Viruses in the marine environment: community dynamics, phage-host interactions and genomic structure
Abstract
There are an estimated 1030 viruses in the world oceans, the majority of which are phages (viruses that infect bacteria). Extensive research has demonstrated the significant influence of marine phages on microbial abundance, community structure, genetic exchange and global biogeochemical cycles. In this thesis, we contribute to increase the knowledge about the ecological role of viruses in marine systems, but also we aimed to provide a better understanding about the interactions between phages and their hosts and the genetic pool and biogeography of some the isolated phages genomes.
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UNIVERSIDAD DE LAS PALMAS DE GRAN CANARI A Departamento de Biología community dynamics, phage-host interactions and genomic structure. Elena Lara de la Casa, Enero 2014 Elena Lara de la Casa community dynamics, phage-host interactions and genomic structure. 2014
3 D/Dª José Manuel Vergara Martín SECRETARIO DEL DEPARTAMENTO DE BIOLOGÍA DE LA UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA, CERTIFICA, Que el Consejo de Doctores del Departamento en su sesión de fecha.............................tomó el acuerdo de dar el consentimiento para su tramitación, a la tesis doctoral titulada “Viruses in the marine environment: community dynamics, phage-host interactions and genomic structure” presentada por el/la doctorando/a D/Dª Elena Lara de la Casa y dirigida por las Doctoras Dolors Vaqué y Silvia González Acinas. Y para que así conste, y a efectos de lo previsto en el Artº 6 del Reglamento para la elaboración, defensa, tribunal y evaluación de tesis doctorales de la Universidad de Las Palmas de Gran Canaria, firmo la presente en Las Palmas de Gran Canaria, a...de....................................de dos mil............
5 Virusesinthemarineenvironment:communitydynamics, phagehostinteractionsandgenomicstructure (Losvirusenlosecosistemasmarinos:dinámicadelacomunidad, interaccionesentrefagoyhospedadoryestructuragenómica) ElenaLaradelaCasa TesisDoctoralpresentadaporDªElenaLaradelaCasaparaobtenerel gradodeDoctorporlaUniversidaddelasPalmasdeGranCanaria, DepartamentodeBiologFa,ProgramaenGceanograHFa (Bienio20082010) Directores:Dra.DolorsVaquéyDra.SilviaG.Acinas UniversidaddelasPalmasdeGranCanaria InstitutdeCiènciesdelMar(ICMCSIC) EnBarcelona,adede2014 LaDoctoranda ElenaLaradelaCasa Eldirector DolorsVaqué ElCodirector SilviaG.Acinas
7 This thesis has been funded by the Spanish Ministry of Science and Innovation (MICINN) through a PhD fellowship to Elena Lara de la Casa, under the program “Formación de Personal Investigador (FPI)”, and was ascribed to the project: “Aislamiento, identificación y especificidad de virus que infectan a microorganismos marinos-MICROVIS” (Ref. CTM2007-62140/MAR, P.I.Dr Dolors Vaqué). Other projects that contributed partially to the completion of this thesis were: “Population Ecology of Model Marine Heterotrophic Flagellates–FLAME” (CGL2010-16304, funded by the MICINN, P.I. Dr. Ramón Massana), “Arctic tipping points-ATP” (contract #226248 in the FP7 program of the European Union, P.I.: P.F. Wassman. University of Tromso, (Norway), and P.I. from CSIC, C.M. Duarte. IMEDEA), “The Role and Mechanisms of Genomic Microbial Microdiversity: a perspective integrating genomics and ecological approaches-MICRODIVERSITY” (CGL2008-00762/BOS, funded by the MICINN, P.I. Dr. Silvia G. Acinas) and “Microbial Ocean Pangenomes: from single cell to bacterial population genomics–PANGENOMICS” (CGL2011-26848/BOS, funded by the MICINN, P.I. Dr. Silvia G. Acinas) and.
