy-metal-polluted soil. The bacterial community wascharacterised using bacterial strain isolation, TTGEfingerprinting and proteomics. Without pollutant, or withphenanthrene or arsenic, there were no significant differencesin the abundance of bacteria and the communities weredominated by Pseudomonas and Paenibacillus genera.

However, at the concentrations used, both phenanthrene orarsenic were toxic as shown by the decrease in mineralisationactivities. Using community-level physiological profiles(Biolog Ecoplates™) or differential proteomics, we observedthat the pollutants had an impact on the community physiology,in particular phenanthrene induced a general cellular stressresponse with changes in the central metabolism and membraneprotein synthesis. Real-time PCR quantification of functionalgenes and transcripts revealed that arsenic induced thetranscription of functional arsenic resistance and speciationgenes (arsB , ACR3 and aioA), while no transcription of PAH-degradation genes (PAH-dioxygenase and catechol-dioxygenase) was detected with phenanthrene. Altogether, inour tested conditions, pollutants do not have a major effect oncommunity abundance or taxonomic composition but ratherhave an impact on metabolic and functional bacterial properties.

Introduction

The dismantling of coal industries, steel factories, cokingplants and gas plants has resulted in large areas where the soilhas been multi-contaminated with heavy metals (HM) andorganic compounds. Problems with arsenic (As) andpolycyclic aromatic hydrocarbon (PAH) contamination inthe environment are a global concern due to the high toxicityto living organisms and worldwide distribution. Polycyclicaromatic hydrocarbons (generated from the incompletecombustion of organic matter) can be degraded in the soil bymicroorganisms [31]. Microorganisms have been shown tochange the speciation of arsenic through both oxidation andreduction, which may alter the distribution of this element inthe soil [47]. Numerous studies have reported the adverseeffects of these various contaminants on microbialcommunities [4, 65]. After decades of contamination,biological compartments have adapted to the pollutants by

Electronic supplementary material The online version of this article(doi:10.1007/s00248-013-0313-1) contains supplementary material,which is available to authorized users.

A. Cébron (*) : P. Billard : J. Poirel :C. LeyvalLIEC UMR7360, CNRS-Université de Lorraine, Faculté desSciences et Technologies, BP 70239, 54506 Vandoeuvre-lès-NancyCedex, Francee-mail: [email protected]

P. Bauda : S. DevinLIEC UMR7360, CNRS-Université de Lorraine, Campus Bridoux,rue du Général Delestraint, 57070 Metz, France

F. Arsène-Ploetze : P. N. Bertin : F. Goulhen-CholletGMGM, Département Microorganismes, Génomes, Environnement,CNRS UMR7156, Université de Strasbourg, 28 rue Goethe,67083 Strasbourg Cedex, France

C. CarapitoLaboratoire de Spectrométrie de Masse Bio-organique, InstitutPluridisciplinaire Hubert Curien, UMR7178 Université deStrasbourg/CNRS, 25 rue Becquerel, 67087 Strasbourg, France

Microb EcolDOI 10.1007/s00248-013-0313-1

Author's personal copy

http://dx.doi.org/10.1007/s00248-013-0313-1

selecting for contaminant-resistant microorganisms andefficient organic pollutant degraders. Turpeinen et al. [57]found that the proportion of arsenite-resistant bacteria wasdependant on the concentration of As in the soil. Similarly,Cébron et al. [14] demonstrated that PAH-ring hydroxylatingdioxygenase genes, which are specific to PAH-degradingbacteria, were detected at higher levels in aged PAH-contaminated soils and sediments from the steel industrialLorraine region (France) than in unexposed soils andsediments. However, the multi-contamination of soils oftenmasks the mechanisms of action of one pollutant on themicrobial community. One community may react differentlyto various pollutants. It is thus important to study the impact ofboth types of contaminants. In heavily polluted soils withaged- and multi-contamination (e.g. inorganic and organicpollutants), microbial communities are expected to resist thedifferent stresses and adapt to the pollution [53]. It is ofecological interest to determine which functional andmetabolic capabilities are selected for in a bacterialcommunity under drastic selective pressure by differentpollutants.

The structure and activity of bacterial community found insuch soil may depend on the presence of pollutants but also onthe composition of the soil. In particular, the presence of plantscan modify the microbial density, diversity and activity in thevicinity of the roots via carbon and nutrient input during rootexudation [51]. Positive effects of the rhizosphere have beenobserved on the fate of the organic pollutant, including anincrease in the PAH-degradation activity [49], functionalcatabolic diversity [64] and density of PAH degraders [15,55]. Aside from phytoextraction, plants and rhizosphericprocesses can also stimulate the microbial community inarsenic-contaminated soils. It has been reported that thecarbohydrates exuded by plant roots could play a crucial rolein arsenic metabolism in the rhizosphere [23]. Moreover,Xiong et al. [63] reported that arsenic-resistance andarsenate-reduction genes were more abundant in rhizosphericsamples than in bulk soils. Therefore, the rhizosphere canselect for microbes involved in phenanthrene degradationand the availability, transport, speciation and detoxificationof As.

The aim of this study was to determine how a bacterialcommunity originating from an aged PAH and heavy-metal-contaminated soil could react to pollutants found in theiroriginal soil. We hypothesized that organic and inorganicpollutant can shape differently the microbial community byselecting specific bacterial strains with degradation orresistance capacities. To test this hypothesis, the impact ofone organic pollutant, phenanthrene, and of one inorganiccompound, arsenic, on the bacterial community was analysedin controlled laboratory conditions. After extraction from theiroriginal polluted soil, microorganisms were incubated in sandbatches with root exudates, in the absence of pollutant, in the

presence of phenanthrene, or in the presence of mixture ofarsenite [As(III)] and arsenate [As(V)]. Only 6 days afterpollutant exposure start, the impact of these pollutants on thebacterial community structure and diversity, the microbialcommunity-level physiological profile, the abundance andexpression of functional genes involved in PAH degradationpathways and metalloid speciation or resistance and thebacterial proteome was evaluated.

Materials and Methods

Batch Experiment

A batch experiment was set up with sand that was spiked witheither artificial root exudates (ARE), ARE plus phenanthrene(ARE + PHE) or ARE plus arsenic (ARE + As). These threeconditions were performed in triplicate for the initial and finalincubation times. A control condition without a carbon sourcewas also performed in triplicate. Altogether, 21 flasks wereprepared. Sterile quartz sand (120 g, 2 mm sieved) was addedto 250-ml flasks. Because of its low solubility in water, aphenanthrene (PHE) solution in chloroform (30 ml of a 2-g/lsolution) was added to six flasks resulting in a finalconcentration of 500 mg PHE kg!1 sand. The same volumeof chloroform (30 ml) was added to the other flasks. Aftercomplete solvent evaporation under a fume hood, a solutioncontaining ARE and Bushnel Haas (BH) medium (18.72 ml,corresponding to 80 % of the water retention capacity of thesand) was added to the ARE andARE + PHE flasks. A solutionof ARE, BH medium and arsenic (18.72 ml) was added to theARE + As flasks. The same volume of BH medium was addedto the control flasks. The ARE solution was prepared accordingto Griffiths et al. [24]. The ARE contained fructose (20 mM),glucose (20 mM), sucrose (20 mM), succinate (10 mM), malate(10mM), arginine (5mM), serine (5mM) and cysteine (5mM).The quantity added to the flasks represented 1.16 g C kg!1 sand.The BH medium contained the following components (g l!1):MgSO4 (0.2), CaCl2 (0.02), KH2PO4 (1), K2HPO4 (1), KNO3

(1) and FeCl3 (0.05). The BH medium was supplemented withthe following trace elements (mg l!1 in distilled water):nitriloacetic acid (15), CaCl2·2H2O (15), MnCl2·2H2O (6),FeSO4·7H2O (6), Co(NO3)2·6H2O (1), ZnSO4 (1), CuSO4

