Bacterial communities in mining soils and surrounding areas under regeneration process in a former ore mine

brazilian journal of microbiology49(2018)489–502

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

Environmental

Microbiology

Bacterial

communities

in

mining

soils

and

surrounding

areas

under

regeneration

process

in

a

former

ore

mine

夽

Camila

Cesário

Fernandes

a,1

_,

_Luciano

_Takeshi

_Kishi

a,1

_,

_Erica

_Mendes

_Lopes

a

_,

Wellington

Pine

Omori

b

_,

_Jackson

_Antonio

_Marcondes

_de

_Souza

b

_,

Lucia

Maria

Carareto

Alves

a

_,

_Eliana

_Gertrudes

_de

_Macedo

_Lemos

a,∗

a_{UNESP–UnivEstadualPaulista,FaculdadedeCiênciasAgráriaseVeterinárias,DepartamentodeTecnologia,Jaboticabal,SP,Brazil} b_{UNESP–UnivEstadualPaulista,FaculdadedeCiênciasAgráriaseVeterinárias,DepartamentodeBiologiaAplicadaàAgropecuária,}

LaboratóriodeGenéticaAplicada,Jaboticabal,SP,Brazil

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received19June2017 Accepted4December2017 Availableonline13February2018 AssociateEditor:VâniaMelo

Keywords: Ironmine Bacterialdiversity Functionaldiversity Braziliansoils

a

b

s

t

r

a

c

t

HumanactivitiesontheEarth’ssurfacechangethelandscapeofnaturalecosystems. Min-ingpracticesareoneofthemostseverehumanactivities,drasticallyalteringthechemical, physicalandbiologicalpropertiesofthesoilenvironment.Bacterialcommunitiesinsoil playanimportantroleinthemaintenanceofecologicalrelationships.Thisworkshows bacterialdiversity,metabolicrepertoireandphysiologicalbehaviorinfiveecosystems sam-pleswithdifferentlevelsofimpact.TheseecosystemsbelongtoahistoricalareainIron Quadrangle,MinasGerais,Brazil,whichsufferedminingactivitiesuntilitstotaldepletion withoutrecoverysincetoday.TheresultsrevealedProteobacteriaasthemostpredominant phylumfollowedbyAcidobacteria,Verrucomicrobia,Planctomycetes,andBacteroidetes.Soilsthat havenotundergoneanthropologicalactionsexhibitanincreaseabilitytodegradecarbon sources.Therichestsoilwiththehighdiversitywasfoundinecosystemsthathavesuffered anthropogenicaction.Ourstudyshowsprofileofdiversityinferringmetabolicprofile,which mayelucidatethemechanismsunderlyingchangesincommunitystructureinsitumining sitesinBrazil.Ourdatacomesfromcontributingtoknowthebacterialdiversity, relation-shipbetweenthesebacteriaandcanexplorestrategiesfornaturalbioremediationinmining areasoradjacentareasunderregenerationprocessinironminingareas.

©2018SociedadeBrasileiradeMicrobiologia.PublishedbyElsevierEditoraLtda.Thisis anopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/

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

夽_{Thedata setsresults ofthisarticleare available inthe NCBI BioProjectPRJNA290860(https://www.ncbi.nlm.nih.gov/bioproject/}

PRJNA290860).

∗ _{Correspondingauthor.Tel.:+551632097409.}

E-mail:[email protected](E.G.Lemos).

1 _{Theseauthorscontributedequallyforthepaper.}

https://doi.org/10.1016/j.bjm.2017.12.006

1517-8382/©2018SociedadeBrasileiradeMicrobiologia.PublishedbyElsevierEditoraLtda.ThisisanopenaccessarticleundertheCC BY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Introduction

Brazilisoneofthelargestproducersofironorealongwith ChinaandAustralia.Iron oreisarawmaterialusedinthe manufacturingofsteel.Ametalsuppliesthesteelindustry and is used in a varietyof consumerproducts. Brazil has extracted320milliontonsofironoreaccountingforalmost 10%ofworldproductionandfeatureextractionoccursinrocks and further processingof the ore because this material is mixed withother elements.1 _{Increasingly, this feature has}

beenexploitedinthewildbecauseofglobaldemand,which is,inturn,controlledbypopulationgrowthandintensification ofindustrialsectors.Miningactivityiscausingenvironmental changein thelandscape, particularly bychangingthe veg-etation cover and affecting soil and water from the mine boundaryregion.2_{Furthermore,theminingactivitiesrequire}

anadditionalareatobeusedfordisposalofwaste,andyears ofintensificationaffectthequalityofsurroundingsoilsand aquifers and threaten humanhealth and ecosystems with seriousenvironmentalimpact.3–5

Many mining companies are aware of the detrimental effects of mining. Companies have invested research and technologyintohowtopracticesustainableexploitationand environmentalprotection,buttheseeffortshavebeen asso-ciatedwithaneconomicallylesscostlyproductionchain.In mining-contaminated regions, soilplays an importantrole inthetransportandstoringofcontaminants.These charac-teristicsdeterminetheecologicalbalanceandbiodiversityof theecosystem.6_{There isaconsensusinthe literaturethat}

metal-contaminatedsoilexhibitsanextremelycomplexand well-adaptedmicrobialcommunity.7,8_{Itshouldbenotedthat}

thesecommunitiesplayanimportantroleinbiogeochemical cyclingandare involvedinthetransformation ofnutrients suchasNandC.8

Fromanenvironmentalstandpoint,miningpracticesare considered unsustainable due to the removal of natural resourcesuntilitstotaldepletion(i.e.,mineralresourcesare nonrenewable)andthehighnegativeimpactonecosystems, especiallyonsoilhabitats,withtheremovalofsoillayers.9

Procedurestominimizetheimpactsofminingpracticesmight includethemaintenanceofvegetationcoveraswellas preser-vationofsoilmacroand microfauna.Therefore, theuse of practicaltools(i.e.,bioindicators)becomescrucialwhen eval-uatingbiologicalprocessesandpatternsthatmayindicatethe currentenvironmentalcondition ofsoilsystems previously subjectedtominingactivities.10

Consideringthat microorganisms play an essential role inenvironmentalbiogeochemicalcyclingandmayinfluence the speciation and bioavailability of metals, it is relevant toobtainmorecomprehensiveknowledgeofthetaxonomic qualities and diversity of the prokaryotic community in metal-contaminatedsoil.11,12_{Althoughpreviousstudiesofthe}

microbialcommunityinmetal-contaminatedsoilhavebeen performed,3,13_{noneofthemhaveassessedthemicrobial}

com-munity ofa preserved mine area with five different soils, throughtaxonomicdiversityandfunctionalinference evalu-ation.

