The effect of heavy metal contamination on the bacterial community structure at Jiaozhou Bay, China

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h tt p : / / w w w . b j m i c r o b i o l . c o m . b r /

Environmental

Microbiology

The

effect

of

heavy

metal

contamination

on

the

bacterial

community

structure

at

Jiaozhou

Bay,

China

Xie-feng

Yao

a

_,

_Jiu-ming

_Zhang

c,d

_,

_Li

_Tian

c,d,∗∗

_,

_Jian-hua

_Guo

b,∗

a_Institute_of_Vegetable_Crops,_Jiangsu_Academy_of_Agricultural_Sciences,_Jiangsu_Key_Laboratory_for_{Horticultural}_Crop_Genetic

Improvement,Nanjing,China

b_Nanjing_Agricultural_University,_College_of_Plant_Protection,_Department_of_Plant_Pathology,_Nanjing,_China c_First_Institute_of_{Oceanography,}_State_Oceanic_{Administration,}_Qingdao,_China

d_Qingdao_University_of_Science_&_Technology,_Qingdao,_China

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received28October2015 Accepted14July2016

Availableonline4October2016 AssociateEditor:WelingtonLuizde Araújo

Keywords:

Heavymetalcontamination PCR-DGGEﬁngerprinting Multivariateanalysis

Bacterialcommunitystructure

a

b

s

t

r

a

c

t

In thisstudy,determination ofheavymetalparametersandmicrobiological characteri-zationofmarinesedimentsobtainedfromtwoheavilypollutedsitesandonelow-grade contaminatedreferencestationatJiaozhouBayinChinawerecarriedout.Themicrobial communitiesfoundinthesampledmarinesedimentswerestudiedusingPCR-DGGE (dena-turinggradientgelelectrophoresis)fingerprintingprofilesincombinationwithmultivariate analysis.ClusteringanalysisofDGGEandmatrixofheavymetalsdisplayedsimilar occur-rencepatterns.Onthisbasis,17sampleswereclassifiedintotwoclustersdependingonthe presenceorabsenceofthehighlevelcontamination.Moreover,theclusterofhighly con-taminatedsampleswasfurtherclassifiedintotwosub-groupsbasedonthestationsoftheir origin.Theseresultsshowedthatthecompositionofthebacterialcommunityisstrongly influencedbyheavymetalvariablespresentinthesedimentsfoundintheJiaozhouBay. ThisstudyalsosuggestedthatmetagenomictechniquessuchasPCR-DGGEfingerprinting incombinationwithmultivariateanalysisisanefficientmethodtoexaminetheeffectof metalcontaminationonthebacterialcommunitystructure.

licenses/by-nc-nd/4.0/).

Introduction

Pollutionofcoastalzones causedbyheavymetals, suchas Cd,Pb, Hg, and Ni,isone oftheimportant environmental problems facedin many parts of the world.1 _Heavy _metal

∗ _{Corresponding}_author. ∗∗ _{Co-corresponding}_author.

E-mails:wshws68@126.com(L.Tian),jhguo@njau.edu.cn(J.Guo).

pollution canleadtoseverechanges inthecompositionof microbial communities that inhabit these zones.2,3

Micro-bial communities present in marine sediments primarily decomposeorganicmatterderivedfromplantlitterbutalso playavitalrole inthetransformationofpollutants.4,5 _They

can alsoinﬂuencethe availabilityofheavymetals andare

http://dx.doi.org/10.1016/j.bjm.2016.09.007

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metalson bacteriafoundinmarine sediments, theylack important ecological information, such as that offered by polymerasechainreaction(PCR)fingerprintingofthe micro-bialcommunityDNAextractedintheseenvironments.Ithas beenwidelyacceptedthatmetagenomictechniques,suchas PCR-DGGE (denaturing gradient gel electrophoresis) finger-printing,aresomeofthewidelyusedmethodsforexamining the diversityof prokaryoticcommunitiesin environmental habitats.12–15_The_PCR-DGGE_profiles_were_used_to_construct_a

