ContentslistsavailableatScienceDirect
Aquacultural Engineering
j ou rn a l h o m epa ge :w w w . e l s e v i e r . c o m / l o c a t e / a q u a - o n l i n e
Deep-sequencing of the bacterial microbiota in commercial-scale recirculating and semi-closed aquaculture systems for Atlantic salmon post-smolt production
Ida Rud
a,∗, Jelena Kolarevic
a, Astrid Buran Holan
a, Ingunn Berget
a, Sara Calabrese
b,c, Bendik Fyhn Terjesen
aaNofima—NorwegianInstituteofFood,FisheriesandAquacultureResearch,N-1430Ås,Norway
bUniversityofBergen,DepartmentofBiology,HighTechnologyCentre,N-5020Bergen,Norway
cMarineHarvestASA,N-5835Bergen,Norway
a r t i c l e i n f o
Articlehistory:
Received9September2016 Accepted18October2016 Availableonlinexxx
Keywords:
Microbiota Deep-sequencing
Recirculatingaquaculturesystem Semi-closedaquaculturesystem Biofilm
Water Salinity Pathogens
a b s t r a c t
NewaquacultureproductionsystemsareevolvingforprolongproductionofAtlanticsalmonsmoltsor post-smoltsbeforestockingintraditionalnetpens,suchassemi-closedcontainmentsystems(S-CCS) insea(Fig.1)andrecirculatingaquaculturesystems(RAS)onland.Themicrobiotainthesesystemscan potentiallyhavegreatimpactontherobustnessandhealthofthefish.Thesetwotypesofaquaculture systemsarelikelytohavedifferentchallengesregardingpathogenicinvasionduetothedifferentwater management,e.g.differenttreatmentoftheintakewateranddifferentturnoverofthewater.Inthisstudy, weinvestigatedthebacterialmicrobiotaofbothwaterandbiofilmsinacommercialRASandinS-CCSin seaduringathreemonthsperiodofpost-smoltproduction.Deep-sequencingofthebacterial16SrRNA gene(V4)wasusedforthefirsttimetoobtainindepthcompositionalanalysisofmicrobialcommunities incommercialscalefacilities.Highlydiversecommunitiesweredetected,withupto2000differentOper- ationalTaxonomicUnits(OTUs)withinsamples.BothsystemsweredominatedbyProteobacteriawith Rhodobacteraceaeasthedominatingtaxa,followedbyBacteroidetesthatwasdominatedbyPolaribacter amongothers.However,themicrobiotacompositionwasclearlydifferentbetweenthetwoaquaculture systems,andbetweenwatersamplesandbiofilms.InRAS,itwasalsoshowndifferentmicrobiotacom- positionwithwatersalinityof12vs22partsperthousand(ppt).Higherabundanceofe.g.Myxococcales andNitrospiraceaewasobservedat12ppt,whichcoincidedwithlowertotalammonianitrogen(TAN) levels.BothtaxawerealsomoreabundantintheMovingBedBioreactor(MBBR)-biofilmsthaninwater, aswellasPlanctomycesamongothers.InS-CCS,cleartemporalchangesofthemicrobiotawasobserved duringtheproduction,wherepotentialpathogenslikeTenacibaculum,Aliivibrio,Alteromonadaceaeand Polaribacterwereincreasinginthespringtime,aswellasoneunassignedtaxaandchloroplastDNAlikely fromalgae.Theimplicationofthesepotentialpathogensonfishhealthisunknown.Acommonobserva- tionforbothRASandS-CCSwashigherabundanceofthepotentialpathogensinthewatercomparedto thebiofilms.Furtherstudiesonthemicrobiotainclosed-containmentaquaculturesystemsareneededto obtainmoreknowledgeabouttheirimpactonpost-smoltproductionperformance,welfareandhealth.
©2016TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Newaquacultureproductionsystemsareevolvingsuchaspro- ductionofsalmonpost-smoltsinfloatingsemi-closedcontainment systems(S-CCS)insea(Fig.1)oronlandinrecirculatingaqua-
∗Correspondingauthor.
E-mailaddress:ida.rud@nofima.no(I.Rud).
culturesystems(RAS)(Iversenetal.,2013;Terjesenetal.,2012, 2013).Thesenewproduction methodsfor thepost-smoltstage of Atlanticsalmonhave evolvedas a responsetochallenges in openseacageproduction,suchassea-liceinfestations,escapees, mortalitiesandinfectionsfrompathogenicmicroorganisms.The microbiotaintheseclosed-containmentsystemscanstillhavea largeimpact ontherobustness and health of thefish, and the farmingindustry experienceschallengeswithdiseaseoutbreaks andlimitedcontroloftheseevents.Increasedknowledgeofthe
http://dx.doi.org/10.1016/j.aquaeng.2016.10.003
0144-8609/©2016TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.
0/).
Please cite this article in press as: Rud, I., et al., Deep-sequencing of the bacterial microbiota in commercial-scale Fig.1.Commercial-scalesemi-closedcontainmentsystem(S-CCS)ofprototype
Neptunduringtransfer(A)andfinallyfloatingandoperatinginMolnessund, Norway(B).Photos:AquaFarmEquipmentASandNofima.
microbialcommunitieswithinthesystemsintermsofcomposi- tion,reservoirs,dynamics,functionalityandeffectsofimportant environmentalparameters(e.g.intakewater,salinity,feedinput, retentiontime,treatmentcompartmentsetc.)wouldbeusefulfor improvedoperationaldesignandsustainablefarming.
Deep-sequencingforcharacterizationofthemicrobiotainaqua- culturesystemsisanemerginghigh-throughputtechnologythat allowsforrapid,costeffective,in-depthtaxonomiccharacteriza- tionofmicrobialcommunitiesincludingunprecedentedbacteria (Caporasoet al., 2012; Metzker, 2010).Thismethod allows for detectionofthousandsofbacteriawithineachsample.Itcanbe usedformonitoringthemicrobiotaover time(e.g.days,weeks, monthsoryears)in aquaculturesystems andtoidentifyacore microbiotathatcanbeusedasreferenceforfuturestudies,aswell aspresenceofpotentialpathogensCurrently,thetechnologyhas beenlimitedtoafewstudiesinRAS(Martinsetal.,2013;Ruanetal., 2015).MoststudiesofmicrobiotainRAS(reviewedbyBlancheton etal.,2013;RurangwaandVerdegem,2015;Schreieretal.,2010) are based oneither traditional microbiological techniques, tar- getedmolecularmethods(e.g.real-timepolymerasechainreaction (PCR))ormolecularfingerprintinganalysis (e.g.PCR-denaturing gradientgelelectrophoresis(DGGE)).
MicrobialcontrolwithinRASandS-CCSisdifferentduetotheir differentoperationaldesignandwatermanagement;however,a commonchallengeisto achievebeneficial microbial communi- tieswithinthesystems,whichdependsonthesupplyofbacterial nutrients,organicmatter,waterexchangeandenvironmentalcon- ditions(Attramadaletal.,2012;DeSchryverandVadstein,2014;
Schreieretal.,2010).