Inthisstudy,weassessthecurrentenvironmentalstatus ofanarea(CórregodoMeioMine)subjectedtoseveraldecades

(1946–2005)ofminingactivities.Duringthisperiod,deeper lay-ersofsoilwereremovedfortheextractionofironinthearea. Theremovedsubterraneansoilwasaccumulatedonthe sur-faceformingpilesofsoilnexttotheopen-pit(Fig.1).In2006, however,theCenterforResearchandBiodiversity Conserva-tionoftheIronQuadrangle(CeBio)wasestablishedandthe area isnowunderrecovery.TheCeBio comprisesthree dif-ferent typesofnaturalvegetation(i.e.,rainforest,savannah andironstoneoutcrops).Additionally,twoimpactedareasare currentlyunderrecoverywiththeintroductionofinput vege-tation.

Monitoringsoilqualityiskeytoimprovingstrategiesfor therecoveryofminingareas.Inthisstudy,webasedon16S rRNAgenesequencesusinghigh-throughputDNA sequenc-ingandametabolicanalysistoexaminethetaxonomicand inferfunctional compositionofthebacterialcommunityin anoffminingarea,whichcurrentlyisinrecovery,butduring 30 years suffered with mining activities without regenera-tionprocess.Theissueofthisworkisshowingthebacterial diversityoffundamentalimportancetodeepenourcurrent understanding of microbial interactions in mining ecosys-temsinregeneration.Inaddition,themetabolicdiversityof bacterial community present in mining sites makes these environmentsidealforstudiesofgenomes,ecology,evolution, tolerancemechanisms,andtheinteractionsbetweenbacteria andenvironmentalfactors.

Materials

and

methods

Areaofstudy

ThestudywasconductedintheCórregodoMeioMinearea (CeBio),locatednearthecityofSabará,MinasGeraisState, inthesoutheastofBrazil(Fig.1).Themeanannual tempera-tureintheregionis20.8◦Cwithameanrainfallof1500mm. Highyieldingiron(Fe)depositswereextracteduntilmining activitiesceasedin2005duetolowproduction.Basedonthe vegetationandsoilsubstrate,theminingarea(approximately 711ha)isroughlycategorizedintofiveecosystems:Atlantic semi-deciduousforest,neotropicalsavannah,ironstone out-crops,soilcoveredwitheucalyptustreesandsoilcoveredwith grasses.

Soilsamplingandidentification

SamplingwascarriedoutinSeptemberof2013.Three repli-cate sampling plots were randomly establishedin the five ecosystemsforthecollectionofsoilsamples(Fig.1).These includedthreenaturalecosystemswithnolevelorlowlevelof disturbance:AtlanticSemi-deciduousForest(ASF), Neotrop-ical Savannah(BCS) and Ironstone outcrops (SIC); and two disturbedareas:Eucalyptusplantedarea(EFS)andGrass land-scape former Iron-tailing Mound (GIM), with medium/high andhighimpact,respectively(Table1).Thetotalsampled sur-facesoil(0–20cmindepth)wascollected(Fig.1)andpacked insterileplasticbags.Eachbagwassealedandtransportedon icetothelaboratoryforfurtherstudy.Fractionsofthesamples weredividedintwo:onewasusedtostudymetabolicdiversity andtheotherwasstoredat−80◦Cformolecularanalyses.

brazilian journal of microbiology49(2018)489–502

491

Sabará-MG

A

B

ASF EFS BCS SIC GIM

SIC1 SIC6 GIM4 GIM3 GIM2 ASF8 ASF6 ASF7 BCS2 BCS1 EFS1 BCS3 EFS3 SIC2 20m 6m 8m 3m 10m B 12m

Figure1–LocationoftheCórregodoMeiomine,Sabará,MinasGeraisState,inthesoutheastofBrazil.A.Samplingsite distributionwithinthedifferentfiveecosystemsisshownintheenlargedcentralimage(modifiedfromGoogleEarth). Characteristicvegetationofeachecosystem(ASF,EFS,BCS,SICandGIM)isshowninTable1.B.Samplingschemeusedfor collectingsoilssamplesaccordingtoMoreiraetal.(2010)isshowninthehigherright;blackpointrepresentsthesoil samples.

Table1–Ecosystemssampledinthisstudy.

Vegetationtype ID Latitude Longitude Soiltype Impactlevel Vegetationfeatures

Atlantic semi-deciduous

forest(ASF)

ASF6 19◦5141.41S43◦487.90O Clayloam Semi-deciduoussecondaryforestoriginally,

belongingtotheBrazilianAtlanticForestbiome

(BAFB),about12.9%oftheCeBioarea.Itshows

differentstagesofnaturalregeneration

ASF7 19◦5144.60S43◦486.81O SiltyclayloamNonetolow

ASF8 19◦5140.38S43◦484.76O Clayloam

Neotropical

Savannah(BCS)

BCS1 19◦₅₁_48.47_S43◦₄₈_20.08_OClayloam Soilcoveredwithherbaceouslayerdominatedby

mixedgrasses,dicotyledonousherbs,andshrubs

withscatteredtrees(lowerthan8m),about17.8%of

theCeBioarea

BCS2 19◦5145.42S43◦4821.12OLoam Nonetolow

BCS3 19◦5142.38S43◦4822.18OLoam

Ironstoneoutcrops

(SIC)

SIC1 19◦502.06S 43◦447.40O Clayloam CharacterizedbythestrongpresenceofVellosia

compactaandEremanthuserythopappus.Individuals

herbaceousbelongingtoAsteraceaeandCyperaceae

families.