binarymatrixforaquantitativecomparisonbetweendifferent communities.16,17_These_data_were_obtained_either_by_visual

scoringofgelsorbycommerciallyavailablesoftwareprograms (forexample,Bionumerics, AppliedMaths, Belgium).These datacanbepresentedasclusteranalysis,e.g.,anunweighted pairgroupmethodwitharithmeticmeans,18 _in_which

den-drograms are used to illustrate the relationship between differentcommunities.19_{Alternatively,}_PCR-DGGE_proﬁles_can

becombinedwithmultidimensionalscaling,whichiswidely used to study the relationships between microbial diver-sity and various measured environmental parameters.20–22

Multivariate analysis has been utilized to determine the effect of metal contamination on bacterial community structure.23

JiaozhouBayisthelargest semi-enclosedwaterbody in theYellowSea(Fig.1).ItislocatedontheChinesecoastin thewesternPaciﬁcOcean.Theenvironmentalqualityofthis bayhasdeteriorateddramaticallyinthepastthreedecades duetorapidincreaseinagriculture,industry,urbanization, andmaricultureinthesurroundingareas.24_This_region

con-tainsveryhighlevelsofheavymetalsinthesediments,due tothe discharge ofconsiderable amounts of heavy metals fromtheindustrial plantslocatedattheheadofthe Bay.25

Theheavymetalslevelsintheseregionswerefarexceedthan theircrustalaveragebackgroundvaluesinthesedimentsat JiaozhouBay.Theconcentrationsoftheheavymetalsinthe sedimentsshowaremarkablegradientrangingfromhigh con-centrationsatthe inner bay (Licunheestuaryand Haibohe estuarystations)tobackgroundlevelsattheoutside ofthe bay(ShilaorenBeachstation).Therepresentativeareaschosen forthisstudyincludedtheLouHillestuary,Licunheestuary, Haiboheestuary,andDagongIslandthatarelocatedoutside thearea understudy.Thehighestlevelofconcentrationof heavymetalswasreportedinHaiboheestuarywhereinthe concentrationsofﬁveheavymetalswere2.6–23.4foldshigher thantheircorrespondingbackgroundvalues.Inaddition,the concentrations ofZn and Cu were much higher than that observedforother metals;the highestconcentrationofZn

ing proﬁles incombination withmultivariate analysis.The concentrationsofindividualheavymetalspresentinthe sed-imentswerealsodetermined.

Materials

and

methods

Sitedescription

ThestudylocationwasJiaozhouBay,whichisasemi-enclosed bay locatedon thesouth bankofthe ShandongPeninsula, China. Thisbay is linkedto the YellowSeaby avery nar-rowentrancemeasuringonly3.1kmacross.Itextendsfrom 120◦16-120◦17Eto36◦00-36◦02N(Fig.1).Theaveragedepth ofthisbayis7mwithamaximumof64m.Itcoversanarea of362km2 _of_seawater_and_has_a_population_of_7.2_million. Thelong-termannual rainfallranges from340to 1243mm withanaverageof775.6mm,58%ofthatinsummerand23% inwinter.Morethantensmallseasonalstreamsemptyinto thebaywithvaryingwaterandsedimentloads,notablythe Yanghe,Daguhe,Moshuihe,Baishahe,Haibohe,andLicunhe estuaries.27

Samplecollectionandenvironmentalfactormeasurements Sediment samples were collected from two stations in JiaozhouBayandonestationoutside ofthebayon Decem-ber30,2011usinga0.05m2_stainless_steel_box_corer₍_Fig.₁_). Sevensampleswereobtainedfromdifferentpointslocatedat distancesof0.03,0.06,0.09,0.12,0.15,0.18,and0.21kmfrom theLicunheestuary,whichhasawasteinput,andwere num-beredasLC1,LC2,LC3,LC4,LC5,LC6,andLC7,respectively. Similarly, ﬁvesampleswere obtainedfrom differentpoints locatedatadistanceof0.03,0.06,0.09,0.12,and0.15kmfrom theHaiboheestuary,whichhasawasteinput,andwere num-beredasHB0,HB1,HB2,HB3,andHB4,respectively.Another ﬁvesampleswereobtainedfromdifferentpointslocatedat distancesof0.03,0.06,0.09,0.12,and0.15kmfromShilaoren Beachandweretreatedasthelow-gradecontaminated con-trolstationandnumberedasSLR1,SLR2,SLR3,SLR4,andSLR5, respectively.