EspeciallyinRAS,anydisturbanceofthenaturalmicrobialbal- ancemightinducer-selectionwithinthesystemcausinggrowth ofopportunisticpathogensalreadypresentaspartoftheaquatic microbiota(Blancheton etal.,2013).Forinstanceeliminationof wasteproductsiscriticalinRAS,andthebiofiltermicrobiotaisan importantcomponentinRASwheretheammoniaarenitrifiedby autotrophsintonitratebyammoniaoxidizingbacteria(AOB)and
nitriteoxidizingbacteria(NOB).Theheterotrophicbacteriadegrade organicmatter,andcontributetomaintaininggoodmicrobialwater qualitybyoccupyingnichesandpreventingproliferationofpoten- tialpathogenicspecies,thoughtheyalsoarecompetitorsforoxygen andspacewithautotrophicbacteria(Blanchetonetal.,2013).Lim- itedoutbreaksofpathogenswithinRAShasbeenreported(Bornø andLinaker, 2015), eventhoughpotentialpathogenshavebeen detectedinRAS(Kingetal.,2004;Martinsetal.,2013;Michaud etal.,2009).
S-CCSinseaarebuiltasflow-throughsystems,pumpingwater fromacertaindepthtooptimizerearingtemperatureandtoavoid surfacewaters,wheretheabundanceofsealiceisthehighest.Due tothehighturnoverrateofthewaterandseasonalchanges,the microbialcommunityinS-CCSwillbeexposedtonewenviron- mentalconditionsovertime,e.g.changesintemperatureandlight andanunstableorganicloadandnutrientprofile.Anotherconcern couldbethepotentialofgettingupwellingofsedimentsfromthe seabedintothesystem,inducedbypumpinghighvolumesofwater.
Fromotherstudies,weknowthatmarinesedimentsintheseacan harborpathogenicspecies(e.g.Vibriospp.)(BlackwellandOliver, 2008;ShikumaandHadfield,2010)andMoritellaviscosa(Colwell andMorita,1964;Urakawaetal.,1998).Furthermore,therewill alwaysbeperiodicallyoccurrenceofpathogensintheseadueto e.g.bacterialoralgalblooms.Thesepathogenscouldfindtheirway intothesystemthroughtheintakewatersuchasthewinter-ulcer causingpathogens.M.viscosaisregardedastheetiologicalagent (Lovolletal.,2009;Lunderetal.,1995),buttheetiologyofulcera- tiveoutbreaksislikelycomplexasnumerousbacterialspeciesare associatedwiththediseases,includingTenacibaculumsppandAli- ivibriowodanis(BornøandLinaker,2015;Olsenetal.,2011).Though therouteoftransmissionislikelytheintakewaterorintroduced fish,thereislimitedknowledgeaboutthereservoirofpathogensin aquaculturesystems.Microbialbiofilmsdevelopinginaquaculture tanksandbiofiltersmightrepresentareservoirforopportunistic pathogens.Theymayprovidepathogenswithsurvivaladvantages byprovidingprotectionagainstenvironmentalstressors,suchas disinfectantsandantibiotics(Sanchez-Vizueteetal.,2015).
Theobjectiveofthisstudywastousestate-of-the-artsequenc- ingtechnologytoobtainapreliminaryoverviewofthecomplex microbiotawithinlandbasedRASandfloatingS-CCSinthesea.The studywasdoneduringcommercialscaleproductionsofAtlantic salmonpost-smolts,sincelimitedknowledgeexistsonthemicro- biotainsuchsystems.Tothebestofourknowledgethisisthefirst findingonmicrobiotainS-CCSingeneral.Comparisonofmicro- biotainthewaterandinbiofilmswereperformedtoidentifythe biofilmassociatedandthefree-livingbacteria,andtrytoidentify themainreservoirofpotentialpathogensinthesetwoaquaculture systems.Inaddition,relevantsystem-specificfactorswerestud- ied.InRAStheeffectofsalinityonthemicrobiotawasinvestigated, whereastemporaleffectswereinvestigatedinS-CCS.
2. Materialsandmethods
2.1. Descriptionofexperimentalfacilities 2.1.1. Recirculatingaquaculturesystem(RAS)
The experiment was done at the commercial-scale Grieg Seafood facility in Adamselv, Lebesby municipality (Finmark, Norway).Atlantic salmonsmolts usedin this experiment were hatchedattheGriegSeafoodhatcheryin Adamselv,andsmolts wereproducedinfreshwaterRAS(FW-RAS)atthesamelocation, butinadifferentsystem.Atotalof200,000smoltswithanaverage startingweightof89.3±13.8g(SD)weretransferredaftersmoltifi- cationtotwoseparatebrackish(BK)-RASat8ppt.Thesmoltswere
Fig.2. MicrobiotadiversityinbiofilmsandwaterofRASandS-CCSpresentedinPCAplots.(A)DiversitycomparisonbetweenS-CCS,RAS,biofilmsandwaterwithPC1and PC2,(B)DiversitycomparisonbetweenS-CCS,RAS,biofilmsandwaterwithPC1andPC3,and(C)DiversitycomparisonbetweenMBBR-biofilmandwatersamplesfrom 12pptand22pptwithinRAS(PC1andPC2).
stockedintwo400m3tankspersystempriortothestartofthe experiment.
EachRASconsisted ofthreeparallel mechanicaldrum filters (Hydrotech, Vellinge, Sweeden) with 40m pore size for par- ticleremoval, followed by a ClearwaterMoving bed bioreactor (MBBR)(Inter AquaAdvance,Egå,Denmark;500m3 in volume) forremoval oftoxic NH3/NH4+ and NO2,afterwhich thewater wasoxygenatedandreturnedtotheculturetanks.Atotaloffour tanks,twowithrearingcapacityof400m3andtwowith700m3, receivedrecirculatingwateraftertreatmentineachsystem.The CO2 degassingoccurredinthebioreactor,inadditiontoin-tank degassing.Atthestartoftheexperiment(July–August2013),the watersalinityinoneoftheRASwasgraduallyincreasedfrom8 partsperthousand(ppt)to12ppt,andintheotherfrom8pptto 22pptoveraperiodofoneweek.Theoperationalparametersfor bothBK-RASaregiveninTable1.Totalammonianitrogen(TAN) wasmeasureddailyusingMultiparameterbenchphotometerfor aquaculture (HANNA Instruments, Woonsocket, Rhode Island, USA). An additional group of fish was introduced equally into theremainingtanksofbothBK-RASafter43and47daysofthe
experiment,asapartoftheplannedcommercialproductionatthe Adamselvfacility.However,thisgroupoffishwas,unknownatthe timeoftheexperiment,infectedbythebacterialpathogenYersinia ruckeri,whichwasthusintroducedtobothRAS.
2.1.2. Floatingsemi-closedcontainmentsystem(S-CCS)
Thisstudywaspartofthefirst testof thecommercial-scale floatingsemi-closedcontainmentsystem(S-CCS)-prototypeNep- tunproducedbyAquaFarmEquipmentAS(Haugesund,Norway) (Fig.1).Thissystemisconstructedofglass-fibrereinforcedplastic (GRP)andiscertifiedaccordingtoNS9415:2009.Thesidewallsare coatedwithNorpolgelandtopcoat(Reichold,Durham,NC,USA) andthebottomiscoatedwithaBüfastandard gelandtopcoat (Büfa,Rastede,Germany).Theprototypewaslocatedforthetrial intheMolnessund(N59◦43.191E5◦51.528),attheSouthwest- erncoastofNorway,andthesystemhasacircularshapewitha circumferenceof126m. Thebottomofthetankisangleddown towardthecenteroutletpipegivingatotaltankdepthof20mand agrossvolumeof21,000m3.Seawater(32ppt)waspumpedinto theprototypefromafixeddepthof26mbyfourpumpsinstalled
Please cite this article in press as: Rud, I., et al., Deep-sequencing of the bacterial microbiota in commercial-scale Table1
Operationalinformationforthe12pptand22pptRASduringproductionofAtlanticsalmonpost-smolts.Givenvaluesaremeans±SD.
