host-derived population genomics data provides …host-derived population genomics data provides...
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Host-derived population genomics data provides insights into bacterial and diatom
compositionofthekillerwhaleskin
RebeccaHooper1*,JaelleC.Brealey1*,TomvanderValk1,AnttonAlberdi2,JohnW.Durban3,
HollyFearnbach4,KellyM.Robertson3,RobinW.Baird5,M.BradleyHanson6,PaulWade7,M.
ThomasP.Gilbert2,8,PhillipA.Morin3,JochenB.W.Wolf9,10,AndrewD.Foote11**,Katerina
Guschanski1**
*Theseauthorscontributedequallytothiswork.
**Theseauthorscontributedequallytothisworkandareco-seniorauthors.
1AnimalEcology,DepartmentofEcologyandGenetics,EvolutionaryBiologyCentre,UppsalaUniversity,Norbyvägen18D,75236,Uppsala,Sweden2Centre forGeoGenetics,NaturalHistoryMuseumofDenmark,UniversityofCopenhagen,ØsterVolgade5-7,CopenhagenK1350,Denmark3MarineMammalandTurtleDivision,SouthwestFisheriesScienceCenter,NationalMarineFisheriesService,NationalOceanicandAtmosphericAdministration,8901LaJollaShoresDr.,LaJolla,CA92037,U.S.A.4SR3,SeaLifeResponse,Rehabilitation,andResearchP.O.Box1404Mukilteo,WA98275,USA5CascadiaResearch,4thAvenue,Olympia,Washington98501,USA6NorthwestFisheriesScienceCenter,NationalMarineFisheriesService,NationalOceanicandAtmosphericAdministration,2725MontlakeBoulevardEast,Seattle,Washington98112,USA7National Marine Mammal Laboratory, Alaska Fisheries Science Center, National MarineFisheriesService,NationalOceanicandAtmosphericAdministration,7600SandPointWayNE,Seattle,Washington98115,USA8NTNUUniversityMuseum,N-7491Trondheim,Norway9ScienceofLifeLaboratoriesandDepartmentofEvolutionaryBiology,EvolutionaryBiologyCentre,UppsalaUniversity,Norbyvägen18D,UppsalaSE-75236,Sweden10SectionofEvolutionaryBiology,FacultyofBiology,LMUMunich,GroßhadernerStrasse2,Planegg-Martinsried82152,Germany11MolecularEcologyandFisheriesGeneticsLaboratory,SchoolofBiologicalSciences,BangorUniversity,Bangor,Gwynedd,LL572UW,UKCorrespondenceRebeccaHooper,TremoughHouse,UniversityofExeterCornwallCampus,Penryn,Cornwall,TR109FE,UK.Email:[email protected],AnimalEcology,DepartmentofEcologyandGenetics,EvolutionaryBiologyCentre,UppsalaUniversity,Norbyvägen18D,75236,Uppsala,Sweden.Email:[email protected]
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AndrewFoote,MolecularEcologyandFisheriesGeneticsLaboratory,SchoolofBiologicalSciences,BangorUniversity,Bangor,Gwynedd,LL572UW,UK.Email:[email protected],AnimalEcology,DepartmentofEcologyandGenetics,EvolutionaryBiologyCentre,UppsalaUniversity,Norbyvägen18D,75236,Uppsala,Sweden.Email:[email protected]
Abstract
Recentexplorationintotheinteractionsandrelationshipbetweenhostsandtheirmicrobiota
hasrevealedaconnectionbetweenmanyaspectsofthehost’sbiology,healthandassociated
microorganisms.Whereasampliconsequencinghastraditionallybeenusedtocharacterise
themicrobiome,theincreasingnumberofpublishedpopulationgenomicsdatasetsofferan
underexploitedopportunity to studymicrobial profiles from thehost shotgun sequencing
data.Here,weusesequencedataoriginallygeneratedfromkillerwhaleOrcinusorca skin
biopsiesforpopulationgenomics,tocharacterisetheskinmicrobiomeandinvestigatehow
hostsocialandgeographicfactors influencethemicrobialcommunitycomposition.Having
identified 845microbial taxa from 2.4million reads that did notmap to the killerwhale
referencegenome,wefoundthatbothecotypicandgeographicfactorsinfluencecommunity
compositionofkillerwhaleskinmicrobiomes.Furthermore,weuncoveredkeytaxathatdrive
themicrobiome community composition and showed that they are embedded in unique
networks, one ofwhich is tentatively linked to diatompresence andpoor skin condition.
CommunitycompositiondifferedbetweenAntarctickillerwhaleswithandwithoutdiatom
coverage, suggesting that the previously reported episodic migrations of Antarctic killer
whalestowarmerwatersassociatedwithskinturnovermaycontroltheeffectsofpotentially
pathogenic bacteria such as Tenacibaculum dicentrarchi. Our work demonstrates the
feasibility of microbiome studies from host shotgun sequencing data and highlights the
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importanceofmetagenomicsinunderstandingtherelationshipbetweenhostandmicrobial
ecology.
KEYWORDS
Cetacea,Contamination,Metagenomics,Microbiota,Orcinusorca
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1|INTRODUCTION
Theskinmicrobiomeisanecosystemcomprisedoftrillionsofmicrobessculptedbyecological
andevolutionary forces actingonboth themicrobes and their host (McFall-Ngai,&Eliot,
2005; Byrd et al., 2018). Recent explorations have revealed a tight connection between
almosteveryaspectofthehost’sbiologyandtheassociatedmicrobialcommunity(Reviewed
byMcFall-Ngai,&Eliot,2005;Bordenstein,&Theis,2015;Alberdietal.,2016;Koskellaetal.,
2017;Byrdetal.,2018).Althoughnumerousintrinsicandextrinsicfactorsthatinfluencethe
skinmicrobiomecompositionhavebeenidentified,therelativeimportanceofthesefactors
often appear to differ even between closely related host taxa (Kueneman et al., 2014;
McKenzie,Bowers,Fierer,Knight,&Lauber,2012;Wolzetal.,2017).Intrinsically,thehost’s
evolutionary history, age, sex, and health appear significant (Leyden, McGiley, Mills, &
Kligman,1975;Cho&Blaser,2012;McKenzieetal.,2012;Phillipsetal.,2012;Apprilletal.,
2014;Yingetal.,2015;Chngetal.,2016).Extrinsically,bothenvironmentalfactors,wherea
sub-selectionofenvironmentalmicrobescolonisehostskin(Apprilletal.,2014;Wolzetal.,
2017;Walkeetal.,2014;Yingetal.,2015),andsocioecologicalfactors,suchasahost’ssocial
groupandthelevelofinteractionwithconspecifics(Jain,2011;Laxetal.,2014;Songetal.,
2013;Kolodnyetal.2017),canplayimportantroles.
Mostmicrobiomestudiestodatearebasedon16SribosomalRNAgenesequences,ahighly
conservedregionofthebacterialandarchaealgenome(Hamady&Knight,2009).However,
inaddition topotentialbiases inPCRamplification, inwhich lowreliabilityofquantitative
estimations ariseduemismatches inprimerbinding sites, PCR stochasticity, anddifferent
numbersof16Sgenecopiesineachbacterialspecies(Alberdietal.,2018),analysisofthe16S
regioncanlimitfunctionalandtaxonomicclassification(Quince,Walker,Simpson,Loman,&
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Segata, 2017). In contrast, shotgun metagenomics can facilitate both high-resolution
taxonomicandfunctionalanalyses(Ranjan,Rani,Metwally,McGee,&Perkins,2016;Quince,
Walker,Simpson,Loman,&Segata,2017;Koskellaetal.,2017).Theadventofaffordablehigh-
throughputsequencinghasseenanever-increasingnumberofpopulationgenomicsstudies
inawiderangeofstudysystems(e.g.Jonesetal.,2012;Poelstraetal.,2014;DerSarkissian
et al., 2015; Nater et al., 2017). This affords an unprecedented opportunity to exploit
sequencingdata to secondarily investigate themicrobial communitiesassociatedwith the
sampled tissue of their host (Salzberg et al., 2005; Ames et al., 2015; Zhang et al., 2015;
Manguletal.,2016;Lassalleetal.,2018).
Here, we explore the relative importance of extrinsic factors on the epidermal skin
microbiome of free-ranging killer whales (Orcinus orca) using shotgun sequencing data
derived from skin biopsy samples of five ecologically specialisedpopulations, or ecotypes
(Foote et al., 2016). Given thewidespread geographic range (Forney&Wade, 2007) and
variationinecologicalspecialisationofkillerwhales,eveninsympatry(Durbanetal.,2017;
Fordetal.,1998),thisspeciesprovidesagoodstudysystemforexploringtheeffectsofboth
geographiclocationandecotype(aproxyforbothsocialityandphylogenetichistory)onthe
skinmicrobiome.However,theopportunisticuseofsuchdataisalsofraughtwithpotential
pitfalls.Wethereforedescribeindetailmeasurestakentodisentanglepotentialsourcesof
contaminationfromthetrueskinmicrobiome,thusprovidingauseful roadmapfor future
host-microbiomestudiesthatexploithost-derivedshotgunsequencingdata.