The concentrations of heavy metals (V, Ni, U, Mo, Zn, Se, Sb, Co, Cr, Cd, Pb, As, Cu, and Hg) in the sediments were determined by graphite furnace atomic absorption spectrophotometry (GFAAS)usinganAAnalyst800graphite furnace atomicabsorption spectrometer (Perkin-Elmer, CT, USA).

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Fig.1–MapshowingthesamplingstationsinJiaozhouBay.LC,Licunheestuary;HB,Haiboheestuary;SLR,ShilaorenBeach.

DNAextractionandPCR-DGGEanalysisofbacterial communities

TotalcommunityDNAwasextractedwiththeBIO-101DNA extractionkit(QBIOgene)from0.5gofsamples(wetweight). Thesediment pellets were taken inlysis tubes containing a mixture of ceramic and silica particles, and DNA was extractedaccordingtothemanufacturer’sprotocol.The proce-durecombinedhighlyenergeticmechanicalmeans(FastPrep Instrument,QBIOgene)inthepresenceofdetergentsandsalts intheveryﬁrststeptoallowdisruptionofhard-to-lysecells, minimizeshearing ofDNA,and inactivatenucleases. After DNAelution,asilica matrixwasutilized tobindDNA,and thesampleswere washedwithasalt/ethanolsolution.The GENECLEANSpin Kit(QBIOgene) was usedas described by themanufacturertore-purifytheDNA.Theyieldsofgenomic DNAweredeterminedafterelectrophoresisin0.8%agarose gelsstainedwithethidiumbromideunderanUV transillu-minator.TheconcentrationsofDNAwereestimatedvisually usingtheDL15,000DNAMarker(TaKaRa,China)intheagarose gels. Thegenomic DNA sampleswere diluteddifferentially toobtainconcentrationsof1to5ngDNAtobeusedinthe followingstep.28

PriortoDGGEanalysisofthebacterialproﬁles,16SrRNA genefragmentswereampliﬁedbyPCRfromtotalcommunity DNAusingtheprimerpairGC-F984(5-CGCCCGGGGCGCGCC CCGGGCGGGGCGGGGGCACGGGGGGAACGCGAAGAACCTTAC-3)/R1378 (5-CGGTGTGTACAAGGCCCGGGAACG-3)29,30 _using

a DNA Engine (PTC-200) Peltier Thermal Cycler (Bio-Rad Laboratories, Inc). The reaction mixture was prepared as recommended by Costa et al.,28 _with _some _{modiﬁcations.}

Brieﬂy, the reaction mixture (50␮L) was composed of 1␮L template DNA (1–5ng), 1x ExTaq buffer (TaKaRa, China), 0.2mMdNTPs,3.75mMMgCl2,4%(w/v)acetamide,0.2mMof eachprimer,and5UExTaqDNApolymerase(TaKaRa,China). Aftera5mindenaturationstepat94◦C,30cyclesof1minat

95◦C,1minat53◦C,and2minat72◦Cwerecarriedout.A ﬁnalextensionstepof10minat72◦Cwasusedtoﬁnishthe reaction. Products were checkedbyelectrophoresisin1.5% agarosegelswithethidiumbromidestaining.

