1-s2.0-S0043135423007790-main原版完整文件

Runningtitle:MicrobialcommunitysuccessioninanAMD-impactedriver

Keywords:microbialcommunitysuccession,acidicminedrainage,communityassembly,microbialnetwork

Typeofarticle:Researcharticle

Abstract

Acidminedrainage(AMD)servesasanidealmodelsystemforinvestigatingmicrobialecology,interaction,andassemblymechanisminnaturalenvironments.WhilepreviousstudieshaveexploredthestructureandfunctionofmicrobialcommunitiesinAMD,thesuccessionpatternsofmicrobialassociationnetworksandunderlyingassemblymechanismsduringnaturalattenuationprocessesremainelusive.Here,weinvestigatedprokaryoticmicrobialdiversityandcommunityassemblyalonganAMD-impactedriver,fromtheextremelyacidic,heavilypollutedheadwaterstothenearlyneutraldownstreamsites.Microbialdiversitywasincreasedalongtheriver,andmicrobialcommunitycompositionshiftedfromacidophile-dominatedtofreshwatertaxa-dominatedcommunities.Thecomplexityandrelativemodularityofthemicrobialnetworkswerealsoincreased,indicatinggreaternetworkstabilityduringsuccession.Deterministicprocesses,includingabioticselectionofpHandhighcontentsofsulfurandiron,governedcommunityassemblyintheheadwaters.Althoughthestochasticityratiowasincreaseddownstream,manganesecontent,microbialnegativecohesion,andrelativemodularityplayedimportantrolesinshapingmicrobialcommunitystructure.Overall,thisstudyprovidesvaluableinsightsintotheecologicalprocessesthatgovernmicrobialcommunitysuccessioninAMD-impactedriverineecosystems.Thesefindingshaveimportantimplicationsforin-situremediationofAMDcontamination.

Introduction

Acidminedrainage(AMD),generatedfromthemicrobially-mediatedoxidativedissolutionofsulfide-containingmines,istypicallycharacterizedbyextremeacidity,highconcentrationsofmetal(loid)s,andsulfate(RothschildandMancinelli,2001).AMDcreatesaharshenvironmentforlifeandharborsmicrobialcommunitieswithrelativelylowdiversities,whicharecapableofadaptingandsurvivinginthisextremeenvironment(BakerandBanfield,2003).Asaresult,AMDservesasanidealmodelsystemforstudyingmicrobialecologyandevolutioninnaturalenvironments(Huangetal.,2016).WhiletheproblemofAMDpollutionissevereandwidespread,someAMD-impactedenvironmentsundergonaturalattenuationprocesses,suchasinpitlakes,waterreservoirs,streamsorrivers(Parketal.,2016).Decipheringthemechanismsthatunderliethebiogeochemistryandbioticandabioticinteractionsinthissystemwillprovidevaluablecluesforin-situremediationofAMDpollution.

AMDmicrobialcommunitiesaredominatedbyProteobacteria,NitrospiraandEuryarchaeota,includingironand/orsulfuroxidizerssuchasAcidithiobacillusspp.,Leptospirillumspp.,andFerrovumspp.(ShuandHuang,2021).AbiogeographicalstudyofgeochemicallydiverseAMDrevealedthatpHprimarilyshapedthemicrobialcommunitystructure,regardlessofgeographicalisolation(Kuangetal.,2013).Morerecently,microbialecologyresearchhasshifteditsfocustodecipheringthemicrobialinteractionsandassemblymechanismsunderlyingmicrobialcommunitydiversity,structureandbiogeography(ZhouandNing,2017).However,studyingmicrobialinteractionsposesasignificantchallengesincemostoftheseinteractionscannotbedirectlyobserved.Networkanalysishasprovenusefulininferringpotentialinteractionsbyidentifyingrobust,non-randomassociations(FaustandRaes,2012).

Thenetworkstructureandcomplexityhavebeendemonstratedtoaffectcommunitystabilityandecosystemfunctioning(ThebaultandFontaine,2010).AstudyconductedinanAMDlakefoundthatmicrobialnetworksmainlyconsistedofpositiveassociationswithlownetworkcomplexity,indicatingthatmicrobesmightcooperateindetoxificationorcross-feedinginresponsetoenvironmentalstress(Sheetal.,2021).Accordingly,deterministicfactors,suchashabitatfiltrationandselectionplayedamajorroleinshapingAMDmicrobialcommunitystructure.