System Totalfeedload(kg/day) Totalwaterflow(m3/h) Make-upwater(m3/h) Make-upwater(m3/kgfeed) Degreerecirculation(%)
12pptRAS 541±292 1999±720 24±16 2±1 98±1
22pptRAS 412±268 1768±1004 25±16 1±1 97±2
atfourbargesjustoutsidethetankateachquadrant.Themaxi- malcombinedflowrateinthissystemwas400m3/min.Oxygen wasaddedtoinflowingwaterintheintakepipesandwasauto- maticallyadjustedtomaintainasaturationabove80%inthetank.
Mostofthewater(80%)wasdischargedthrough14sidehatches (1m×1m)at12mdepthand20%ofthewaterthroughthebottom centerpipe.Whenthepumpswereoperatingatmaximalcapacity, theretentiontimeofthewaterinthetankwas52min.Additional informationabouttheS-CCSprototypecanbefoundinSummerfelt etal.(2016),farmlocationH.
A total of 200,000 Atlantic salmon post-smolts weighing approximately118gr(VAKIwellboatsmoltcounter)weretrans- ferredfromthehatcheryVågafossensettefiskAStotheNeptun prototype.Astandard commercialproductionprotocolforpost- smoltsinseawater(MarineHarvestNorway AS)wasfollowed.
Dead fish were removed once a day using a LiftUP system (LiftUP,Eikelandsosen,Norway),countedandnoticeablepatholo- gies recorded. Temperature, oxygen (YSI pro20), pH (YSI pro 10)andsalinity(TetraConprobe,VWR)weremonitoreddailyat 1mdepthintheprototype.Tankwalls,outletandpumpswere cleaned as needed by high-pressure washing and commercial divers.
2.2. Microbiotaanalysis 2.2.1. Samplecollection
Thebiofilmand water samplesformicrobiota analysiswere collectedduringthreemonthsofpost-smoltproductionineither system.Microbialbiofilmswereobservedattachedtothesampling materials,thoughnotquantified.InRAS,approximately4–5car- rierdiscsfromtheMovingBedBioreactors(MBBRs)werecollected persampleonthreeoccasionsduringtheexperiment(2–3samples pertimeofsampling,where1–2samplesfromeachsalinitytreat- ment,intotal7samples):atthestart,aftertwoandthreemonthsof theexperiment,andfrozenat−80◦Cforsubsequentanalysis.The watersamplesof100mleachwerecollectedatthetankoutletlevel aftertwoandthreemonthsoftheexperiment(3–4samplesper timeofsampling,where1–2samplesfromeachsalinitytreatment, intotal7samples),frozenimmediatelyandwassubsequentlyfil- teredusing0.2mmembranefilters(Whatman,Dassel,Germany).
Filterswerefreshfrozenandstoredat−80◦Cuntilanalysis.InS- CCS,thesampleswerecollectedfromstartoftheexperiment(Day 0;February2014)andeveryweekuntilDay70followedbyDay91 (May2014),exceptforonewatersamplemissingonDay63.Sterile drywipes(32cm×39cm,SodiBox®,Névez,France)wereusedto collectbiofilmoftankwalls(3–4wallspersampledate,intotal47 samples).Thewatersamples(onesampleperdate,intotal12sam- ples)werecollectedat1mdepthfromS-CCSbysoakingthesterile drywipeswithwater.Allthesampleswerefrozenandstoredat
−80◦Cuntilanalysis.
2.2.2. DNAextraction
Thecollectedsampleswerethawedonicebeforepreparation ofmicrobialpellets.Peptonewaterwasaddedtothewipes,filters andMBBR-discsbefore2×1minofstomachingtoreleasemicrobial cellsofthesamples.Firstly,themicrobialpelletswerecollectedin falcontubesbycentrifugationforfiveminutesat5000×g.Theclear supernatantswere removed, while the remaining loose pellets underwentadditionalfiveminutesofcentrifugationat13,000×g
inEppendorftubes.Thefinalpelletsweresubsequentlyusedfor DNAextraction.TheFastDNA-96TMFecalDNAKitwithMatrixE (MPBiomedicals,USA)wasusedformechanical lysis,following themanufacture’sprotocol,includingtheMP-96InhibitorRemoval Plate.
2.2.3. Deep-sequencingofthemicrobiota
Themicrobiotaanalysiswasperformedbydeep-sequencingfol- lowingourin-houseprotocol(Moenetal.,2016),whichisindetail presentedin supplementarymethods ofCaporasoet al.(2012).
Thedeep-sequencinginvolvespairedendsequencing(2×150bp) ofthevariableregion4(V4)ofthebacterial16SrRNAgene.The V4 containsconserved and hypervariableregions, whichmakes issuitablefortaxonomicclassifications.Briefly,polymerasechain reaction(PCR)wasperformedintriplicatesperDNAsamplewith region-specificprimers(targetingconservedregions)thatincluded theIlluminaflowcelladaptersequences.Thelatterpermitsbind- ingtoanIlluminaflow cellduringthesequencingprocess.The reverseamplificationprimeralsocontainedatwelvebasebarcode sequenceuniqueforeachsample,whichsupportspoolingofdif- ferentsamples.Beforeandafterpoolingthesampleswerepurified withAmpure(AgencourtBioscienceCorporation)andquantified using the Quant-iT Picogreen ds DNA with picogreen (Invitro- gen,LifeTechnologies).Thesamplepoolwasdilutedto4nM,and sequencedonaMiSeq(Illumina)followingtheprotocolprovidedby Illumina.TheMiSeqrunalsocontainedacontrollibrarymadefrom PhiXControlv3thataccountedfor10%ofreads,whichisrecom- mendedforincreasedsequencingyieldandqualityoflowdiversity samples(e.g.targetedampliconlibraries).Thelibraryquantifica- tionandsequencingwereperformedatNofima.TheMiSeqControl Software(MCS)versionusedwasRTA1.18.54.
2.2.4. Dataprocessingofsequencingdata
Dataprocessingofthesequencingreadswasperformedusing theopen-sourcebioinformaticspipelineQuantitativeInsightInto MicrobialEcology(QIIME)v.1.7.andv.1.8(Caporasoetal.,2010).
Briefly,theforwardandreversereadswerejoinedandbarcodes failedtoassemblewereremoved.Thesequencesweredemulti- plexedintorepresentativesampletaqsandqualityfilteredallowing zerobarcodeerrorsandaqualityscoreof30(Q30).Readswere assignedtotheirrespectivebacterialtaxonomy(OperationalTax- onomic Unit: OTU) by clustering them against the Greengenes reference sequence collection (gg138) using a 97% similarity threshold. Reads that did not hit a sequence in the reference sequencecollectionwereclustereddenovo.Chimericsequences wereremovedusingChimeraSlayer,andallOTUsthatareobserved fewerthan2timeswerediscarded.ThisresultedinanOTUtable containing52,288differentOTUs,andthatwasbasedonatotal of6,180,272reads.EachofthedifferentOTUidentities(OTUid) representsaunique 16SrRNAgene sequence thatare assigned tothenearesttaxa,wherethetaxonomiclevelassignedcanvary fromphylum,class,order,family,genusandsometimesalsospecies level.ThismeansthatseveralOTUidscanbeassignedtothesame taxa.