(0.1), H3BO3 (0.1), Na2MoO4 (0.1) and Al(SO4)2·H2O (0.1).Arsenic was provided as a mixture of sodium arsenite(NaAsO2) and sodium arsenate (Na2HAsO4·7H2O) at finalconcentrations of 0.66 and 12.5 mM, respectively. The flaskswere inoculated with bacteria extracted from the Neuves-Maisons coking plant wasteland soil, which was colonised byspontaneous vegetation as described in Cébron et al. [15] andOuvrard et al. [40], and sampled in April 2010. At this time, theNeuves-Maisons soil characteristics were as follows: pH of 7.5,C/N ratio of 25.4 and pollutant concentrations of 1,205±295, 2,

A. Cébron et al.


279±182, 597±128, 1,077±151, 55.4±4.9 and 2.8±0.3 mg/kgsoil dry weight for the 16 US-EPA PAHs, zinc, lead, chromium,arsenic and cadmium, respectively. Soil bacteria were extractedvia a density gradient using Nycodenz (AbCys SA, Paris,France), as previously described by Lindhal and Bakken [36].Sixteen sub-samples of soil (10 g) were shaken for 1 h with20 ml of NaCl (0.85 %) and 5 g of glass beads (2 mm). Afterdecantation for 5 min, the soil suspension supernatant wasadded to a centrifuge tube. Sterile Nycodenz solution (10 mlwith a density of 1.3 g ml!1) was added to the bottom of thetube to create the higher density fraction. The tubes werecentrifuged for 1 h at 10,000!g . The bacterial halo was thenrecovered and rinsed twice with the NaCl solution (0.85 %).The 16 bacterial extractions were pooled together, and cellenumeration was performed using the Thoma countingchamber procedure. The sand in the 21 flasks was inoculatedwith these bacteria (185μl, approximately 1.5!105 bacteria g!1

of sand). The flasks for the initial analyses were harvested andsampled directly for PAHs and arsenic and cultivable bacteriaenumeration analyses were also performed on the initiallyharvested flasks. The other flasks were hermetically sealedand incubated for 6 days in the dark at 24 °C.

Mineralisation

Every day, 3 ml of headspace gas from the incubatedflasks was removed with a syringe, and the CO2

concentration was measured using a Binos 1004 infraredspectrophotometer, as previously described by Quantinet al. [44]. After the CO2 measurements, the flasks wereopened for 1 h under a fume hood for aeration, resealed andfurther incubated.

Phenanthrene Concentration

The sand samples (1 g w /w) were mixed with hexane (10 ml)in Oak Ridge Teflon centrifuge tubes (Nalgene, Rochester,NY, USA). After sealing, the tubes were shaken overnight ona rotary shaker and then centrifuged (10,000!g ). A 1-mlaliquot was evaporated under a nitrogen atmosphere anddissolved in acetonitrile for HPLC analyses, as described byLouvel et al. [37], using reverse-phase chromatography with aDionex HPLC system (Dionex pump GP40) equipped with aUV–vis detector and a reverse-phase C-18 column.

Arsenic Concentration

The sand samples (2 g w /w ) were mixed with 13.2 ml ofdeionised water (42-fold dilution of the sand solution). Thesuspensions were shaken for 1 h on a rotary shaker to recoveravailable (water-soluble) arsenic. After centrifugation (10,000!g ), the solutions were filtered (0.22 mm). Then, 1.5-mlaliquots were stored at 4 °C. Arsenate As(V) concentrations

were measured using ionic chromatography analyses, and10 ml of the filtered suspensions were acidified with nitricacid to measure the total arsenic concentrations viainductively coupled plasma spectrometry.

Enumeration of Cultivable Bacteria and Strain Isolation

The sand samples (1 g w /w) were mixed with 10 ml of salinesolution (NaCl 0.85 %). The sand suspensions were shakenfor 1 h on a rotary shaker and diluted 10-fold in saline solution(NaCl 0.85 %). The dilutions (100 μl) were spread out onnutrient broth (NB) agar plates and incubated for 5 days in thedark at 24 °C. The colonies were then enumerated. After6 days, five colonies (with different morphologicalcharacteristics) were further isolated on NB agar medium.Approximately 15 bacterial strains were identified percondition (PHE + ARE, ARE and ARE + As). The isolatedstrains were identified by amplifying the 16S rRNA genefragment using 968F/1401R primers [21]. The PCR productswere purified using the High Pure PCR Product PurificationKit (Roche Applied Science) and sequenced (Eurofins MWG-Operon). The bacterial identification was performed usingboth the RDP and GenBank (BlastN) databases. The partial16S rDNA sequences of the isolates were deposited in theGenBank database under the following accession numbers:JX861148-JX861194.

Metabolic Profiles

Biolog Ecoplate™ (Biolog Inc., Hayward, CA) analyses wereused to assess the community-level physiological profiling.The Ecoplate™ contains three replicate wells with 31 carbonsubstrates (see Fig. 3 legend for compound names):predominantly amines (n =2), amino acids (n =6),carbohydrates (n =10), carboxylic acids (n =7), phenoliccompounds (n =2) and polymers (n =4). Briefly, 5 g of eachfresh sand sample was homogenised in 45 ml of sterilephysiological water and shaken for 30 min at 150 rpm. Afterdecantation (2 h), 150 μl of the supernatant was used toinoculate each well in the plates. The plates were incubatedat 25 °C in the dark, and the OD590nm was recorded every 12 hfor 5 days using an Expert Microplate Reader (ASYS,Eugendorf, Austria). The data were normalised by theaverage well colour development, according to Weberet al. [61]. The kinetics of microbial activity were assessedaccording to the area under the absorbance versus timecurves (AUC) [25]. The AUC were obtained from platereadings after 120 h of incubation and were used for furtheranalysis. A principal components analysis (PCA, RDevelopment Core Team, 2008) was performed on thesesamples for the 31 carbon sources. The Shannon index (H !)and Simpson index (1/D ) were calculated to estimate thefunctional diversity.

Rapid impact of phenanthrene and arsenic on bacterial community


DNA and RNA Extractions

The sand samples (15 and 10 g w /w ) were mixed with 10 mland 5 nml of saline buffer (g l!1: Na2SO4·10H2O, 7.5;(NH4)2SO4, 22.5; KCl, 2.5; MgSO4·7H20, 25; KH2PO4, 2.5;Ca(NO3)2·4H2O, 0.7) for the DNA and RNA extractions. Thesamples were vortexed for 2 min, and the supernatants wererecovered. The sand samples were washed twice with 2.5 mlof saline buffer. The supernatants were pooled together andcentrifuged (30 min, 13,000 rpm, 4 °C), and the cell pellet wasresuspended in 200 μl of EDTA buffer (0.5 M, pH 8.0) and in100 μl of RNAprotect bacteria reagent (Qiagen) for furtherDNA and RNA extraction, respectively. Cell pellets werestored at !20 °C until nucleic acid extractions.

Genomic DNAwas extracted using the Fast DNA spin kitfor soil (MP biomedicals, Illkirch, France), according to themanufacturer instructions. The genomic DNA wasresuspended in 100 μl of DES buffer.

RNA was extracted using Fast RNA Pro Soil-Direct kit(MP biomedicals, Illkirch, France) according to manufacturerinstructions and resuspended in 100 μl DEPC-treated water.The RNA samples were treated with RNase-Free DNase I(7 U, 10 min; Qiagen), then the columns RNeasy Mini Kit(Qiagen) were used to eliminate the DNase. First-strandcDNA was synthesized from 400 ng RNA with SuperscriptIII First-Strand Synthesis System kit (Invitrogen) according tomanufacturer instructions.

Genomic DNA and RNA concentrations were quantifiedby measuring the absorbance at 260 and 280 nm usingspectrofluorimetry (Shimadzu) equipped with a specificadaptor (TrayCell).