AMDstreamsandriversareloticsystems,whicharedistinctfortheirrepresentationofacontinuousspatialandtemporalrangefromtheheadwaterstotherivermouth(Readetal.,2015).Theseenvironmentsreceiveadditionalinputsfrombothnaturalandanthropogenicsourcesduringthetransition,andbiologicaldispersaloccurredthroughwatermovement,unlikemostterrestrialecosystemswheredispersalislimited(Marietal.,2014).Therefore,therulesgoverningmicrobialbiogeographyandsuccessioninmorestaticenvironmentssuchasAMDsoils,tailings,sediments,ordiscreteAMDstreamsmaynotbeapplicabletotheconstantturnoverandsuccessioninAMDstreamsorrivers.PollutantsinAMD-impactedriver,suchasacidity,sulfate,andheavymetals,tendedtodecreasefromheadwaterstodownstreamduetoprecipitationandadsorptionintosediments,orwaterdilution(Linetal.,2007).Correspondingly,bacterialdiversityandtheabundancesofmetal-resistantmicrobesexhibitedclearsuccessionpatterns,coincidingwithchangesinwaterchemistry(Desoeuvreetal.,2016).Nevertheless,thesuccessionpatternsofmicrobialinteractionsandassemblymechanismsinAMDloticsystemsremainuncertain.

Inthisstudy,weaimtoinvestigatehowmicrobialcommunitycompositions,networks,andassemblymechanismsinthewaterandsedimenthabitatchangedcoincidingwith

physiochemicalgradientsalonganAMD-impactedriver,revealingmicrobialresponsepatterntoenvironmentalstress.Weproposethreehypothesestotest:(i)themicrobialcommunitiesshiftfromacidophile-dominatedtofreshwatertaxa-dominatedwiththeincreaseinpH;(ii)themicrobialdiversityandnetworkcomplexityincreasedownstream;(iii)deterministicprocessesmainlygovernthemicrobialcommunitiesinacidicheadwaters,whilestochasticprocesseshavemoreinfluencedownstreaminneutralenvironments.Ourresearchrevealsclearsuccessionpatternsofmicrobialcommunitycompositions,functionsandassociationnetworksinbothwaterandsedimentalongtheAMDriver,andweidentifythebioticandabioticfactorsthatcontrolthemicrobialcommunitystructure.

Materialandmethods

Studysiteandsamplecollection

TheriverwestudiediscalledHouziriverinShaanxiProvince,China.TheriverisatributaryinHanrivercatchment,withatotallengthof27kmandacatchmentareaof133km2.Thisareahasacontinentalmonsoonclimate,withanannualaveragetemperatureof15.1oCandanannualprecipitationof787.4mm.TheriversourcewasheavilypollutedbyAMDwithapHofapproximately2.49.Wecollectedwaterandsedimentsamplesfrom5sitesalongadistanceof0.5–26kmfromthesourceoftheriver,whichweredesignatedasSiteA,B,C,D,andE(Fig.S1).ThepHvaluesatthesefivesitesrangedfrom2.49to6.35(TableS1&S2).Ateachsite,werandomlycollectedfivereplicateswithinadistanceof10meters.Forsedimentsamples,wecollectedapproximately250gramsandstoredtheminsterilecentrifugetubes.Forwatersamples,wecollected0.5litersofwaterandstoredtheminsterileplasticbottles.Sampleswere

immediatelyplacedontoiceandtransportedtothelaboratorywithin48hours.Subsequently,samplesweredividedintotwosubsamples:onewasstoredat4℃forphysiochemicalanalyses,theotherwasstoredat-80℃formicrobialanalyses.Microbialmaterialsinwatersampleswerecollectedusing0.22μmporesizemembranefilterspriortofreezing.OurcollectionofsamplestookplaceinMarch2021.

