Psoriasis : A Study of the Skin Transcriptome and Microbiome
GEOGRAPHIC INFLUENCES ON THE SKIN MICROBIOME OF …€¦ · Ethics Statement Skin samples from...
Transcript of GEOGRAPHIC INFLUENCES ON THE SKIN MICROBIOME OF …€¦ · Ethics Statement Skin samples from...
GEOGRAPHICINFLUENCESONTHESKINMICROBIOMEOFHUMPBACKWHALES
By
KevinCharles(KC)BierlchDr.DaveJohnston,DukeUniversityMarineLab
Dr.AmyApprill,WoodsHoleOceanographicInstituteApril21,2016
Mastersprojectsubmittedinpartialfulfillmentofthe
requirementsfortheMasterofEnvironmentalManagementdegreeintheNicholasSchooloftheEnvironmentof
DukeUniversity
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ExecutiveSummary
Assessing thehealth stateofwildmarinemammalsand theirpopulations is challenging, and
thereisagrowingneedtodevelopreliableproxiesforhealthdetermination.Climatechangeandother
anthropogenicfactorsareinfluencingdiseaseprevalenceandvirulenceinthemarineenvironmentand
thereisaneedtoimprovetoolsandtechniquesformonitoringthehealthstatusofwildmarinemammals
thatarelistedasthreatenedorendangered.
Theskinisthelargestmammalianorganandservesasthefirstlineofdefensebetweenthehost
andtheirexternalenvironment.Mostresearchhasfocusedonhumanhealthandhasfoundthattheskin
microbiomecanserveasaprotectivemechanismbyaddingtotheskin’sdefenseagainstcolonizationof
potentialpathogenicbacteria.Theskinisrelativelywell-sampledinmarinemammalsandmayserveas
ausefulproxyforhealthstatus,asdemonstratedinhumans.However,beforeskinmicrobiomesbecome
useful health diagnostic tools for marine mammals, more information is needed about the factors
influencingvariabilitywithintheskinmicrobialcommunity.
I analyzed the skin microbiome of 72 apparently healthy humpback whales primarily from
Antarctica,aswellasAlaska,Hawaii,AmericanSamoa,andtheGulfofMaine.Phylogeneticandstatistical
analysesrevealedtwodominantfamiliesofbacteria(MoraxellaceaeandFlavobacteriaceae)foundon
each individualwhale.However, thereweresignificantdifferences in theskinmicrobiomesamongst
whales fromdifferent geographic areas, both globally aswell as amongst regionswithin Antarctica.
Thesefindingsprovidesupportthatthereisaspecies-specificmicrobiomeonhumpbackskinthatvaries
accordingtogeographicfactors.Thisinitialcharacterizationofthehealthyhumpbackskinmicrobiome
in Antarctica is helpful for future health diagnostic efforts aimed especially at heath-compromised
animals.Thisresearchultimatelyaimstobethebuildingblocksforexploringhowtheskinmicrobiome
canbeusedasadiagnostictoolformonitoringmarinemammalhealth.
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Introduction
Climatechangeisinfluencingdiseaseinthemarineenvironment(Burgeetal.2014).Withmany
speciesandpopulationsofmarinemammalslistedasthreatenedand/orendangeredundertheIUCN
RedListandtheEndangeredSpeciesAct,weneedtoimproveourtoolsandtechniquesformonitoring
thehealthstatusofindividualsandtheirwildpopulations.Mostofthehealthinformationwehaveon
infectious diseases in wild populations is gained from photographs and stranded marine mammals
(ThompsonandHammond,1992;Pettisetal.,2004;Wellsetal.,2004).Thesemethodsprovidealimited
glimpse into the current health state of the animal, and there is a need to develop better health
diagnostictoolsfortheseanimals.Recently,theskinmicrobiomeofhumpbackwhaleswasshownto
contain a core group of bacteria, and that shifts in this core group, as well as the emergence of
pathogens, were correlated with severe shifts in health status (Apprill et al. 2014). While marine
mammals,andtheecosystemstheyinhabit,arecurrentlyexperiencingthreatsfromclimatechange,it
isnecessarytounderstandmoreabouttheskinmicrobiomeanditspotentialuseasadiagnostictoolfor
monitoringthreatenedandendangeredmarinemammals(EvansandBjørge2013;Burgeetal.2014;
McFall-Ngaietal.2013;Apprilletal.2014).
Theskinisthelargestmammalianorganandservesasthefirstlineofdefensebetweenthehost
andtheirexternalenvironment(MoutonandBoth2012;Mahmudetal.2012).Thetermmicrobiome
refers to a specific assemblage of microorganisms, and the microbiome of the skin can serve as a
protective mechanism by adding to the skin’s defense against colonization of potential pathogenic
bacteria(Cogenetal.2010;RothandJames1988).Mostresearchontheskinmicrobiomehasfocused
onhumansanditislessexploredinothermammals(GriceandSegre2011;Fredricks2001;Larsenetal.
2010). Therefore, there is a need to explore the skin microbiomes of other mammalian species,
particularlymarinemammalsbecausetheirskinisconstantlyincontactwithseawater,whichtypically
containsordersofmagnitudemoremicroorganismsthaninair(Bowersetal.2012;DeLongetal.1999).
Humpback whales (Megaptera novaeanglia) are a particularly interesting species to study
because they are found in every ocean and undergo the longest knownmigration of anymammal
(Jacksonetal.2014).Humpbackwhalesmigratethousandsofkilometersbetweenhighlatitudesummer
feedinggroundsandlowlatitudewinterbreedinggrounds,thusexposingtheirskintoawidevarietyof
oceanic environments (Baker et al. 1990; Johnson andWolman 1984). The humpbackwhale is also
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recoveringfromoverexploitationbycommercialwhalingduringthe20thcentury(Johnstonetal.2012).