Bacterial Community Structure

Bacterial 16S rRNA gene fragments were amplified using auniversal primer set, as described by Felske et al. [21]. Theprimer sequences are as follows: 968F (5!-GAA CGC GAAGAA CCT TAC-3!) and 1401R (5!-CGG TGT GTA CAAGAC CC-3!) with an additional GC clamp on the forwardprimer. PCR was performed using an iCycler (Bio-Rad) in a50-μl volume with Taq DNA polymerase (Invitrogen). Atouchdown temperature profile was chosen according toCébron et al. [15]. The PCR products were loaded onto atemporal thermal gradient gel electrophoresis (TTGE) DCodesystem (Bio-Rad). TTGE was performed as previouslydescribed in Cébron et al. [15] (7 M urea, 6 % acrylamide/bisacrylamide, run at 110 V with a temperature gradient from57 to 67 °C and an increment of 2 °C h!1). After migration, thegels were stained with SYBRGold (1/10,000 final, MolecularProbes) and analysed on a GelDoc transilluminator (Bio-Rad).QuantityOne 4.0.1 software (Bio-Rad) was used for imageand band pattern analysis. A matrix of the relative abundanceof each band in the samples was then generated and used first

for principal component analysis based on Pearson correlationmatrix using XLStat 2011 software and second to calculateShannon diversity indexes H !.

Distinct bands were excised from the TTGE gel, dissolvedovernight in 30 μl of distilled water at 4 °C, amplified with thesame primers and analysed a second time using TTGE. Theexcision was repeated until a single band was obtained. ThePCR products were purified using the High Pure PCR ProductPurification Kit (Roche Applied Science) and sequenced(Eurofins MWG-Operon). The partial 16S rDNA sequencesof the TTGE bands were deposited in the GenBank databaseunder the following accession numbers: JX861133-JX861147. The phylogenetic affiliation and closest sequenceswere obtained from the RDP and GenBank (BlastN)databases.

Quantification of 16S rRNA and the Functional Genesand Transcripts

Real-time PCR quantification of the genes and transcripts wasperformed according to the procedure described in Cébronet al. [14] and Poirel et al. [43]. The bacterial genes encoding16S rRNA, PAH-ring hydroxylating dioxygenases fromGram-negative and Gram-positive bacteria, catechol-1,2-and 2,3-dioxygenase, ArsB andACR3 transmembrane arsenicefflux pumps and the AioA subunit of the arsenite oxidasewere quantified using the primers 968F/1401R [21], PAH-RHDα GN and GP [14], CATA-F/CATA-R [19] and C23O-F/C23O-R [48], darsB1F/darsB1R and dacr1F/dacr1R [1] andaoxBM1-2F/aoxBM2-1R [45], respectively. Quantificationswere performed in triplicate in three independent reactionsusing an iCycler iQ apparatus (Bio-Rad) that used the iCyclerOptical System Interface software (version 2.3; Bio-Rad) fordata collection and the subsequent melting curve analyses.Amplification reactions were carried out in a volume of 20 μlas described previously [14]. Nineteen microliters of the PCRmix was prepared using the SYBR green PCR Master Mix(2X iQ SYBR Green Supermix, Bio-Rad, France) and 1 μlconsisted of either the template DNA (non-diluted samples or10-fold dilution series from 108 to 101 copies μl!1 of thestandard plasmid) or cDNA or distilled water (negativecontrol). The conditions for amplification consisted of 5 minat 95 °C followed by 50 cycles of 30 s at 95 °C, 30 s at theannealing temperature (53 °C for arsB and ACR3 and 57.6 °Cfor aioA ; [43]), 30 s at 72 °C and 10 s at 80 °C for fluorescencedetection and acquisition.

Protein Extractions

The sand samples (70 gw /w) were mixed with 15ml of the saltsolution (0.15 g l!1 Na2SO4, 0.45 g l!1 (NH4)2SO4·10H2O,0.05 g l!1 KCl, 0.5 g l!1 MgSO4·7H2O, 0.05 g l!1 KH2PO4,0.014 g l!1 Ca(NO3)2·4H2O) and gently shaken for 2 h at 4 °C.

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After decantation (5 min), the supernatant was recovered, andthe sand was mixed a second time with 15 ml of the saltsolution. After decantation, the supernatants were pooled andcentrifuged (10,000!g , 30 min, 4 °C). The cell pellets werewashed twice, first with 1 ml of the salt solution and secondwith 1 ml of NaCl 0.9 %. The cells were lysed in the presenceof protease inhibitors, DNAse and RNAse, as previouslydescribed [62]. Sonication freeze–thaw lysis cycles wereperformed (seven times, 30 s of sonication with 30 s intervalon ice, 28 % duty cycle). The cell debris was removed bycentrifugation (13,000!g , 90 min). The protein concentrationsweremeasured using the Bio-Rad protein assay kit and checkedon a 12 % SDS-PAGE gel.

Proteomic analysis was performed for taxonomicdetermination as previously described [8, 26, 50], where insome cases the bacteria expressing the identified protein couldbe identified unambiguously at the genus or species levelthanks to discriminant peptides. The proteins were liquiddigested with trypsin and analysed by NanoLC-MS/MSperformed on an Agilent 1200 Series HPLC-Chip/MSsystem (Agilent Technologies, Palo Alto, USA) coupledto an amazon ion trap (Bruker Daltonics, Bremen,Germany). The MS/MS data were analysed as previouslydescribed [6] with a mass tolerance of 150 ppm and 0.2 Dafor the MS and MS/MS data, respectively. MS/MS datasearches were performed against two in-house-generateddatabases. The spectra were first searched against a target-decoy version [20] of a subset of the NCBInr databasethat was restricted to the bacteria found in the 16S rRNAgene community analysis (Dehalococcoides, Micavibrio ,Sphingomonas , Azospirillum , Gammaproteobacteria,Th i o b a c i l l u s , Pa e n i b a c i l l u s , S po ro s a rc i n a ,Verrucomicrobiaceae, Solirubrobacterales, Micrococcineae,Clostridium , Enterobactericeae, Herbaspirillum andPandoreae; see Table 2). The MS/MS spectra were thensearched against a database containing all of the GroELannotated proteins in UniProtKB (20,250 sequences in March2011) [8]. Protein identification was confirmed when at leasttwo peptides with high-quality MS/MS spectra (Mascot ionscore higher than 42 and less than 10 points below Mascot’sthreshold identity score at a 95 % confidence level) weredetected. For the one-peptide hits, the score of the uniquepeptide had to be higher than 56 and at least 5 points higherthan the 95 % confidence Mascot threshold level. With thesevalidation criteria, no reverse hit was identified.

Prior to the 2D experiments, the proteins were precipitatedand labelled as previously described [7]. Six samples werelabelled that corresponded to the three replicates of the AREand ARE + PHE batches. Minimal labelling was performedwith either Cy3 or Cy5 differential gel electrophoresis (DIGE)fluors (400 pmol) (GE Healthcare Biosciences) to prevent anybias that may result from differential labelling efficiency. Theprotein samples that were separated on the same gel were

pooled as follows: one Cy3- and one Cy5-labelled sampleand one sixth of the volume of the pooled set of the internalstandards labelled with Cy2 (as described in [7]). Theseparation and image acquisitions were performed aspreviously described [7]. The analyses of variance(ANOVA) value and the ‘ratio’ (mean of the volumes obtainedfrom the ARE + PHE condition relative to the mean of thevolumes obtained from the ARE condition) were calculatedfor each spot according to the 2D platinum software manual.Only the spots having a ratio greater than 1.5 and an ANOVAvalue with p <0.1 were analysed. The selected spots were cutfrom the 2D gel and digested as previously described [62].The resulting peptides were analysed by nanoLC-MS/MS aspreviously described [6]. The spectra were searched with amass tolerance of 5 ppm for MS and 0.2 Da for the MS/MSdata against a target-decoy version of a subset of the NCBInrdatabase that was restricted to the bacteria found in the 16SrRNA gene community analysis (phylum described above;see Table 2). Protein identification was performed using thesame criteria as described above.