The OTU tablewas used for alpha diversity (within-sample diversity)andbetadiversityanalysis(between-samplediversity), usinganequalnumber ofsequencesacrosssamples,wherethe OTUtablewasresampledtoanevendepthof27,000sequencesper sample.ThealphadiversitywasmeasuredusingthemetricsChao1
(estimatesspeciesrichness bycountingnumbersofrareOTUs), observed species (counts the number of OTUs), PDwholetree (quantitativemeasure ofphylogenetic diversity).TheOTUtable wasfurthersplitintooneforeachsystembeforefiltration,where onlyOTUsthatpassedthecriteriaofminimumcountof0.01%ofthe totalsequencespersystemwerekept.Thisfilterwasusedinorder tohaveafeasiblenumberofOTUsasinputforthetaxonomicchar- acterization.Intotal633and607OTUspassedthisfilterinRASand S-CCS,respectively,andtheseOTUtableswerefurthersummarized intotaxonomicsummarytablesforeachofdifferenttaxonomiclev- els(i.e.phylum,class,order,family,genus).Taxonomicanalysiswas mainlyperformedatthephylumorgenuslevel.OnlyOTUsatthe genuslevelwereusedinthestatisticalanalyses,andcontained176 OTUsforRASand141OTUsforS-CCS.
Asurvey fora listof15 potentialpathogenswasperformed withinthe OTUtables at thegenus level, which wereselected based on the Norwegian Fish Health Report of 2014 (Bornø and Linaker, 2015) and involved Tenacibaculum, Moritella, Vib- rio,Aliivibrio,Flavobacterium,Yersinia,Aeromonas,Renibacterium, Piscirickettsia, Pseudomonas, Photobacterium, Alteromonas, Pseu- doalteromonas,PsychrobacterandPolaribacter.Thebacteriawere definedaspotentialpathogenssincetheirgenusorsomeoftheir species have been isolated from clinically diseased fish or are knownas pathogens or opportunists. Nucleotide search within BLASTdatabaseof16SribosomalRNAsequenceswasperformed witha16SrRNAgenesequencecorrespondingwithanOTUidof EnterobacteriaceaedetectedinRAS,inordertoininvestigateany similaritieswithYersinia.
2.2.5. Statisticalanalysis
For S-CCS differences in alpha diversity (Chao1, observed species,PDwholetree)betweenbiofilmandwater,aswellastem- poralchangesinthetankwastestedusingfollowingregression model:
y=b0+b1x1+b2x2+e
Intheabovemodel,yisthemeasuredalphadiversity,x1the sampletype(x1=1isbiofilm,andx1=0iswater)andx2thenum- berofdaysaftertheinitiationoftheexperiment.Thesamemodel wasappliedontheselectedOTUsdefinedaspotentialpathogens totestfordifferencesbetweenbiofilmandwater,aswellastheir temporalchangesinS-CCS.ForRAS,effectofsalinity(12vs22ppt) andsampletype(biofilmvswater)onalphadiversityandpotential pathogenswastestedusingtwowayANOVAwithoutinteraction.
Minitab(version17.2.1)wasappliedforregressionand ANOVA.
ResultsofthealphadiversityanalysisisshownwithChao1metric (Chao,1984),butsimilarresultswerealsoobtainedwiththeother alphadiversitymetrics.
PartialLeastSquaresRegression(PLSR,MartensandMartens, 2000)wasusedonthestatisticalanalysisoftheOTUtablesatthe genuslevel(UnscramblerX10.3).PLSRisatoolformultivariate regressionwhereoneormoreresponses(Y)aremodelledfroma setofexplanatoryvariables(X).APLSmodelextractlinearcombi- nationsoforiginalvariablesthatmaximizethecovariancebetween XandY.MorespecificallylinearcombinationsinX(OTUs)areused aspredictorsfortheY’s(salinity/sampletype).UsuallyaPLSmodel isinterpretedusingscore plots,loadingplotsandbylookingat regressioncoefficients.Inordertolookatstabilityofmodels,cross- validationareusedtoselectthenumberofcomponentstoinclude inthemodel.HeretheYvariableswerecategorical(salinity,12/22 andsampletypebiofilm/water),whichmeansthattheregression modelcanbeformulatedasaclassificationproblem.Inotherwords, salinityandsampletypewerepredictedfromtheOTUdata,using PLSRwithacategoricalresponse.Thisapproachisusuallyreferred
toasPLSDiscriminantAnalysis(PLS-DA,BarkerandRayens,2003;
Indahletal.,2007;Woldetal.,2001).
PLS-DA with cross-validationand Jack-knifing (Martens and Martens, 2000) was applied to identify OTUs significantly dif- ferent between biofilm and water within RAS and S-CCS, and betweenthetwo salinitylevelsintheRAS.PLS-DAwasapplied withbiofilm/waterascategoricalresponsesinthefirstmodel,and 12/22pptinthesecond model.ThesignificantOTUs (variables) withpositive loadings onthefirstcomponent willhave higher abundanceintheconditioncorrespondingtobiofilmor22pptin thefirstandsecondmodel,respectively,whereasthosewithneg- ativeloadingswillhavehigherabundanceintheothercondition.
PLSRwasappliedtoinvestigatetemporalchancesofOTUsin theS-CCSsystem.Inthefirstmodel,samplesfrombothbiofilm andwaterwereincludedandtheOTUdatawerecorrectedwith respectedtothesampletype(meanofeachsampletypesubtracted fromrawdata).Inthesecondmodel,onlydatafrombiofilmwas included.Cross-validationandJack-knifingwereappliedtoextract significantvariables.OTUsidentifiedsignificantlyintheprediction modelforthetemporalvariationwasclusteredusingthefunction clustergraminbioinformaticstoolboxofMatlab(release,2013b) withstandardisedeuclideandistanceandwardslinkagebetween theOTUs.Theresultsareshownasheatmapdiagrams.
3. Results
3.1. Microbiotadiversityinwaterandbiofilmsof commercial-scaleRASandS-CCS
3.1.1. Betadiversity
The difference in microbiota composition between samples from RAS and S-CCS wasanalysed withbeta diversity analysis (unweighted)andpresentedinPrincipalComponentAnalysis(PCA) plots(Fig.2AandB).AclearseparationofsamplesfromS-CCSand RASwasseenalongPC1,aswellassomeseparationbetweenwater andMBBR-biofilmsampleswithinRAS.PC2wasmainlyseparat- ingsampleswithinS-CCS(Fig.2A).AlongPC3aclearseparation wasobservedbetweenwaterandbiofilmsamples(Fig.2B)espe- ciallyinS-CCS.SincePC1explains15%,PC27.8%andPC35.6%of thevariance,thismeansthatthedifferencebetweenS-CCSand RASsamplesweremuchlargerthanthedifferencewithinS-CCS orbetweenwaterandbiofilmsamples.Betadiversityanalysiswas alsoperformedonRASsamplestoinvestigatetheimpactofsalin- ity(12pptvs22ppt)onthemicrobiota(Fig.2C).Aclearseparation betweenthemicrobiotafrom12pptand22pptsampleswasseen alongPC2,whilePC1wasseparatingsamplesfromMBBR-biofilm andwater.
3.1.2. Alphadiversity
Thediversityofthemicrobiotawithinsampleswereinvesti- gatedbyperformingalphadiversityanalysis.Almost2000observed OTUspersampleweredetectedinbothRASandS-CCS.Thealpha diversitywassignificantlyhigherinthebiofilmsamplescompared tothewatersamplesin bothRAS andS-CCS (Chao1,p<0.001).