DGGE was performed with an INGENY phorU-2 system (INGENYInternationalBV,Leiden,TheNetherlands)usinga doublegradientgelcontaining6–10%polyacrylamide (acry-lamide/bisacrylamide,37.5:1)withagradientof46.5–65%of denaturant(ureaandformamide).ThePCRproducts contain-ingapproximatelyequalamountsofDNAofsimilarsizeswere separatedonagelcontainingalineargradientofthe denatu-rants.Therunwasperformedin1×Tris-acetate-EDTAbuffer at58◦Cataconstantvoltageof120Vfor16hour.TheDGGE gelsweresilver-stainedaccordingtoHeueretal.(2001).31

Statisticalanalysis

Thedenaturinggradientgelswerescannedinthe transmis-sivemode(Epson1680Pro,Seiko-EpsonCorp.Suwa,Nagano, Japan)athigh-resolutionsettings.TheGELCompar® _II_v.4.0 softwarepackage(AppliedMathsBVBA,Sint-Martens-Latem, Belgium) was used to analyze the community ﬁngerprints of each denaturing gradient gel as recommended in the literature,32 _with_modiﬁed _settings _as _described _by _Smalla

et al.(2001).33 _The_comparison_between_samples_loaded _on

differentDGGEgelswasperformedusingnormalizedvalues derived from known standards. The relative abundanceof each phylotype(band)wasestimatedasthe relative inten-sityofindividualbandsforeachcommunity(Lane)andwas expressed asa proportionofthe totalcommunity volume, thusnormalizingthedataandstandardizingtheloading dif-ferences.Abinarymatrix ofidentiﬁedbandswasmadefor all gellanes:the term‘OTU’isusedtorefertoeach DGGE band.ThePearsoncorrelationindexforeachpairoflaneswas calculated asameasureofsimilarity betweenthe commu-nityﬁngerprints.Clusteranalysiswascarriedoutbyapplying

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Results

Chemicalanalysisandsimilaritymatrix

Theconcentrationsofheavymetalsinsedimentsare given inTableS1.Alargevariationintheconcentrationsofheavy metalsinsedimentsampleswasfound.Theconcentrationsof metals,suchasZn,As,Pb,Cu,andCd,werehigherinheavily contaminatedLicunhe(LC)andmediumcontaminated Hai-bohe(HB)estuariesthanthoseinthelow-gradecontaminated controlstationShilaorenintertidalzone(SLR)(Fig.2).

Theresultsofcharacterizationofthemetalsobtainedat thethreesamplingstationsareshowninTable1.The con-centrationsofalloftheheavymetalsinvestigated,withthe exceptionofSbandAs,weresignificantlydifferentbetween the differentsampling stations (ANOVA, p<0.05). From the similaritymatrixoftheheavymetalparameters, verylarge differencesinthe sedimentsampleprofiles wereobserved. The17 samples obtainedwere classified into two clusters dependingon the presenceor absenceofhigh contamina-tion,andsampleswithinthe samesampling stationswere generallyclusteredintoagroup(Fig.2).Thesimilarity matri-ces ofthe heavy metal parametersin LC and HB samples were significantly more related to each other. The cluster ofhighlycontaminated sampleswasalso divided into two

Fig.2–Aheatmapofheavymetal(columns)occurrences ineachsedimentsample(rows).Thenumberofblueboxes indicatesthatagivenheavymetalhasahighoccurrencein thatsedimentsample.Dendrogramsrepresenthierarchical clusteringofsedimentsamplesorheavymetals.

sub-groups based on the stations from which they were obtained (LC and HB) and were characterizedby 84% pat-ternsimilarity(Fig.2).Thegreatestdifferenceswereobserved betweenthetwoheavilycontaminatedstations(LCandHB) andthelow-gradecontaminatedcontrolstation(SLR)(Fig.2). DCA ordinationofthe heavymetals(data notshown)also

Table1–Concentrationoftheheavymetalswithineachstation.