SIC2 19◦505.13S 43◦446.35O Sandyloam Nonetolow

SIC6 19◦507.25S 43◦441.99O Clayloam

Eucalyptusplanted

area(EFS)

EFS1 19◦5140.35S43◦4826.58OClayloam OriginallypartoftheBAFB,itrepresentsabout43.8%

oftheCeBioarea.In2006,treesofEucalyptusspp.

wereintroducedintheareatoremediatethe

impactsoftheminingactivity

EFS2 19◦5136.27S43◦4824.25OClayloam Mediumtohigh

EFS3 19◦5139.32S43◦4823.29OLoam

Grasslandscape

formerIron-tailing

Mound(GIM)

GIM219◦5140.56S43◦4753.75OSandyloam Soilcoveredwithgrass,mostlyMelinisminutiflora

andBrachiariaspp.,about18.8%oftheCeBioarea.

Vegetationwasalsointroducedin2006toremediate

theimpactsoftheminingactivity.

GIM319◦5139.62S43◦4750.61OLoam High

Soilandheavymetalanalyses

SoilanalyseswereconductedintheLaboratoryofSoil Analy-sesattheUniversidadeFederaldeLavras(UFLA).Astandard soilanalyseswascarriedoutforeachcompositionsampleto determinetotalconcentrationsofthefollowingchemicals:K, P,Ca,Mg,Al,Zn,Fe,Mn,Cu,BandS.Additionally,pH,organic matter content and soil texture (clay, silt and sand) were determined.Allthesoilanalyseswere performedfollowing thestandardprotocols ofEMBRAPA.14 _{Cu,Ni andCr}

analy-seswereconductedintheLaboratoryofSoilAnalysesatthe UniversidadeEstadualPaulista(FCAV/UNESP),concentrations were determined by atomic absorption spectroscopy with air-acetyleneflame, according3050Bmethodology USEPA,15

determiningtheconcentrationsofmetalsbyMelichmethod 1.16 _{Allstatisticalanalyseswere implementedwithANOVA}

varianceandTukey’stest(p<0.05)insoftwareR. DNAextraction

MicrobialDNA wasextractedfrom 0.5-gsoilsamplesusing thePowerLyzer® PowerSoil® DNAIsolationKit(MoBio Labo-ratories,SolanaBeach,CA)accordingtothemanufacturer’s protocols.

Bacterialdiversityanalyses

TheV4–V5regionsofthebacterial16SrRNAwereamplified usingPCR(95◦Cfor3min,followedby35cyclesat95◦Cfor30s, 55◦Cfor90s,72◦Cfor45s,andafinalextensionat72◦Cfor 5min)usingprimers17_515F5

-TGTGNCAGCMGCCGCGGTAA-3and926R5-barcode-CCCCGYCAATTYMTT-3;bothprimers aredegenerate.Thebarcodeisatwelve-basesequenceunique toeachsample.PCRreactions wereperformed intriplicate in a 25-␮L mixture containing 8␮L of KAPA HiFi HotStart ReadyMix(2X)PCRkit(KapaBiosystems,Boston,USA),1␮L ofeachprimer(10␮M), and50ngoftemplateDNA. Ampli-conswereextractedfrom2%agarosegelsandpurifiedusing the ZymocleanTM _{Gel DNA Recovery Kit w/Zymo-Spin}TM

IC Columns (Zymo Research, Irvine, CA) according to the manufacturer’sinstructions,andquantifiedusingQubit Fluo-rometricQuantitation(ThermoFisherScientific,USA).Purified ampliconswere pooledinequimolar quantitiesand single-readsequenced(1x400pb)onanIonTorrentPGMSequencer (ThermoFisherScientific,USA)onaseparateIon316v2chip, accordingtothemanufacturer’sprotocols.

Raw data files from sequencing of 16S rRNA gene, region V4-V5, adapter were trimmed using Scythe 0.991

(https://github.com/vsbuffalo/scythe) and Cutadapt 1.7.1.18

The quality of data was filtered by PHRED 20, and dupli-cate readswere removed using the Prinseqprogram.19 _We

used QIIME v. 1.7.020 _{to filter reads and determine}

Opera-tionalTaxonomicUnits(OTUs)atthe≥97%similaritylevel.21

Briefly,we usedthe open reference OTUpicking workflow, wheresequences were first clustered with the Greengenes (GG13899).Chloroplast-derived contaminant andchimera reads that could not be assembled were removed prior to downstream analyses. For computingalpha diversity met-ricsamong substrates,weused alphadiversity.py script in QIIME(chao1,phylogeneticdiversityandequitability),using

the number ofthe smallergroup ofsamples.Weused the betadiversitythroughplots.pyscripttocomputebeta diver-sitydistancesbetweensamples(weightedUniFrac)forboth thephylogeneticcompositionandtherelativeabundanceof taxa.

Predictedmetabolicprofile

Predictfunctionalbacterialcommunityprofilewasperformed byPICRUSt.22_{OTUtablewasbuiltwithQiimeprogram(97%}

similarity)usingGreenGenesbank.23_{OTUswerenormalized}

innormalize by copynumber.pyscriptandmetagenomics infer-encewasmadebypredictmetagenomes.pyscript.

Metabolicdiversity

BiologEcoPlates(Biolog,Inc.,Hayward,CA,USA)wereusedto determinethemetabolicprofilediversityevaluationof micro-bialpopulationsinthefivesoilsofironminingarea,basedon thecarbonsourcestheyutilize.TheEcoPlateiscomposedof31 differentcarboncompoundsdividedintosixcategoriesalong withthecontrolwellsina96-wellmicroplate(TableS3).Three replicatesofeachsubstrateandno-carbonsourcecontrolwere includedonasinglemicroplate.Utilizationofsubstrateswas spectrophotometricallymeasuredbymeansofa tetrazolium-formazanreaction,inwhichcoloredformazandyewasformed from thetetrazolium saltbythemetabolicactivity ofcells. Ecoplatewaspreparedinthefollowingway:2.5gofeach sam-plesoilfromeach regionwaspooled,andatotalof10gof pooledsoilwassuspendedin190mLof0.85%salinesolution andshakenfor30minat30◦C.Next,eachwelloftheBiolog EcoPlatewasinoculatedby120␮Lofthepreparedsuspension andincubatedat28◦C.24_{Absorbanceat590nmwasmeasured}

onBio-RadBenchmarkMicroplateReaderafter24,48,72,96, 120,and144incubationhours.Opticaldensity(ODi)valuefrom eachwellwascorrectedbysubtractingthecontrol(blankwell) valuesfromeachplatewell,accordingtoWeberandLegge.25