Climate change may be posing a new threat to this recovery, as the number of infectious disease
outbreaksisexpectedtoincreaseinthemarineenvironment(Burgeetal.2014).Thus,understanding
themicrobialcommunityontheskinmayhelpprovidesomeinsightformonitoringthehealthstatusof
thisrecoveringpopulation.
Arecentstudyanalyzed56humpbackwhaleskinsamplesfromtheNorthPacific,SouthPacific,
andNorthAtlanticOceansandfoundgeographic-relateddifferencesincommunityabundance,butwith
acommonalityofcoremembersofbacteriawithinthiscommunitythatwasindependentofageorsex
(Apprilletal.2014).Thiscoregroupofbacteriamayhelpserveasawayformonitoringthehealthstatus
ofthisrecoveringpopulation.Here,weexpanduponthisresearchandanalyzethemicrobiomeof72
humpbackwhaleskinsamplesfromAntarctica,apreviouslyunstudiedportionofthegeographicrange
ofhumpbackwhales,andcomparethesecommunitiestoasubsetofskinfromhumpbacksresidingin
Alaska,Hawaii,AmericanSamoa,andtheGulfofMaine.Humpbacks intheseregionsareexposedto
differentenvironmental conditionsandexhibitdifferentbehaviorswithin these regions thatmaybe
contributingtodifferencesinthecommunityabundance.Thegoalofthisstudywastodetermineifthere
isacommonalityinthemicrobialcommunitybetweenhumpbackwhalesinthesediversehabitats.This
researchultimatelyaimstobethebuildingblocksforexploringhowtheskinmicrobiomecanbeusedas
adiagnostictoolformonitoringmarinemammalhealth.
Methods
EthicsStatement
SkinsamplesfromAlaska,Hawaii,AmericanSamoa,andtheGulfofMainewerecollectedunder
NOAA permits, #1000-1617, 1071-1770-00, 774-1714, 932-1489, 633-1778, 633-1778, and with the
approvalfromtheGovernmentofAmericanSamoa.SkinsamplesfromAntarcticawerecollectedunder
NOAApermit808-735andACApermit2009-14.Allofthesampleswerecollectedinaccordancewith
permitguidelines.
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Samples
Atotalof72skinsampleswerecollectedin5differentgeographicregionsthroughouttheworld
andarelistedinTable1.
Table1.Descriptionofskinsamplesexaminedformicrobiomes
Geographiclocation Yearcollected Behavior Numberofsamples
SoutheastAlaska Summer2009 Feeding 2
Hawaii Winter2007,2008 Breeding 4
AmericanSamoa Winter2009 Breeding 2
GulfofMaine Summer2009 Feeding 3
AntarcticPeninsula Summer2010,2013 Feeding 61
SkinsampleswerecollectedfromAntarcticaandHawaiiviabiopsytechniquesusingcrossbows
equippedwithcustommadefloatingboltsandscrew-onhollowpointbiopsyplugstoobtainapieceof
skinfromtheupperflanknearthedorsalfinoftheanimal.Theseboltsaredesignedtopenetratethe
skintotheendoftheplug(0.5or1inch)andbouncebackout,securingasample(Thieleetal.2003).
Steriletoolswereusedtoretrievetheskinsample,whichwereeasilyseenbythefloatingbolt.Allofthe
skin samples fromAlaska andGulf ofMainewere collected from freshly sloughed skin. Of the two
samples fromAmericanSamoa,oneof themwascollectedvia thesebiopsytechniques listedabove,
whiletheotherwasfromsloughedskin.Marinemammalsundergosheddingofthesurfaceepidermis
quiterapidlyandthisskinisthereforeonlyslightlyolderthanbiopsiedskin(MoutonandBoth2012).We
collectedsloughedskinsamplesusingskinnetsandsieves,whichwereonlyindirectcontactwiththe
skinforamatterofseconds,andwerethenrinsedwithseawater.Weplacedcollectedsamplesonice
fornomorethan10hoursbeforefreezingthemat-80°C.Oneskinsample,ENT1a,wascollectedfrom
anentangledwhaleinHawaii.
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DNAExtraction,Amplification,andSequencing
DNAwas extracted from 1-30mg of skin,with an average of 13mg, from each sample using
QiagenDNeasyTissueKit(Cat.#69504)andquantifiedusingtheInvitrogenQubit®2.0AssayFluorometer
(LifeTechnologies,Beverly,Ma,USA).TheV4regionoftheSSUrRNAgenewasamplifiedusingbarcoded
primers(515FBand806RB)(Caporasoetal.2011;Apprilletal.2015;Paradaetal.,2015).Sampleswere
amplified in triplicate using polymerase chain reaction on a S1000® Thermal Cycler (Bio-Rad
Laboratories,Hercules,Ca,USA)asfollows:for30-35cyclesstartingwiththelidat105˚Candthen2min
95˚C,20sec95˚C,15sec55˚C,5min72˚C,andthen10minof72˚C.TriplicatePCRreactionscontained
1µLDNAsolutioncontainingof0.5xGoTaqflexibuffer,14.75µLH2O,2.5µLofa25nMMgCl2solution,
200nMofeachdNTPs,and0.25µLofa5u/µLGoTaqDNApolymerasesolutionpersample,alongwith
200nMofeachprimer.AfterPCRreaction,amplificationwasassessedbymixing5µLofeachsample
with1µLof10,000xSybrSafeDyeandrunningthisona1%/1xTBEBufferDilutiongel.Thereplicate
reactionswerepurifiedusingAgencourt®AMPure®XP(BeckmanCoulterInc.,Pasadena,Ca,USA)and
quantifiedusing InvitrogenQubit®2.0AssayFluorometer.Barcodedampliconsweresequencedusing
pairedend2x250bpMiSeqIlluminaformatattheUniversityofIllinoisW.M.KeckCenterforComparative
andFunctionalGenomicsforsequencing.