2|MATERIALSANDMETHODS
2.1|Studysystem
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ThroughoutmostofthecoastalwatersoftheNorthPacifictwoecotypesofkillerwhalesare
foundinsympatry:themammal-eating‘transient’andfish-eating‘resident’ecotype(Fordet
al.,1998;Saulitisetal.,2000;Matkinetal.,2007;Filatovaetal.,2015).Fourdecadesoffield
studieshave found that theyare socially andgenetically isolated (Hoelzel&Dover,1991;
Barrett-Lennard,2000;Hoelzeletal.,2007;Ford,2009;Parsonsetal.,2013;Filatovaetal.,
2015;Morinetal.,2015;Foote&Morin,2016).Killerwhaleshavealsodiversifiedintoseveral
ecotypesinthewatersaroundtheAntarcticcontinent,includingaformcommonlyobserved
hunting seals in the pack-ice of the Antarctic peninsula (type B1), a form that feeds on
penguinsinthecoastalwatersoftheAntarcticpeninsula(typeB2),andadwarfformthought
toprimarilyfeedonfishinthedensepack-iceoftheRossSea(typeC)(Pitman&Ensor,2003;
Pitman&Durban,2010,2012;Durbanetal.,2017;Pitmanetal.2018).
2.2|Samplecollectionanddatageneration
Weusedtheunmappedreadsfromapublishedpopulationgenomicsstudyofkillerwhale
ecotypes(EuropeanNucleotideArchive,www.ebi.ac.uk/ena,accessionnumbers:ERS554424-
ERS554471;Footeetal.2016),whichproducedlowcoveragegenomesfromatotalof49wild
killerwhales,correspondingtofiveecotypes;10sampleseachoftheNorthPacificfish-eating
resident and sympatric mammal-eating transient ecotypes, and 8, 11 and 10 samples,
respectivelyfromAntarctictypesB1,B2andC(seeFigure1forthesamplinglocations).DNA
wasextractedfromepidermalbiopsiescollectedbyfiringalightweightdartwithasterilised
stainless-steelcuttingtipfromasterilisedprojector(e.g.Palsbølletal.,1991;Barrett-Lennard
etal.,1996)at theflankof thekillerwhale.Asastudyoncaptivekillerwhales found low
variability in the taxonomiccompositionof the skinmicrobiome fromdifferentbodysites
(Chiarelloetal.2017),smallvariationintheexactlocationontheflankfromwhichthebiopsy
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wastakenshouldnotbiasourresults.Biopsieswerestoredinsteriletubesat-20°C.Atno
pointwerebiopsysamplesindirectcontactwithhumanskin.
DNAextraction,librarybuilding,andsequencinghavebeenpreviouslydescribed(Footeetal.,
2016). All lab work was conducted in a sterile flow hood to prevent contamination.
SequencingwasdoneattheDanishNationalHigh-ThroughputDNASequencingCentrewithin
theUniversityofCopenhagen.ThefacilityisspecificallygearedforlowDNAquantitylibrary
sequencingfromancientandenvironmentalDNA.Samplesofthesameecotypewerepooled
andsequencedacrossmultiplesequencinglanes.Samplesofdifferentecotypeswerealways
runondifferent sequencing lanes,with theexceptionof several typeB1 andB2 samples,
whichwere initially grouped as ‘type B’ (Pitman& Ensor, 2003) and some sampleswere
thereforesequencedonsharedlanes.
2.3|Sequencingreadpre-processing
Asameanstoenrichthedatasetforbacterialsequences,wefirstusedSAMtoolsv1.5(Liet
al.,2009)toremoveallsequencingreadsthatmappedtothekillerwhalereferencenuclear
genome (Oorca1.1, GenBank: ANOL00000000.2; Foote et al., 2015) and mitochondrial
genome (GU187176.1) with BWA-mem (Li & Durbin, 2009). The remaining reads were
adapter-trimmedusingAdapterRemovalV2.1.7(Schubert,Lindgreen,&Orlando,2016).We
then removed duplicates generated during library indexing PCR by merging reads with
identical sequences using in-house python scripts (Dryad doi:10.5061/dryad.c8v3rv6). All
readswithanaveragequalityscore<30werefilteredoutusingprinseqV0.20.4(Schmieder
&Edwards,2011),andallreadsof<35bpwereremovedusingAdapterRemoval.
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2.4|Investigatingcontamination
Despitetheprecautionsoutlinedabove,contaminationcanbeintroducedatseveralstages
ofthesequencedatageneration,andsubsequentlymistakenforthegenuinehost-associated
microbiomesignal.ContaminatingDNAcanbepresentinlaboratoryreagentsandextraction
kits(Lusketal.,2014;Salteretal.,2014).Forexample,silicainsomecommercialDNAspin
columns is derived from diatom cells and therefore can be a potential source of
contaminationwithdiatomDNA(Naccacheetal.,2013).However,theQiagenQIAquickspin
columnsused in this studydonotcontainsilica frombiologicalmaterial,according to the
manufacturer.Crosscontaminationcanalsooccurbetweensamplesprocessedinthesame
sequencing centre (Ballenghien et al., 2017). The impact of contamination increases in
sampleswithsmallamountsoftrueexogenousDNAandcanswampthesignalfromthehost’s
microbiome (Lusk et al., 2014; Salter et al., 2014). Contamination can be assessed using
negativecontrols(e.g.Davisetal.,2017).However,thedatausedinthisstudywasinitially
produced with the sole focus on the host organism. Including extractions and library
preparationblanksisnotaroutineprocedureinpopulationgenomicsstudiesbasedonhigh-
qualityhosttissuesamplesand,assuch,blankswerenotincludedinthelaboratoryworkflow
and hence not sequenced. Therefore, we instead implement an ad hoc workflow that
attemptstodifferentiatebetweencontaminantandrealexogenousDNAfromhostspecies
shotgunsequencingdata.
2.4.1|PhiXcontamination
The contaminationofmicrobial referencegenomesbyPhiX,which isusedas a control in
Illumina sequencing, is a known potential source of error inmetagenomics studies using
shotgunsequencingdata(Mukherjeeetal.,2015).Therefore,toavoiderroneousmappingof
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PhiX-derived reads to contaminatedgenomes,we removedall readsmapping to thePhiX
genome used by Illumina (NC_001422) with BWA-mem 0.7.15 (Li, 2013) with default
parameters.
2.4.2|Environmentalandlaboratorycontamination
If the amount of contamination (derived from laboratory reagents or environment) is
relatively equal among samples, we expect the relative proportion of contaminant
sequencingreadstobeinverselycorrelatedtothequantityofsample-derivedDNA:i.e.low
DNAquantity sampleswillbedisproportionatelyaffectedbycontaminantDNAsequences
comparedwithhigh-quantitysamples (Lusketal.,2014;Salteretal.,2014).Wetherefore
estimatedthecorrelationbetweentheproportionofthetotalsequencingreadsassignedto
eachmicrobial taxon(seebelowforhowtaxonomicassignmentwasconducted)andtotal
DNAreadcountpersample(priortoremovalofhostDNAandbeforePCRduplicateremoval).
Microbialtaxaforwhichthereadcountwassignificantlynegativelycorrelatedwiththetotal
numberofreadspersample(includinghostDNA), i.e. thosethatconsistently increased in
abundanceinlow-quantityDNAsamples,wereflaggedaspotentialcontaminants.
2.4.3|Humancontamination
Toaccountforthepossibilityofcontaminationwithhuman-associatedmicroorganisms,we
nextquantifiedtheamountsofhumanDNAinoursamplesandusedthisasaproxyofhuman-
derived microbial contamination (see Supporting Information for the details of read
processing).Onlyreadsuniquelymappingtoasingleregionofthegenomewithhighquality
(samtools -q30 -F4 -F256)wereretained,andweremovedallduplicatesusingsamtools
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rmdupinsingle-endmode.Humancontaminationlevelswereestimatedbycalculatingthe
percentageoffilteredreadsmappedtothehumangenome(TableS1).Weincludedthese
values as a covariate in statistical models as a way to, at least partially, control for
contaminationwithhuman-associatedmicroorganisms.
2.4.4|Knownbacterialcontaminants
Next,weinvestigatedwhetherspecificbacterialtaxathathavepreviouslybeenreportedto
belikelycontaminantsarepresentinourdataset.Followingread-basedanalyses,wefound
that our samples were dominated by Cutibacterium (Propionibacterium) acnes, which is
abundantonhumanskin(Byrdetal.,2018)andaknowncontaminantofhigh-throughput
sequencing data (Lusk et al., 2014;Mollerup et al., 2016).We therefore investigated the
distribution of sequence identity between ourC. acnes reads and theC. acnes reference
genomes,with the expectation that human or laboratory contaminantswould showhigh
(close to 100) percent identity, whereas killer whale-derived C. acnes would be more
divergent.