9 A mis padres.
17 LIST OF PUBLICATIONS This thesis is based on the following papers: Lara, E1. .; Sà, E.L.; Ruiz-González, C.; Massana, R.; Gasol, J.M.; Acinas, S.G. and Vaqué, D. Absence of seasonality on viral dynamics and salinity as a main driver modulating viral abundance in the NW Mediterranean Sea. Manuscript. Lara, E2. .; Arrieta, J.M.; Garcia-Zarandona, I.; Boras, J.A.; Duarte, C.M.; Agustí, S.; Wassmann, P.F. and Vaqué, D. 2013. Experimental evaluation of the warming effect on viral, bacterial and protistan communities in two contrasting Arctic systems. Aquatic Microbial Ecology 70:17-32. Lara, E3. ., Sà, E.L.; Salazar, G.; Santos, F.; Sánchez, P.; Antón, J.; Vaqué, D. and Acinas, S.G. Marine phage-bacteria interactions at fine-scale within Pseudoalteromonas spp. phages. Manuscript. Lara, E4. .; Holmfeldt, K.; Solonenko, N.; Sà, E.L.; Ignacio-Espinoza, J.C.; Verberkmoes, N.C.; Vaqué, D.; Sullivan, M.B. and Acinas, S.G. Life-style and mosaic genome structure of marine Pseudoalteromonas siphovirus B8b isolated from the Northwestern Mediterranean Sea. Manuscript. Lara, E5. .; Duhaime, M.B.; Ignacio-Espinoza, J.C.; Sà, E.L.; Vaqué, D., Sullivan, M.B. and Acinas, S.G. Comparative genomics and biogeography of Pseudoalteromonas phages. Manuscript.
General Introduction
21 Introduction A LITTLE BIT OF HISTORY Bacterialvirusesorbacteriophages(termderivedfrom‘bacteria’andtheGreekφαγεῖν phagein“todevour”)werefirstdescribedbetween1915and1917bybothTwortandd’Herelle (Duckworth,1987).Rightaftertheirdiscovery,theywereconsideredasapotentialtherapeutic tool to fight bacterial pathogens (Levin and Bull, 1996). The role of phages in the marine environment was not significantly appreciated until 1968 when, Wiebe and Liston in 1968 suggestedthatphages couldexertan influenceonbacterialpopulations andon biochemical capabilitiesofmicroorganisms.Atthesametime,itwasreportedthefirstisolatedmarinephage from Pseudomonas, Photobacterium and Cytophaga(Spencer,1955,1960,1963). It was not until thelate1980s,whenitwasdemonstratedthatinmanyaquaticenvironmentsbacteriophageswere presentinveryhighconcentrationsandthattheyoftenexceedbyonetotwoordersofmagnitude theconcentrationsofbacterioplankton(Berghetal.,1989;Borsheimetal.,1990;Bratbaketal., 1990).Thesefindingsencouragedtheresearchontheecologyofmarineviralcommunitiesand theirimpactonmicrobialfoodwebsandbiogeochemicalcycles. WHAT IS A VIRUS? Avirusisanon-cellulargeneticelementthatusesalivingcellforitsownreproduction. Prokaryoticvirusesaretermedbacteriophages(phages).Theyusuallyhaveasmallsize,between 30and60nm,althoughsmallerandlargerphagescanalsobefound(Weinbauer,2004).Phages cancontaindouble-stranded(ds)DNA,single-stranded(ss)DNA,ssRNA,ordsRNA.Aprotective coatingcalledcapsidthatiscomposedofphage-encodedproteinssurroundsthenucleicacid. Afewtypeshavealipid-containingenvelopeorcontainlipidsaspartoftheparticlewall.For manyphagetypes,thecapsidisattachedtoatailstructurethatisalsomadefromphage-encoded proteins(Fig.1A).Phagesarehighlystructuraldiverseandaccordingtothesymmetryofaviral particle,theycanbedividedintailed,cubic,filamentousandpleomorphic.Mostaquaticphages belong to Caudovirales,whicharetailedphagesandcharacterizedbyanicosahedralhead.The order consist of three main families: (i) Myoviridae with contractile tails, (ii) Siphoviridae with longnon-contractiletails,and(iii)Podoviridaewithshortnon-contractiletails(Fig.1B,CandD). However,inthepastyear,studieshavedemonstratedthedominanceofnon-tailedviralparticles (Brumetal., 2013),andthepresenceofotherviralgroupsin theoceansassingle-stranded viruses(ssDNA),RNAvirusesorlargeDNAviruses(Hingampetal.,2013;LabontéandSuttle, 2013;Stewardetal.,2013).