Regressionanalysisrevealedtemporalchangesofthealphadiver- sity(Chao1,p<0.05)inS-CCS(Fig.3).Nosignificantdifferencein alphadiversitybetweensamplesfrom12and22pptinRASwas detected(datanotshown).
3.2. Taxonomiccharacterizationofmicrobiotain commercial-scaleRASandS-CCS
3.2.1. Overviewofthedominatingmicrobiota
ThemicrobiotainRASandS-CCSwascharacterizedatphylum andgenuslevel.ThedominatingOTUsatphylum(>0.1%)andgenus (>1%)ofRASandS-CCSarepresentedinFigs.4and5,respectively.
Please cite this article in press as: Rud, I., et al., Deep-sequencing of the bacterial microbiota in commercial-scale Fig.3.Alphadiversity(Chao1)inbiofilmsandwateratdifferenttimepointswithin
S-CCS.
InRAS,ProteobacteriaandBacteroideteswerethemaindominat- ingphylaaccountingfor89%ofthemicrobiota(Fig.4A).Relevant forRASwasalsothedetectionofNitrospirae(0.4%).Thedominat- ingphylainS-CCSareshowninFig.5A,withhighestdominanceof Proteobacteria,Bacteroidetes,Cyanobacteria,anUnassignedOTU
andVerrucomicrobia(all >5%).Proteobacteriaand Bacteroidetes accountedfor69%ofthemicrobiotainS-CCS.Characterizationat thegenuslevelrevealedmany missinggenera,and inthis case thetaxaata higherlevelwasreported(Figs.4Band 5B).The correspondingphylaandorderwasalsoindicatedinthefigures.
CharacterizationatthegenuslevelinRASresultedinonlydom- inanceoftaxawithinProteobacteriaandBacteroidetes(Fig.4B), whichweredominatedbyoneOTUofRhodobacteraceae,account- ingfor22%ofthetotalmicrobiota,andSaprospiraceae(7%)and Polaribacter (5%),respectively. In S-CCS, theProteobacteria was highlydominatedbythesameOTUofRhodobacteraceae(12%)as withinRAS,whilePolaribacter(9%)wasthedominatinggeneraof theBacteroidetes(Fig.5B).AnUnassignedOTU(6%)attheking- domlevelwasalsodominatinginS-CCS.EightoftheOTUswere commonbetweenRASandS-CCS(Figs.3Band4B).
3.2.2. Differencesinmicrobiotacompositionbetweenbiofilmand water
Differencesinmicrobiota composition betweenbiofilmsand waterwaspreviouslyrevealedwithbetadiversityanalysis(Fig.2).
PartialLeastSquaresDiscriminantAnalysis(PLS-DA)wasfurther appliedtoidentifyOTUswithsignificantlydifferentabundancein biofilmversuswaterwithinbothRASandS-CCS,whichresultedin totalof66significantOTUsinRASand31OTUsinS-CCS.Thefirst
Fig.4.TaxonomicassignmentofdominatingOTUsinRASpresentedatphylum(A)andgenus(B)levelshownaspercentageofthetotalmicrobiota.Dataisbasedonthe meanofallsampleswithinRAS.PhylaOTUs>0.1%andgeneraOTUs>1%areshown.Atthegenuslevel,thecorrespondingphylaandordernameisincluded,andwhentaxa nameismissingthehigherleveloftaxaisincluded.k,kingdom;p,phylum;c,class;o,order;f,family;g,genus.
Table2
OTUssignificantlydifferentbetweenMBBR-biofilmandwaterinRASwhenapplyingPLS-DAwithfullcross-validationandjackknifinga.
Phylum Class Order Family Genus MBBR-biofilm(%) Water(%)
MBBR-biofilm
Actinobacteria Acidimicrobiia Acidimicrobiales 0.779 0.179
wb1P06 0.870 0.138
Bacteroidetes Cytophagia Cytophagales Cytophagaceae 6.192 1.816
Chloroflexi Anaerolineae Caldilineales Caldilineaceae 1.627 0.075
Planctomycetes Planctomycetia Pirellulales Pirellulaceae 1.593 0.369
Planctomycetales Planctomycetaceae Planctomyces 0.883 0.173
Proteobacteria Alphaproteobacteria 1.837 0.186
Kiloniellales Kiloniellaceae 1.189 0.442
Rhizobiales 1.752 0.125
Hyphomicrobiaceae 1.318 0.243
Devosia 3.236 0.847
Phyllobacteriaceae Mesorhizobium 0.619 0.074
Rhodospirillales 0.545 0.003
Betaproteobacteria Burkholderiales Comamonadaceae 1.426 0.757
Deltaproteobacteria Myxococcales 5.887 0.892
Gammaproteobacteria Alteromonadales OM60 1.255 0.326
Thiotrichales Piscirickettsiaceae 0.515 0.119
[Marinicellales] [Marinicellaceae] Marinicella 2.956 0.753
Water
Bacteroidetes Cytophagia Cytophagales Cytophagaceae Leadbetterella 0.638 2.151
Flavobacteriia Flavobacteriales Flavobacteriaceae Maribacter 0.440 2.039
Polaribacter 1.999 8.380
Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 13.809 29.238
Phaeobacter 0.310 0.703
Gammaproteobacteria Alteromonadales Alteromonadaceae Glaciecola 0.077 0.737
aOTUswhere>0.5%ofmeanwithinMBBR-biofilmsamplesorwatersamplesareincluded.
Table3
OTUssignificantlydifferentbetweenbiofilmandwaterinS-CCSwhenapplyingPLS-DAwithfullcross-validationandjackknifinga.
Phylum Class Order Family Genus Biofilm(%) Water(%)
Biofilm
Unassigned 7.110 4.380
Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae Other 0.125 0.042
Phyllobacteriaceae 1.382 0.573
Rhodobacterales Rhodobacteraceae Other 6.282 2.006
Rhodobacteraceae 16.089 8.210
Loktanella 1.193 0.367
Phaeobacter 1.426 0.454
Pseudoruegeria 0.404 0.171
Gammaproteobacteria Thiohalorhabdales 0.394 0.160
Water
Acidobacteria [Chloracidobacteria] RB41 0.001 0.130
Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 0.022 1.189
Bacteroidetes Flavobacteriia Flavobacteriales 0.065 0.179
Cryomorphaceae 0.080 0.322
Fluviicola 0.033 0.282
NS9 0.005 0.119
Proteobacteria Betaproteobacteria Burkholderiales Burkholderiaceae Burkholderia 0.005 0.425
Gammaproteobacteria Enterobacteriales Enterobacteriaceae Enterobacter 0.006 0.174
Oceanospirillales 0.016 1.173
Halomonadaceae CandidatusPortiera 0.009 0.546
Pseudomonadales Pseudomonadaceae Pseudomonas 0.002 0.141
Xanthomonadales Xanthomonadaceae 0.002 0.275
aOTUswhere>0.1%ofmeanwithinbiofilmsamplesorwatersamplesareincluded.
componentexplained27%ofthevariationinRASand23%inS-CCS, hencemuchofthevariabilityinthemicrobiotawasalsorelated tootherfactors.ArepresentativeofthesignificantOTUsarelisted inTable2(RAS)andTable3(S-CCS),whileallsignificantOTUsare listedinSupplementarymaterial(TableS1andTableS2).