Additionally,weanalyseddatafromaNorthPacifickillerwhalesequencedat~20xcoverage
inapublishedstudy,inwhichsamplecollection,DNAextraction,andsequencingwasentirely
independentofourdataproduction(accessionnumber:SRP035610;Mouraetal.,2014).IfC.
acneswaspresentinthesedata,itwouldsuggestthateitheritwasarealcomponentofthe
killerwhaleskinmicrobiome,oritwasindependentlyintroducedascontaminationinboth
studies.
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Contaminanttaxaareunlikelytobeintroducedinisolation.C.acneswasconfirmedtobea
likelycontaminant(seebelow)andwethereforeremovedalltaxawithwhichitsignificantly
co-occurred.Usingnetassoc0.6.3(Morueta-Holmeetal.,2016),wecalculatedco-occurrence
scoresbetweenalltaxonpairsintherawtaxadataset.Wesetthenumberofnullreplicates
to999,andcorrectedp-values formultiplecomparisonsusing theFDRmethod.Fromthe
resultingmatrix,weselectedtaxawiththetop10%absolutesignificantco-occurrencescore
withcandidatecontaminanttaxa,andremovedthesetaxafromdownstreamanalyses,along
withC.acnes.
2.4.5|Investigatingsourcesofcontamination
Finally,toascertaintheauthenticityofourdataandtoestimatethelevelandpossiblesource
ofcontamination,weusedSourceTrackerV2.0.1(Knightsetal.,2011),atoolthatimplements
a Bayesian classification model to predict the proportion of taxa derived from different
potentialsourceenvironments.Thisapproachallowedustocomparethecompositionofthe
free-rangingkillerwhaleskinmicrobiometoothermarinemammalskinmicrobiotaandtoa
number of potential contaminating and environmental sources. We obtained data from
publicrepositoriesand includedmicrobialcommunitiesreflectingthemarineenvironment
(oceanwater from Southern Ocean and the North Pacific, Sunagawa et al., 2015), other
marinemammalskin(captivebottlenosedolphinsTursiopstruncatusandkillerwhalesalong
withtherespectivepoolwatersamples,andfree-ranginghumpbackwhales,Chiarelloetal.,
2017,Bierlichetal.,2018),likelycontaminantssuchashumanskinandgut(Ohetal.,2014,
Lloyd-Priceetal.,2017,Meiseletal.,2016),andlaboratorycontaminationfromcommonly
usedreagents(sterilewater,Salteretal.,2014)(TableS2).Weattemptedtospecificallyselect
sourcesthatwereobtainedwiththeshotgunsequencingapproachtoavoidpotentiallocus-
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specific effects that can produce distinct microbiome profiles in amplicon-based studies.
However,only16SrRNAamplicondatawasavailableforthemarinemammalskinandthe
laboratorycontaminants,eachstudytargetingadifferentregionwithinthislocus(TableS2).
Therefore,tocontrolforlocus-specificeffects,wealsoincludedsamplesfromahumanskin
16Sampliconstudy(Meiseletal.,2016)andlimitedourdatatoreadsmappingtothe16S
rRNA gene for those comparisons (see Supporting Information for more detailed
methodologyofreadprocessing).
WeusedtheRpackageVeganV2.4.6(Oksanenetal.,2017)tocalculatedistancesbetween
microbiome profiles derived from these different datasets. After Total Sum Scaling (TSS)
normalization, abundance-based Bray-Curtis and presence/absence-based binary Jaccard
distanceswerecalculatedandvisualisedusingprincipalcoordinatesanalysis.Subsequently,
a subsetof sourceswasused in SourceTracker andweusedour killerwhaledata as sink
without applying rarefaction to either sink or source samples. We also repeated the
SourceTrackeranalysisusingfree-ranginghumpbackwhalesasthesinksamples.
2.5|Taxonomicassignment
We used MALT (MEGAN Alignment Tool) version 0.3.8 (Herbig et al., 2016) to create a
reference database of bacterial genomes downloaded from the NCBI FTP server
(ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA,accessed26thJanuary2017).Weperformeda
semi-global nucleotide-nucleotide alignment against the reference database. Semi-global
alignmentsaremoresuitableforassessingqualityandauthenticitycriteriacommontoshort-
readdata,andarealsousefulwhenaligning16SrRNAdataagainstareferencedatabasesuch
asSILVA(Herbigetal.,2016).Sequenceidentitythresholdwassetto95%asperVågeneet
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al.,(2018),butwithamoreconservativethresholdofincludingonlytaxawithfiveormore
alignedreadsinsubsequentanalysis,
ThenucleotidealignmentsproducedinMALTwerefurtheranalysedinMEGANversion6.7.6
(Husonetal.,2016).Genomeswiththepresenceofstackedreadsinsomegenomicregions
and/or large gapswithout anymapped reads,were flaggedusing a custompython script
(Dryad doi:10.5061/dryad.c8v3rv6), and manually assessed in MEGAN. This step was
necessarytoidentifyspuriousandincorrectlysupportedbacterialtaxa,whichwereremoved
from furtheranalysis if they representedhighlyabundant species (Warinneret al., 2017).
TaxonomiccompositionofthesampleswasalsointeractivelyexploredinMEGAN,andthe
numberofreadsassignedtoeachtaxonwasexportedforsubsequentanalysis.
Taxonomic assignment was also carried out using an assembly-based approach. Filtered
metagenomicsequencesofallsamplesweremergedtoperformaco-assemblyusingMegahit
1.1.1(Lietal.,2015)withdefaultsettingsandk-list:21,29,39,59,79.Assemblyqualitywas
assessed using Quast 4.5 (Gurevich et al., 2013). Contigs were subsequently mapped to
reference bacterial genomeswithMGMapper (Petersen et al., 2017) using bestmode to
assigntaxonomy.TheassemblyfilewasindexedusingBWA-indexandSamtools-faidx.BWA-
memwassubsequentlyusedtomapthereadsofeachsamplebacktotheassemblycontigs
tofinallyretrievethemappedreadsusingSAMtools-view.Individualcoveragevalueswere
calculatedwithBedtools2.26.0(Quinlanetal.,2010)andcontigcoveragetablenormalised
usingCumulativeSumScale(CSS)asimplementedinMetagenomeSeq(Paulsonetal.,2013).
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Thesequencingdatausedisthisstudyisrathershallowintermsofcoverageofmicrobialtaxa,
correspondingtolowcoveragekillerwhalegenomes(meanof2x).Therefore,weexplored
howlowsequencingdepthmayaffecttheinferredbacterialprofiles.Tothisend,weusedan
independentlysequenced20xcoverageresidentkillerwhalegenome(Mouraetal.,2014).By
drawing a random subset of reads from this genome using SAMtools, we compared the
taxonomiccompositionofthemicrobiomeofthesameindividualat20x,10x,5xand2xmean
sequencecoveragedepth.
2.6|Diversityanalyses
WecalculatedalldiversitymeasuresinVegan(Oksanenetal.,2017),usingreadsthatwere
assignedtothespecieslevelinMEGAN.Byfocussingontaxaatthespecies-level,wewere
able to explore the skinmicrobiome at a high resolution, an advantage of shotgun over
amplicon-basedanalyses.However,resultsofthisanalysisshouldbeinterpretedinlightofa
species-levelfocus,whereweareexploringasmallyetwell-resolvedrepresentationofthe
microbiome,whichmaypotentiallybeenrichedwithpathogensandcommonenvironmental
bacteria,ratherthanaholisticrepresentationoftheentireecosystem.
Tocontrol forbias introducedbyvaryinggenomesize (specieswith largergenomesshow
higherreadcounts,whicharetranslatedintohigherabundancescores;Warinneretal.,2017),
wedividedallreadcountsbythesizeoftherespectivefullbacterialgenome.Ifthetaxonwas
mappedtothelevelofthestrain,wedividedthereadcountbythepublishedgenomesizeof
thatstrain;ifidentifiedtothespecieslevel,wedividedthereadcountbytheaveragegenome
sizeacrossallpublishedstrainsofthatspecies.