Marineviruses 22 DEBATE ON PHAGES: TO BE OR NOT TO BE ALIVE? Are viruses alive? The origin of life, published in 1929 considered that viruses were themissinglinkbetweenthenon-livingandthefirstcell.However,thediscoveryin1944that DNAencodesgeneticinformation(Averyetal.,1944)createda”lifeisDNA”definitionthatheld informationandevolutionconceptsasprincipalvalues.Forterre(2010)suggestedthatviruses can be viewed as complex living entities that transform the infected cell into a novel organism (the virus)producingvirions.However,otherauthorssuggestedseveralreasonstoexcludeviruses fromthetreeoflife(MoreiraandLópez-García,2009).Forinstance,theystatedthataviruscan notbeconsideredanaliveorganismbecauserequiresportionsofanotherorganism,andtheydo nothavethemachinerytoexpresstheirowngenes.Theyalsoarguethatvirusesdonotreplicate andtheydonotevolvesincetheyareevolvedbycells.Thereforetheanswertothequestionif virusesarealivedependsonourdefinitionoflife. VIRAL INFECTION MECHANISMS Phagescannotsurviveindependentlybecauseoftheirlackofacompletemetabolicsystem andtherefore,theydependonthehostenzymaticsystemsforproliferation(Weinbauer,2004). Inordertomaximizethesustainableuseofthehost,phageshaveevolvedavarietyoflifecycles: Figure 1.(A)DiagramofatypicalT4bacteriophage(myovirus)structureandthethreemorphological familiesoftaileddsDNAbacteriophages:(B)Myoviridae,(C)Siphoviridae and (D) Podoviridae.
23 Introduction lytic,lysogenic,pseudolysogenicandchronicinfections.However,filamentousphagesableto causechronicinfectionhaveonlybeendetectedrarelyinfreshwateraquaticenvironments(Pina etal.,1998;HoferandSommaruga,2001). Lyticinfectionisoneofthemosttypicalstrategiesforphagereplication;phagesrelyingin thismechanismarecalledvirulentphages(Fig.2).Theycanquicklycompleteproliferationand causethereleaseoflargeamountsofprogenyinashorttime.Virulentphagesareabundantin theocean(Zhangetal.,2011).Forinstance,lyticphagescanaccountforthe65%oftheisolated phagesintheAtlanticOcean(MoebusandNattkemper,1981).Thelyticwayisasurvivalstrategy for phages to adapt to nutrient rich environments where bacterial abundance and production are high.Theyaregenerallyconsideredtober-selectedwhichischaracterizedbyhighburstsizeand shortgenerationcycle(Suttle,2007). Lysogenic infection refers to the process of a lysogen or lysogenic phage (also called temperate phage) integrating its own genome into the host genome for a long time, and replicating alongthehostgenome(Fig.2).Thebacteriathatcontainthegenomeofalysogenicphage(existing asaprophage)arecalledlysogenicbacteria.Theprophagecanspontaneously,orbeinducedby physicalorchemicalfactors(e.g.pH,temperature,UV,nutrientconditions,etc.)toenterintoa lyticcycle.Lysogenicphagesaremorecommoninoligotrophicmarineenvironments,because a low nutrient concentration results in a lower bacterial abundance and, therefore the infection frequencyisreduced(WommackandColwell,2000).Accordingly,lysogenicbacteriaaremore abundant in open sea than in coastal waters, as well as in depth waters than in surface (Jiang andPaul,1996,1998a;WeinbauerandSuttle,1999;Weinbaueretal.,2003).Althoughthisis notalwaystheruleasitisobservedinthesubtropicalnortheastAtlanticOcean(Borasetal., 2010a). Thepseudo-lysogenyischaracterizedbytheindependent existenceofphagegenome (called preprophage) in the host cytoplasm (Fig.2). Thedetailed mechanism of this survival strategyisstillcontroversial,butMoebus(1996)consideredthatpseudo-lysogenyisatemporary immunestateofhostbacteria.Thebacteriaandphagecancoexistduringthisstageandithas beensuggestedthatthissurvivalstrategycanhelpmarinephagessurviveinanunfavorable environmentalconditions(WommackandColwell,2000).K-selectedphagesareusuallylysogenic orpseudo-lysogenicwithsmallgenomeandburstsizesandtheyinfectthemostabundant,slowgrowingmembersofthemicrobialcommunity(Suttle,2007).