Heavymetals(ppm) LC HB SLR p

Mean SD Mean SD Mean SD

V 46.81 4.45 34.54 14.75 20.44 3.51 0.000 U 3.21 0.44 2.54 0.66 1.45 0.09 0.000 Sb 12.73 1.30 12.01 4.09 9.53 2.14 NS Cr 57.73 9.61 37.70 20.13 10.26 2.00 0.000 Co 10.12 1.31 5.36 2.69 2.48 0.42 0.000 Ni 22.70 1.83 13.13 7.60 4.14 0.53 0.000 Cu 57.05 10.75 30.51 15.56 4.58 0.50 0.000 Zn 564.91 266.02 83.44 47.82 18.66 3.18 0.003 Hg 1.53 0.91 0.76 0.76 0.03 0.00 0.009 As 13.65 0.87 10.89 4.33 13.22 0.68 NS Se 1.21 0.20 1.43 0.61 0.58 0.19 0.002 Mo 4.65 2.03 2.14 1.34 0.73 0.09 0.004 Cd 1.18 0.26 0.60 0.29 0.23 0.01 0.000 Pb 72.87 17.56 38.65 9.26 19.09 1.86 0.000

p,levelofsigniﬁcance(ANOVA,p<0.05)betweendifferentstations(LC,n=7;HB,n=5;SLR,n=5);NS,notsigniﬁcant;LC,Licunheestuary;HB,

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Fig.3–AheatmapofDGGEband(columns)occurrencesineachsedimentsample(rows).Thenumberofblueboxes indicatesthatagivenDGGEbandwasobservedinalargerproportioninthatsedimentsample.Dendrogramsrepresent hierarchicalclusteringofsedimentsamplesorDGGEbands.

showedthatthesamplescouldbeclassiﬁedintothreegroups onthebasisofthestationfromwhichtheywereobtained.

CompositionofbacterialcommunitiesasdepictedbyDGGE SomeoverlapoccurredbetweenDGGEbandsobservedinthe heavily contaminated sediment samples (LC and HB) and thecontrol sample(SLR). Around100 definedDGGE opera-tionaltaxonomicunits(OTUs)wereobservedatagivensite across the sampleseries. Approximately 54 ofthese OTUs wereexclusivelyobservedintheheavilycontaminated sed-imentsamples(LCandHB),andonly15were foundinthe controlsamples(SLR).Theremaining31OTUswereobserved inboth,heavilycontaminated(LCandHB)andcontrol(SLR) sedimentsamples.Two-dimensionalhierarchicalclusteringof OTUsandsamples,combinedwithaheatmapofoccurrence frequency,revealedgroupsofsampletypesandOTUsthathad similarpatternsofoccurrence(Fig.3).ThedistributionofOTUs amongsampletypeswasuneven;themajorityoftheobserved OTUswerepredominantlydetectedineitherLCorHB sedi-mentsamples(groupsBandC,Fig.3).TheOTUsingroupA (Fig.3)weremostoftenfoundinSLRsedimentenvironments. Thecomparison ofprofiles through the construction of similaritydendrogramsrevealed higherdegreeofsimilarity withinonestationthanthatbetweenthesamplesfrom differ-entstations.Afactthatheavymetalcontaminationbelongs toaspecificgroupingofmicrobialcommunitieswaspreferred tovisible(groupsBandC,Fig.3).UPGMAclusteringofDGGE profilesrevealedthatthe17sampleswereseeminglydivided intotwo clustersdependingon thepresenceorabsence of highcontamination.DGGEpatternsinhighlypolluted sam-plesweresignificantlymorerelatedtoeachother.Thecluster ofhighlycontaminated sampleswasalso dividedinto two sub-groupsbasedontheirstations,whichwerecharacterized byapproximately25%patternsimilarity(Fig.3).CCA ordina-tionofthesamedataalsoconfirmedthesegroupings(Fig.4). Thesamples from each station formeda cluster that was markedlydifferentfromothers inboththeanalyses.These resultsshowedthatheavymetalvariablesinthesediments exertastronginfluenceonthecommunitycompositionatthe JiaozhouBay.