Statistics

Statistical analyses were performed using XLStat 2011software (Addinsoft) with p <0.05. One- or two-ways analysesof variance, followed by Newman-Keuls multiple comparisontest were performed to detect the effects of treatment andincubation period (1 to 6 days of incubation) on the CO2

production data and to detect the effect of treatments oncultivable bacteria enumeration or 16S rRNA gene andtranscript quantifications. Student’s t tests were performed tocompare functional gene and transcripts quantifications.

Results

Bacterial Community Development during the 6 daysIncubation

Carbon mineralisation was monitored during the batchincubation (Fig. 1). No CO2 production was observed in thecontrol batch without carbon substrate. In the presence ofcarbon substrate (ARE), and in the presence or in the absenceof pollutants, the CO2 production increased after 1 day oflatency and a stationary phase was reached in all of theconditions after 6 days incubation. Within this short time ofincubation, the bacterial density increased in all theseconditions by more than 4 logs (Table 1), e.g. from 1.1!104

to 1.3±0.3!108 CFU per gram of sand, and the 16S rDNAlevel reached 2.1!109 gene copies per gram of sand for thenon-polluted batches.

The microbial community composition was analysed bysequencing the dominant bands visualised by TTGE



fingerprinting (Fig. 2 and Table 2), by sequencing the 16SrRNA gene fragment from the isolated bacterial strains(Table 2) and by taxonomic affiliation of the peptides fromthe proteomic analyses (Table 2). Even when the same initialsoil inoculumwas used, slight differences in the structure of thebacterial communities (Fig. 2) and in the identified species(Table 2) appeared depending on the batch conditions. Whenno pollutant was added, the most common bacteria belonged toboth γ-Proteobacteria, which are closely related toPseudomonas sp. Stenotrophomonas sp. and Enterobactersp., and Firmicutes, which are closely related to Paenibacillussp., Clostridium sp. and Staphylococcus sp. Only one speciesof Arthrobacter belonging to the Actinobacteria was detectedthrough strain isolation.

The metabolic profiles of the bacterial communities weredetermined using Biolog Ecoplates™, which allow for thetesting of 31 carbon substrates. The different batch conditionswere spatially separated on PCA and the replicates weregrouped together (Fig. 3), which indicated slight differencesin the types of substrate used and the kinetics of carbonsubstrate utilisation. In the absence of pollutant the bacterialcommunity preferentially degraded carbon substratesbelonging to the following three guilds: two polymers(Tween 80, Tween 40), one phenolic compound (2-hydroxybenzoic acid) and one amino acid (L-phenylalanine).

Effect of Phenanthrene on the Community Characteristics

In the presence of phenanthrene, the mineralisation wassignificantly lower than in non-polluted batches after 2, 3and 4 days of incubation (ANOVA, p <0.05; Fig. 1).

Densities of cultivable bacteria and 16S rRNA genes weresimilar to non-polluted batches and reached 2.4±0.9!108 CFU per gram of sand and 1.9!109 gene copies per gramof sand (Table 1), respectively. No significant decrease in theconcentration of phenanthrene was observed during the 6 daysof incubation (Student t test, p =0.11; Table 3); but the PHEdegradation most likely started to occur in replicates 2 and 3,with 20 to 35 % of potential degradation.

Based on PCA generated from the TTGE band patterns, thebacterial community structure was not significantly differentbetween the non-polluted condition (ARE) and the conditionwith phenanthrene (ARE + PHE) due to the variabilitybetween the replicates (Fig. 2). Moreover, the Shannondiversity indexes H ! calculated from TTGE band matrix andthe identified bacteria were very similar in the ARE + PHEcondition and in the ARE condition (Table 2). Besides thedominance of Pseudomonas sp. and Paenibacillus sp.,bacteria closely related to Stenotrophomonas sp. andClostridium sp., also found in non-polluted condition, weredetected in the presence of phenanthrene. Among the minorbacteria some γ-Proteobacteria (i.e. Serratia sp., Rahnellasp., Klebsiella sp. and Ewingella sp.) and one strainbelonging to the β-Proteobacteria (i.e. Alcaligenes sp.) wereonly detected in the ARE + PHE condition. Buttiauxella sp.(γ-Proteobacteria) was detected in the two pollutedconditions. On the contrary, bacteria related to Enterobactersp. (γ-Proteobacteria) were absent in polluted as compared tonon-polluted samples. Altogether these data revealed thatphenanthrene had no impact on the global communitystructure but rather on the presence or absence of specificminor species.

replicate 1

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replicate 3

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Fig. 1 Mineralisation, expressedas cumulative CO2 production,during batch incubation

A. Cébron et al.


To identify the impact of phenanthrene on the presence andthe activity of bacteria actors in phenanthrene degradation,quantification of copy number of functional genes andtranscripts involved in the PAH degradation pathway werecompared in the ARE and ARE + PHE conditions (Table 1).No significant differences were observed in the levels of thePAH-RHDα genes and transcripts. The proportion of thecatechol-1,2-dioxygenase and catechol-2,3-dioxygenase geneswere significantly higher in theARE+ PHE samples than in thenon-polluted ARE samples (Table 1). In contrast, no significantdifference was found in catechol-dioxygenase genetranscription, suggesting that the catechol-1,2-dioxygenaseand catechol-2,3-dioxygenase activities were not yet inducedafter 6 days with phenanthrene.

Based on metabolic profiles analysis (Biolog Ecoplates™),the data treatment of the AUC analysis showed no significantdifferences in the diversity, equitability and richness indicesbetween the PHE-polluted condition (ARE + PHE) and the

non-polluted condition (ARE; Table 4). These indices indicatethat in the two conditions, the bacterial communities degrade anequivalent number of substrates (richness), with equivalentdegradation kinetics (equitability) and thus the substratediversity indices were similar. However, slight variations inthe type of substrate used and small differences in degradationkinetics of each substrate, cumulated on almost 30 substrates,allowed differentiation of the conditions. Indeed, thecommunities from the batches supplemented with phenanthrene(ARE + PHE) were separated from the ones developed in thebatches with only exudates (ARE) on the second PCA axis(Fig. 3). In the presence of phenanthrene (ARE + PHE), thebacterial community preferentially degraded the carboncompounds belonging to the following two guilds: threecarbohydrates (glucose-1-phosphate, D-xylose, D,L-α-glycerolphosphate) and one carboxylic acid (D-malic acid).