Environmentalvariableanalyses

Sedimentsampleswereairdried,fullygrounded,andsievedthrougha20-mmmeshbeforeconductingphysicochemicalanalysis.ThepHvaluesandelectricalconductivity(EC)weremeasuredina1:2.5(w/v)aqueoussolutionusingapHmeter(PB-10,Sartorious,Göttingen,Germany)andanECmeter(DDSJ-308F,RexElectricChemical,Shanghai,China),respectively.Netacidgenerationcapacity(NAG)andNAG-pHweremeasuredaspreviouslydescribed(Shuetal.,2001).Sulfatewasmeasuredina1:5(w/v)aqueoussolutionusingtheturbidimetricmethod,whiletotalsulfur(S)wasmeasuredafterHNO3-HClO4digestionusingthesamemethod.Totalorganiccarbon(TOC)wasmeasuredusinganultravioletspectrophotometer(UV2800S,SunnyHengping,Shanghai,China)afterK2Cr2O7-H2SO4digestion.Totalnitrogen(N)andtotalphosphorus(P)weremeasuredusingtheNessler'sreagentspectrophotometrymethodandmolybdenumbluemethod,respectively,afterH2SO4-HClO4digestion.Ferrous(Fe(III))andferricirons(Fe(II))weremeasuredusingthe1,10-phenanthrolinemethodafterextractionin0.5MHCl.Theconcentrationsoftotalheavymetalsofcopper(Cu),manganese(Mn)andzinc(Zn)weremeasuredusinganatomicabsorptionspectrophotometer(AA-7000,Shimadzu,Kyoto,Japan)afterHCl-HNO3-HClO4digestion.Theconcentrationsofavailableheavymetals(Cu,Mn,

Zn)wereextractedusingdiethylenetriaminepentaaceticacid(DTPA)andalsomeasuredbytheAA-7000equipment.

Watersampleswerefilteredthroughtwolayersofquantitativefilterpaper(poresizeof30-50μm)beforeconductingphysicochemicalanalysis.ThepHvaluewasmeasuredusingthepHmeter.Sulfatewasmeasuredusingtheturbidimetricmethod.TOCwasmeasuredusingaTOC-VCPHTotalOrganicCarbonAnalyzer(Shimadzu,Kyoto,Japan).Ferrousandferricironsweremeasuredusingthecolorimetricmethod.Heavymetals(Cu,Mn,Zn)weredeterminedusingtheAA-7000AtomicAbsorptionSpectrophotometer.Sincethewaterwashighlyhomogenousateachsite,theenvironmentalvariableswereonlymeasuredinonesample.

DNAextraction,PCRamplification,sequencinganddataprocessing

ThegenomicDNAwasextractedusingaFastDNASpinkit(MPBiomedicals,CA,USA)accordingtothemanufacturer'sinstructions.ThequalityoftheextractedDNAwasassessedusingaNanoDropSpectrophotometer(ThermoFisherScientific,MA,USA).TheV4regionofthe16SrRNAgenewasamplifiedusingprokaryoticuniversalprimers515F(GTGYCAGCMGCCGCGGTAA)and806R(GGACTACNVGGGTWTCTAAT).Triplicate

PCRamplificationswereperformedforeachsampleandthenpooled.ThePCRproductswerepurifiedusinganE.Z.N.A.GelExtractionKit(Omega,CT,USA),andthensequencedonanIlluminaNova6000PE250platform(Illumina,CA,USA).

Therawpaired-endsequenceswereanalyzedusingtheQIIME2platform(Bolyenetal.,2019).ThesequencesweredenoisedandassembledusingtheDADA2pluginandclusteredasampliconsequencevariants(ASVs)ata100%identitythreshold(Callahanetal.,2016).The

taxonomicannotationof16SrRNArepresentativesequenceswasperformedusingtheNaiveBayesclassifiertrainedfortheV4regionof16SrRNAgene(versionSilva-138-99-515-806).Sequencesannotatedaschloroplastsormitochondria,aswellasthosethatcouldnotbeannotatedatthephylumlevel,wereremoved.ASVswithatleast97%similaritycomparedwiththesequencesinFreshwaterTrainingSet(FreshTrain)weredefinedasfreshwatertaxa(Rohweretal.,2018).Tominimizebiasesresultingfromdifferencesinsequencingdepthamongthesamples,ASVswererandomlyresampledtoadepthof26,245forallsamples(Fig.S2).ThefunctionalprofileswerepredictedusingPICRUSt2(Douglasetal.,2020).