TheOTUs more abundantin theMBBR-biofilm in RAS were in several phyla, e.g. including Proteobacteria (dominated by orderRhizobiales,MyxococcalesandMarinicellales),Bacteroidetes (Cytophagales)andPlanctomycetes(Table2).OTUsmoreabun- dantinthewaterinRASwerewithinProteobacteria,includingthe dominatingOTUofRhodobacteraceae,andPolaribacterwithinBac- teroidetes.InS-CCS,theOTUsmoreabundantinthebiofilmwere
withinProteobacteria,includingthedominatingOTUofRhodobac- teraceaeandOTUsofRhizobiales,aswellastheUnassignedOTU (Table3).ThesignificantOTUsmoreabundantinthewaterthanin thebiofilminS-CCSwererelativelowinabundance(Table3).
3.2.3. SalinityeffectsonthemicrobiotacompositioninRAS
Differencesinmicrobiotacompositionbetweensalinitytreat- ments,12and22ppt,inRASwaspreviouslyrevealedbyusingbeta diversityanalysis(Fig.2C),andPLS-DAwasappliedandidentified 26OTUswithsignificantlydifferentabundancewithinRASofeither 12or22ppt.Thefirstcomponentexplained16%ofthevariance, hencemuchofthevariabilityinthemicrobiotawasalsorelated
Please cite this article in press as: Rud, I., et al., Deep-sequencing of the bacterial microbiota in commercial-scale Table4
OTUssignificantlydifferentbetween12and22pptinRASwhenapplyingPLS-DAwithfullcross-validationandjackknifinga.
Phylum Class Order Family Genus 12ppt(%) 22ppt(%)
12ppt
Actinobacteria Acidimicrobiia Acidimicrobiales SC3-41 0.948 0.047
Nitrospirae Nitrospira Nitrospirales Nitrospiraceae 0.867 0.003
Proteobacteria Alphaproteobacteria Rhodospirillales 0.631 0.006
Rhizobiales Phyllobacteriaceae 1.697 0.713
Sphingomonadales 0.515 0.103
Deltaproteobacteria Myxococcales 6.221 1.266
22ppt
Bacteroidetes Cytophagia Cytophagales Cytophagaceae 1.947 5.547
Leadbetterella 0.849 1.803
Flavobacteriia Flavobacteriales Flavobacteriaceae Ulvibacter 0.025 0.568
Proteobacteria Alphaproteobacteria Kiloniellales Kiloniellaceae 0.449 1.091
Rhizobiales Phyllobacteriaceae Other 0.322 1.022
Rhodobacterales Rhodobacteraceae Other 1.193 4.146
Loktanella 0.273 1.366
Verrucomicrobia Verrucomicrobiae Verrucomicrobiales Verrucomicrobiaceae 0.144 1.322
aOTUswhere>0.5%ofmeanwithin12pptor22pptareincluded.
Fig.5.TaxonomicassignmentofdominatingOTUsinS-CCSpresentedatphylum (A)andgenus(B)levelshownaspercentageofthetotalmicrobiota.Dataisbased onthemeanofallsampleswithinS-CCS.PhylaOTUs>0.1%andgeneraOTUs>1%
areshown.Atthegenuslevel,thecorrespondingphylaandordernameisincluded, andwhentaxanameismissingthehigherleveloftaxaisincluded.k,kingdom;p, phylum;c,class;o,order;f,family;g,genus.
tootherfactors.ArepresentativeofthesignificantOTUsarelisted inTable4,whileallsignificantOTUsarelistedinSupplementary material(TableS3).MyxococcalesandPhyllobacteriaceae(order
Rhizobiales)werethemostdominatingOTUthatweresignificantly moreabundantwith12ppt.AlsoNitrospiraceaewassignificantly moreabundantwith12ppt,whileCytophagaceaeandRhodobac- teraceae(other)weretwoofthedominatingOTUsmoreabundant with22ppt.ThesalinitymighthavehadaneffectonTANsince highestTANwasobservedwith22ppt(Supplementarymaterial, Fig.S1A).
3.2.4. Time-dependentchangesofthemicrobiotainS-CCS
TemporalchangesofthemicrobiotacompositioninS-CCSwas shownwithPCA(Fig.6AandB).Acleartime-dependentchange inthemicrobiotacompositionwasobservedespeciallywithinthe biofilmsduringthe91daysoftheexperiment(Fig.6A),whereas thetrendwasnotthatclearforthewatersamples(Fig.6B).PLSR wasappliedtoidentifyOTUssignificantlychangingovertime,using bothacombineddatasetofbiofilmandwatersamples(wherethe firstcomponentexplained20%ofthevariation)andonbiofilmsam- ples(wherethefirstcomponentexplained32%ofthevariation).
Hence,thelattermodelshowedthattimeexplainedmoreofthe microbiotavariationinthebiofilmsthaninwater.
OTUs with a significant temporal change were clustered in heatmaps, where 11 significantOTUs commonfor biofilm and watersamplesarepresentedinFig.6C,while33OTUssignificantly changinginthebiofilmsamplesarepresentedinFig.6D.Twomain clustersweregenerated,whereClusterAcontainedOTUswho’s relativeabundancedecreasedovertimeandClusterBwherethe relativeabundanceincreasedovertime(Fig.6CandD).Common OTUsforClusterAinbothheatmapswereDevosiaandHyphomi- crobiaceae(orderRhizobiales),SC3-56(orderCytophagates)and Sphingomonadales.CommonOTUsforClusterBwereAlteromon- adaceaeandAliivibrio.Interestingly,Tenacibaculumwasoneofthe significantOTUsinCluster Bwithacommonresponsefor both biofilmandwater(Fig.6C).MostofthesignificantOTUschang- ingovertimeinbiofilmwereinClusterA(Fig.6D),includingthe dominatingOTUofRhodobacteriaceae.OTUsinClusterB(Fig.6D) includedPolaribacter,Aliivibrio,RhodobacteralesandseveralOTUs intheorderofAlteromonadales.
3.2.5. PotentialpathogensinRASandS-CCS
InRAS,fourofthe15potentialpathogenswereidentifiedatthe genuslevel(filteredat0.01%),andincludedFlavobacterium,Polarib- acter,PseudoalteromonasandPhotobacterium.ThegenusYersinia wasnotdetectedinanyofthesamplesfromRAS,eventhoughit wasrevealedthattheintroducedfishwascontaminatedbyYersinia ruckeri.However,anOTUidofitsfamilyEnterobacteriaceae was detectedthatwasnotresolvedtoanygenus,andsearchwithin
Fig.6.Time-dependentchangesofthemicrobiotainS-CCSduringaperiodof91days.(A)PCAplotofbiofilmOTUdataovertime(D=days),(B)PCAplotofwaterOTU dataovertime(D=days),(C)HeatmapofOTUswithsignificantchangeovertime(days)forcombineddataofbiofilmandwatersamples,(D)HeatmapofbiofilmOTUswith significantchangeovertime(days).k,kingdom;p,phylum;c,class;o,order;f,family;g,genus.ClusterAandBareindicated.