As the replicates were reproducible and the bacterialcommunities were very similar, the bacterial activity and

Table 1 Cultivable bacterial quantification (CFU), and genes andtranscripts quantification (real-time PCR on genomic DNA and cDNA).The percentage of functional genes relative to 16S rDNA, and offunctional gene transcripts relative to 16S rRNA, is shown in brackets.The data shown are the means (n =3). Statistical significance wasdetermined by performing a one-way analysis of variance (ANOVA)

followed by a Newman–Keuls multiple comparison test for the 16SrRNA genes and transcripts data and Student’s t test for the functionalgenes and transcripts data (p <0.05). Significant differences are shown bydifferent lower case letters, when no significant differences were found noletters were indicated

ARE + PHE ARE ARE + As

CFU T0 (cells/g sand) 1.1!104

T + 6 days 2.4!108 1.3!108 1.8!108

Genes 16S rDNA (gene copies/g sand) 1.9!109 2.1!109 2.1!109

PAH-RHDα GN 9.5!106 (0.57 %) 1.9!107 (0.93 %) –

PAH-RHDα GP 4.9!105 (0.030 %) 2.9!105 (0.013 %) –

Cat-1,2-Diox 5.9!107 (3.10 %a) 3.4!107 (1.57 %b) –

Cat-2,3-Diox 4.7!106 (0.21 %a) 9.2!105 (0.05 %b) –

arsB – 5.5!106b (0.27 %b) 1.6!107a (0.73 %a)

ACR3 – 4.4!105b (0.02 %b) 1.1!107a (0.54 %a)

aioA – 9.3!104b (0.004 %b) 8.8!105a (0.044 %a)

Transcripts 16S rRNA (RNA copies/g sand) 1.0!109 9.8!108 1.0!109

PAH-RHDα GN 1.5!104 (1.5!10!3 %) 3.6!104 (3.5!10!3 %) –

PAH-RHDα GP nd nd –

Cat-1,2-diox 4.1!102 (4.1!10!5 %) 1.8!102 (1.8!10–5 %) –

Cat-2,3-Diox nd nd –

arsB – nd 4.6!102 (3.9!10–5 %)

rep 1: 1.2!102 (2.0!10!5 %)

rep 2: 1.1!103 (8.2!10!5 %)

rep 3: 1.5!102 (1.3!10!5 %)

ACR3 – nd 8.4!102 (7.6!10!5 %)

rep 1: 9.1!101 (1.5!10!5 %)

rep 2: 5.7!102 (4.2!10!5 %)

rep 3: 1.8!103 (17.1!10!5 %)

aioA – nd 1.3!103 (1.6!10!4 %)*

nd not detected, values lower than the real-time PCR detection limit

*Mean of two values from replicates 1 and 2, aioA transcripts were not detected from replicate 3



metabolic response to phenanthrene stress was also studiedusing proteomic analyses. To identify the proteins that areinduced in the presence of phenanthrene, differential gelelectrophoresis was performed to compare the samplesincubated in ARE and ARE + PHE conditions (Fig. 4b).Twenty-nine differentially expressed spots were analysedusing highly sensitive mass spectrometry techniques(Fig. 4b, Table S1). No identification was obtained for 11spots, most likely due to the limited genome informationavailable for the uncultured bacteria. For several of the otherspots, more than one protein was identified. In these cases, itwas difficult to establish whether the variation of the spotintensity was due to one particular protein. Nevertheless, theresults indicated that the proteins from Enterobacter sp. andPantoea sp. decreased in the presence of PHE, whereas theproteins expressed by the strains related to Pseudomonas sp.increased in the presence of phenanthrene. One protein (spot22), which increased in the presence of PHE, has a yetunknown function. Based on the gene sequence of this proteinas well as the other genes found in the vicinity of thePseudomonas fluorescens genomes, no function could beelucidated (data not shown). Several of the other proteins thatincreased in the presence of PHE are involved in centralmetabolism or the stress response (i.e. DnaK chaperones),suggesting that in the presence of phenanthrene, bacteriamodified their central metabolism and induced a general stressresponse.

Effect of Arsenic on the Community Characteristics

The CO2 production was significantly lower in the batchesspiked with arsenic (ARE + As) than in the absence ofpollutants after 2, 3 and 4 days of incubation (ANOVA, p <0.05; Fig. 1). The three replicates from the ARE + As batchharboured contrasted mineralisation rates (Fig. 1), replicates 1and 2 being lower. The concentrations of the available (water-soluble) arsenic varied between the batch replicates of theARE + As condition, probably because the communitytransformed arsenic (e.g. oxidation, reduction or methylation),rendering this toxic metal soluble or not. In particular, weobserved that the proportion of As(V) remained constant inreplicates 1 and 2 but decreased in replicate 3 (Table 3). Theseresults suggest that arsenate reduction is clearly favoured in thereplicate 3 as compared to the other replicates where balancedactivities of arsenate reduction and arsenite oxidation appearedto occur. Densities of cultivable bacteria and of 16S rRNAgenes were similar to non-polluted (p <0.05) and reached 1.8±0.9!108 CFU per gram of sand and 2.1!109 gene copies pergram of sand (Table 1), respectively.

The first axis of the PCA generated from the TTGE bandpatterns and representing 33 % of the variability, separated thecondition with arsenic (ARE + As) from the non-pollutedcondition (Fig. 2) based on community structure. Moreover,the Shannon diversity indexes H ! calculated from TTGE bandmatrix were significantly lower than in non-polluted condition,

ARE+PHE ARE ARE+As

123

4

65

87

91011

12

13

1415

16

ARE

ARE+As

ARE+As

ARE+As

ARE

ARE

ARE+PHE

ARE+PHE

ARE+PHE

a

F1 (33%)F

2 (1

8%)

Shannon diversity indexes H’2.40 2.33 2.43 2.44 2.31 2.24 1.62 1.66 2.16

b

Fig. 2 The structure of the bacterial community at the end of the batchincubation. a 16S rDNA TTGE fingerprints from the communityincubated in the presence of artificial root exudates alone (ARE), AREplus phenanthrene (ARE + PHE) or ARE plus arsenic (ARE + As). The

bands labelled with arrows were cut, re-amplified and sequenced forbacterial taxonomic assessment. Values of Shannon diversity indices H !calculated from TTGE pattern are shown; b principal component analysisbased on the TTGE band intensity matrix

A. Cébron et al.


Table 2 Identification of bacteria in the sand batch with only artificial root exudates (ARE) or ARE spiked with either phenanthrene (ARE + PHE) orarsenic (ARE + As) after 6 days

Sample Closest relative bacteria(at least 98 % similarity)

Affiliation Method TTGE band number/clonesequence/identified protein

ARE + PHE Clostridium jejuense Firmicute TTGE 1

Paenibacillus borealis Firmicute TTGE 12

Ewingella americana γ-Proteobacteria TTGE 3

Klebsiella pneumonia γ-Proteobacteria TTGE 4

Pseudomonas sp. γ-Proteobacteria TTGE 9 and 10

Pseudomonas fluorescens γ-Proteobacteria TTGE 11

Rahnella sp. γ-Proteobacteria TTGE 2

Serratia sp. γ-Proteobacteria TTGE 6

Stenotrophomonas maltophila γ-Proteobacteria TTGE 7

Alcaligenes faecalis β-Proteobacteria Strain isolation ARE + PHE19-1, ARE +PHE21-B and ARE + PHE21-C

Buttiauxella gaviniae γ-Proteobacteria Strain isolation ARE + PHE20-C

Pseudomonas sp. γ-Proteobacteria Strain isolation ARE + PHE20-B

Pseudomonas putida γ-Proteobacteria Strain isolation ARE + PHE19-D, ARE +PHE19-E and ARE + PHE20-D

Rahnella sp. γ-Proteobacteria Strain isolation ARE + PHE19-B

Rahnella aquatilis γ-Proteobacteria Strain isolation ARE + PHE21-A

Serratia sp. γ-Proteobacteria Strain isolation ARE + PHE21-2

Serratia fonticola γ-Proteobacteria Strain isolation ARE + PHE20-A

Stenotrophomonas sp. γ-Proteobacteria Strain isolation ARE + PHE21-1

Stenotrophomonas rhizophila γ-Proteobacteria Strain isolation ARE + PHE19-C

Stenotrophomonas maltophilia γ-Proteobacteria Strain isolation ARE + PHE19-A

Pseudomonas fluorescens SBW25 γ-Proteobacteria Proteomics 50S ribosomal protein L9,glutamine synthetase

Pseudomonas fluorescens Pf-5 γ-Proteobacteria Proteomics 50S ribosomal protein L7/L12, flagellin

Pseudomonas syringae pv. oryzae 1_6 γ-Proteobacteria Proteomics Extracellular ligand-binding receptor

Pseudomonas putida F1 γ-Proteobacteria Proteomics 60 kDa chaperonin

Pseudomonas fluorescens Pf0-1 γ-Proteobacteria Proteomics 60 kDa chaperonin, Flagellin