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Beta diversity was explored using two dissimilarity matrices in the R-package Vegan:
abundance-based Bray-Curtis and presence/absence-based binary Jaccard distances. To
assess the strength and significance of ecotype and geographic location (longitude and
latitude) in describing variation in community composition, we conducted permutational
multivariateanalysisusingthefunctionAdonisinVegan.Wecontrolledfordifferingdepths
ofcoveragebetweensamplesusingtwotechniques.First,weusedgenomesize-controlled
data (see above) and included the number of reads mapping to the species level as a
covariate. Second, TSS normalisation of the genome-size controlled data was conducted,
followedbyconversiontotheBray-Curtisdistancematrix.TSSnormalisationisirrelevantfor
presenceabsencedata,asonlyspeciespresence,ratherthanspeciesabundance,isretained
inthebinarypresenceabsencematrix.Asaresult,threemodelswereexplored:twoBray-
Curtismodelswith differing depth-control techniques, and one Jaccardmodel using read
counts as covariate. Eachmodel consistedof the following covariates: latitude (numeric),
longitude(numeric),ecotype(categorical),andpercentagehumancontamination(numeric),
with library size included only when TSS normalisation was not used. For each model,
residualswerepermuted9999times.WeusedthefunctionBetadisper(Vegan)followedby
ANalysisOfVAriance(ANOVA)totestforhomogeneityofgroupdispersions.Betadispercan
beusedtoensure that i) theAdonismodel resultsarenotconfoundedbyheterogeneous
variances (Anderson, 2001) and ii) to make biological inferences about between-group
varianceincommunitycomposition.
Weused the function capscale from theVeganpackage to performPrincipal Co-ordinate
Analysis(PCoA).ThefourbacterialtaxathatdescribedthemostvariationonPCoA1,andthe
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four that described the most variation on PCoA2, were designated as ‘driving taxa’. We
thereforeclassifiedatotalofeightuniquedrivingtaxathatdescribeindividualdifferencesin
microbiomecomposition(TableS4).
2.7|Networkanalysis
To venture beyond single microbial taxa and explore microbial interactions that include
interspecific dynamics, we expanded our analyses to networks of bacterial communities
associatedwiththedrivingtaxaidentifiedthroughthePCoA.Usingnetassoc(Morueta-Holme
etal.,2016),wecomparedtheobservedpartialcorrelationcoefficientsbetweentaxawitha
nulldistributionestimatedfromidenticalspeciesrichnessandabundancesastheobserved
data.Again, taxaco-occurrencescoreswerecalculatedbetweenall taxonpairs in theraw
dataset,withnull replicatesset to999.TheFDRmethodwasusedtocorrectp-values for
multiple comparisons. From the resulting matrix of significant co-occurrence scores, we
selectedthe20taxawiththehighestabsoluteco-occurrencescoreforeachofthe8unique
drivingtaxa.Wecreatedanewmatrixincludingonlythesetaxaandvisualisedco-occurrence
networks.
2.8|Functionalprofiling
Communitycompositioncanbeapoorpredictorofthefunctionaltraitsofthemicrobiome,
duetoprocessessuchashorizontalgenetransfer(HGT)betweenbacterialtaxa,whichcan
decouple species compositionand function (Koskellaetal.,2017). Shifting focus fromthe
taxonomiccompositiontothegeniccompositionofthemicrobiomereducestheimpactof
HGTonfunctionalcharacterisation(Koskellaetal.,2017).
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To explore functional profiles of the samples, we used DIAMOND v0.9.10 with default
parameters(Buchfink,Xie&Huson,2015)tocreateareferencedatabaseofnon-redundant
proteinsequencesfromfullysequencedbacterialgenomesdownloadedfromtheNBCIFTP
server (https://ftp.ncbi.nlm.nih.gov/genomes/genbank/ accessed 9th March 2017).
Nucleotide-to-amino-acid alignments of the sample reads to the reference database was
performedinDIAMONDandthetop10%ofalignmentsperqueryreported.TheMEGANtool
daa-meganizerwasthenusedtoassignreadstoproteinsbasedontheDIAMONDalignments
andtoassignfunctionalrolestotheseproteinsusingtheSEED(Overbeeketal.,2005)and
eggNOG(Huerta-Cepasetal.,2017)databases.Sinceoneproteincanhavemorethanone
function,itispossibleforonereadtobeassignedtomultiplefunctionalsubsystems.Theraw
countdata(numberofreadsassignedtofunctionalsubsystem)wereexportedfromMEGAN
and further processed in R. To control for differences in library depth, read counts per
functionalgroupwerenormalisedbytotalreadnumbersmappingtoSEEDoreggNOGterms.
Weusedprincipalcomponentsanalysis(PCA)performedintheRpackageprcomptovisualise
differencesinfunctionalgroupsbetweenindividuals.
Weadditionallyperformedanassembly-basedfunctionalprofilingtoovercometheindividual
weaknessesofbothassemblyandread-basedmethodologies(Quinceetal.,2017).Abinitio
genepredictionwasperformedoverthemetagenomicassemblyusingProdigal2.6.3(Hyatt
etal.,2010).ThelistofpredictedgenesequenceswasindexedusingBWAandSAMtoolswas
usedtomapthereadsofeachsamplebacktothegenesequences.WeusedBedtools2.26.0
(Quinlan et al., 2010) to calculate individual coverages. Gene coverage table was
subsequentlyCSSnormalisedusingMetagenomeSeq(Paulsonetal.,2013).
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2.9|Diatom-associationanalyses
Antarctickillerwhalesareoftenobservedtohaveayellowhue,whichhasbeenattributedto
diatom coverage (Berzin & Vladimirov, 1983; Pitman & Ensor, 2003) and identifiable
individualshavebeenobservedtotransitfromthisyellowskincolourationtoa‘clean’skin
condition (Durban & Pitman, 2012). This change is hypothesised to occur during brief
migrationstosubtropicallatitudes,whereturnoveroftheouterskinlayertakesplacewitha
reduced thermal cost (Durban & Pitman, 2012). If this hypothesis is correct, diatom
abundanceshouldbecorrelatedtoskinageandcolouration(Hart,1935;Konishietal.2008;
Durban&Pitman,2012).Inter-individualvariationinmicrobiomeprofileswithintheAntarctic
ecotypescouldthereforereflectvariationintheageoftheouterskinlayer.Duringnetwork
analysis,weidentifiedapossibleassociationbetweenakeybacterialtaxadrivingbetween-
sample differences in community composition (Tenacibaculum dicentrarchi) and bacterial
taxa associated with diatoms. Following from our observations that three samples from
AntarcticecotypeshadhighabundancesofT.dicentrarchiandthatinthePCoAthesesamples
weredifferentiated frommostother samples,we investigated the linkbetweenobserved
diatom coverage, abundance of T. dicentrarchi and abundance of other algae-associated
bacterialtaxa.WeconductedqualitativecolourgradingoftypeB1andtypeB2 individuals
usingphotographs takenat the timeofbiopsy collection, ranging from ‘clean’ through to
‘prominent’yellowcolouration.
Onlyalimitednumberofdiatomreferencegenomesareavailableandthereforeweusedtwo
methodologiestoquantifythelevelofdiatomDNAinoursamples.First,weusedMALTand
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MEGAN in the same taxonomic pipeline as previously described, but with a reference
database comprised of NCBI RefSeq nucleotide sequences from the diatom phylum
Bacillariophyta (downloaded 30th October 2017). To date, only seven diatom reference
genomesareavailable,thusidentificationatthespecieslevelwasnotattempted.Instead,
numbersofreadsmappingtoBacillariophytawereexportedandfurtherprocessedinR.Raw
diatomreadcountswereconvertedtoproportionoftotalnumberofsequencingreadsper
sample.Second,wealignedallreadsagainsttheSILVArRNAdatabase(release128,Quastet
al.,2013)usingBWA-mem0.7.17,retainedreadsmappingwith>10mappingqualitywith
SAMtools and used uclust (Edgar, 2010) inQIIME 1.9.1 (Caporaso et al., 2010) to assign
taxonomybasedontheSILVA18Sdatabaseat97%similarity.FromtheresultingOTUtable
weretainedreadsthatmatchedtoknowndiatomtaxonomy.
We explored the correlation between latitude (by grouping North Pacific and Antarctic
ecotypes)andproportionofreadspersamplemappingtodiatomsusingageneralisedlinear
modelwithaquasipoissonerrorstructureandloglink.Ascovariates,weincludedlongitude,
numberofreadsmappingtothebacterialspeciesleveltocontrolforlibrarysize,andnumber
ofhumanreadstocontrol forhuman-associatedmicrobialcontamination.Usingthesame
model structure,we then tested the correlationbetweenproportionof readsper sample
mapping to diatomsand the presence/abundance ofT. dicentrarchi reads, aswell as the
correlationwithpresenceofknownalgae-associatedbacterialtaxa(includingT.dicentrarchi,
Cellulophagabaltica,Formosa sp.Hel1_33_131,Winogradskyella sp.,Marinovumalgicola,
Agarivorans gilvus, Pseudoalteromonas atlantica and Shewanella baltica: Bowman 2000;
Aminetal.,2012;Goeckeetal.,2013a,b,incorporatedasabinaryvariable).