Networkconstructionandanalyses

Themolecularecologicalnetworkswereconstructedaccordingtoapreviouslydescribedmethod(CsardiandNepusz,2006).Inbrief,theASVsthatpresentedinmorethan80%ofthesamplesateachsitewereselectedfornetworkconstruction.TheabundanceofeachASVwaslog-transformedbeforecalculatinganassociationmatrixwithPearson'scorrelationsusingtheHmiscpackage.Thenetworksweregeneratedbasedonrobustlinkswith|R|>0.80andstatisticalsignificancep<0.01usingtheigraphpackage.Sub-networksforeachsamplewerethenextracted.Thedivisionofthenetworksintodifferentmoduleswasaccomplishedusingthefastgreedyalgorithm.Networktopologicalproperties,includingaveragedegree,averageclusteringcoefficient,connectanceandmodularity,werecalculatedviaigraphpackage.Sincethesizeandconnectivityofnetworksvariedsubstantiallyamongdifferentnetworks(TableS3),relativemodularity,representinghowmodularisintheobservednetworkascomparedwiththeexpectedmodularityfromtherandomnetworks,wascalculatedaspreviouslydescribed

(ThebaultandFontaine,2010):

Relativemodularity=𝑀−̅𝑀̅̅𝑟̅

̅𝑀̅̅𝑟̅

whereMisthemodularityoftheobservednetwork,and

̅𝑀̅̅𝑟̅isthemeanofthemodularityfrom

100randomnetworks.

Cohesion,anabundance-weightedmetricbasedonpairwisecorrelations,wascalculatedto

quantifythenegativeandpositiveconnectivityofmicrobialcommunitiesaspreviouslydescribed(HerrenandMcMahon,2017):

𝑚

cohesion=∑abundance𝑖×connectedness𝑖

𝑖=1

wheremisthetotalnumberoftaxainanetworkcommunity.Foreachtaxon,thepositiveandnegativecorrelationswereseparatelyaveragedastheconnectednessvalues.Cohesionvalueswerecalculatedbymultiplyingtherelativeabundancetablebytheconnectednessvaluesaccordingtotheprecedingformulaforeachsample.

Thetopologicalroleofeachnodewasdeterminedusingwithin-moduleconnectivity(Zi)andamong-moduleconnectivity(Pi),classifyingthemasnetworkhubs(Zi>2.5andPi>0.62),modulehubs(Zi>2.5andPi≤0.62),connectors(Zi≤2.5andPi>0.62),orperipherals(Zi≤2.5andPi≤0.62)(Dengetal.,2012).ThenetworkwasvisualizedusingGephi0.9.

Statisticalanalyses

AllstatisticalanalyseswereconductedinRenvironmentversion4.0.5().ShannonindexandFaith’sphylogeneticdiversitywerecalculatedusingtheveganandpicantepackage,respectively.ThevariationsoffactorsacrosssamplingsiteswereexaminedusingthenonparametricKruskal-WallistestandDunn’sposthoctestatsignificantlevelof0.05usingthe

FSApackage.Thevariationsofmicrobialcommunitycompositionsacrosssamplingsiteswereevaluatedusingpermutationalmultivariateanalysisofvariance(PERMANOVA)basedonBray-Curtisdissimilaritymetrics,andvisualizedusingprincipalcoordinateanalysis(PCoA)usingtheveganpackage.Thesignificanceofthedistance-decayrelationshipbetweenthemicrobialcommunitysimilarityandriverdistancewasassessedusingManteltest.Community-levelprokaryoticribosomalRNAgeneoperon(rrn)copynumberswerecalculatedaspreviouslydescribed(Daietal.,2022).TheabundantASVs(relativeabundance>1%atanyofthesamplingsites)thatshowclearsuccessionpatternswereexaminedbyANOVAanddisplayedintheheatmapusingthepheatmappackage.