ARE Clostridium jejuense Firmicute TTGE 1

Paenibacillus borealis Firmicute TTGE 12

Enterobacter sp. γ-Proteobacteria TTGE 5

Ewingella americana γ-Proteobacteria TTGE 3

Pseudomonas sp. γ-Proteobacteria TTGE 9

Pseudomonas fluorescens γ-Proteobacteria TTGE 11

Stenotrophomonas rhizophila γ-Proteobacteria TTGE 8

Arthrobacter sp. Actinobacteria Strain isolation ARE23-C

Staphylococcus sp. Firmicute Strain isolation ARE23-D

Acinetobacter sp. γ-Proteobacteria Strain isolation ARE24-4

Enterobacter sp. γ-Proteobacteria Strain isolation ARE23-A

Pseudomonas sp. γ-Proteobacteria Strain isolation ARE22-B and ARE23-B

Pseudomonas fluorescens γ-Proteobacteria Strain isolation ARE22-1

Pseudomonas syringae γ-Proteobacteria Strain isolation ARE22-A and ARE22-C

Serratia sp. γ-Proteobacteria Strain isolation ARE24-1 and ARE24-A

Stenotrophomonas sp. γ-Proteobacteria Strain isolation ARE24-3

Stenotrophomonas maltophilia γ-Proteobacteria Strain isolation ARE22-2

Stenotrophomonas rhizophila γ-Proteobacteria Strain isolation ARE23-1 and ARE24-2

Enterobacter cloacae ATCC 13047 γ-Proteobacteria Proteomics Dihydrolipoamide dehydrogenase

Pantoea ananatis LMG 20103 γ-Proteobacteria Proteomics OmpF



i.e. the balance between dominant genera and minor speciesvaried. Although, the TTGE profiles between replicates weredifferent, the sequencing of the dominant TTGE bands revealed

that the dominant bacteria in the ARE + As condition alsobelonged to the Pseudomonas sp. and Paenibacillus sp.(Table 2), as observed in absence of arsenic. Although the

Table 2 (continued)

Sample Closest relative bacteria(at least 98 % similarity)

Affiliation Method TTGE band number/clonesequence/identified protein

Pseudomonas putida F1 γ-Proteobacteria Proteomics Flagellin, 60 kDa chaperonin

Pseudomonas fluorescens Pf0-1 γ-Proteobacteria Proteomics Superoxide dismutase

ARE + As Arthrobacter oxydans Actinobacteria TTGE 13

Paenibacillus sp. Firmicutes TTGE 15

Paenibacillus xylanexedans Firmicutes TTGE 16

Pseudomonas putida γ-Proteobacteria TTGE 14

Arthrobacter phenanthrenivorans Actinobacteria Strain isolation ARE + As27-5

Achromobacter sp. β-Proteobacteria Strain isolation ARE + As27-A

Buttiauxella gaviniae γ-Proteobacteria Strain isolation ARE + As25-2, ARE + As27-3and ARE + As27-B

Enterobacter sp. γ-Proteobacteria Strain isolation ARE + As25-1

Pantoea agglomerans γ-Proteobacteria Strain isolation ARE + As25-4

Pseudomonas sp. γ-Proteobacteria Strain isolation ARE + As25-3, ARE + As26-2and ARE + As27-2

Pseudomonas fluorescens γ-Proteobacteria Strain isolation ARE + As26-A

Pseudomonas hibiscicola γ-Proteobacteria Strain isolation ARE + As26-3

Pseudomonas putida γ-Proteobacteria Strain isolation ARE + As25-A

Pseudomonas syringae γ-Proteobacteria Strain isolation ARE + As26-4, ARE + As26-Band ARE + As27-1

Serratia fonticola γ-Proteobacteria Strain isolation ARE + As26-1

Stenotrophomonas sp. γ-Proteobacteria Strain isolation ARE + As27-4

Pseudomonas fluorescens Pf0-1 γ-Proteobacteria Proteomics Elongation factor Tu, superoxidedismutase, 60 kDa chaperonin,flagellin

Pseudomonas fluorescens SBW25 γ-Proteobacteria Proteomics Trigger factor, chaperone protein dnaK,50S ribosomal protein L1, glutaminesynthetase, dihydrolipoyllysine-residueacetyltransferase component ofpyruvate dehydrogenase complex

Pseudomonas putida W619 γ-Proteobacteria Proteomics Histone family protein DNA-bindingprotein, 50S ribosomal protein L1,arginine deiminase, 50S ribosomalprotein L3, 50S ribosomalprotein L19

Pseudomonas putida GB-1 γ-Proteobacteria Proteomics 30S ribosomal protein S14, ATPsynthase subunit alpha, sulphatase

Pseudomonas sp. CT07 γ-Proteobacteria Proteomics 60 kDa chaperonin

Pseudomonas fluorescens Pf-5 γ-Proteobacteria Proteomics 50S ribosomal protein L7/L12,branched-chain amino acid ABCtransporter, Succinyl-CoA ligase

Pseudomonas putida F1 γ-Proteobacteria Proteomics Flagellin, 60 kDa chaperonin

Pseudomonas fluorescens F113 γ-Proteobacteria Proteomics Flagellin

Pseudomonas entomophila strain L48 γ-Proteobacteria Proteomics Flagellin, hypothetical proteinPSEEN5249

Serratia fonticola γ-Proteobacteria Proteomics Elongation factor Tu A

Achromobacter piechaudii ATCC 43553 β-Proteobacteria Proteomics Flagellin

The data were assessed by TTGE band sequencing, bacterial strain isolation and proteomics

A. Cébron et al.


genera diversity of the strains isolated from the ARE + Assamples was very similar to the diversity found in the AREsamples, the affiliation of the Pseudomonas strains at thespecies level was different due to slight variations in 16SrDNA sequences. Moreover, using TTGE or proteomicsAchromobacter sp., Pantoea sp. and Serratia sp. were onlydetected in the presence of arsenic. Altogether these datarevealed that arsenic had an impact on the global communitystructure as the level of diversity decreased while dominantgenera were similar.

To identify the impact of arsenic on the presence and theactivity of bacteria involved in arsenic speciation andresistance, functional genes and transcripts were measured inthe ARE and ARE + As conditions (Table 1). The copynumbers of the three genes involved in arsenic speciation orresistance were significantly higher in the ARE + As samplesthan in the non-polluted ARE samples, e.g. arsB , aioA andACR3 increased 3-, 11- and 27-fold, respectively. It has to benoticed that the gene quantification data were equivalentbetween the replicates. Interestingly, arsB , ACR3 and aioAgene transcription were not detected in the ARE conditionprobably because cDNA levels were lower to the detectionlimit, whereas transcription of these three genes were detectedand cDNA level could be quantified in the ARE + Asconditions. However, the transcription levels were different inthe three replicates. In particular, in the replicate 3, ACR3 genetranscription was four to 11 times higher than in the replicates 1and 2, and aioA transcripts were not detected. These data were

in agreement with chemical data showing that the proportion ofarsenate was reduced in the third replicates as compared to thetwo other replicates, suggesting that arsenate reduction isfavoured in this sample. Because of these significant differencesbetween the three replicates in term of population structure andactivity and because the proteins extracts from bacteria grownin these replicates were clearly different (Fig. 4a), DIGEanalysis was not possible. Nevertheless, metabolic profilesanalysis (Biolog Ecoplates™) was possible and the substratediversity, equitability and richness indices were similar to non-polluted condition. Nevertheless, the communities from thebatches supplemented with arsenic (ARE + As) were separatedfrom the ones of the batches with only exudates (ARE) on thefirst PCA axis (Fig. 3). In the presence of arsenic (ARE + As),the bacterial community preferentially degraded the carbonsubstrates belonging to the following four guilds: one aminoacid (L-asparagine), one polymer (α-cyclodextrin), onecarboxylic acid (γ-hydroxybutyric acid) and one amino sugar(N-acetyl-D-glucosamine). Altogether revealed that arsenic hadan impact on the community functions.