Marineviruses 24 Figure 2.Strategiesofphagereplication.Inthisfigurearerepresented(a)lysis,(b)lysogenyand(c) pseudolysogeny.
25 Introduction UNDERSTANDING MARINE VIRUSES IN A GLOBAL CONTEXT Viruses are incredible abundant in the oceans!1. In the past two decades, methods for direct counts have evolved from transmission electron microscopy (TEM) to epifluorescence microscopy and flow citometry (Børsheim et al.,1990;Haraetal.,1991;Haraetal.,1996;WeinbauerandSuttle,1997;NobleandFuhrman, 1998;Marieetal.,1999;Brussaard,2004),andviruseshavebeenenumeratedfromthousands ofsamplesthroughouttheworld’soceans.Accordingtothesetechniques,thereareanaverage of107 viruses ml-1intheocean’ssurface(Marieetal.,1999;WommackandColwell,2000).The generalagreementisthatmostofvirusesinthemarineenvironmentaretaileddsDNA(Wommack andColwell,2000;Weinbauer,2004),buttherecentdiscoveryofssDNAvirusesandRNAviruses suggeststhattheseviralgroupsaremoreprevalentthanpreviouslyrecognized(Langetal.,2009; LabontéandSuttle,2013;Stewardetal.,2013).Moreover,recentstudieshavedemonstratedthe dominanceofnon-tailedviralparticles(Brumetal.,2013)andhaveevidencedthatevenlarger ssDNAphagesaredifficulttostainandvisualizeusingepifluorescencemicroscopy(Holmfeldt et al., 2012). Therefore, methodological limitations may lead to an underestimation of viral abundanceanddiversityinenvironmentalsamples. Spatial variability2. Latitudinal variations Thecontrollingfactorsofviralabundanceoverlargespatialscaleintheoceanssurface arestillpoorunderstood.Inpolarregions,speciallyintheArctic,temperatureislowandnutrient concentration is limited, thehostabundanceandproductionarereducedprobablyduetothese lowtemperatures;thus,viralabundanceis10timeslowerthanintemperatewaters(Middelboeet al.,2002;Säwströmetal.,2007;Borasetal.,2010b).Astudycarriedoutinsurfacewatersalong3 transectsdeployedinbroadregionsofthecentralPacificandSouthernOceansalsodemonstrate thatviralabundancetendtobemoreabundantinlowerlatitudesthanintheAntarcticregion (Yangetal.,2010).Theauthorssuggestedthatthehighabundancesofvirusesinsubtropicaland tropicalwatersmightbeaccountedforhighabundancesofcyanophages.Andfinally,temperate regionsgothroughseasonsgraduallychangingfromadeepmixedwinterwatercolumntoa morestratifiedsummerwaters.Studiesperformedintheseregionsalsoshowlinkagesbetween viral abundance and Synechococcus(Bettareletal.,2002)orcorrelatedwithbacterialactivityand host cell abundance (Corinaldesietal.,2003). Thesefindingsevidence that viral abundance is distinctamongdifferentoceanographicregions.Understandingchangesinphysical,chemicaland
Marineviruses 26 biological characteristics occurring across different oceans it is needed for a better assessment of factorsandprocessesinfluencingviraldynamicsanddistribution. Depth variations Physico-chemicalchangeswithdepthcanhaveasignificantimpactonmarineviruses. Deep-sea ecosystems are dark and extreme environments that lack photosynthetic primary production, depend on prokaryotic production, including heterotrophic reactions using importedorganicmatterfromupperlayersorchemosyntheticreactionsusingreducedinorganic compoundssuchasammoniaorcarbonmonoxide(Dicketal.,2013).Giventhathostsareatleast anorderofmagnitudelessabundantinbathypelagicwatersthaninsurfacewaters(Tanakaand