Regressionsbetweenvariablesandriverdistance.Toexaminethesuccessionpatternofphysicochemicalandmicrobialcommunityvariablesalongtheriver,regressionswereperformedagainstriverdistancefromeachsamplingsitetotheAMD-pollutedriversource.Forwatercontaminants,theexponentialdecayfunctionwasusedtodelineatethevariationsinnaturalattenuationprocessesaspreviouslydescribed(Acunaetal.,2015).However,therearenotheoreticallyestablishedmodelforothervariables,suchasthecontaminantsinsedimentormicrobialattributes.Therefore,bothlinearandnon-linearregressions(includingexponential,exponentialdecay,andlogarithmicmodels)wereperformedusingtheBasicTrendLinepackage.ThemodelwiththelowestAICvalueandresidualstandarderrorwasselectedforvisualization(DataS1).

Assemblymechanismanalyses.Toevaluatetherelativeimportanceofstochasticanddeterministicprocessesunderlyingmicrobialcommunityassembly,thephylogeneticandtaxonomicstochasticityratiowereestimatedusingamodifiedmethodbasedonnull-model

algorithms with abundance-weighted beta-mean-nearest-taxon-distance (βMNTD) andBray-CurtisdissimilaritymetricsviatheNSTpackage.NichebreadthindexofmicrobialcommunitywascalculatedusingthestandardizedLevin’sindexasimplementedinthespaapackage.Therelativecontributionsofdeterministicprocesses,includingabioticselection(physicochemicalfactors),bioticselection(networktopologicalfactors)andriverdistance,instructuringprokaryoticcommunitieswereassessedusingmultipleregressiononmatrices(MRM)approachusingtheecodistpackage.Riverdistancewasln-transformed.ThedissimilaritymatricesofabioticandbioticfactorswerecalculatedusingtheEuclideanmethod.Toreducetheinfluenceofcollinearity,thefactorswithhighcovariation(Spearman’sρ2>0.8)wereexcludedbeforeconductingtheMRMmodelusingthevarclusfunctionintheHmiscpackage.Tofurtherexploretherelativecontributionsofeachabioticandbioticfactor,anothertwoMRMmodelswereperformed.TheMRMmodelwasinitiallyruntoeliminatethenonsiginificantfactors,therebypreventingtheeffectfromspuriousrelationshipsbetweenfactors.ThefinalMRMmodelforeachfactorwasobtainedbyrunningitasecondtimewiththeremainingvariables.Therelativeimportanceofdifferentcommunityassemblyprocesses,includingheterogeneousselection,homogeneousselection,dispersallimitation,homogenizingdispersal,anddrift,wasquantitativelyinferredbyphylogeneticbin-basednullmodelanalysisusingtheiCAMPpackage.

Results

Environmentalvariables

ThepHvaluesofwaterexhibitedalinearincreasewithriverdistance(Fig.1a),rangingfrom

2.49atSiteAto6.35atSiteE(TableS1).Thesulfateconcentrationdisplayedadecrease

followinganexponentialdecaymodel(Fig.1b),decliningfrom3.188g/Lto0.357g/L(TableS1).Theconcentrationsofseveralmetals,includingFe(III),Cu,Mn,andZn,alsoshowedexponentialdecreaseswithriverdistance(Fig.1d–1g),whereastheconcentrationsofFe(II)decreasedlogarithmically(Fig.1c).Dissolvedoxygenconcentrationsandtotalorganiccarbondidnotdisplayanyclearpatternsofvariation(Fig.S3a&S3b).

Inthesediment,thepHvaluesincreasedlinearlyfrom2.65atSiteAto6.11atSiteE(Fig.1h&TableS2),followingapatternobservedinthewaterhabitat,whiletheNAG-pHvaluesdisplayedalogarithmicincreasewithdistanceupstream(Fig.1i).Thecontentsofsalt,totalsulfur,sulfate,Fe(II),andFe(III)allexhibiteddecreasesalongtheriver(Fig.1j–1n).Ontheotherhand,thecontentsoftotalorganiccarbonincreasedlinearlyalongtheriver(Fig.1o),whilethoseoftotalnitrogendecreasedlogarithmically(Fig.1p).However,thecontentsoftotalphosphoruswererelativelystable,expectforahighvalueatSiteB(TableS2&Fig.S3c).Incontrasttothedecreaseofheavymetalsinthewaterhabitat,thetotalcontentsofMnandZn,aswellasDTPA-extractableMn,Cu,andZn,increasedwithriverdistance(Fig.1q–1u),indicatingtheaccumulationofheavymetalsinthesediment.However,thetotalcontentsofCuremainedrelativelystable(Fig.S3d).