Discussion

The objective was to determine how a bacterial communityextracted from an aged PAH and heavy-metal-contaminated soilcould react to organic (phenanthrene) and inorganic (arsenic)pollutants found in their original soil, shortly after pollutant

F1 (43%)

ARE+As

ARE+As

ARE+As

ARE+PHE

ARE+PHE

ARE

ARE

ARE

ARE+PHE

F2

(25%

)

Fig. 3 Principal component analysis and correlation circles showing the31 carbon substrates (labelled using their localisation in the plate) asvariables generated from the Biolog Ecoplates™ data. Carbon substratesare as follows: water (A1),β-methyl-D-glucoside (A2), D-galactonic acid γlactone (A3), L-arginine (A4), pyruvic acid methyl ester (B1), D-xylose(B2), D-galacturonic acid (B3), L-asparagine (B4), Tween 40 (C1), i-erythritol (C2), 2-hydroxybenzoic acid (C3), L-phenylalanine (C4), Tween

80 (D1), D-mannitol (D2), 4-hydroxy benzoic acid (D3), L-serine (D4),α-cyclodextrin (E1),N-acetyl-D-glucosamine (E2), γ-hydroxybutyric acid(E3), L-threonine (E4), glycogen (F1), D-glucosaminic acid (F2), itaconicacid (F3), glycyl-L-glutamic acid (F4), D-cellobiose (G1), glucose-1-phosphate (G2), α-ketobutyric acid (G3), phenylethylamine (G4), α-D-lactose (H1), D,L-α-glycerol phosphate (H2), D-malic acid (H3) andputrescine (H4)



exposure. Our study was done in simplified laboratoryconditions where the bacterial community was inoculated insand rather than soil to avoid matrix heterogeneity. Our resultsgive insight regarding the rapid impact of two pollutants on thetaxonomic and functional diversity a pre-adapted bacterialcommunity.

In the three conditions studied, i.e. without pollutant, withphenanthrene and with arsenic, the bacterial density and thetaxonomic bacterial community structure were highly similar,with the dominance of Pseudomonas sp. and Paenibacillussp.. Among others, Pseudomonas sp. and Paenibacillus sp.dominated the bacterial community in the Neuves-Maisonsmulti-contaminated soil from which the inoculum came from[15]. Firstly, these strains brought within the initial inoculumwere adapted to PAH and heavy metal contamination. Indeed,these genera are commonly found in contaminated sites, in thepast they have been isolated from organic- and heavy-metal-contaminated sediment [42] and organo-chlorates and arsenic-polluted soils [3]. Secondly, these strains are fast-growingbacteria, selected during the short incubation period, by theexperimental physicochemical conditions (i.e. sand matrix,BH medium and oxygenation) and the presence of easilydegradable carbon sources provided through ARE [24].Pseudomonas sp. and Gram-positive spore-formingPaenibacillus sp. are known as r-strategist copiotrophs [22,52] and are favoured in C-rich rhizosphere soils comparatively

to C-poor bulk soils [18, 22]. It seems that the addition of AREreduced the bacterial diversity compared to the initial Neuves-Maisons soil [15] and favoured rapidly growing bacteria aspreviously demonstrated [37]. Similarly, Kozdrój and vanElsas [32] observed decreased cultivable bacterial diversityin the polluted soil after ARE amendment toward dominationof r-strategists, as shown by the uneven distribution ofbacterial classes and an increase in colony development(CD) indices.

In our tested conditions, phenanthrene had an impact onmicrobial activity because mineralisation was slightly lower inthe presence of phenanthrene. Other authors found thatdepending on its concentration, phenanthrene could decreasethe bacterial biomass [54] or activity [58]. Despite the PHEtoxicity, a very similar bacterial community structuresdominated by Pseudomonas sp. and Paenibacillus sp. wasobserved with PHE and without pollutant. Although thetaxonomic diversity was not affected, the physiology of thebacteria was modified. Indeed, the DIGE experiment revealedthat the bacteria, mainly affiliated to Pseudomonas sp., reactto phenanthrene pollution by overexpression of cellular stressresponse proteins (e.g. chaperon protein DnaK), modulationof the expression of enzymes involved in central carbonmetabolism (e.g. overexpression of 3-hydroxyacetyl-coA-dehydrogenase and acetyl-coA-synthase or underexpressionof pyruvate- and glucose-6-phosphate dehydrogenase)and underexpression of membrane proteins (e.g. OmpF,ABC-transporter), indicating a possible toxic effect onbacterial activity. Bacteria belonging to Enterobacter sp.(γ-Proteobacteria) were repressed, and were never detectedwithin strain isolation, TTGE band sequencing and proteomicpeptides analyses in phenanthrene-polluted batches. Thiswas also confirmed with DIGE data, where Enterobactermetabolism proteins were underexpressed most likely due toa higher sensitivity to PAH toxicity.

Besides being a pollutant with potential toxicity, phenan-threne is also a carbon source that could support bacterialgrowth [31]. However, no significant phenanthrene degradationoccurred during the short time of our study (6 days), as shown

Table 3 Phenanthrene andarsenic concentrations in thebatch experiment on day 0 andday 6 of the incubation period

At T0, the data show the meanvalues (n =3) and standarddeviations

Phenanthrene Arsenic (mM, in the sand solution)

(mg kg sand!1 ww) Astotal As(V) Ratio As(V)/Astotal

ARE + PHE T0 320.1±34.6

T+6 days rep 1 321.9



ARE + As T0 9.3±1.4 6.78±0.42 0.73±0.07

T+6 days rep 1 12.9 9.76 0.75

T+6 days rep 2 11.2 7.65 0.68

T+6 days rep 3 7.7 2.02 0.26

Table 4 Diversity and equitability indices (mean, n =3) calculated fromthe area under the curve (AUC) data following 120 h of substrateconsumption during the Biolog Ecoplate™ assays

Indices ARE + PHE ARE ARE + As

Shannon diversity H ! 4.67 4.58 4.68

Simpson equitability 0.98 0.97 0.96

Substrate richness 27.67 26.67 29.33

Statistical significance was determined by performing a one-way analysisof variance (ANOVA) followed by a Newman–Keuls multiplecomparison test. No significant differences (p <0.05) were shown

A. Cébron et al.


by no mineralisation increase and no significant phenanthreneconcentration decrease. In a similar experiment performed withnatural root exudates, approximately 30 % of the phenanthrene,shown to be totally bioavailable, was degraded during the first 6out of 10 days [37]. It is likely that the excess of easilydegradable carbon compounds provided within ARE resultedin a catabolic repression inhibiting phenanthrene degradation.Indeed, root extracts or compounds such as pyruvate, glucose,acetate and glutamate have been shown to repress phenanthrenedegradation in Pseudomonas sp. by carbon cataboliterepression [46]. Nevertheless, phenanthrene could be partlyused by some bacteria as a carbon source that could supportbacterial growth. Few minor genera were favoured in thepresence of phenanthrene, (i.e. γ-Proteobacteria: Serratia sp.,Rahnella sp., Ewingella sp. and Buttiauxella sp. and β-Proteobacteria: Alcaligenes). Some of these strains and thetwo dominant phyla (i.e. Pseudomonas sp. and Paenibacillussp.) have previously been identified as PAH degraders [2, 27,38, 56]. For example, in a stable isotope-probing experiment,Cébron et al. [16] have shown that Pseudomonas sp. andPaenibacillus sp. were involved in phenanthrene degradationin the presence of root exudates. Interestingly, in phenanthrene-polluted batches, the bacterial population potentially able todegrade PHE is present as shown by the detection of PAH-RHDα GN and a higher level of catechol-dioxygenase genes.However after 6-day incubation, these bacteria seems to be notactive in PHE degradation, as shown by no significanttranscription and no overexpression of proteins involved inPAH degradation pathways. The percentage of intradiolcatechol-1,2-dioxygenase genes was ten times higher thanextradiol catechol-2,3-dioxygenase genes. Similarly, El Azhariet al. [19] detected a wide diversity of catechol-1,2-dioxygenase

genes in the DNA extracted from the Neuves-Maisons soil,which was the source of our inoculum. Although some strainshave been shown to be versatile in their catechol metabolism[12], intradiol catechol-1,2-dioxygenase has been detected and/or purified from a variety of soil organisms, includingPseudomonas sp., Alcaligenes sp., Nocardia sp.,Rhodococcus sp. and Acinetobacter sp. [28]. All together thesedata suggest that the bacterial communities adapted to thepresence of phenanthrene by selection of specific functionsand by adapting their physiology even if this organic pollutantwas not immediately degraded.