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3|RESULTS
Metagenomicprofilesfromtheskinmicrobiomeof49killerwhalesfromfiveecotypes(Figure
1)weresuccessfullyreconstructedusingshotgunsequencingdatafromDNAextractedfrom
skinbiopsies.Ofthereadsretainedfollowingourstringentfilteringprocedure,butbeforeour
investigationsintoC.acnesasapossiblecontaminant,8.20%(n=7,984,195)wereassigned
tomicrobialtaxausingtheread-basedapproach,with2.45%(n=2,384,587)assignedatthe
specieslevel(seeDryadrepositorydoi:10.5061/dryad.c8v3rv6).Overall,845taxaofmicrobes
were identified. The co-assembly yielded a 33.01 Mbp-long metagenome comprised of
45,934contigs(N50=970bp,average=730bp,max=48,182bp).Taxonomywasassigned
to41.73%ofthecontigs.Resultsfromtheassembly-basedapproachwereconcordantwith
theread-basedresults,wethereforereportonlythelatter.
3.1|Investigatingcontamination
Onaverage0.16%ofreads(range0.01-5.43%)readsmappedtothehumangenome(Table
S2),suggestingpresenceofhumancontaminationandmakingitpossiblethathuman-derived
bacteria were present in our dataset. After correcting for multiple testing, we found no
significantnegativecorrelationbetweentheproportionofreadsassignedtoeachbacterial
taxonandthetotalnumberofsequencedreads(SupportingInformation,Fig.S1).Negative
trends (although not significant) between some bacterial taxa and the total number of
sequenced reads were largely driven by one outlier sample with the lowest coverage
(B1_124047).Followingthededuplicationstepofourprocessingpipeline,thesetaxawereno
longerpresentinthedataset,astheyfellbelowourdefinedthresholdoffivealignedreadsin
MALT(Fig.S2).
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C.acneswasidentifiedasthemostabundantbacterialtaxon,withanaverageabundanceof
39.57%(standarddeviation=24.65;Fig.S3),butitmayhavebeenintroducedviahumanor
laboratorycontamination(Lusketal.,2014).Percentidentitytothehuman-derivedC.acnes
genomewas100%for245andover97%for505ofthe527contigsidentifiedasC.acnesby
MGMapper(Fig.S4),supportingtheideaofalikelyexogenoussourceofC.acnes.Killerwhale
samplespooledbyecotypeweresequencedacrossmultiplesequencinglanes,allowingusto
investigateifcontaminationwithC.acneswasintroducedatthesequencingstep.RelativeC.
acnes abundanceper samplewashighly similar between sequencing lanes (Coefficient of
Variation=0.076;Fig.S5),suggestingthatthecontaminationoccurredpriortosequencing.
However,C.acneswasalsopresenttoahighabundance(18.06%ofreadsaligningatspecies
level)intheindependentlysequencedresidentkillerwhale(Mouraetal.,2014),suggesting
thatcontaminationwithC.acneswasnotspecifictoourworkflow.Weconcludedthatthere
wasahighprobabilitythatC.acneswasalabcontaminantandthereforeremovedallC.acnes
reads/contigsfromourdatasetbeforecontinuingwithanalysis.
3.1.1|NetworkanalysisresultsforC.acnesassociatedtaxa.
Followingitsidentificationasalikelycontaminant,weusednetworkanalysistoidentifyand
remove the top 10% of species which significantly co-occurred with C. acnes, which
correspondedtoco-occurrencescoresabovetheabsolutevalueof1000(Fig.S6).Overall,82
specieswereremoved(Dryaddoi:10.5061/dryad.c8v3rv6),manyofwhichareknownhuman-
associatedbacterialtaxa.Followingthisfilteringstep,onetypeCsamplehadnoremaining
taxa.Wethereforeexcludedthissamplefromfurtheranalyses.
3.1.2|Metagenomicaffinitiesofwildkillerwhaleskinmicrobiome
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Only10killerwhalesampleshad50ormore16SreadswithassignedSILVAtaxonomy(eight
killerwhalesamplesremainedafterfilteringforC.acnesassociatedtaxa,Figure2).Overall,
priortoC.acnesfiltering,thekillerwhaledatasethad273taxaincommonwiththedataset
of2,279bacterialtaxaderivedfromsources(e.g.human,marinemammalandenvironmental
samples,seesection2.4.5).AfterfilteringforC.acnesandassociatedtaxa,236ofthe273
killerwhaleassociatedtaxaremained.Free-rangingkillerwhaleandhumpbackwhaleskin
microbiomesoverlappedontheprincipalcoordinates,independentoftheapplieddistance
measureandpresenceofC.acnesassociatedbacteria(Figure2a,Fig.S7a,b).Incontrast,data
fromthecaptivestudy,includingkillerwhaleandcaptivedolphinskinsamplesandtheirpool
water, clustered separately from all other studies. General separation by sequencing
approach (i.e. shotgun versus amplicon) was not observed: For instance, amplicon- and
shotgun-sequencedhumansamplesgroupedtogether(Figure2a,b).Itisthereforepossible
that theseparationof thecaptivestudysamples isduetoeithertheuseofaspecific16S
targetlocusorotherfactorsassociatedwithcaptiveversuswildenvironments(notethatthe
poolwasfilledwithseawaterfromtheMediterraneanSea;Chiarelloetal.,2017).
The threemarinemammal species formed one cluster irrespective of study on the third
dimensionintheabundance-basedBray-Curtisdistanceanalysis(Fig.S7c,d),suggestingthat
thereisacommonfactortothemarinemammalskinmicrobiomecomposition.Importantly,
thefree-rangingkillerwhalemicrobiomeprofilesgenerallygroupedawayfromthehuman
skinsamples,gutsamplesandlaboratorycontaminants.Theywerealsoseparatedfromthe
oceanwatersamples,suggestingthatthekillerwhaleskinmicrobiomescharacterisedinour
studyrepresentamicrobialcommunitythatisclearlydistinctfromsurroundingoceanwater.
Here,itisnoteworthythatfilteringofourdataforC.acnesassociatedtaxaatthegenuslevel
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ishighlyconservativeandalsoremovesanumberofmicrobialtaxathatareabundantinthe
marineenvironment,astheybelongtothesamegeneraassomeC.acnesassociatedspecies.
Samplesrepresentinglaboratorycontaminationconsistentlyclusteredwiththehumanskin
samples(Figure2a,b,Fig.S7),suggestingthatonesourceofcontaminantsinlaboratorywork
are human-associated skinmicrobes. All results presented above were confirmed with a
largerdatasetthatincluded16killerwhalesampleswithatleast20bacterial16Sreadswith
SILVAtaxonomyassignment(Fig.S8).
Based on the principal coordinates analysis and for greater clarity of presentation, we
restrictedtheselectionofsamplesthatwereusedassourcesintheSourceTrackeranalysisto
captivedolphinskin(n=4),captivekillerwhaleskin(n=4),waterfromthecaptivekillerwhale
pool(n=4),wildhumpbackwhaleskin(n=4),SouthernOceanwater(n=4),humangut(n=4),
shotgun-derivedhumanskindatafromasebaceoussite(n=4),andlaboratorycontamination
(n=3;thefourthsamplehad<2016Sreadsandwasexcludedfromanalysis)(TableS2).The
SourceTrackerresultssupportedthoseoftheprincipalcoordinatesanalysis(Figure2c,d),with
humanskintaxacontributingonaverageonly3.4%tothewildkillerwhaleskinmicrobiome
(range0.0-18.4%).Thispercentagedecreasedto2.2%(range0.0-9.6%)afterfilteringoutC.
acnes associated taxa. The contribution of lab contaminantswas also low (average 4.2%,
range0.0-28.6) inallbutoneresidentkillerwhale individual (31868),whichwasremoved
afterC.acnesfilteringduetolow(<50)readnumbers(average1.7%,range0.0-7.1%after
removalofC.acnesassociatedtaxa).Thesourcescontributingthemosttothefree-ranging
killerwhale skinmicrobiomes after removingC. acnes associated taxa included Southern
Ocean (mean 32.3%, range 4.5-69.4%), humpback whale skin (11.9%, range 0-36.7% in),
captivekillerwhaleskinandcaptivedolphinskin(mean13.2%,range2.1-64.8%andmean
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12.5%, range 0.2-40.8%, respectively). A high proportion of taxa observed in free-ranging
killerwhalescouldnotbeassignedtoanyofthesourcesincludedintheanalysis(‘Unknown’,
mean>25%).Thesetaxamayrepresentuncharacteriseddiversityspecifictothewildkiller
whale skinmicrobiome, a source thatwas not included in our analysis, e.g. oceanwater
collectedatthesametimeasthekillerwhaleskinbiopsies,ormarinemammalskintaxathat
are poorly characterised by the 16S locus targeted in othermarinemammalmicrobiome
studies.
ToverifytheSourceTrackerresultsforfree-rangingkillerwhalesamplesstudiedhere,wealso
ranSourceTrackerusingthefourwildhumpbackwhalesasthesinksampleswhileassigning
free-rangingkillerwhalesasasource(Fig.S9).Twohumpbackwhalessampledearlyinthe
foraging season around the Antarctic Peninsula closely resembled the wild killer whale
profiles, containing amixture of taxa attributed to the wild killer whale skin (41.7% and
65.3%),thecaptivedolphinskin(31.1%and2.7%)andunknownsources(21.3%and24.5%).