SequenceProcessing
Thesequenceswereprocessedusingmothurv.1.36.1(Schlossetal.2009).Barcodesandprimers
werefirstremoved,whichresultedwith4,139,807remainingsequencesthathadanaveragelengthof
253basepairs(bp).TheSilvaribosomalRNAsequencedatabase(v.123)alignmenttemplate(Pruesseet
al. 2007)wasused toalign sequences to the16S rRNAmolecule. Sequences thatwere identifiedas
Eukaryota,chloroplasts,mitochondria,“unknown”phylum,orasgroupswith<20,000sequenceswere
removed.Thisprocessreducedthetotalnumberofsequencesto3,913,100,withanaveragelengthof
253bp.Chimerasweredetectedandremoved,furtherreducingthenumberofsequencesto3,893,275.
ThesequenceswerethenclusteredusingMinimumEntropyDecomposition(MED)nodes.MEDapplies
shannonentropyandusestheinformation-richnucleotidepositionsacrossreadstoiterativelypartition
largedatasetswhileomittingstochasticvariation(Erenetal.2015).MEDnodesprovideamoreefficient
wayforgroupingsequencesintohomogeneousoperationaltaxonomicunits(OTUs)andwillbereferred
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to as “OTUs” for the remainder of this study. The final dataset contained 3,528,790 sequences
categorizedinto212“MEDnodes”,orOTUs.
StatisticalAnalysis
Usingmothur,thealphadiversityoftheskinmicrobialcommunitywasanalyzedbycomputing
observedOTUs,Chao’sSpeciesRichnessEstimator,Shannon’s(H’)DiversityIndex,andInverseSimpson’s
DiversityIndex,andthencomparedsamplelocationsusingANOVAfromthestatspackageinR.Both
Shannon’s and Simpson’s Diversity Indicesmeasure species richness and evenness in a community,
where Chao’s Species Richness Estimator outputs the number of taxa that likely would have been
observedatadeepersamplinglevel.Becausetherangeofsamplesizesforalllocationswassovariable
(nmin,max=1-12),withmostlocationshavingasamplesizelessthan5,moststatisticalprocessesfocused
onsixAntarcticalocationsthathadasamplesizegreaterorequalthan5(AndvordBay:10,FlandresBay:
5,GerlacheStrait:9,MargueriteBay:10,PalmerStation:5,andWilheminaBay:12).Figure1showsa
mapoftheselocationsandFigure2showsthemonthandyearthatbiopsysampleswerecollected.
PRIMERv.7(PRIMER-ELtd.,PlymouthUK)(ClarkandWarwick2001)wasusedtoassessbeta
diversity, or between sample comparisons, of the skinmicrobial community. TheMED nodes were
squareroottransformedandassembledintoadistancematrixusingBray-Curtissimilarity.Sampleswere
arrangedinpredefinedfactors;“Antarctica/non-Antarctica”,“Population”,“Location”,and“Sex”.These
factors were explored using nonmetric multidimensional scaling (nMDS) ordination to create a 2-
demnsional representation of the microbial community and with hierarchical clustering analysis
(CLUSTER) to create similaritydendograms.PERMANOVA tests inPRIMERv.7wereused for testing
significant differences between the composition of the microbial communities and the pre-defined
factorsusingaBray-Curtissimilaritymatrixwith999permutationsunderTypeIII(partial)sumofsquares,
withsignificantlevelsconfirmedusingaMonteCarlosimulation.
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Figure1.MapdisplayssamplelocationforthesixAntarcticalocationscontaining≥5skinbiopsysamples.Greenbox indicatesMarguerite Bay location and the red box indicates the location of Andvord Bay, Flandres Bay,GerlacheStrait,PalmerStation,andWilheminaBay.Notethatsomesampleshadthesamelongitudeandlatituderecordedandthushavelessthan5pointsdisplayed,i.e.PalmerStation,FlandresBay.
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Figure2.MapdisplaysmonthandyearofsamplecollectionforthesixAntarcticalocationscontaining≥5skinbiopsysamples.GreenboxindicatesMargueriteBaylocationandtheredboxindicatesthelocationofAndvordBay,FlandresBay,GerlacheStrait,PalmerStation,andWilheminaBay.Notethatsomesampleshadthesamelongitudeandlatituderecordedandthushavelessthan5pointsdisplayed,i.e.PalmerStation,FlandresBay.
Results Anonmetricmultidimensionalscaling(nMDS)analysisanddendogramclusteringanalysisofSSU
rRNAgenes frombacteriaandarchaeacomparedusingtheBray-Curtissimilarity indexrevealedthat
Antarcticasamplesaregroupedseparatelyfromsamplesobtainedfromtheothergeographiclocations
(Figure 3a&b). PERMANOVA analysis confirmed that skin microbiomes were significantly different
betweentheAntarcticaandnon-Antarcticawhales(Table2).Althoughsamplesizeswerelowforthe
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non-Antarcticregions,theAntarcticskinmicrobiomeswerefoundtobethemostdistinctfromtheother
geographicregions(Table3).ThenMDSanalysisforsexbetweenalllocationsshowednodistinctpattern,
andthePERMANOVAanalysisdidnotrevealasignificantdifferencebetweenanimalsofdifferentsex
(Figure4,Table2).