BLAST withthecorresponding16S rRNAgene sequence of this OTUidresultedin100%hits againstdifferentspeciesofYersinia andSerratia,andwith99% identitymatchagainstone strainof Yersiniaruckeri.ThisOTUwasdetectedmainlyinthewaterafter infectedfishwasintroducedtotheRASafter2months(Supplemen- tarymaterial,Fig.S1D),butwasalmostdepletedonemonthlaterin thewaterof22ppt.Polaribacterwassignificantlyhigherinbiofilm thanwater(alsoidentifiedwithPLS-DA,Table2),whereasPhoto- bacteriumhadhigherabundanceinthewatersamples(Fig.7A).No significantdifferencewasobservedbetweenthetwoRASoperated atdifferentsalinitieswithanyofthefourpathogenstested.InS- CCS,nineof15potentialpathogenswereidentifiedatthegenus level(filteredat0.01%),andincludedFlavobacterium,Polaribacter, Tenacibaculum, Moritella, Psychrobacter, Pseudomonas, Pseudoal- teromonas, Aliivibrio and Photobacterium. It hasbeen shown by PLS-DAthatPseudomonaswassignificantlymoreabundantinthe waterofS-CCS (SupplementaryTableS2),whereasPLSR insec- tion3.2.4identifiedincreaseofseveralpotentialpathogensover time,includingAllivibrio,Tenacibaculum,Alteromonadaceae(fam- ilyofAlteromonas)andPolaribacter,andthelattersignificantlymore abundantin thebiofilm (Fig. 6C and D). Regression analysisof theninepotentialpathogensconfirmedtheseresults,aswellas decreaseofPseudomonaswithinthewater(Fig.7B).Thefourgen- erabestexplainedbytheregressionmodelareshowninFig.7B,also showingsignificantlyhigherrelativeabundanceofthesepotential pathogensinthewatersamples,exceptforPolaribacter.
4. Discussion
4.1. OverallmicrobiotadifferencesbetweenandwithinRASand S-CCS
The microbiota in commercial-scale RAS and S-CCS during post-smolt production of Atlantic salmon was investigated by deep-sequencingtoincreaseourknowledgeofthecomplexityof themicrobiotainnewemergingclosed-containmentaquaculture systems for post-smolts. To ourcurrent knowledge, this is the firststudyonmicrobiotainRASduringpost-smoltproductionof Atlanticsalmon,andthefirststudyofmicrobiotainS-CCSingen- eral.
Deep-sequencingofbacterial16SrRNAgeneampliconswasthe chosenmethod,allowingforindepthcompositionalanalysisof thesehighlycomplexmicrobialcommunities.Indeed,thecomplex- itywasverifiedbydetectionofuptomorethan50,000different OTUsintotalandalmost2000uniquespeciespersampleinboth RASandS-CCS.Asexpected,themicrobiotacompositionwasdif- ferentbetweenthetwoaquaculturesystems,likelyduetoawide rangeofexternalfactors,includingmajordifferencesintechnology, water source,water treatment, turnover-rate,fish introduction, seasonalclimatevariationsetc.Thus,theobservationsinRASand S-CCSwasnotmeantfordirectcomparison,andthemainscopewas togetin-depthoverviewofthemicrobiotawithineachsystem.
Please cite this article in press as: Rud, I., et al., Deep-sequencing of the bacterial microbiota in commercial-scale Fig.7.Potentialpathogens(%)inbiofilmandwaterofRAS(A)andS-CCS(B)withsignificantdifferencesbetweenbiofilmandwater(*),significantdifferentovertimein termsofdays(**),bothsignificantdifferencesbetweenbiofilmandwaterandovertime(***).
In both RAS and in S-CCS, clear differences in microbiota betweenwaterandbiofilmswereobservedintermsofdiversity andcomposition,whichreflectsthatmanyaquaticmicroorganisms arecapableofcolonizingsurfaces,leadingtoformationsofbiofilms withspecialized processes (Costertonet al., 1995; Petrova and Sauer,2012).Otherauthorshavealsoreporteddifferentmicrobiota compositionattachedtoRASbiofilterthanthefree-livinginthe waterphaseofaquaculturesystems(reviewedinBlanchetonetal., 2013).Higherspeciesdiversitywasdetectedinthebiofilmsamples ofbothRASandS-CCScomparedtothewaterinthepresentstudy, whichhasalsobeenobservedinanindustrialrecirculatingcooling watersystem(Wangetal.,2013),thusindicatingthatheterogenic maturebiofilmshavebeenestablishedinthesystems.
4.2. RASandS-CCSweremainlydominatedbytaxaof ProteobacteriaandBacteroidetes
Taxonomicassignmentofthemicrobiotarevealedmorethan 1000OTUsatthegenuslevel,thustheanalysisallowedforidenti- ficationofthemorelowabundantbacteriaincontrasttothemore traditionaltypingmethodologiesbasedon16SrRNAsequencing, e.g.DGGE.Agenerallimitationwith16SrRNAgenetaxonomyis thatthegenehaslimitedresolutionoftaxonomyamongclosely
relatedspecies,andweexperiencedespeciallylimitationsoftaxo- nomicresolutionatthegenuslevelwhensequencingtheV4region ofthe16SrRNAgene.Thenext-generationsequencingtechniques are generallylimited by short read lengths obtained;however, longersequencereadsarecurrentlyavailable(Buermansandden Dunnen,2014),andwillprobablyexpandinthefutureimproving thetaxonomicresolution.
ThedominatingphylaofbothRASandS-CCSwereProteobacte- riafollowedbyBacteroidetes.Bothphylaarefrequentlyaccounted asdominating in aquaticecosystems,e.g. in biofilterofmarine RAS(Ruanetal.,2015), inwaterofa RASfortheproductionof turbotandsole(Martinsetal.,2013),inananaerobicsludgeofa wastewatertreatmentplant(Shuetal.,2015),andintheSouthSea ofKorea(Suhetal.,2015).TheProteobacteriaisahighlydiverse phenotypicand phylogeneticlineage(Kerstersetal.,2006), and especiallyoneOTUofRhodobacteraceaewasdominatinginboth RAS and S-CCSin ourstudy.The Rhodobacteraceaeareaquatic bacteriathatfrequentlythriveinmarineenvironmentsandcon- tainsof100genera(Pujalteetal.,2014),andhasbeenshownto behighlypresentasheterotrophsinamarineRAS(Michaudetal., 2009).ItislikelytoassumethatthisOTUrepresentsbacteriawithin theRoseobacterclade,alineageofRhodobacteriaceshowntobe amongthedominatingbacteriaintheocean,comprisingupto20%
ofthemicrobialcommunityandabundantasbothfree-livingandas attachedtoparticles,cellsorsurfaces(Buchanetal.,2005;Dangand Lovell,2016).TheBacteroidetescontainsmembersdominatingthe marineheterotrophicbacterioplanktons (Kirchman,2002).They areassumedtobespecializedindegradationofcomplexpolymers andhavepreferencesforgrowthattachedtoparticles,surfacesor algalcells,confirmedbygenomesequencesofmarineBacteroidetes (Fernandez-Gomezetal.,2013).InbothRASandS-CCS,Polaribac- terwasthemostdominatingOTUoftheBacteroidetes.Thisisin agreementwithastudyofBacteroidetesacrosstheNorthAtlantic Ocean,whichshowedPolaribactertobethemostabundantgenera oftheBacteroidetes(Gomez-Pereiraetal.,2010).Polaribacterhas recentlyalsobeenshowntobeamongthedominatingbacteriain RAScompartmentsandbiofilters(Martinsetal.,2013;Ruanetal., 2015).