In contrast, the microbiome of the two humpback whales sampled late in the Antarctic
foragingseasonweredominatedbySouthernOceantaxa(both>95%).Thisisconsistentwith
thetemporalvariationinthecompletehumpbackwhaledatasetreportedbyBierlichetal.,
(2018). Overall, the detailed analyses of contributing sources of the killer whale skin
microbiome revealed a large proportion of taxa that are also found on the skin of other
marinemammalsandanimportantcontributionofenvironmentaloceanwatertaxa.Thisis
inlinewithpreviousreportsthatfoundasignificantcontributionofseawaterto,yetdistinct
composition of, marinemammalmicrobiomes (Bik et al., 2016). Expected contaminating
sources, such as human skin and laboratory contaminants, contributed only a small
proportiontoourkillerwhaleskinmicrobiomedataobtainedfromhostshotgunsequencing.
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3.2|Taxonomicexploration
Read-basedandassembly-basedapproachesproducedconcordanttaxonomicprofiles.The
mostabundantconstituentsof thekillerwhaleskinmicrobiomeat thephylum levelwere
Proteobacteria, Actinobacteria, Bacteroidetes and Firmicutes (Fig. S3a), which have been
identifiedinpreviousstudiesofbaleenwhaleskinmicrobiota(Appriletal.,2014;Shottset
al.,1990),includingthrough16Samplificationofskinswabsfromcaptivekillerwhalesunder
controlledconditions(Chiarelloetal.,2017).Atthespecies level,wefoundahighlevelof
inter-individual variation (Figure 3a, Fig. S3b), as previously found for four captive killer
whaleshousedinthesamefacility(Chiarelloetal.,2017).
Subsettingan independentlysequencedresidentkillerwhalegenometo lowersequencing
depth,weinferredthatwhilefivemostcommontaxawerefoundinsimilarproportionsin
highandlowcoveragedata,theidentificationofrarertaxabecamemorestochasticatlower
sequencingdepths(TableS3).Ourresultsmaythereforesufferfromthisbiasassociatedwith
lowcoveragedata,whichwouldbemostprominentinpresence/absence-basedanalyses.As
ameanstocontrolforthisbias,weincludelibrarysizeasacovariateinmodelsinvestigating
betadiversity.
3.3|Diversityanalyses
Human contaminationwas not a significant driver in themodels exploring beta diversity
(Table1),explainingatmost2%ofthevariation intaxonomiccomposition ineachmodel.
Ecotypewasasignificantvariable inallmodels,explaining10-11%ofvariation inthedata
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(Table1).LatitudewassignificantinbothBrayCurtismodelsbutnotintheJaccardpresence-
absencemodel.Where significant, it explained 4 – 5% of variation in the data (Table 1).
Longitudewasnotsignificantinanyofthemodels.Betadisperanalysisrevealednosignificant
heterogeneity in the variation of community composition between ecotypes (non-TSS
normalisedBray-Curtis:df=4,F=0.52,p=0.72;TSSnormalisedBray-Curtis:df=4,F=1.74,
p=0.16;binary Jaccard:df=4,F=0.63,p=0.64).This suggests thatbetween-individual
variationinmicrobialcompositionissimilaramongecotypes.
TheBray-CurtisPCoAexplainedmorevariationthanJaccard(24.13%versus16.06%onthe
firsttwoaxes),wethereforefocusontheBray-Curtisresults.Anetworkbasedonsignificant
co-occurrencesbetweeneightbacterialtaxadrivingvariationattheindividuallevel(TableS4)
andthetop20co-occurringtaxaforeachofthedrivingtaxa,showedclearlydifferentiated
and distinct community groups (Figure 3). Further investigation into the top 20 taxa that
significantly co-occurred with the driving taxa found that three of the taxa showing the
highest co-occurrence scores with the driving taxon T. dicentrarchi (Formosa sp.
Hel1_33_131,Cellulophagaalgicola andAlgibacteralginolytica)areassociatedwithalgae
(Bowman,2000;Beckeretal.,2017;Sunetal.,2016).
3.4|T.dicentrarchianddiatoms
Bothapproachestodiatomidentificationproducedconcordantresults(Fig.S10,TableS5).
AntarcticecotypeshadasignificantlyhigherabundanceofdiatomDNAthanNorthPacific
ecotypes (β = 0.65, SE = 0.29, p = 0.03; Figure 4a). Individuals with “prominent” yellow
colourationshowedhigherdiatomabundance(Figure4b),supportingthelinkbetweenskin
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colouranddiatompresenceinAntarctickillerwhales.Furthermore,theabundanceofdiatom
DNApersamplewassignificantlypositivelycorrelatedwiththeabundanceandpresenceof
T. dicentrarchi reads (number ofT. dicentrarchi reads: β = 0.014, SE = 0.003, p = <0.001;
presenceofT.dicentrarchi:β=0.915,SE=0.207,p<0.001;Figure4c)andthepresenceofat
leastonealgae-associatedbacterialtaxa(β=0.98,SE=0.17,p=<0.001;Figure4d).
3.5|Functionalanalysis
Intheread-basedfunctionalanalysisatotalof3,611,441readsmappedtoeggNOGfunctions
and1,440,371readsmappedtoSEEDfunctions.Inthecontig-basedfunctionalanalysiswe
identified 56,042 potential genes in ourmetagenome, out ofwhich EggNog functionwas
assignedto35,182.Bothapproachesidentifiedenergyproductionandconversion(classC)
andaminoacidmetabolismandtransport(classE)asthemostabundanteggNOGfunctions
inourdataset.TheeggNOGPCArevealedhighvariabilitybetween individuals (Figure4e),
however,aclusterofAntarcticwhaleswasobservedinprincipalcomponent2.Thesesamples
had high abundances of T. dicentrarchi (Figure 4e) and were associated with functions
corresponding to the COG functional categories J (translation, ribosomal structure and
biogenesis,β=0.007,SE=0.002,p=<0.001),F(nucleotidetransportandmetabolism,β=
0.005,SE=0.002,p=0.004)andI(lipidtransportandmetabolism,β=0.004,SE=0.002,p=
0.03). The same cluster of high abundance T. dicentrarchi Antarctic samples was also
identifiedintheSEEDPCA(Fig.S11).Thesesampleshadincreasednumbersofreadsmapping
toDNAmetabolism,aminoacidsandderivativesandcofactors/vitamins,althoughnoneof
thesefunctionsweresignificantlycorrelatedwithT.dicentrarchiabundance.
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4|DISCUSSION
Ourstudyhighlights thatcommunitiesofexogenousorhost-associatedmicrobiotacanbe
geneticallycharacterisedfromshotgunsequencingofDNAextractedfromthehosttissue.
However, dedicated analysis and treatment of contamination is necessary and requires
careful consideration in studies such as this, whereby samples were not collected nor
sequencedwiththeintentionofgeneticallyidentifyingmicrobiota.Insuchcases,thenormal
stringentcontrolmeasureswhichareroutineinmicrobialstudies,suchasthesequencingof
blanks, may not be possible. We have therefore presented an array of approaches for
estimating the proportion and sources of contamination and accounting for it in shotgun
studies.Overall,ouranalysessuggestthatwithcarefulconsideration,theminingofmicrobial
DNAfromhostshotgunsequencingdatacanprovideusefulbiological insightsthat inform
future targeted investigations intomicrobiome composition and function under stringent
laboratoryconditions.
After carefully filtering our data,wewere able to identify species interactions, ecological
networksandcommunityassemblyofthemicrobesanddiatomsthatcolonisekillerwhale
skinbyutilisingunmappedreadsfromshotgunsequencingdatageneratedfromskinbiopsies.
Akeyadvantageof this approachoveramplicon-based sequencing is theability toassess
functionalvariationbasedongenecontent,andtoidentifytaxatospecieslevel(Koskellaet
al., 2017; Quince, Walker, Simpson, Loman, & Segata, 2017). However, despite ongoing
efforts to describe bacterial species diversity, the breadth of the reference database is a
limitingfactorintheunbiasedcharacterisationofbacterialcomposition.Thus,taxaidentified
inouranalysesarenecessarilylimitedtospecieswithavailablegenomicinformation,andin
somecasesare likely to represent their closephylogenetic relatives (Tessleret al., 2017).
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Hence,wereferto‘taxa’ratherthan‘species’whereappropriate.Wealsodemonstratethe
impactofcontaminationonthelownumbersofreadsfromtruehost-associatedmicrobes,
whichcandilutethesignalofbiologicallymeaningfulvariationamongsamples.