Figure3.a)NonmetricMultidemensionalScaling(nMDS)analysisandb)adendogramclusteranalysisofskinmicrobiomesamplesfromthedifferentpopulations.
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TherelationshipbetweenthehumpbackskinmicrobiomeandspecificlocationswithinAntarctica
wasexaminedfurther.AnMDSordinationshowsaslightgroupingpatternamongstthesixAntarctica
locationswith≥5samples(Figure6).Additionally,PERMANOVAanalysisrevealedsignificantdifference
betweenthesesixlocations(Table5).GerlacheStraitwassignificantlydifferentfromtherestofthefive
locationsexceptforFlandersBay,whileFlandersBaywasonlysignificantlydifferentthanMarguerite
Bay (Table 5).Marguerite Baywas significantly different fromall five other locations except Palmer
Station(Table5),whilePalmerStationwassignificantlydifferentthanGerlacheStraitandAndvordBay
(Table5).AndvordBayissignificantlydifferentthanalllocationsexceptFlandersBayandWilheminaBay
(Table5).WilheminaBaywassignificantlydifferentfromGerlacheStraitandMargueriteBay(Table5).
Nosignificantdifferencewasdetectedbetweencommunityrichness (measuredwithobserved
OTUs)andlocationsforallsamples(F9,41=0.8,p=0.618).Whenlookingfurtherintothedifferentlocations
withinAntarctica,therewasnosignificantdifferencebetweencommunityrichnessandthesixAntarctic
locationswith≥5samples,althoughthep-valuewasjustabove0.05(F4,45=2.331,p=0.0577)(Table4).
Antarcticavs.Non-Antarctic allsamples 1 19881 8.7221 0.001*** 0.001***Globalregions allsamples 4 29713 3.324 0.001*** 0.001***Antarcticlocations samplesize≥5 15 70096 2.3933 0.001*** 0.001***Sex(m,f,u) allsamples 2 7006.3 1.4018 0.131 0.13***p≤0.001
Table2. PERMANOVAresultsexaminingtheimpactofAntarcticaandnon-Antarcticasamples,globalpopulations,Antarcticalocations,andsexontheskinmicrobiome(u=unknown)
Variation Data df SS t p p(MC)
Groups t p p(MC)Antarcticavs.GulfofMaine 1.9498 0.001*** 0.004**Antarcticavs.Hawaii 1.9951 0.001*** 0.001***Antarcticavs.AmericanSamoa 2.0181 0.001*** 0.004**Antarcticavs.Alaska 1.5358 0.011 0.04*GulfofMainevs.Hawaii 1.1605 0.119 0.293GulfofMainevs.AmericanSamoa 1.6656 0.121 0.113GulfofMainevs.Alaska 1.2677 0.099 0.232Hawaiivs.AmericanSamoa 1.0174 0.529 0.415Hawaiivs.Alaska 1.2094 0.134 0.229AmericanSamoavs.Alaska 1.3346 0.351 0.273
Table3. PERMANOVAresultscomparingskinmicrobiomesbetweenthe5majorgeographicregionssampled.df=15,SS=70096,MC=MonteCarlo
*p≤0.05,**p≤0.01,***p≤0.001
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TheaverageobservedOTUrangedbetween102-128(Table4andFigure5).DiversityIndicesremained
relativelyconsistentacrossthesixlocations(Figure5).
Figure4.NonmetricMultidemensionalScaling(nMDS)analysisofskinmicrobiomesbelongingtoanimalsofdifferentsexfromAntarctica,Alaska,Hawaii,AmericanSamoa,andtheGulfofMaine.PERMANOVAresultsrevealnosignificancebetweenthesexandtheskinmicrobiomecomposition(p=0.13).
AndvordBay(10,10) 128(17) 103-156 146.94(21.85) 113.11-179.14 2.14(0.28) 5.29(2.04)FlandresBay(5,5) 107(30) 70-147 123.78(23.51) 94-149.55 2.04(0.48) 4.84(2.19)GerlacheStrait(9,9) 118(22) 87-147 144.68(27.80) 98-188.09 2.23(0.44) 5.65(2.84)
MargueriteBay(10,10) 121(16) 91-146 138.80(22.63) 114.58-178.60 1.87(0.50) 3.48(1.91)PalmerStation(5,5) 112(28) 72-143 136.60(23.59) 103.67-166 1.90(0.90) 5.25(5.29)
WilheminaBay(12,11) 102(14) 71-121 118.44(20.00) 82.67-151 2.10(0.54) 5.72(2.84)
Table4.ObservedOTUs,Chaosrichnessestimator,andtheShannonandInverseSimpsondiversityindicesfortheskinmicrobiomesfromthesixAntarcticalocationsthathave≥5skinsamples
Sample(samples,individuals)
Avg.ObservedOTUsOTURange
Chao(st.dev.) ChaoRange
Shannon(H')(st.dev.)
InverseSimpson(st.dev.)
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Figure5.BoxplotsoftheobservedMEDnodes,richness,anddiversityindicesamongsttheskinmicrobiomesfromsixAntarcticalocationswithfiveormoresamples.Nosignificantvariationwasfoundbetweeneachdiversityindexandlocation(AB=AndvordBay,FB=FlandersBay,GS=GerlacheStrait,MB=MargueriteBay,PS=PalmerStation,WB=WelheminaBay).
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Figure6.NonmetricMultidemensionalScaling(nMDS)analysisofhumpbackskinmicrobiomesfromAntarcticalocationswith≥5samples.