Opticaldensityvaluesobtainedat144hofincubation repre-sented theoptimalrangeofopticaldensityreadings.Thus, we used the incubation results from 144h forthe assess-mentofmicrobialfunctionaldiversityandstatisticalanalyses. Microbialactivityineachmicroplatewasexpressedas aver-agewell-colordevelopment,AWCD.26_{Hierarchicalclustering}

ofeachsoilwasusedtoassessmetabolicdiversity.

Clusteranalyseswereusedtoevaluatethesubstratesthat weremostutilizedineachpoolofthesamplesoil.Thedata werestandardizedbytheaveragewellcolordevelopmentin eachmicroplatetoremoveinoculumdensityeffects.27_All

sta-tisticalanalyseswereperformedwithsoftwareR,version3.128

andPASTz.29

Results

Soilsamplesenvironmentalparameters

ThechemicalcharacteristicsofsoilsamplesfromCeBioare presented in Table 2. Data displayed in Table 2 revealed that metal concentration in the CeBio did not exceed the maximumallowableconcentrationsestablishedbyBrazilian

b r a z i l i a n j o u r n a l o f m i c r o b i o l o g y 4 9 (2 0 1 8) 489–502

493

Table2–ChemicalparametersandmetalconcentrationfromsoilsamplesofCeBioandthelimitspermittedbylaw.

Soilsample Organicmatter pH K P Zn Fe Mn Cu B S Cr Ni

(gkg−1_{) (mgkg}−1₎ ASF 4.77(+0.92)c _4.65(+0.11)b _68.5(+0.88)a _{1.9325(+0.16)}a_7.56(+1.44)a _{94.545(+2.35)}a _67.83(+2.50)a _{3.0275(+2.46)}a _7.08(+7.34)a _{11.155(+2.39)}b _12.9(+0.92)ab _15.05(+1.50)ab BCS 3.46(+0.84)b _4.95(+0.11)b _48(+0.32)ab _{1.4975(+0.29)}a_0.88(+0.08)a _{102.735(+8.30)}a _22.46(+4.13)a _{2.1025(+9.05)}abc_{0.0875(+1.23)}a _{11.1175(+6.75)}b _26.65(+0.84)a_16.3(+3.90)a EFS 5.09(+0.25)b _4.725(+0.05)b_38(+0.16)ab _1.935(+0.77)a _0.765(+0.11)a _{122.66(+4.32)}a _{18.2625(+5.44)}a_{2.7625(+5.07)}ab _{0.1225(+3.00)}a _{17.465(+2.65)}ab _27.45(+0.25)a_15.9(+1.75)a SIC 9.57(+0.41)a _4.5(+0.58)b _56(+0.11)ab _3.81(+0.50)a _{1.8025(+0.58)}a _{236.7175(+4.75)}a _{17.0975(+3.40)}a_{0.6975(+2.00)}c _0.25(+1.30)a _{14.4475(+0.58)}ab_9.01(+3.41)b _6.05(+1.75)b GIM 1.32(+0.17)b _5.95(+0.92)a _32.5(+0.16)b _6.65(+0.62)a _{0.6125(+0.40)}a _{43.3275(+2.88)}a _{41.9075(+2.90)}a_0.735(+4.84)bc _0.185(+5.33)a _{28.9375(+0.15)}a _19.4(+0.17)ab _13.95(+1.22)a Background (Nascimento, 2014) 180.9 492.8 1284.5 387.7 17.3 9.00 Background (CONAMA) 12,300 NE NE 35.7 NE NE 37.3 1800

Tukeyanalysis–(smalllettersnodifferencebetweenecosystems). NE–notestablished.

environmentalregulation30 _{forsoil.Fe,Mn,Cr,andNiwere}

themetalspresentinthehighestconcentrationsinthesoil samplesanalyzed.

The physicochemical analyses revealed that except for GIM, pH value from other sites changes slightly from 4.5 to4.95.Another characteristic from GIMisthat it had the highestconcentration ofphosphorus and sulfate, 6.65 and 28.93mgkg−1, respectively. On the other hand,the organic matterpresentsthe lowestconcentrationincomparisonto theotherssoils,1.32gkg−1.

ThemaximumconcentrationvaluesofFe,Mn,Cr,andNi wereobservedinSIC,ASF,EFS,andBCS,respectively.Although the Fe concentration was high in all samples, this is not regardedasanindicatorofmetalpollutionintheSICsoil,a nativeandminedsoilfromIronQuadrangle(Table2). Bacteriadiversityanalyses

ThesequencingofV4andV5regionof16SrRNAgenefrom metagenomicDNAwasusedtostudythebacterialdiversity ofsoils. Atotal of8,290,316 reads and 39,858OTUs ofthe wholedataset wereobtainedfrom fifteensamplesthrough IonTorrentPGMsequencinganalyses.Eachlibrarycontains 85,313–221,574totaltrimmedreads(Table3).Sequenceswere trimmed and quality control Phred ≥20 applied. All the resultswere submitted in Sequence ReadArchive (SRA) in

NCBI(TableS1).Thesegroupsofsequenceswerealignedin

databaseSILVA.31 _{Wecheckedthegene region(V4–V5)and}

usedsequenceswith0.97identitycutoff.Forstatistical analy-ses,datawerenormalizedusingBCS1(28,750reads)asabasis, becauseit wasthe groupthat showedless sequencereads

(Table3).Filteringthe matrix ofdistance0.03 betweenthe

sequences(3%different),OTUsfor␣-diversitywereobtained togeneratetherarefactioncurve,richness(ACEandChao1), andindexdiversity(ShannonandSimpson)(Table3).