Communityordinationanalysis

Twelveheavymetalvariableswerefoundtobesigniﬁcantly related(p<0.05)tobacterialcompositioninhighly contami-natedsediments.CCAordinationofDGGEpatternsindicated

Fig.4–CCAordinationoftheOTU–environment relationships.Symbols:LC(),HB(䊉),SLR().Agroupof samplesinthesamesamplingstationsisdividedintothe sameenclosingenvelope.Thesamplesweredividedinto twogroups(groupAandgroupB)dependingonthe presence(HB,LC)orabsence(SLR)ofhighcontamination. ThegroupBincludedthesamplesfromlow-grade contaminatedcontrolstation(SLR).ThegroupAofhighly contaminatedsampleswasdividedintotwosub-groups (groupA1andA2)basedontheirstations(HBandLC). Arrowsindicatethedirectionofincreasingvaluesofthe metalvariable;thelengthofarrowsindicatesthedegreeof correlationofthevariablewithcommunitydata.

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Mo −0.1019 −0.2265 0.2932 −0.4624 0.8483 0.3074 Cd −0.0784 0.4242 0.1914 −0.4372 0.0050 0.5001 Sb −0.0635 0.3020 0.0283 0.6048 −0.0301 0.2426 Pb 0.2412 −0.0013 0.4615 −0.4401 0.1233 0.5349 U 0.0022 0.0913 0.6078 −0.2582 −0.4108 0.6724 Eigenvalues 0.672 0.632 0.557 0.459 0.530 0.275 Cumulativepercentage varianceof OUT-environment relation 26.2 50.7 31.7 57.8 47.2 71.6

Heavilyandmediumcontaminatedstations:LC,Licunheestuary;HB,Haiboheestuary;Low-gradecontaminatedcontrolstation:SLR,Shilaoren

Beach.BoldtypeindicatesthethreestrongestfactorscorrelatedwiththeﬁrsttwoCCAordinationaxes.

thatheavymetalsaffectthebacterialcomposition.TheCCA explained44.2%ofthevariationintheﬁrsttwoaxes. More-over,therewerestrongrelationshipsbetweentheOTUsand theheavymetalvariables(OTUsandheavymetalcorrelations oftheﬁrsttwoaxeswere0.999and0.997,respectively).

IntheCCAanalyses,50.7–71.6%ofthecumulativevariance oftheOTUs–heavymetalrelationshipwithineachstation wasexplainedbythe ﬁrsttwoCCAs(Table2).Heavy metal factorsthatcorrelatedstronglywiththegeneticdiversityof thebacterialcommunitiesdifferedamongthestations.The strengthsofthecorrelationsbetweenOTUsandheavymetal factorsexplainedbytheﬁrsttwoaxesareshowninTable2. Thethreestrongestfactorscorrelatedwiththeaxes(shown inbold)generallydifferedamongthestations.The concen-trationsofCo, Zn,Hg, As, and Sewere stronglycorrelated withoneorbothoftheaxeswithineachstation. Addition-ally,theconcentrationsofAswerestronglycorrelatedwithall samplingstationsandplayedanimportantrole(Table2).

Discussion

DGGEbandsareconsideredtobedominantuniquesequence types (operational taxonomic units or OTUs) within the methodologicalconstraintsimposedbyPCR.20,22_As_reported

earlier,two-dimensionalhierarchicalclusteringbasedonboth DGGEbandrelativeintensitiesandsamplechemistryresults wereusedtodeterminewhethermicrobialcommunitieswere relatedtoenvironmentalparameters.36,37_By_combining_with

a heat map of occurrence frequency, the results revealed groups of samples from OTUs or heavy metal parameters thathad both consensusmatriceswithsimilar patterns of occurrence.ThemajorityofobservedOTUs(groupsBandC,

Fig.3)werepredominantlydetectedineitherofLCorHB sed-imentsamples.TheOTUs(groupA)weremostoftenfoundin

theSLRsedimentenvironment.Thissuggeststhatthe domi-nantmajorityofOTUs(groupsBandC)maybedispersedto beadaptedtothecontaminatedsediment.Theconsistently dispersedbacterialtaxaarestronglyselectedagainstheavy metals.Underthestressofcombinedheavymetalpollution, theshiftsinbacterialcommunitystructuresweresigniﬁcant betweenthe pollutedand thereference sedimentsamples. TheincreasedOTUsinheavymetalpollutedsedimentscould becausedbyanacquiredtolerancebyadaptation,andashift inspeciescomposition.Theorganismsthatwerealready tol-erantbecamemorecompetitiveandthusmorenumerous.We foundthattheseresults,similartootherinvestigators,further supportthehypothesisthatbacteriainheavymetal contam-inatedsedimentsareincreasedinnumber.38