Groups t p p(MC)GerlacheStraitvs.WilheminaBay 1.4817 0.044 0.047*GerlacheStraitvs.FlandresBay 0.99194 0.453 0.412
GerlacheStraitvs.MargueriteBay 2.2731 0.002 0.002**GerlacheStraitvs.PalmerStation 2.0244 0.004 0.009**GerlacheStraitvs.AndvordBay 2.0531 0.002 0.002**WilheminaBayvs.FlandresBay 0.82266 0.722 0.622
WilheminaBayvs.MargueriteBay 1.6559 0.035 0.039*WilheminaBayvs.PalmerStation 1.5352 0.057 0.081WilheminaBayvs.AndvordBay 0.97911 0.403 0.395FlandresBayvs.MargueriteBay 1.6596 0.012 0.023*FlandresBayvs.PalmerStation 1.5622 0.04 0.064FlandresBayvs.AndvordBay 1.2925 0.107 0.14
MargueriteBayvs.PalmerStation 0.80539 0.758 0.648MargueriteBayvs.AndvordBay 2.0684 0.003 0.004**PalmerStationvs.AndvordBay 2.0149 0.007 0.012*
Table5. PERMANOVAresultscomparingtheskinmicrobiomesofthesixAntarcticalocationswithsamplesizesgreaterorequalto5.df=3,SS=17186,MC=MonteCarlo
*p≤0.05,**p≤0.01
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MembersoftheMoraxellaceaeandFlavobacteriaceaefamilywerethemostconsistentmembers
ofthehumpbackskinmicrobialcommunity(Table6,Figure7).Whilesequencesfromthesetwofamilies
werepresentineverysamplefromalllocations,thereisstillvariationinthepercentageofabundance
betweenlocations(Table6,Figure7).TheaverageabundanceofMoraxellaceaefoundoneachsample
from each population was 54.37, 21.61, 20.47, 30.62, and 65.79% for Antarctica, Alaska, American
Samoa,GulfofMaine,andHawaii,respectively(Table6).TheaverageabundanceofFlavobacteriaceae
foundoneachsamplefromeachlocationwas32.81,71.99,62.55,51.29,and22.66%forAntarctica,
Alaska,AmericanSamoa,GulfofMaine,andHawaii,respectively(Table6).Antarcticaexperiencedthe
greatestranges forbothfamilies,with6.89%–99.81%forMoraxacellaceaeand0.15%–75.60%for
Flavobacteriaceae(Table6).
average stdv min max average stdv min maxAndvordBay 52.84 24.93 19.96 92.19 43.09 25.77 2.14 75.60BransfieldStrait 24.56 - - - 33.78 - - -CharlotteBay 34.20 - - - 42.53 - - -FlandresBay 51.70 29.86 29.32 91.38 34.29 23.41 4.99 68.19GerlacheStrait 32.72 30.42 6.89 89.31 46.02 24.40 4.52 70.88LTERGrid 77.00 25.67 47.37 92.36 19.57 22.51 5.80 45.55MargueriteBay 72.69 30.50 18.31 99.33 21.71 27.85 0.28 75.16PalmerDeepCanyon 49.16 43.63 13.36 97.76 22.18 18.54 0.81 34.00PalmerStation 72.38 30.48 25.73 99.81 23.78 28.66 0.15 72.40Unknown 78.18 - - - 5.07 - - -WilheminaBay 46.57 27.89 11.24 98.76 34.89 22.82 0.23 61.69
54.37 30.84 6.89 99.81 32.81 25.03 0.15 75.6021.61 29.62 0.67 42.56 71.99 36.33 46.30 97.6820.47 4.83 17.05 23.88 62.55 11.15 54.66 70.4430.62 5.38 25.36 36.12 51.29 26.70 20.99 71.3965.79 21.47 37.87 86.84 22.66 19.18 0.22 42.90
Table6.Averagepercent(%)abundanceofwhaleskin-bacterialsequencescontainingMoraxellaceaeandFlavobacteriaceaeforeachlocation.Locationswithasamplesizeof1receivea"-"forstdv,min,andmax
Antarctica
Moraxellaceae Flavobacteriaceae
Antarctica
Location
Hawaii
AlaskaAmericanSamoaGulfofMaine
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Figure 7. Total abundance and taxonomic composition of bacterial sequences, classified at the family level,presentoneachhumpbackwhaleskinsample.Discussion
Phylogeneticconsistencyofthemicrobiomeandimplicationsforhealthmonitoring
Sequencesbelonging to the familiesMoraxellaceae andFlavobacteriaceaewerepresent in all
samples. A large portion of the family Moraxellaceae sequences were identified as the genus
Psychrobacter,whiletherestremainedunclassifiedatthegenuslevelunderthereferencetaxonomy
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databaseandalgorithmusedhere.Psychrobacterhasbeenfoundinmanydifferentmarineorganisms,
includingfish,sponges,seaweeds,andalgae(Pegoraroetal.2015;TeixeiraandMerquior2014).The
genusPsychrobacterhasahighlydevelopedosmotoleranceandaremostlymadeupofpsychrophilic
bacteria,meaningtheycanhandleextremelycoldtemperatures,andarethusfoundinmanynatural
coldsalineenvironments(TeixeiraandMerquior2014).Thisgenushasalsobeenfoundtothriveinwide
temperatureranges,-10to28°C(BakermansandNealson2004).Thismayhelpexplainwhytheyarea
dominant member of the microbial community because humpback whales travel between vastly
differentenvironmentswithextremelyvariedtemperatures.Little isknownabouttheecologicalrole
thisgenusplays,butitislikelythattheyplayacommensalrolewithdegradingorganiccompoundsother
than sugars (Teixeira andMerquior 2014). Flavobacteriaceae have been found in a wide range of
habitats,includingfreshwaterandmarineenvironment(BernardetandNakagawa2006).Theyhavealso
been found in diseasedmarine and freshwater fish, and have been considered to be an emergent
pathogenundervariousfishfarmingtechniques(BernardetandNakagawa2006;Pegoraroetal.2015).