Allrarefactioncurves tendedtoapproachthesaturation plateau,indicatingthepresenceofalargevariationinthetotal numberofOTUsindifferentsamples(1291–3780OTUs). Com-paredsoilsamplesGIMhavethehighestOTUdiversityvalues; moreover,thecalculationofspeciesrichness(Chao1), even-ness(ACE),and ShannonandSimpson indexallconfirmed increasingdiversity, evenin samplesinwhich the total of sequencesandOTUswerelower.GIMsoilpresentedahigher valueforallindicesofdiversity:4042OTUsforACE,9.52and 0.99forShannonandSimpson,respectively.ASFsoilhadlower valuesforthe sameindices, indicatingthatGIMisanarea thatisnotinbalanceyet,inrecovery,orunderconstruction

(Table3).

BacterialcommunitycompositionofCeBio

Sequencesthatcouldnotbeclassifiedintoanyknowngroup, Bacteriaand Archaea domains, were assigned as unclassi-fied.ThebacterialOTUswereassignedinto30differentphyla, 275 families, or 395 genera.Bacteria were by far the most abundantprokaryoticdomain.Thedominantphylaacrossall sampleswereProteobacteria,Acidobacteria,Verrucomicrobia,and Planctomycetes.However,Bacteroideteswasthemostabundant phylum(p<0.001)(SupplementMaterialS2)inGIMsoil com-paredtotheotherssoils.Althoughtherearedifferencesinthe

abundanceofphyluminallsamples,thereplicateshavethe sameprofile.

BacterialfamilydiversityisshowninFig.2.MostOTUswere affiliatedwithHyphomicrobiaceae,Chthoniobacteraceae, Koribac-teraceae,Gemmataceae,andBradyrhizobiaceae.Similarpatterns ofbacterialcommunitystructurewereobservedwithinBCS, SIC, EFS, and ASF soils, while it was different within the GIMsoil.ThefamilyChitinophagaceaedistributionvariedunder differentsoils,itwasmoreabundantinGIMsoilwhen com-paredtotheothers.Ontheotherhand,representationofthe Chitinoniobacteraceae family was very low inGIM soilwhen comparedtoBCS,SIC,EFS,andASF. Thefamily Chitinopha-gaceaebelongstotheBacteroidetesphylum,whichisconsistent withourresultsforGIMsoilsamplesthathadahigher pro-portionofthesebacteriumphyla.

Whenweanalyzedthegenerafromthebacteria popula-tionineachsoil(Fig.3),wecanseethatthemostabundant generainallsamplesfromCeBioareRhodoplanes,despitethe factthatunclassifiedandothergenerahavemorethan30%in abundance,mainlyinGIMsoil.AnotherfindingisthatGIMsoil hadthehighestnumberofrepresentativesfromFlavisolibacter genera.TherepresentativesfromthesegeneraareTerrimonas, Niastella,andChitinophagainthephylumBacteroidetes. Predictedmetabolicprofile

Predictfunctionaldiversityfromecosystemswasassessedby the abundance ofgenesidentified inPICRUSt in theKEGG database,andweusedmetabolicvialevel-2(Fig.4).According tothisanalysis,wecanobserveagroupingintothreedistinct groups,onecomprisingBCSandASF,non-minedareasfrom CeBio, the EFSan intermediate groupand, the other com-prising SICand GIMnative soiland soilmining recovering areafrom CeBio.Therewereafewdifferencesinthe abun-danceoflevel-2KEGGpathwaysinecosystemsoils.Onlythree pathways changedsubstantially:“amino acid metabolism”, “carbohydratemetabolism”,and“membranetransport”.The mostfunctionaldiversityforthesepathwayscanbeobserved in SIC and the lowest in BCS. Others pathways have the sameabundanceinthedifferenttypesofsoilbutinsmaller amounts.

Metabolicdiversityandcommunity-levelphysiological profile(CLPP)

ThemetabolicprofileofthebacterialcommunityofCeBiosoils wasassessedusingBiologEcoplate(Biolog,Inc.).Thelistof carbon substrates are shown inTableS3. Carbon substrate utilization byBiologEcoPlatesshowedadifferentmetabolic activity in bacterial community from CeBio, the ability of degradedcarbonsourcesinGIMisverydifferenttoSIC,ASF, EFS,andBCS(Fig.5).

Principal coordinates analyses (PCoA) was used to ver-ify the correlation in the community structure differences underdifferentsoilsamples.TheresolutionsofPCoAbetween physicochemical parameters, metal concentration, carbon source, and ecosystems were different (Fig. 6). Soil sam-ples exhibited different preferences for consumption of carbonsources,highlightingcarbohydrates,carboxylicacids, amino/amide,andphenoliccompounds.Ecosystems,suchas

brazilian journal of microbiology49(2018)489–502

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Table3–Sequencingresultsanddiversityestimates.

Sample Sequencingresults Diversityestimates

Totalsequences TotalOTUs ACE Chao1 Shannon Simpson

BCS1 85,313 1931 2534 2522 8.274 0.986 BCS2 207,062 2011 2368 2429 7.538 0.974 BCS3 576,501 3280 3502 3577 9.012 0.993 MeanvalueBSC 2407 2801 2843 8.27±0.73 0.98±0.009 SIC1 535,103 3109 3383 3487 8.810 0.992 SIC2 562,264 2489 2743 2674 7.740 0.983 SIC6 446,362 3037 3372 3463 8.348 0.990