DespitewidespreaduseofDGGEproﬁledata,afewofthe studieshaveusedthesemultivariateapproachestodetermine relationshipsbetweencommunitycompositionand environ-mentalvariablesasappliedinthisstudy.Forexample,afew studiesidentiﬁedtherelationshipbetweencommunity com-positionandgeochemicalvariablesusingPCAorCCA.10,39,40

Theanalysis,presentedinthisstudy,allowedthecorrelation ofDGGEproﬁleswithawiderrange ofenvironmental vari-ables.Aconsiderablenumberofstudieshavereportedastrong relationshipbetweencommunitycompositionand environ-mentalchemistryinmarinesediments.10,39,41–45 _Our_results

alsosuggestthatcommunitycompositionisstronglyrelated toheavy metalvariables inthesediments ofJiaozhouBay. Therewere significantcorrelationsbetweenmicrobial com-munity profiles and the concentrations ofCo, Zn, Hg, As, and Se inthe sediments. In the CCA analyses, 50.7–71.6% ofthecumulativevarianceoftheOTUs–heavymetal rela-tionshipwithineach stationwasexplainedbythefirsttwo

CCAs(Table2).Now,itappearsreasonabletocombine

clus-teringanalysisofDGGEbandingpatternswithordinationof theheavymetalsusingtheCCAapproachtoanalyzemarine

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sediments.Thebacterialcommunityproﬁlesinmetalpolluted sedimentsamplesweredissimilartothoseintheclean ref-erencesamples(Fig.4).Similarresultswerealsoreportedby Bradetal.(2008).46_Our_results_strongly_support_the_hypothesis

thatenvironmentcontrolsthecompositionanddistributionof microbialcommunities.47,48

Inthisstudy,moleculartechniquesincombination with multivariate analysis were used to identify the associated microbialcommunitiesinbothpristineandheavymetals con-taminatedmarinesedimentsinJiaozhouBay.However,itis importanttobearinmindthatacausalconnectioncannot bedeterminedfromstatisticalanalysisalone.Inaddition,the obtainedcorrelation valuesmight bearesult ofother fac-tors,asCCAdisclosedonly44.2%ofthevariationonthefirst twoaxes; thus 55.8% ofthe variables remain unexplained. Itmustbenoted,however,thatDGGEhasits own method-ologicallimitations,anditsfingerprintingpatterns maynot perfectlyreflectthecommunities.49_The_results_of_the_DGGE

andCCAanalyseswereintuitive,althoughtheconsequences maybeoversimpliﬁed.Duetotheseshortcomings,inherent toallmoleculartechniques,theparameterscalculatedfrom theﬁngerprintsmustbeinterpretedasanindicationandnot anabsolutemeasureofthedegreeofdiversityinabacterial community.50

Asreviewedinliterature,PCR-DGGEincombinationwith multivariate analysis isan efﬁcient methodfor examining theeffectofmetalscontaminationonbacterialcommunity structure.23_This_was_also_conﬁrmed_by_the_observation_that

bacterialcompositionasdepictedbyDGGEwassubstantially relatedtoheavymetalscontamination.Anin-depthstudyof thefactors other than metalsmay explainthedistribution observed.However,theinformationpresentedheremightbe usefulinpredictingthelong-termeffectsofheavymetal con-taminationinthemarineenvironment.

Conﬂicts

of

interest

Theauthorsdeclarenoconﬂictsofinterest.

Acknowledgements

ThisworkwassupportedbytheprogramforNational Natu-ralScienceFoundationofChina(31471812,31171809),andthe FundamentalResearchFundsforJiangsuAcademyof Agricul-turalSciences(ZX(15)4007).

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