It isunlikelythatFlavobacteriaceae isplayinganypathogenicroleforthesehumpbackwhales. Ithas
beenobservedthatsomeFlavobacteriaceaearebacteriolytic,andwilllysepreycellsaftersurrounding
them(Banning,Casciotti,andKujawinski2010).ThissuggeststhatFlavobacteriaceaemayhelpwardoff
infectiouspathogenicbacteria,andmaycontributetomaintainingthehealthoftheirhost.
TheconsistencyofMoraxellaceaeandFlavobacteriaceae-affiliatedmicroorganismsontheskin
ofallhumpbackwhalessuggestsaco-evolutionaryrelationshipthatmayserveasanimportanthealth
indicatorforhumpbacks.Althoughusingmicrobiomesforhealthdiagnosisisstillanemergingareaof
research,identifyingcoremicrobiomesinhealthyindividualsisoneofthekeyfactorsindevelopingthis
tool.Forexample,studieshaveshownthatchangesinresidentmicrobialcommunitieshaveinfluenced
disease such as antibiotic-associated diarrhea, human immunodeficiency virus, bacterial vaginosis,
obesity,andcardiovasculardiseaseinhumans(Rosenthaletal.,2011).
Developing a health assessment tool for cetaceans is critically important. An increase in the
numberofdifferentkindsofskinlesionsreportedoncetaceansoverthepastdecadeshasbeennoted,
with a larger number of viruses, pathogenic bacteria, and fungi associated with lesions of
immunocompromised cetaceans and of thosewith higher exposure to pollution (Mouton and Both
2012).Climatechangeisinfluencinginfectiousdiseasesinthemarineenvironmentandthenumberof
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diseasebreakouts in certain taxaareexpected to increase,basedonphysiological state (stressedor
immunocompromised)and/ormicrobialactivity(increasedgrowthandvirulence)(Burgeetal.2014).
Duetolackofdataandtheindirectnatureofclimatechangeonthesediseases,nostudieshaveshown
adefinitivecausalrelationshipbetweenclimatechangeandincreasesininfectiousdiseasesinmarine
mammals(Burgeetal.,2014).Despitethislackofdata,insightcanbegainedfromthefewexamples
available that have shown associations between climate events and infectious diseases caused by
microbialinfectionsinmarinemammals.BelowareafewexamplestakenfromBurgeetal.,2014:
• 1988:Phocinedistempervirusoutbreakinharborsealswasassociatedwith“unseasonably”
warmtemperaturesinnorthernEurope.
• 1990–1992:CetaceanmorbillivirusaffectingmultiplecetaceanspeciesintheMediterranean
wasassociatedwithhighwinter seasurface temperatures, lowrainfall, and reducedprey
availability.
• 2000: Canine distemper virus epizootic in Caspian seals was associated with warm
temperaturesandearlydisappearanceoficecoverintheCaspianSea.
It may seem unlikely that skin microbes could provide insight into something like cetacean
morbillivirus,butimbalancesordisruptionsintheskin-microbiomecanaltertheimmuneresponseof
thehostatseverallevels(ScharschmidtandFischbach2013).Alaboratorystudyfoundthatmicewith
skin disorders had an altered abundance of the core bacterial members of their skin microbiome
(Scharschmidtet al. 2009). Following thisexample, changes in coremembersofbacteria in the skin
microbiomeofhumpbackwhales,suchasMoraxellaceaeandFlavobacteriaceae,couldposeahigher
riskofattaininginfectiousdiseases.Moreresearchisneededtolookatwhatrolesthemembersofthese
bacterialcommunitiesareplaying.
Withclimatechangealteringtheoceanenvironment,itisimportanttounderstandhowdifferent
species and the ecosystems they inhabitwill be affected. Burge et al., 2014 calls formore adaptive
managementstrategiesthatincludebetterlong-termhealthandpopulationmonitoringandforecasting
toolstounderstandhowmobilevertebrateswillbeaffected.Whileadditionalresearchisneeded,the
skin-microbiomeisapotentiallyusefultoolthatwillhelpaidinassessingthepopulationstateofacertain
species.
19
VariationintheskinmicrobiomewithinAntarctica
It is interesting that while Antarctica was significantly different from the other populations,
variationstillexistsamongstthelocationssurroundingtheWesternAntarcticaPeninsula(WAP).Thisis
especially interestingbecause there is evidence that humpbackwhales donot reside in constrained
locationsalongWAPandwillmovetodifferentlocationsthroughouttheforagingseason(Curticeetal.
2015). No significant differencewas detected between the six locationswith ≥ 5 samples and their
diversity(richnessandevenness)indices,suggestingthatbehaviorandotherenvironmentalfactorsat
thelocallevelmaybeinfluencingthislocalvariation.Inhumans,ithasbeenfoundthatthereisashared
common type of bacteria found on the palms of hands, but also interpersonal variation linked to
individualhabits,evenbetweenanindividuals’dominantandnon-dominanthand(Fiereretal.2008).