4.3. MicrobiotainMBBR-biofilmaffectedbysalinity
Bacteria are known to have different sensitivity to salinity (Herlemann et al., 2011), and here we have shown that salin- ityalsoinfluencedthemicrobiota inRAS.Changesinsalinityin aRAS resultalsoinchanges innitrificationcapacity(e.g.Nijhof and Bovendeur,1990);likely a consequenceof varyingactivity oftheoriginal nitrifyingspeciespresent, but alsoduetoquali- tativechangestothemicrobialspeciescomposition.Indeed,the levelsofTANinthepresentstudymighthave beenaffectedby salinity(lowestTANwith12ppt),indicatingimpactonthenitrifica- tionefficiency,reportedbothfortricklingbiofiltersandsubmerged fixedbedbioreactor(NijhofandBovendeur,1990;Cortes-Lorenzo etal.,2014).ThiswasinagreementwiththatNitrospiraceae,likely thenitriteoxidizerin thesystem,wasmore abundantwith12 pptandwithhighestabundancewithintheMBBR-biofilm,though almostdepleted at 22pptin both MBBR-biofilm and thewater (Supplementary material,Fig.S1B).Nitrosomonaceae, likelythe ammoniaoxidizerinthesystem,wasalsomoreabundantinthe MBBR-biofilmat12ppt(Supplementarymaterial,Fig.S1B),though notsignificantlydifferentbetweenthetwosalinitiesandbetween MBBR-biofilm and water, becauseof high presence alsoin the waterat22ppt.Thepresentfindings,coupledtopreviousresults (e.g.NijhofandBovendeur,1990)haspracticalimplications.Any required changein salinityin this range (12–22ppt)shouldbe gradual,andtheRASsystemloadbecontrolledorreducedfora period,asaconsequenceof reducednitrificationandforallow- ingthenewmicrobiotatostabilize,includinganynewnitrifying species.Otherinvestigatorshavealsoreportedsensitivitytosalin- itywithinammoniaandnitriteoxidizingbacteria,wherethenitrite oxidizingbacteriawereshowntobemoresensitive(Bassinetal., 2011;Cortes-Lorenzoetal.,2014;Moussaetal.,2006;Zhaoetal., 2014).MyxococcaleswasamongthedominatingtaxainRAS,and more abundant with12 pptand in the MBBR-biofilm. Though Myxococcalesareconsideredsoilbacteriawithlowtolerancefor salt(Reichenbach,1999),halophilicandhalotolerantmyxobacte- riahavebeenisolatedfrommarineenvironments,e.g.Jiangetal.
(2010).MembersofMyxococcaleshavealsobeenassociatedwith production of off-flavours like geosmin(Dickschatet al., 2005, 2007;Schulzetal.,2004),and alsowithgeosminproductionin aRAS(Auffretetal.,2013).
Awiderange ofdifferentbacteriawereshowntobesignifi- cantlymoreabundantwithintheMBBR-biofilm,manylikelywith specializedcapabilitiestoattach,colonizeandsurviveonsurfaces.
Movementoversurfacesbyglidingmotilityisforinstancecom- monwithinmembersofthehighlyabundantMyxococcalesand Cytophagaceae(Kaiser,2003;McBrideetal.,2014).Planctomyces wasalsosignificantlymore abundantwithintheMBBR-biofilm, alsopreviouslyidentifiedinRASbiofilters(Lahavetal.,2009;Ruan etal.,2015; Taletal.,2003,2006),suggesting arole foranam-
moxinthesystem.Indeed,manyspeciesofPlanctomycetesgrow attachedtosurfacesviaaholdfast anchoredatthetipofalong stalk(Wardetal.,2006),andreproducebybudding(Fuerstand Sagulenko,2011).Thisisalsocommonforothertaxadominating theMBBR-biofilm,includingRhizobiales,itsfamilyHyphomicro- biaceaeandPirellulaceae(Brownetal.,2012;OrenandXu,2014;
Wardetal.,2006).TheHyphomicrobiaceae,aswellasPirellulaceae, Marinicella, Chloroflexiand Actinobacteria that also weremore abundantwithintheMBBR-biofilmshavepreviouslybeeniden- tifiedinmarineandfreshwaternitrificationfilters(Martinsetal., 2013;Michaudetal.,2009;Ruanetal.,2015;Sugitaetal.,2005;
Taletal.,2006).ThedominatingOTUofRhodobacteraceaewassig- nificantlymoreabundantinthewaterofRAS,whereastheother OTUofRhodobacteraceaedominatinginRASwasmostabundant with22ppt,indicatingdifferentpropertiesofthesetwotaxonomic cladesofRhodobacteraceae(alsocommentedin4.2).
4.4. TemporalchangesofthemicrobiotainS-CCS
Thetemporalchangesinthemicrobiotadiversityandcomposi- tioninS-CCSwasmainlyobservedinthebiofilmsduringthethree monthsstudy.HighturnoverofthewaterinS-CCS,duetothehigh flowrateofintakeofwaterfromthesea,mighthavecausedamore stochasticmicrobiotainthewaterthaninthebiofilms,aswellas lowermicrobiotadiversity.Thebiofilmmicrobiotadetectedmight alsohavebeenmorerepresentativeduetoameanoffoursamples comparedtoonesamplingpointpertimeofthewater,andlarger samplingvolumeofthewaterwouldhavebeenmoresatisfactory.
ThehighturnoverrateofwaterinS-CCS(<60min)mightalso introduce a microbiota reflecting the diversity in sea, contain- inghighamountofbacterianotyetculturedandtaxonomically assigned,alsoreportedbyAravindrajaetal.(2013).Indeed,high abundanceofanunassignedOTUwasdetectedinS-CCSandnot intheRAS,whichischaracterizedbymuchlowersystemwater turnoverrate.HighabundanceofthephylumCyanobacteriawas alsodetectedinS-CCS;however,atlowertaxonomicdepththiswas resolvedtobecontaminationofChloroplastDNAinthe16SrRNA geneanalysis,alsoreportedbyHanshewetal.(2013).TheChloro- plastDNAmightindicatedetectionofalgae(phytoplankton),and investigationofthetemporalchangeswithinS-CCSidentifiedan increaseoftheStreptophytaalgaeespeciallyinMaymonths(also increaseoftheCyanobacteriaSynechococcaceaewasobservedin thewater,datanotshown).Thismightindicatedetectionofanalgal bloomthatistypicallydevelopinginthespringmonths(Buchan et al., 2014). In parallel, several potential pathogens including Tenacibaculum,Polaribacter,AliivibrioandthefamilyAlteromon- adaceaecontainingpotentialpathogenicspecieswereincreasingin thelastperiod.Indeed,membersoftheFlavobacteriiaareknownto dominatecommunitiesofalgalblooms(seereviewbyBuchanetal., 2014).Forinstance,Teelingetal.(2012)haveidentifiedPolaribacter andTenacibaculumwithinsuchacommunity.Itshouldbemen- tionedthatnomeasurementsintheS-CCScouldconfirmanalgal bloomduringtheexperimentalperiod,andourmethodologyneeds tobevalidatedintermsofalgaldetectionaccuracy.Theincreaseof Polaribacterduringthelastperiodmightalsobeduetoproduction ofspecificbacterialcompounds(rhodopsins)thatinducegrowth whenassociatedwithlight,afeatureknownwithinPolaribacter (Gomez-Consarnauetal.,2007).Polaribacterwasonlysignificantly increasinginthebiofilm,whereasinRASit wasmoreabundant inthewaterthanintheMBBR-biofilm.Ithasbeensuggestedthat Polaribactercanalternatebetweentwolifestyles,onefree-living whennutrientsarepoorandoneattachedlifeformtosurfacesand glidingtosearchfornutrients(Gonzalezetal.,2008).
RhodobacteraceaeOTUsandbacteriawithinRhizobialeswere more abundant in the biofilm than in the water in S-CCS, but interestinglytheysignificantlydecreased duringtheexperimen-