MeanvalueSIC 2878 3166 3208 8.29+0.53 0.99+0.004

GIM2 540,761 3438 3745 3770 9.329 0.995

GIM3 832,481 3780 3999 4103 9.560 0.996

GIM4 171,291 2792 3259 3360 8.764 0.993

MeanvalueGIM 3777 4042 3744 9.52+0.18 0.99+0.005

EFS1 531,479 2500 2731 2753 8.440 0.990

EFS2 263,852 2483 2796 2837 8.473 0.988

EFS3 97,912 2029 2482 2494 8.405 0.982

MeanvalueEFS 2337 2669 2695 8.43+0.03 0.99+0.004

ASF6 570,726 3341 3544 3561 9.146 0.993

ASF7 221,574 2347 2719 2764 7.785 0.985

ASF8 653,469 1291 1389 1411 7.687 0.986

MeanvalueASF 2326 2550 2579 8.20+0.83 0.92+0.004

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

BCS1 BCS2 BCS3 SIC1 SIC2 SIC6 GIM2 GIM3 GIM4 EFS1 EFS2 EFS3 ASF6 ASF7 ASF8

Streptomycetaceae Pseudonocardiaceae Nocardioidaceae Gaiellaceae Pirellulaceae Oxalobacteraceae Comamonadaceae Sphingomonadaceae Planctomycetaceae Bacillaceae Xanthobacteraceae Ktedmnobacteraceae Isosphaeraceae Caulobacteraceae Methylocystaceae Burkholdenaceae Thermogemmatisporaceae Syntrophobacteraceae Ellin515 Mycobacteriaceae

Others and unclassified Hyphomicrobiaceae Chthoniobacteraceae Koribacteraceae Gemmataceae Bradyrhizobiaceae Rhodospirillaceae Sinobacteraceae Acidobacteriaceae Acetobacteraceae Solibacteraceae Chitinophagaceae Conexibacteraceae Beijerinckiaceae Nitrososphaeraceae

Figure2– TaxonomicdistributionofbacterialfamilyinallsoilsamplesfromCeBio.

GIMand SIC,showed higherdegradationofcarbon sources

relatedto carbohydratesand phenolic compounds. Indeed,

forASF,EFS, andBCS, ecosystemsdisplayedcarbon source

degradation related to carboxylic acids, amino/amide, and

carbohydrates. These results demonstrated that bacterial

communityinGIMandSIC,miningareas,hadsignificant dif-ferencesindegradationofcarbonsourcescomparedtoASF, EFSandBCS,areasthatwerenotmined.

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

BCS1 BCS2 BCS3 SIC1 SIC2 SIC6 GIM2 GIM3 GIM4 EFS1 EFS2 EFS3 ASF6 ASF7 ASF8

Candidatus_nitrososphaera Flavisolibacter Burkholderia Mycobacterium Candidatus_solibacter Bosea DA101 Candidatus_koribacter Bradyrhizobium Candidatus_xiphinematobacter Rhodoplanes

Others and unclassified

Figure3–TaxonomicdistributionofbacterialgenerainallsoilsamplesfromCeBio.

Discussion

Theenvironmentalimpactofminingiscurrentlyamajor con-cernbecauseitsprocessesreleasetoxicmetals,suchasFe,Mn, Cr,andNi,insoil.Oneofthemostpersistentandcomplex environmentalissuesismetalpollution,becauseofthreatto ecosystemsandhumanhealth.InBrazilandinotherpartsof theworld,metalsarerawmaterialsusedforthe manufactur-ingofsteel.Ironoreisthemostwidelyusedmetal,which suppliesthe steelindustry aimedatavarietyofconsumer productsbasedonthisore.Ontheotherhand,thecompanies thatexploretheseareasshouldpracticehighcontrolofthese miningareastoavoidcontaminationinthesoil,sediment,and water.

Microorganismsconstitute the oldestmembers ofliving systemsandpossesshigheradaptabilitytothriveinadverse conditions.Themicrobialcommunityplaysanimportantrole inthe soilsystem,especiallyinextremeecosystemswhere theyareresponsibleforthemajorityoforganicmatter decom-position.Thedatasetpresentedinthisstudyisataxonomical andfunctionalcharacterizationofthemicrobialcommunityin oneofthelargestminingregions(CeBio),usingmetagenomics approach.

Taxonomicanalysesrevealedthatahighlycomplex bac-terialcommunitywaspresentinCeBiosoils.Taxonomicdata indicatedthatProteobacteriawasthemostabundantdivision, comprising approximately 50% ofthe bacterialcommunity inall soils.This finding is inaccordance withother stud-ies on bacterial community soils,32–34 _{in which the most}

dominantwasnotedtobeProteobacteria.Theseauthors con-cluded that Proteobacteria could represent 25–40% of total sequencesbyclonelibrarystudiesor42–50%obtainedbynext

generationsequencing.Lieberetal.,35 _{summarizedthat, ␣-,}

␤-,and␥-Proteobacteria,Actinobacteria,Acidobacteria,andtoa lesserextentFirmicutes,Bacteroidetes,andPlantcomyceteshave been identified as major phyla; however, recognizing that their relativeabundancesand presenceinvarioustypesof soils.StudiessuggestthattheabundanceoftheActinobacteria and Firmicutesphyla issignificantly correlated with metal-contaminatedenvironments.36–38_{However,donothavemetal}

contaminationfoundinCeBiosoils,becauseofitthesephyla donotappearinabundance.

In a work with NeutralMine DrainageChannel, Pereira etal.,39_{showedthatthephylumProteobacteriawasthemost}

abundantfollowedbyAcidobacteriaandActinobacteriainareas withhighamountofcopperandthatpHinfluencedShannon andSimpsonindicesshowinghighervalues,suggestingless dominanceandgreaterdiversityinthealkalineenvironments, revealingthattherewasnosignificantdifferencebetweenthe drainageandsoilsamplesintermsofdiversity.

Ourresults,bothsoilpHandsoiltotalcarboncontent dom-inantly affectedthe composition and diversityofthe soil’s bacterialcommunity,andtheeffectofsoilpHwasstronger thanthatofsoilcarboncontent.ThepHisanintegrating vari-able indirectly influencing microbial community structure. ThereasonofthehighestpHvalue,concentrationof phos-phorusandsulfateandthelowestorganicmatterinGIMsoil, indicatingahighenvironmentalheterogeneity.This hetero-geneitycanbeattributedtothedifferencesintimeofmining disposal, local availability of airand water, and the levels of nature of microbial metabolic activities of the different samplingsites.Changesinsoilmanagement,includingland coverage,mayleadtochangesinenergyresources,thus influ-encingtheabundance,diversityandtrophiccompositionof soilmicroorganisms.40,41

brazilian journal of microbiology49(2018)489–502

497

Metabolism of cofactors and vitamins Translation

Cellular processes and signaling Nucleotide metabolism Metabolism

Cell motility

Xenobiotics biodegradation and metabolism Lipid metabolism

Energy metabolism Poorly characterized Republication and repair Genetic information processing Signal transduction