Rassoulzadegan,2002),viralabundancesdecreasesinthedeeperwatercolumn(Suttle,2005, 2007).Moreover,thedecayofvirusesinthedeepseaishigherthancanbesupportedbyratesof viralproduction,whicharebelievedtoberelativelow(Paradaetal.,2007).Nevertheless,high abundancesofvirusesinthedeepoceanhavebeenreported(Paradaetal.,2007;DeCorteetal., 2010).Themechanismformaintaininghighviralabundancesinthebathypelagicisnotclearbut onepotentialviralinputtothedeepseaisthroughsinkingparticleswherebacterialcellsmay attachinaggregatesinhighabundance(Haraetal.,1996;Paradaetal.,2007). Temporal variability3. Seasonal variation in viral abundance has been observed since the earliest reports ofvirusesinseawater(Berghetal.,1989;JiangandPaul,1994)andithasalsobeennotedin coastalsystems wheretheviralabundance ishigherin summerandautumn thanin winter (reviewedinWommackandColwell,2000),probablyreflectingthatviralproliferationdepends ontheabundanceandactivityofhostcells.Forexample,recurringpatternsinviralabundance wereobservedintheNorthAtlanticsubtropicalgyreandshowedstrongcorrelationsbetween viralabundanceandSAR11,Prochlorococcus and Rhodobacteraceae(Parsonsetal.,2012).But becausethevolatilenatureofvirioplankton,theirabundancechangesaremoreevidentinshortterm temporal studies. Winget and Wommack (2009) demonstrated significant variations in viralproductionratesover24-hcyclesandWinteretal.(2004)determinedthatthefrequencyof infectedcellswasgenerallyhigheratnightthanduringthedaytimeandsuggestedthatinfection occurredduringthenightandvirallysisintheafternoon.Thisclearstrongtemporalvariabilityin viralpopulationisnotonlyimportantbecauseitshowsthatvirusesareaveryactivecomponent, butalsoasamainfactortoconsiderforcomparingviralabundancedatafromdifferentsystems.
33 Introduction onehostorcloselyrelatedhosts.However,thetwomostfrequentlyexaminedpatternsinthe studyoftheseauthorswerenestednessandmodularity.Nestednessistheresultspatternof phages that evolve to broader host ranges and bacteria evolve to increase the number of phages towhichtheyareresistant.Ontheotherhand,modularitycontainsinteractionsthattendto occuramongdistinctgroupsofphagesandhosts.Theseauthorsanalyzed38studiesofphagehostinfectionnetworksatnarrowtaxonomicscaleandtheyfoundthattheinteractionspatterns weremostlynested,whichimpliesahierarchystructureinwhichthemostspecialistphages infectthosehostwithhighersusceptibilityofinfection(themostgeneralisthosts)matching withtheideaofgeneforgene(GFG)co-evolutionarymodel(Floresetal.,2011).However,a recentstudyfromthesameauthorsshowedthattheinteractionspatternsweremodularwhen bacteria and phages interactions are taking into account at larger geographical/or taxonomic scales(Floresetal.,2013).Theseresultsindicatethatmorehost-rangeanalysesshouldbedone includingawiderangeofphylogeneticbacterialtaxa.Additionally,thecomplexityofhost-phage relationshipareoftenoverlookedbystudiesbasedonconservativegenemarkers(suchas16S rRNA)sincephylogeneticallyidenticalbacteriacanshowdifferencesinthephage-susceptibility patterns(Holmfeldtetal.,2007)andthereforeothergenemarkersorgenomicprofilesshouldbe includedinhostrangeanalyses. Figure 6.Schematicnetworksrepresentationofinfectionbetweenphagesandbacteria.