In arsenic-polluted condition, major phyla belong toPseudomonas sp. and Paenibacillus sp. as shown for non-polluted and phenanthrene-polluted batches, but the level ofdiversity was decreased, as shown by Shannon index calculatedbased on TTGE patterns, indicating a higher proportion ofdominant strains. Arsenic is toxic to almost all organisms andinduces oxidative stress and DNA damage [33]. Surprisingly,arsenic-induced stress affected the community activity andstructure differently between the replicates. The carbonmineralisation was affected differently in the presence ofarsenic, microbial activity was lower in the first and secondreplicates, while it was higher in the third replicate. Despitesuch decrease of bacterial activity and diversity, some bacteriaresist to such stress within the 6 days, such as Pseudomonas sp.and Paenibacillus sp.. It has also been previously reported thatthe bacteria belonging to these phyla have a high tolerance toarsenite and arsenate [41]. Pseudomonas species are frequentlyisolated from arsenic-contaminated sites [1, 9] and includesstrains that have been described as arsenite-oxidising [10, 11]and arsenate-reducing bacteria [35]. A Pseudomonas isolateshowing dual capacity for both As(III) oxidation and As(V)

1

45

109 1112

14

16 17 18 19 20

22

2526

28 29

M

a bARE+PHE ARE ARE+As

Fig. 4 Proteomic analyses of the community at the end of the batchincubation. a SDS-PAGE separation of the proteins extracted from thecommunity incubated in theARE,ARE+PHE orARE+As conditions;Mprotein markers. b Photo of a 2D-DIGE gel analysis. The protein extractswere obtained from the bacteria incubated with ARE (labelled with Cy5, ingreen) or ARE + PHE (labelled with Cy3, in red). The spots that arecircled show significant differences (induced in the presence of PHE in red ,repressed in green ). The spots were identified, and supplementalinformation is given in Table S1 (1 pyruvate dehydrogenase, 4 not

identified, 5 formate acetyltransferase, 9 chaperone protein Dnak, 10polyribonucleotide nucleotidyltransferase, 11 transketolase, 12 3-hydroxyacyl-CoA dehydrogenase, 14 acetyl-coenzyme A synthetase, 16phosphoenolpyruvate carboxykinase, 17 pyruvate dehydrogenase, 18pyruvate dehydrogenase, 19 glucose-6-phosphate isomerase, 20 glucose-6-phosphate 1-dehydrogenase, 22 protein PFL_5115, 25 porin, 26 OmpFfamily protein, 28 dihydrolipoamide dehydrogenase, 29 dihydrolipoamidedehydrogenase)



reduction has been reported recently [60]. Corsini et al. [17]have identified one arsenic-resistant Paenibacillus strain thathas the ability to reduce arsenate to arsenite, but the authorscould not detect the arsC or arsB genes.Moreover, the bacteriabelonging to these two genera are often able to form biofilmsthat are protective again toxic elements [41]. In addition, someminor bacterial strains belonging to the Achromobacter sp. orSerratia sp. (β- and γ-Proteobacteria, respectively) weredetected only in these conditions. The strains of the samegenera were previously described to be resistant to arseniteand arsenate [57] and to oxidise As(III) to As(V) [5].Nevertheless, because such processes in these bacteria havenot been fully characterised, studying such isolates would be ofprime interest.

High arsenic resistance was proposed to be correlated withthe detection of the arsenite transporter genes in bacterialisolates [13], and of the arsenite oxidase gene in bacteria[29], in surface water samples (Quéméneur et al. [45] andmore recently in soil columns exposed to As(III) [34]. Poirelet al. [43] also reported that the abundance and expression ofarsB and ACR3 genes were significantly increased above0.1 mM of As(III). In our study, the proportion of bacteriacontaining genes involved in arsenic metabolism or resistance(arsB , ACR3 and aioA genes) was higher in the presence ofarsenic, and the expression of these functional genes wasinduced by arsenic within the 6-day incubation period. Inreplicates 1 and 2, the arsB , ACR3 and aioA genes weretranscribed, while in replicate 3 only arsB , ACR3 genes weretranscribed. This was in agreement with the observation that inthe replicates 1 and 2, the As(V) compared to Arsenic totalwas higher than in the third replicate. Although not measuredin this study, we speculate that the detoxifying arsenatereductase arsC gene was co-expressed with arsB and ACR3genes and may balance the arsenite oxidase activity of thebacterial community. Altogether, these data indicate that thepresence of arsenic in these batch experiments shifted themicrobial community to favour arsenic-tolerant bacteriacapable of As detoxification via either As(III) oxidation andreduction, as suggested previously for oxic As-contaminatedenvironments [39], and that the fate of arsenic relied on therelative abundance and activities of these populations.

Most studies published so far, describe the adaptation andselection of bacterial population consecutively to the pollutionbut without pre-exposure [34, 54, 59, 64]. In the present study,as the inoculum came from an aged-contaminated soil, thebacteria were already adapted to pollutants. The present studytherefore may be considered as amodel experiment that mimicsthe bacterial community adaptation when the pollutantconcentration varies or when the pollutant-bioavailabilityincreases. Remarkably, some common observations were donein the presence of phenanthrene or arsenic. The presence oforganic or inorganic pollutants affected nor the density nor theidentity of dominant bacteria (i.e. Pseudomonas sp. and

Paenibacillus sp.), but selected for functional populationsspecifically adapted to the pollutant. Indeed, isolates affiliatedto Pseudomonas sp. and Paenibacillus sp. were slightlydivergent (based on 16S rDNA sequences) from one conditionto the other, indicating that these bacteria belong to distinctspecies and most likely have distinct metabolisms or functionalproperties. Specific functional populations developed inpresence of pollutants as shown by contrasted community-level physiological profiles observed for the three conditions.Similar substrate diversity indices were obtained in thepresence or absence of pollutants, but the range and kineticsof substrates utilisation was different with and withoutpollutants highlighting the functional adaptation of thecommunity. These observations suggest that the presence ofphenanthrene or arsenic modifies the range of substrates usedand degradation efficiency rather than decreasing the metabolicdiversity, contrarily to what was previously reported forcommunity exposed to PAH and heavy metal [30, 63].Finally, the use of complementary taxonomic and functionaltools has allowed us to better describe the adaptation in a shortperiod of a bacterial community when exposed to an increase ofphenanthrene or arsenic concentration.

Acknowledgment We gratefully acknowledge the ANR program(MULTIPOLSITE, ANR-2008-CESA-010) for financial support, theAgence de l’Environnement et de la Maîtrise de l’Energie (ADEME)and the Bureau de Recherches Géologiques et Minières (BRGM) for J.Poirel PhD grant funding.

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Rapid Impact of Phenanthrene and Arsenic on Bacterial Community Structure and Activities in Sand Batches