3.2Microbialcommunitydiversityandcomposition

Atotalof1,312,250high-qualitysequenceswereobtainedforallsamples,whichwereclusteredinto117to1,062ampliconsequencevariants(ASVs)inthewater,and205to1,744ASVsinthesediment(Fig.S2).Astheriverfloweddownstream,thealphadiversity(Shannonindex)increasedlinearlyfrom2.459to5.131inthewaterandincreasedlogarithmicallyfrom3.599to

5.726inthesediment(Fig.2a–2c).Similarly,Faith'sphylogeneticdiversityincreasedlogarithmicallyfrom13.66to60.61inthewaterandfrom21.37to65.19inthesediment(Fig.2d–2f).

Microbialcommunitycompositionsatdifferentsitesvaried(PERMANOVAp<0.001,Fig.3a)anddifferedwitheachother(p<0.011,TableS4).Onlyasmallproportion(0.36%–1.16%)ofASVsweresharedbyallthesamplingsites(Fig.S4).ThemicrobialcommunitycompositionsatSiteAwereordinatedclosetoSiteBinboththewaterandsedimenthabitats(Fig.3a),suggestingthatmicrobialcommunitycompositionswerestronglyaffectedbyAMDintheheadwaters.MicrobialcommunitycompositionsatSiteC,DandEwereseparatedfromthoseatSiteAandB,reflectingasuccessionofmicrobialcommunitycompositionsalongtheriver.Significantdistance-decayrelationshipswereobservedformicrobialcommunitiesinboththewaterandsedimenthabitats(Water:slope=-0.133,p<0.001;Sediment:slope=-0.135,p<0.001,Fig.3b).Furthermore,thecommunity-levelrrncopynumberofmicrobialcommunitiesincreasedlogarithmicallyalongtheriver(Fig.3c),indicatingasuccessionofmicrobiallifetraits.Therelativeabundancesoffreshwatertaxaincreasedlinearlywithriverdistanceinboththewaterandsedimenthabitats(Fig.3d).

UponfurtherexaminationofmicrobialcommunitycompositionsattheASVlevel,theabundantASVswereseparatedintotwogroups:GroupIweretheASVsthatenrichedatSiteAandBwhileGroupIIwerethoseenrichedatSiteC,DandE(Fig.4).ThisresultwasconsistentwiththatinPCoAanalysis,suggestingaclearsuccessionpatternalongtheriverinboththewaterandsedimenthabitats.Inthewaterhabitat,ASVsofFerrovum,Gallionella,groupSva0485,andAcidiphilium,whichareaffiliatedwithinProteobacteria,aswellasASVofLeptospirilium,

affiliatedwithinNitrospirota,thrivedatheadwaters(SiteAandB)(Fig.4a).ASVsofAcidocella,Pseudarcobacter,Rhodanobcter,Acinetobacter,Acidithrix,Nitrosotalea,andThiomonas,thrivedatSiteCinalessacidicenvironment,whileASVsofNovosphingobium,Flavobacterium,Pseudanabaena,Polaromonas,Prevotella,Rhodoferax,Bacteroides,Methylotenera,andBdellovibrioproliferatedatSiteDandE(Fig.4a).Consistently,therelativeabundancesofProteobacteriaandNitrospirotadecreaseddownstream,whilethoseofBacteroidota,Campilobacterota,Cyanobacteria,andBdellovibrionotaincreased(Fig.S5a).