Withthisspecificvariationinhumans,itisnotunreasonablethatvariationintheskinmicrobiomemay
becausedbyfactorssuchastheyearandtimeduringtheforagingseasonskinsampleswerecollected,
offshore vs. inshore location, ocean currents, behavior, close proximity to other animals, and even
freshwaterreleasedfromameltingglacier.Itisimportanttonotethatnoseawatersamplescollected
inproximitytothebiopsysiteswereanalyzedforthisstudy.However,ithasbeenshowninprevious
studies that the microbial community on humpback whale skin is significantly different than the
communitypresentinseawater(Apprilletal.2011;Apprilletal.2014)andthereforevariationinthe
skinmicrobiomeisnotattributedtocontaminationfromthesurroundingwater.
Humpbackwhalestravelthousandsofkilometersbetweenforagingandbreedingsites,andthus
needanefficientwaytoobtainandstoreenergy.Antarcticaisaprolifichotspotbecausecircumpolar
currents travel up deep canyons along the continent and bring up phytoplankton-rich waters that
support the abundance of krill (Prézelin et al. 2000). Krill are an essential part of the food web in
Antarctica,especiallyforhumpbackwhales(Curticeetal.2015).Thereisevidencethatthemovement
patternsofhumpbackwhalesreflectkrill,astheymovefromoffshoretoinshoreinAntarcticwatersover
thecourseoftheaustralsummerforagingseason,which isdefinedasJanuarytoJune(Curticeetal.
2015).Thesemovementpatternsmayhelpexplainsomeofthevariationseenbetweenthedifferent
locationswithinAntarctica.
20
Inearlysummer,krillarefoundfartherawayfromthecontinentalshelfandaggregateneardeep
water canyonswherenutrient richupwelling creates ideal conditions for themtodeposit theireggs
(Klincketal.2004;Nicol2006).Duringthelaterpartofsummerandautumn,krillmoveinshore,closer
intothebaywheretheygrouptogetherandlettheseaicecoverandprotectthemforthewinter(Nicol
2006).ThismayhelpexplainwhytheskinmicrobiomeofhumpbackwhalessampledinMargueriteBay
issignificantlydifferentfromallotherlocationsexceptPalmerStation.Allbutoneoftheskinsamples
fromMargueriteBayandPalmerStationwerecollectedduringthebeginningoftheforagingseasonin
January2013andbothlocationssitrightontopofadeepwatercanyon(Figure2).Thesamplelocations
inMargueriteBayarefartheroutsideofthebaycomparedtotheotherbaylocations(Figure1),sowe
wouldthusexpectthatasthesummermonthscontinued,thelocationsofthesewhaleswouldmove
closer towards shore. The fact that the sampleswere collected in January and in 2013may alsobe
contributingtosomeofthedifferencesweareseeing,sincealotoftheothersampleswerecollectedat
different times (Figure 2). Both Palmer Station andMarguerite Bay are also the two locationsmost
exposedtoopenocean(Figure1).TheotherfourlocationsareprotectedfromopenoceanbyAnvers
andBrabantIsland(Figure1).Acombinationoftheopenoceancurrents,deepwaterupwelling,theyear
andtimeofsamplecollection,andahighabundanceofkrillandtheireggspotentiallymaybeinfluencing
thecolonizationofthemicrobialenvironment.
WhileGerlacheStraitandFlandres,Andvord,andWelheminaBayareallprotectedfromopen
ocean,GerlacheStraitisstillsignificantlydifferentfromallotherlocationsexceptforFlandresBay.This
isparticularly interestingsincethereisoverlapbetweenGerlacheStraitsamplesandWelheminaand
AndvordBay(Figure1).Thisalsomaybeattributedtothedateandyearthesampleswerecollected.
MostofthesamplesfromGerlacheStraitandFlandersBaywerecollectedlateintheforagingseason,
MayandJune,andin2010(Figure2).Thissuggeststhateitherorbothyearandthetimeduringthe
foragingseasonmaybeinfluencingthesevariationsinthemicrobialdiversity.
Asidefromthetimeframethatsampleswerecollected,otherbehaviorandlocalenvironmental
factors may be influencing some of the variation between the Antarctic locations.Welhemina and
AndvordBaybothhavesamplesdeeperinthebaycomparedtoonesinFlandersBay,whichhasallofits
samplesrightatthemouthofthebay(Figure1).Previousstudieshavefoundthathumpbackwhales
travelingthroughGerlacheStraithaveshortresidencytimesandvariablehomeranges(Nicol2006).This
21
suggeststhatthewhalesaremostlyintransitandwillbeactivelyswimmingcomparedtowhaleswithin
baysthatfocustheiractivityonforaging(Curticeetal.2015).Sincestraitstypicallyhaveagreaterwater
velocitythanbays,wewouldexpectthesewhalesmaybeexperiencingconsistentlystrongercurrents.
Whalesforagingclosertoshoreinbaysaremostlikelyincloserproximitytootherwhalesforagingwhich
couldpotentially influencetheskin-microbialcommunityviaclosercontact.Theyalsoarefeedingon
denserkrillpatchesandaremoreexposedtofreshwaterfromthemeltingseaicedeepinthesebays,
whichcouldpossiblychangetheconditionsforthemicrobialcommunitypresent.
Thecombinationoftheyearanddateduringtheforagingseasonskinsampleswerecollected,
swimminginhighorlowvelocitywaters,beingincloseproximitytootherwhalesandgroupsofkrill,and
the increase in freshwater from melting sea ice, may all be contributing to the differences in the
colonizationofthemicrobialcommunity.