Socialandgeographicfactorshavebeenfoundtoinfluencemicrobialdiversityinterrestrial
andsemi-terrestrialanimals(Koskellaetal.,2017).However,thereislessunderstandingof
howthesefactorsinterplayinawide-rangingsocialmarinemammaliansystem(Nelsonetal.,
2015).We found that beta diversity of the killerwhale skinmicrobiomewas significantly
influencedbyecotypeandlatitude.Temperaturehasbeenshowntobeakeydeterminantof
marinemicrobialcommunitystructureataglobalscale(Salazar&Sunagawa,2017;Sunagawa
etal.,2015).However,theeffectofecotypeasthemostimportanttestedvariablehighlights
the significance of social and phylogenetic factors in shaping microbiome richness and
composition.Inaddition,itunderscoresthatalthoughkillerwhaleskinisinfluencedbythe
localenvironment(Romano-Bertrandetal.,2015),itrepresentsauniqueecosystemthatis
separatefromthatofthesurroundinghabitat.Concordantwithourresults,astudyofthe
microbiomeoffourcaptivekillerwhalesandtheseawaterfromtheirpoolfoundthatthe
skin microbiota were more diversity and phylogenetically distinct from the sea water
microbialcommunity(Chiarelloetal.,2017).Killerwhalesarehighlysocialmammals(Baird,
2000; Ford, 2009), and thus are likely to have a high potential for horizontal transfer of
microbes between individuals during contact (Nelson et al., 2015). Ecotype-specific social
behaviour, organisation and population structure, as well as other variables related to
ecotypeecology,suchasrangesize,anddiet(duetotransmissionofbacteriafromdifferent
preyspecies;Menkeetal.,2017),arealllikelytoaffectthediversityofmicrobialspeciesthat
individuals areexposed to, andalso influence the levelofhorizontal transferofmicrobes
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betweenwhales.Thestrongsocialphilopatryinkillerwhales(Baird,2000;Ford,2009)and
thephylogeneticandphylogeographichistoryofecotypesisalsolikelytoplayarole,whereby
dueto limitedsocial transmissionbetweenecotypes, thephylogenyofbacterialspecies is
likelytoreflectthatofthehost(Leyetal.,2008,butseeRothschildetal.,2018).Itisalsolikely
to be influenced by the host’s evolutionary history, including secondary contact between
ecotypes(Foote&Morin,2016),wherebothverticalandhorizontaltransmissionofmicrobes
betweenecotypesispossible.
Despitethesignificanceof‘ecotype’asadriverofskinmicrobiomediversityinkillerwhales,
atleast79%ofthevariationinthemicrobiomeisunexplainedbythedriversconsideredin
ourmodels (Table 1). There is a strong overlap among ecotypes in the PCoA (Figure 3b),
suggesting a shared coremicrobiomewhichmay be partially sharedwith other cetacean
species(Figure2).Additionally,thePCoAshowssubstantialvariationwithinecotypes(Figure
3b), furtherhighlighting the roleof someotherdriver(s) ofmicrobiomevariation.Among
Antarcticecotypes,individualvariationwasassociatedwithdiatompresenceandadiscrete
subnetworkofmicrobialtaxa.Theoccurrenceofa‘yellowslime’attributedtodiatomsonthe
skinofwhales,includingkillerwhales,wasrecordedasearlyasacenturyago(Bennett1920;
Pitman et al., 2018). The extent of diatom adhesion on Antarctic whales is thought to
correlatewithlatitudeandthetimethewhalehasspentincoldwaters(Hart,1935;Konishi
etal.2008).Theskinmicrobiomeofhumpbackwhaleshasbeenreportedtochangethrough
theAntarcticforagingseason(Bierlichetal.,2018),andourSourceTrackeranalysisfoundthat
humpback whales sampled during the late foraging season (i.e. individuals who had
presumablyspentlongerintheSouthernOceanwatersatthetimeofsampling)hadmore
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similarity toSouthernOceanmicrobial communities than those collectedduring theearly
foragingseason.Thisraisestheintriguingquestionastowhetherthetimespentinthefrigid
Antarcticwaterscouldbeadriverofvariation in theskinmicrobiomeanddiatom loadof
Antarctickillerwhales.
Satellite tracking of type B1 and B2 killer whale movements, Durban & Pitman (2012)
documentedrapidreturnmigrationstosubtropical latitudes, inwhichindividualstravelled
upto9,400kmin42days.Basedonthestrongdirectionalityandvelocityoftravelduring
thesemigrations,Durban&Pitman(2012)hypothesisedthattheywerenotassociatedwith
breedingorfeedingbehaviour.Instead,theyarguedthatthesemigrationscouldbedrivenby
theneedtoleavethefrigidAntarcticwatersandtemporarilymovetowarmerwaters,toallow
forphysiologicalmaintenanceincludingtheregenerationoftheouterskinlayer(Durban&
Pitman,2012).TheidentificationofthesameindividualsinAntarcticwaters,sometimeswith
a thick accumulation of diatoms, and at other times appearing ‘clean’, supports the
hypothesisthatskinregenerationisanintermittentratherthancontinuousprocess(Durban
&Pitman,2012).Furthermore,bitemarksfromcookiecuttersharksIsistiusspp.,whichare
typicallyfoundinwarmandtemperatewaters,areregularlyobservedontypeCkillerwhales
(Pitman et al., 2018),which have also been directly tracked towarm sub-tropicalwaters
(Durbanetal.,2013).
WepresentgeneticsupportforthehypothesisofDurban&Pitman(2012),that‘clean’and
yellow-tintedtypeB1andB2killerwhalesrepresentdifferencesindiatomload.Inaddition,
we provide the first evidence that the extent of diatom coverage is also associatedwith
significantvariationintheskinmicrobiomecommunity.WefoundthatAntarctickillerwhales
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with the highest diatom abundance also had skinmicrobiomesmost similar to Southern
Oceanmicrobialcommunities,suggestingthatatthetimeofsamplingtheseindividualshad
spent longer in theAntarcticwaters,consistentwiththehypothesis thatdiatomcoverage
accumulateswithtimespentinthecoldSouthernOceanwaters.Perhapsmostsignificantly,
diatomabundancewaspositivelycorrelatedwiththeabundanceofT.dicentrarchi,aknown
pathogeninseveralfishspecies,whichisassociatedwithskinlesionsandseveretailandfin
rot(Piñeiro-Vidaletal.,2012;Habibetal.,2014;Avendaño-Herreraetal.,2016).
Ouranalyses revealed that sampleswithhighabundancesofT.dicentrarchi showdistinct
functionalprofiles.Functionalanalysesremainexploratoryatthisstage,constrainedbythe
difficultyoftranslatingbroadfunctionalcategories intobiologicalmeaning.However,with
more data that links individual health status and microbiome composition, functional
analyses may provide a tool for identifying individuals at risk. Therefore, whether T.
dicentrarchi represents a pathogen to killerwhalehosts remainsunknown. Type B1 killer
whales in apparently poor health andwith heavy diatom loads have been observedwith
severeskinconditions(skinpeelingandlesions;Figure4f),however,Tenacibaculumsp.have
beenreportedinupto95%ofhumpbackwhalessampledinrecentstudies,whichincluded
apparently healthy individuals (Appril et al., 2011, 2014; Bierlich et al., 2018). Skin
maintenancemaythusrepresentabalancingactforAntarctickillerwhalesofmanagingthe
costsofpathogenload,thermalregulation,reducedforagingtimeandlong-rangemovement.
Researchintotheskinmicrobiomeshouldthereforecontinuetoformacomponentofthe
ongoing holistic and multidisciplinary research programme to investigate the health of
Antarctic killer whale populations and more broadly in studies on the health of marine
mammals(e.g.Appriletal.,2014;Ravertyetal.,2017).
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Ongoing field efforts provide the opportunity to further explore the relationships and
interactionsbetweenkillerwhalehosts,theirskinmicrobiome,otherexogenoussymbionts
suchasdiatoms,andtheenvironment.Ourcommunity-basedanalysessuggestthepresence
ofadistinctenvironmentaltaxanetworkcentredonP.haloplanktisasadrivingtaxon(Fig.
3c). Collection andmetagenomic characterization of environmental samples, such as sea
water, alongside host biological samples would allow further explorations into the
contributionoflocalecologicalfactorstothehostmicrobiome.Asameansofreducingthe
impact of contamination with DNA from laboratory environment, microbiome
characterisation can be conducted by means of RNA sequencing. This has an additional
advantage of generating metatranscriptomic data, which, in combination with the
metagenomic data, can facilitate the comparison/contrast between community function
(usingRNAtranscript)andcommunitytaxonomiccomposition(usingDNAsequence;Koskella
et al., 2017). Thismay further reduce thepotential impactof common lab contaminants,
allowing the exploration of the bacterial functional repertoire that is in use in a given
ecologicalcontext, includingreconstructionofmetabolicpathways(Bashiardes,Zilberman-
Schapira,Elinav2016).Contaminationinthelaboratorycouldbefurthercontrolledforand
characterisedthroughinclusionofextraction,librarypreparationandPCRblanksasnegative
controls(Lusketal.,2014;Salteretal.,2014)andmeasuressuchasdouble-indexing(Rohland
&Reich,2012;vanderValketal.,2018),whichcantheninformtheemergingdownstream
filteringmethods for separating truemicrobiomes from contamination (Delmont & Eren,
2016; Davis et al., 2018). Lastly, the advances in long-read sequencing using portable
nanopore-based platforms make it possible to generate data suitable for reconstructing
completebacterialgenomeswhileinthefield(Parkeretal.,2017),includingintheAntarctic
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(Johnsonetal.,2017).Thisisapromisingdevelopmentwithrespecttoimprovingthebreadth
ofhost taxa fromwhichbacterial taxaarederivedandshould improve futuremappingof
metagenomicsdataandtaxonomicassignment.