Glycan biosynthesis and metabolism Folding, sorting and degradation Metabolism of other amino acids Enzyme families

Transcription

Metabolism of terpenoids and polyketides Endocrine system

Transport and catabolism Infectious diseases Neurodegenerative diseases Signaling molecules and interaction Cell growth and death

Cancers

Environmental adaptation Nervous system Metabolic diseases Immune system diseases Circulatory system Cardiovascular diseases Immune system Digestive system Excretory system Cell communication Sensory system

Biosynthesis of other secondary metabolites Amino acid metabolism

Carbohydrate metabolism Membrane transport

BCS ASF EFS GIM SIC

Color key

HeatMap

1e+07 Value

2e+07

Figure4–AheatmapofhierarchicalclusteringshowingPICRUSt-predictedcommunitymetagenomesoffiveCeBiosoils samplesand41level-2KEGGpathways.

Inforestsoil,Rodrigues etal.,42 _{described that the}

con-versionofaforestintoarablesoilsthelocaltaxonomicand phylogeneticdiversityofsoilbacteriaincreasesafter conver-sion,but communitiesbecomemoresimilarinspace. This homogenizationisdrivenbythe lossofforestsoilbacteria withrestricted(endemic) bandsand thus resultsinlossof diversity.

Thepositiverelationshipbetweentheabundanceof Acti-nobacteriaand Bacteroidetes andsoil pHhavebeen reported indifferentsoils,43,44 but were notobservedinCeBiosoils, unlessinGIMsamples,whereBacteroidetesarepresentinhigh abundance.Lauberetal.,43_{alsofoundapositivecorrelation}

betweenthepHofthesubstrateandtherelativeabundance ofBacteroidetes.Indifferentstudiesofbacterialcommunities in soilBacteroidetes phylum have been found in cultivated fields,38,43_{greenhousesoils,}44_{andunexploitedareas.}45–47

EnvironmentalBacteroidetesare specializedin the degra-dationofcomplex organic matter,48 _{they are versatilein a}

range of biopolymers that they can use as carbon and as

an energy source; these biopolymers include plant, algal, or animal compounds. The description of new taxa often resultsfromscreeningofenvironmentalsamplestodiscover originalenzymaticactivitieswithpotentialbiotechnological applications.49–51

OurresultsindicatedthatBradyrhizobiaceaeand Rhodospir-illaceae are present in highamounts in CeBiosoils, mainly inGIMsamples,wherethesoilispredominantcoveredwith grass,mostlyMelinisminutifloraandBrachiariaspp,andthese familiesaremembersofnitrogen-fixingandphotosynthetic bacteria. It has been suggested that native nitrogen-fixing plant management combined with nitrogen-fixing bacteria couldbeaneffectivemeasurefortherecoveryofadegraded ecosystem.52_{Theseideascanbeconvenientforre-vegetation}

in the CeBio mining area;the success ofcreeperlegumes, suchasDolichoslablab,ViceasativaL,CanavaliaensiformisDC, Desmodiumovalifolium,Arachispintoi,Lotononisbainesii, Stylosan-thesspp.,canthusbelargelyattributedtotheirabilitytoform anitrogen-fixingsymbiosis.Becauseofthebacterialactionof

Color key Heatmap 0 0.05 Value 0.1 GIM SIC . EFS ASF . BCS . D,L-alpha-glycerol phosphate L-threonine Teween 40 D-mannitol I-erythritol L-serine D-xylose Beta-methyl-D-glucosidase Glycogen Itaconic acid Alpha-ketobutyric acid 2-hydroxy benzoic acid Alpha-cyclodextrin Hydroxybutyric acid Alpha-D-lactose Glycyl-L-glutamic acid Glucose-1-phosphate Phenylethyl-amine D-glucosaminic acid 4-hydroxy benzoic acid Putrescine

D-malic acid

D-galactonic acid lactone D-cellobiose

Teween 80 L-asparagine

Pyruvic acid methyl ester N-acetyl-D-glucosamine D-galacturonic acid L-phenylalanine

L-arginine

Figure5–Heatmapofhierarchicalclusteranalysesfrom15soilsamplesand31sourcesofcarbonshowingmetabolic diversityandcommunity-levelphysiologicalprofile(CLPP)inalltimeofincubation.

absorbing,oxidizing,decomposing,andreducingpollutants, bacteriahad asignificant contributiononecological health andsoilpurification.

Themicrobial communityphysiological profile analyses based on the ability to use different carbon sources has beensuccessfullyusedtocharacterizemicrobialdiversityin differentenvironments.53–55_{Soilsunderstressandtheir}

pre-viously adapted microbiota are an alternative to areas of recoverystudies usingbioremediationor phytoremediation processesandtoxiccompoundsreleasedtotheenvironment becausetoxiccompoundsareharmfultothehealthofliving organisms.56–58_{Ourdatashowedahighmetabolicdiversityin}

theCeBiosoils,thedifferencesintheabundanceof microor-ganismsobservedbyrichnessanddiversityindicesarisewith apossibleapplicationinbioremediationorphytoremediation. Furthermore,otherstudieshavealsoreportedthatinmining areasshowedawiderangeofprokaryotic.13,59–61

DuringtheFeextractionperiodwhentheminewasactive, deepersoillayersweredepositedonthesurface.Additionally, thenaturalvegetation(ASF)inthissitewasentirelyremoved

withminingactivities.Theintroductionofgrassvegetation on the almost sterile soil in the GIM may have primarily facilitatedthere-colonisationandincreaseoftheabundance oftheBradyrhizobiaceaeandRhodospirillaceae,environmental stressors can alsoresultinadominance ofthesebacterial communitieswhennaturalvegetationisreplacedby agricul-turecultivars,andespeciallywhencultivationisinmonocrops (e.g.,grasslandinthisstudy).52