Inthesedimenthabitat,taxaannotatedasLeptospirillum,Acidiphilium,Metallibacterium,Ferrovum,Thermoplasmataceae,AD3,Aquisphaera,CPla−3_termite_group,andPseudomonasthrivedatSiteAandB(Fig.4b),resultinginhighrelativeabundancesofProteobacteria,Nitrospirota,Thermoplasmatota,andPlanctomycetota(Fig.S5b).ASVsofAcidocella,Granulicella,RhodanobacterandgroupWPS-2thrivedatSiteC,whileASVsofRhodoferax,Polaromonas,Alkanindiges,Novosphingobium,Massilia,Pseudanabaena,Sediminibacterium,Arenimonas,Flavobacterium,Methylotenera,andenv.OPS_17thrivedatSiteDandE(Fig.4b).

Sulfatereducingbacteria(SRB)areknowntobeeffectiveintreatingAMDwastewater;however,theSRBtaxaconsideredcompetentwerenotamongtheabundanttaxaatanyofthesites(Fig.4).FurtherinvestigationrevealedthattherelativeabundanceofSRBwasextremelylowinboththewaterandsedimenthabitatsatSiteAandB(0–0.007%,Fig.S6a,S6b).Downstreamsitesshowedincreasedrelativeabundancesrangingfrom0.070%to0.242%,withdominanttaxasuchasDesulfobulbus,Desulfomicrobium,Desulforegula,Desulfovibrio,andDesulfurivibrio.Similarly,dissimilatorysulfatereductionalsoexhibitedlowrelativeabundances(0–0.004‰,Fig.S6c),particularlyatSiteAandB,asindicatedbythefunctionalprofileofdsrA

anddsrBpredictedbyPICRUSt2.Conversely,assimilatorysulfatereduction(cysIJandsir)displayedhigherrelativeabundancesrangingfrom0.064%to0.098%acrossthefivesites(Fig.6d).

Microbialnetworks

Aswaterfloweddownstreamalongtheriver,thenumberofnodesandedgesinthenetworksincreasedsubstantiallyinboththewaterandsedimenthabitats(Fig.5a).Specifically,inthewaterhabitat,thenumberofnodeswasincreasedfrom57atSiteAto598atSiteE,whilethenumberoflinkswasincreasedfrom51to2,213(Fig.4a).Similarly,inthesedimenthabitat,thenumberofnodeswasincreasedfrom108to573,andthenumberoflinkswasincreasedfrom276to3,668.Thepositiverelationshipbetweennetworkcomplexityandriverdistancewasevident,asindicatedbythesignificant(p<0.001)regressionsofseveralnetworktopologicalparameters,includingnetworksize(totalnumberofnodes,n),connectivity(totalnumberoflinks,L),andaverageconnectivity(averagelinkspernode,averageK)(Fig.5b–5d).PositivelinksdominatedamongallofthelinksinthenetworksofSiteAandB(64.5%–90.9%,TableS3).Inaddition,cohesion,arecentlydevelopedmetric,wasquantifiedtoestimatethedegreeofcommunitycomplexity.Negativecohesionincreasedsignificantlyoverdistance(p<0.001,Fig.5e),suggestingthatnegativeassociationswereenhancedalongtheriver.Asfornetworkorganizationalstructure,relativemodularityincreasedsignificantlyoverdistance(Fig.5f),indicatingthatmicrobialnetworksshiftedtohigh-modularstructureindownstream.

Topologicalroleofeachindividualnodewasinferredonthebasisoftheirwithin-moduleconnectivity(Zi)andamong-moduleconnectivity(Pi).Atotalof6modulehubsand33

connectorsacrossallthenetworkswereidentified(Fig.S7),allofwhichcouldbeconsideredaskeystonenodes.Thenumberofkeystonenodesincreasedoverriverdistance(Fig.5g).Keystonenodesinupstreamsiteswereassociatedwithiron/sulfurcycling,whilethoseindownstreamsiteswereassociatedwithnitrification,nitratereduction,andorganicmatterdegradation(DataS2).

Microbialcommunityassemblyprocesses

Theestimatedphylogeneticandtaxonomicstochasticityratioswereincreasedoverriverdistanceinboththewaterandsedimenthabitats(Fig.6a,6b).Specifically,theestimatedstochasticityratiosatSiteAandBwereintherangeof0.032–0.444,lowerthan0.50,indicatingstrongselectionforcesduetoheavycontami