Conclusion
Thisstudysupportsthenotionthathumpbackwhalesshareacoregroupofbacteriaontheskin,
Moraxellaceae andFlavobacteriaceae. These two families of bacteriamay helpmaintain the overall
healthoftheirhost,althoughmoreresearchisneededontherolestheyareplayinginthemicrobial
community on the skin of humpback whales. Greater phylogenetic resolutionmay also help better
identifymembersofthehumpbackwhalemicrobiome.Antarcticskinsamplesharborauniquemicrobial
communitycomparedtootherlocationsaroundtheworld.Still,localvariationwithinAntarcticaseems
likelyandmaybeattributedtotheyearanddateduringtheforagingseasonskinsampleswerecollected,
swimminginhighorlowvelocitywaters,beingincloseproximitytootherwhalesandgroupsofkrill,and
exposure to salinity changes frommelting sea ice. Future studies examining specific drivers of skin
microbiomevariabilityareneeded.Overall,thisresearchservesasthepreliminarystepsforexploring
howtheskinmicrobiomecanbeusedasahealthdiagnostictool.
22
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Appendix(onnextpages,26-28:
26
Samples Sex SkincollectionmethodMn_13_32d F BiopsyMn_13_32i U BiopsyMn_13_36a F BiopsyMn_13_36b F BiopsyMn_13_36d M BiopsyMn_13_36e M BiopsyMn_13_36f F BiopsyMn_13_36g F BiopsyMn_13_39b F BiopsyMn_13_47c M Biopsy
BransfieldStrait B142b M BiopsyCharlotteBay Mn_13_48f M Biopsy
B156a F BiopsyB156d M BiopsyMn_13_38a F BiopsyMn_13_38b F BiopsyMn_13_38c M BiopsyB133a F BiopsyB135b F BiopsyB135c M BiopsyB144a M BiopsyB144b M BiopsyB144c F BiopsyB151a F BiopsyB151b F BiopsyMn_13_32a F BiopsyMn_13_10a M BiopsyMn_13_10b M BiopsyMn_13_10c M BiopsyMn_13_15a F BiopsyMn_13_15c M BiopsyMn_13_15h M BiopsyMn_13_15i M BiopsyMn_13_16b M BiopsyMn_13_16d M BiopsyMn_13_16e F BiopsyMn_13_16f F BiopsyMn_13_16g M BiopsyMn_13_16h M BiopsyMn_13_30c M BiopsyMn_13_6c M BiopsyMn_13_6d F BiopsyMn_13_30d M BiopsyMn_13_30e F BiopsyMn_13_30f F BiopsyMn_13_30g F BiopsyMn_13_35b M BiopsyMn_13_52a F Biopsy
Unknown Mn_13_30a U BiopsyB139a F BiopsyB139b F BiopsyMn_13_37a F BiopsyMn_13_37b F BiopsyMn_13_37d M BiopsyMn_13_40a F BiopsyMn_13_40b M BiopsyMn_13_40m M BiopsyMn_13_42a F BiopsyMn_13_42b F BiopsyMn_60a M BiopsyMn_60b M BiopsyMn_SEAK1_a U SloughedMn_SEAK2_a U SloughedMn_FBNMS015 M SloughedMn_FBNMS016 M BiopsyMn_CCS44 U SloughedMn_CCS64a M SloughedMn_CCS93 F SloughedMn_ENT1a M SloughedfromentanglementMn_WH44 M BiopsyMn_WH45a U BiopsyMn_WH57 M Biopsy
Alaska
AmericanSamoa
GulfofMaine
Hawaii
LocationSupplementaryTable.Summaryofhumpbackwhaleskinsamplesanalyzedinthisstudy
Antarctica
AndvordBay
FlandresBay
GerlacheStrait
LTERGrid
MargueriteBay
PalmerDeepCanyon
PalmerStation
WilheminaBay
27
RCode:#KCBierlich#March,2016#AnalysisofSequenceddata#MastersProject##Checkingdistributionsread.csv("Matrix_Count_Group.groups.summary.csv",header=T)count<-read.csv("Matrix_Count_Group.groups.summary.csv",header=T)par(mfrow=c(2,2))hist(count$sobs,main="Observed")hist(count$chao,main="Chao")hist(count$invsimpson,main="invSimpson")hist(count$shannon,main="Shannon")#RelativelyNormal.invSimpsonistheleastnormalofthegroup##Checkingdistributionsofthe6Locationsdiv<-read.csv("DiversityTests.csv",header=T)par(mfrow=c(2,2))hist(div$observed,main="Observed")hist(div$chao,main="Chao")hist(div$invsimpson,main="invSimpson")hist(div$shannon,main="Shannon")#ANOVA-DiversityTestaov(lm(chao~factor(location),data=div))summary(aov(chao~factor(location),data=div))aov(lm(observed~factor(location),data=div))summary(aov(observed~factor(location),data=div))aov(lm(invsimpson~factor(location),data=div))summary(aov(invsimpson~factor(location),data=div))aov(lm(shannon~factor(location),data=div))summary(aov(shannon~factor(location),data=div))#Sonowlet'sboxplotthemobs<-read.csv("Observed.csv",header=T)chao<-read.csv("Chao.csv",header=T)simp<-read.csv("invsimpson.csv",header=T)shan<-read.csv("Shannon.csv",header=T)
28
par(mfrow=c(2,2))boxplot(obs,col="beige",#horizontal=T,main="ObservedOTUs",#xlab="",angle=-45,ylim=c(60,200),ylab="",las=1)boxplot(chao,col="beige",#horizontal=T,main="ChaoRichnessEstimate",ylim=c(60,200),ylab="",las=1)boxplot(simp,col="beige",#horizontal=T,main="Simpson'sReciprocalIndex",ylim=c(0,16),ylab="",las=1)boxplot(shan,col="beige",#horizontal=T,main="Shannon'sDiversity",#ylim=c(0,16),ylim=c(0,3.5),ylab="",las=1)#QuickExamplesofSummaryStatssummary(div$chao)sd(div$chao)