Thepredictivefunctionalprofilingofthebacterial commu-nitiesofCeBiosoilsshowedthat“metabolism”wasthemost dominantcategory,ofwhich“aminoacidmetabolism”, “car-bohydratemetabolism”and“membranetransport”werethe topthreeabundantpathways.Inthe“aminoacidmetabolism” pathway,whichiscommonlyusedbybacteriafor osmoregu-lation,soilshavebeenexposedtofrequentmoisturestress, mainlyGIMarea.62,63 _{“Carbohydratemetabolism”involvesa}

complex seriesofenzymatic stepstoconvert external sub-strateintometabolicprecursors,suchasacetyl-CoA,pyruvate, andd-fructose-6-phosphate,anditinvolvesahydrolysis reac-tioninwhichpolymersarehydrolyzedtomonomers.64,65_The

b r a z i l i a n j o u r n a l o f m i c r o b i o l o g y 4 9 (2 0 1 8) 489–502

499

Polimers Amino acids L-threonine Teween 80 Glycogen L-serine D-cellobiose D-mannitol GIM SIC i-erythritol D-xylose Fe S pH Cu Zn B Alpha-cyclodextrin D-malic acid Organic matter 2-hydroxy benzoic acid Beta-methyl-D-Glucosidase

Itaconic acid Alpha-ketobutyric acid

Putrescine Ni L-Arginine BCS Cr Phenylethyl-amine N-acetyl-D-glucosamine L-asparagine Teween 40

Alpha-D-lactose D-glucosaminic acid L-phenylalanine ASF Mn D-galacturonic acid Glycyl-L-glutamic acid EFS Hydroxybutyric acid

Pyruvic acid methyl esterD,L-Alpha-glycerol phosphate D-galactonic acid latone Glucose-1-phosphate 4-hydroxy benzoic acid

-0.030 -0.024 -0.018 -0.012 -0.006 Phenolic Carbohydrates -0.024 0.012 0.018 0.006 Carboxylics acids Carbohydrates PC1 71,9% 0.024 0.018 0.012 0.006 -0.006 -0.012 -0.018 -0.024 -0.030 Carboxylics acids Amino/Amide

“membranetransport”pathwayinvolvesaneffluxofthe irri-tantmetalsoutsidethecellbytransporters,transformationof themetalsinto non-toxic/lesstoxicforms,andbiosorption. Often,biosorption and enzymatic conversion ofmetalinto anotherformoccuratthesametime;ametalmaybeabsorbed intothecell,whichprecipitatethemetalassalt.61,64,66

Thespatial distribution ofsoilbacterial communitiesis stronglylinkedtophysicalpropertiesofthesoil.Wefound thatthespatialdistributionofbacterialcommunities’ assem-blagesseemstobedeterminedbyacombination offactors suchassoilproperties,typeofsubsystems/vegetationaswell asimpactlevel,andgeographicdistributionofsamples.For example,withinBCSarea,sampleswere closelyassociated withASF,aswellasSICandGIM.

Inrelationtovegetationcoverage,notedthatalmost all high bacteria taxa diversity and intense metabolic profile weremoreabundantinareasnocoveredbyvegetationwhen comparedwithsimilar areaswithvegetation,showingthat bacterialcommunityiswelladaptedtoenvironmentalunder stress,suggestingatransitionfromsoilswithsparsegrass veg-etation(GIM)tothoseofdensewoodedareas(ASF,BCSand EFS),observedinthePCoAanalyzes.

Ontheotherhand,areasre-coveredwithEucalyptusspp. have positively affected bacterial composition, resulting in similarcharacteristicstothosefoundinnaturalareas.This factinfersthatEFSareadidnotdifferentiatefromnaturaland none/low impactarea,but diddifferentiate from GIMarea (highimpact). Altogether,thesefindings suggest apossible strategytowardrecoveryEFStopreviousrainforestconditions.

Conclusion

Thestudyofmicroorganismsbymetagenomics isa transi-tion from classical microbiology to modern one. This new areaislikelytobeanimportantcontributortosolve differ-entproblems.Metagenomicsisanewbranchesofknowledge andspecialization.67,68_{Inconclusion,ourdatarevealthatthe}

microbialcommunitiesfromCeBiosoilshavesignificantly dif-ferentfeaturesthatdependontheparticularareasthathave undergoneanthropological actions,including mining activ-ities (GIM and EFS), from areas that have not experienced anthropologicalactions(SIC,ASFandBCS).Thisstudy pro-videsimportantinsightsintothestructureoftheprokaryotic communityofaminingandsurroundingareaunder regen-erationprocessindicatingapossibleroleforthiscommunity. Theinferenceoffunctionaldiversityanalysesunveiledahigh degreeofdiversity,indicatingthatthismicrobialcommunity iswelladaptedtoenvironmentsunderstress,likeGIMsoil. Finally,theresultsreportedhereexpandthecurrent knowl-edgeofthemicrobial taxonomicinminingareas;our data maybearelevantcontributiontotheexplorationofnatural bioremediationorphytoremediationstrategiesinminingand surroundingareasunderregenerationprocessinaformeriron mine.

Conflicts

of

interest

Theauthorsdeclarenoconflictsofinterest.

Funding

information

ThisworkwassupportedbyResearchProjectsforTechnologic Innovation–PITE–FAPESP/FAPEMIG/FAPESPA/VALES.A.,n◦ 01/2010(grants:2010/51316-0;2012/20022-6;2014/14234-6).

Acknowledgements

WegratefullyacknowledgeUniversidadeEstadualPaulista– UNESP and Universidade Federal deLavras –UFLA forsoil sampleanalysis,supportedbyDepartmentofSoilScience.We wouldliketothanktheProgramadePósGraduac¸ãoem Micro-biologia Agropecuária,UNESP, Jaboticabal, São Paulo State, Brazil.WealsothankUniversidadeEstadualPaulista–UNESP, DepartamentodeTecnologia,LaboratórioMultiusuário Cen-tralizado para Sequenciamento de DNA emLarga Escalae AnálisedeExpressãoGênica–LMSeqforsequencing(grants: FAPESP2009/53984-2).

Appendix

A.

Supplementary

data

Supplementarydataassociatedwiththisarticlecanbefound, intheonlineversion,atdoi:10.1016/j.bjm.2017.12.006.

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