AcknowledgementsThesuggestionofharvestingtheskinmicrobiomefromhostshotgundatawasfirstmootedbyGeraldPaooftheSalkInstituteduringameetingbackin2012whenwefirstembarkingontheshotgunsequencingproject,andwearegratefultoGeraldforsowingthisseed.WewouldliketothankBobPitmanwhowasinvolvedinthecollectionofmanyofthesamplesusedinthisstudyandgreatlycontributedthroughmanydiscussionsonthevariationamongkillerwhaletypes.We’dfurtherliketothankDavidStudholmeforpointingouttherichliteraturesurroundingdiatommicrobiomes,aswellaspotentialdiatom-relatedcontamination.JamesFellows Yates, Linda Rhodes, Morten Limborg and the microbiome journal club of theEvogenomicssectionattheCentreforGeoGeneticsprovidedvaluablefeedbackonthisworkand an earlier draft of this manuscript. We acknowledge The Danish National High-ThroughputDNASequencingCentreforsequencingthesamplesand,particularly,AndaineSeguin-Orlando, Lillian Petersen, Cecilie Demring Mortensen, Kim Magnussen and IanLissimore for technical support. Sample collection from killer whales in Antarctica wassupportedby the LindbladExpeditions-NationalGeographicConservation Fund. Thisworkwas funded by European Research Council grant ERCStG-336536 to J.B.W.W.; a DanishNationalResearchFoundationgrantDNRF94toM.T.P.G,theWelshGovernmentandHigherEducationFundingCouncilforWalesthroughtheSêrCymruNationalResearchNetworkforLowCarbon,EnergyandEnvironment,andfromtheEuropeanUnion’sHorizon2020researchandinnovationprogrammeundertheMarieSkłodowska-CuriegrantagreementNo.663830andaBritishEcologicalSocietygrant(SR17\1227)toAF,theFORMASgrant(project2016-00835) to KG.A.A.was supportedby theDanishCouncil for IndependentResearch -DFF(grant5051-00033)andLundbeckfonden(grantR250-2017-1351).Finally,wewouldliketothank the Linnaeus Scholarship Foundation at the University of Uppsala for providing ascholarshiptoRH,theErasmusMundusMasterProgrammeinEvolutionaryBiology(MEME)for facilitating the collaborative supervision of RH, and to Vanessa and Peter Hooper forprovidingfinancialsupporttoRHthroughoutthisproject.ReferencesAlberdi,A.,Aizpurua,O.,Bohmann,K.,Zepeda-Mendoza,M.L.,&Gilbert,M.T.P.(2016).Dovertebrategutmetagenomesconferrapidecologicaladaptation?.Trendsinecology&evolution,31,689-699.Alberdi,A.,Aizpurua,O.,Gilbert,M.T.P.,&Bohmann,K.(2017).Scrutinizingkeystepsforreliablemetabarcodingofenvironmentalsamples.MethodsinEcologyandEvolution, 9,134–147.AmesS.K.,GardnerS.N.,MartiJ.M.,SlezakT.R.,GokhaleM.B.,AllenJ.E.(2015)Usingpopulationsofhumanandmicrobialgenomesfororganismdetectioninmetagenomes.GenomeResearch,25,1056–1067.Amin,S.A.,Parker,M.S.,&Armbrust,E.V.(2012).Interactionsbetweendiatomsandbacteria.MicrobiologyandMolecularBiologyReviews,76(3),667-684.
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FIGURES
FIGURE1.Summaryofgeographicsampleoriginsandmicrobialdiversitymeasures.Mapofsampling locations of the 49 samples of five killer whale ecotypes, from which skinmicrobiomeswereincludedinthisstudy.TheAntarcticecotypesprimarilyinhabitwaters8–16°CcolderthantheNorthPacificecotypes.
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Figure 2. Composition of the wild killer whale skin microbiomes and other publishedmicrobiomes, for sampleswith≥50 taxonomyassigned16S reads. Principal coordinatesanalysisofJaccardbinarypresence/absencedistancesbefore(a)andafter(b)filteringofC.acnesassociatedtaxafromthewildkillerwhaledata.Proportionsofsourcescontributingtoeachkillerwhalesample,representedbycolumns,fromSourceTrackeranalysisbefore(c)andafter (d) filteringofC.acnesassociatedtaxa.* in (c)denotessamplesthatwereexcludedafterC.acnesfilteringduetolowreadnumbers.
* *
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FIGURE3. (a)Proportionofdrivingbacteriaper individual,afterdata filtering. Individuals,representedbycolumns,aregroupedbyecotype,andtherelativeproportionsofbacterialtaxa are indicatedby column shading (1,Tenacibaculumdicentrarchi; 2,Paraburkholderiafungorum; 3, Pseudoalteromonas haloplanktis; 4, Pseudoalteromonas translucida; 5,Acinetobacter johnsonii; 6, Pseudomonas stutzeri; 7, Stenotrophomonas maltophilia; 8,Kocuriapalustris;9,Other).(b)BetadiversitybetweenecotypesillustratedasaBray-CurtisPCoA estimated from read counts. (c) Positive co-occurrence network built from a co-occurrencematrixofallspecies,subsettedtothe8drivingtaxa(blacknodesnumberedasabove) and their top 20 positive and significant co-occurring species.Only specieswith asignificantco-occurrencescoreof>800areshown.
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FIGURE4Theinfluenceofdiatomabundanceonskinmicrobiomecommunitycompositionandmicrobial functional profiles. (a) Relative diatomabundance is significantly higher inAntarctic killerwhales thanNorth Pacific, but this is largely driven by a subset of outlierAntarcticindividuals.(b)WithinAntarctictypeB1andtypeB2specimens,therelativediatomabundanceissignificantlyassociatedwithskincolourationofthehostkillerwhale,withtheyellowishhuebeingareliableindicatorofdiatomload.InsetimagesareofthesametypeB2killerwhaleindividualdisplayingextremevariabilityindiatomcoverage,bothphotographsbyJohnDurban.Relativediatomabundanceissignificantlyassociatedwith(c)thepresenceofTenacibaculum dicentrarchi and (d) several algae-associated bacteria, including T.dicentrarchi. (e) PCA of variation in functional COGs between individuals, coloured by T.dicentrarchiabundance.IndividualswithhighrelativeabundancesofT.dicentrarchigenerallycluster with high values in principal component 2. The top 10 COGs contributing to PCAvariationareshowningreyarrows(J:translation,ribosomalstructureandbiogenesis,I:lipidtransportandmetabolism,F:nucleotidetransportandmetabolism,H:coenzymetransportandmetabolism,U:intracellulartrafficking,secretionandvesiculartransport,N:cellmotility,P: inorganiciontransportandmetabolism,T:signaltransductionmechanisms,M:cellwallmembraneenvelopebiogenesis,G:carbohydratetransportandmetabolism).(f)PhotographofatypeB1killerwhaleintheGerlacheStraitoftheAntarcticPeninsulaonthe4thDecember2015withhighdiatomcoverageandpoorskincondition.PhotographbyConorRyan.
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TABLESTable1.Factorsinfluencingthekillerwhaleskinmicrobiome.ResultsofAdonismodelsusinggenome-size controlled species data. (a) Bray Curtismodelwith library size included as acovariate; (b)TSSnormalisedBrayCurtismodel; (c)Binary Jaccardmodelwith librarysizeincludedasacovariate.Significantfactorsarehighlightedinbold.
(a) BrayCurtis(b)BrayCurtis
(TSSnormalised) (c)BinaryJaccard
F r2 p F r2 p F r2 p
Latitude 1.8 0.04 0.01 2.35 0.05 <0.01 1.4 0.03 0.05Longitude 0.89 0.02 0.62 0.89 0.02 0.61 0.99 0.02 0.45Ecotype 1.36 0.11 <0.01 1.35 0.11 0.02 1.23 0.10 0.03
Librarysize 1.33 0.03 <0.01 - - - 1.20 0.02 0.23Humancontamination 1.13 0.02 0.35 0.59 0.01 0.89 1.04 0.02 0.42
Residuals 0.79 0.81 0.80
Total 1 1 1
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