Marineviruses 34 Phage-host evolution and mechanisms of resistance4. Phages and hosts are involved in continuous cycles of co-evolution, in which phageinsensitivehostshelptopreservebacteriallineages,whilenon-resistanthostcellscouldresultin newbacterialstrains.Therearemanymechanismsbywhichhostscanbecomeresistanttophage infection:preventionofphageadsorption,destructionofphageDNAorlossofbothphageand hostthroughabortionofphageinfection(Labrieetal.,2010). Adsorptionofphagestohostreceptorsistheinitialstepofinfection,phagesmustrecognizea particularcellcomponentwhicharehighdiverseinhostmembranesandwalls.Themechanisms to avoid the phage adsorption are divided in three categories: blocking phage receptors, the productionofextracellularmatrixandtheproductionofcompetitiveinhibitors. A recently mechanism of phage DNA destruction has been described. Clustered regularly interspacedshortpalindromicrepeats(CRISPR)hasbeenidentifiedanditprovidesacquired immunityagainstvirusesandplasmids.CRISPRlocitypicallyconsistofseveralnoncontiguous directrepeatsseparatedbystretchesofvariablesequencescalledspacersandareoftenadjacent toCasgenes(CRISPR-associated).Casgenesencodealargeandheterogeneousfamilyofproteins thatcarryfunctionaldomainstypicalofnucleases,helicases,polymerases,andpolynucleotidebinding proteins. CRISPR, in combination with Cas proteins, forms the CRISPR/Cas systems (HorvathandBarrangou,2010).CRISPRsystemshavebeenidentifiedinapproximately40%and 90%ofBacteriaandArchaeagenomesrespectively(Mojicaetal.,2000;Grissaetal.,2007)andare usuallylaterallytransferredandfoundoverrepresentedingenomicislands(HoSuietal.,2009). Ithasbeendemonstratedthatthissystemisanadaptivemicrobialmechanismofresistanceto phageinfection(Barrangouetal.,2007;Deveauetal.,2010).Thismechanismisveryspecific,thus itmaydominateinenvironmentswithhighhostdensityandlowdiversity.However,theoceans presenttheoppositescenario,andtheimportanceoftheCRISPR/Cassystemisstillunknown (Breitbart,2012).However,datafromGOS(GlobalOceanSampling)expeditiondemonstrated thatalmost200reliableCRISPRcassettes(Sorokinetal.,2010)andCRISPR/Cassystemhasbeen detectedinsomegenomesofculturedmarinebacteria(Thomasetal.,2008;Fernández-Gómez etal.,2012). Ontheotherhand,phagesevolvedtoavoidthesebacterialmechanismsofresistance.Therefore, thereisaconstant-diversitydynamicmodel,inwhichthediversityofprokaryoticpopulation is maintained by phage predation, because the best-adapted microorganisms are selected (Rodriguez-Valeraetal.,2009).
35 Introduction Figure 7.OverviewofbacteriophagesequencesversusthetotalviralgenomesavailableinNCBIandthe taxonomicdistributionofallsequencedphagesversusallsequencedGammaproteobacteriaphages.Itis also represented the taxonomic distribution of all the Gammaproteobacteriaphagessequenced. Genomic diversity of marine phages5. Recognized the viral numeric importance, scientists have been characterizing them andtryingtodeterminetheextentofmarineviraldiversity.Nevertheless,diversityhasbeen hard to measure because viruses do not have a universally conserved gene. Further, it has beenestimatedthat>99%ofallenvironmentalbacteriaaredifficulttocultureusingstandard techniques(StaleyandKonopka,1985)andthereforethereisapaucityofmarinephagehosts. Moreover,notallphagesproduceidentifiableplaquesonbacteriallawns(Seguritanetal.,2003; Breitbart,2012).Tocircumventtheselimitations,thediversityofviralcommunitieshavebeen analyzed by culture independent approaches: (i) Pulse-Field Gel Electrophoresis (PFGE), a methodtoallowdiscriminatevirusesaccordingtheirgenomesize(Stewardetal.,2000;Steward, 2001),(ii)byRandomlyAmplifiedPolymorphicDNA(RAPD-PCR)togetageneralfingerprint of the whole viral community (Comeau et al., 2006; Winget and Wommack, 2008) and (iii) bymetagenomesequencingofwholeviralcommunities(metavirome)(Breitbartetal.,2002; Rohwer,2003;Anglyetal.,2006;Rodriguez-Britoetal.,2010).Throughviralmetagenomesithas beenshownthatvirusesareexceptionallydiverseandrepresentthelargestreservoirofgenetic diversityintheocean(Pedullaetal.,2003;Rohwer,2003;Anglyetal.,2006).Thesecultureindependentmetagenomicmethodsarepowerful,buttheyarealsoseverelydatabaselimited duetothelackofsequencedviralgenomes.Thus,isolate-basedgenomeanalysesareessential tobettermapviralsequenceandtounderstandviral-hostinteractionsinnature.Infact,most sequencedmarinephagegenomesbelongtocyanophages(PaulandSullivan,2005),andalthough recentlyithasbeendescribedphagesinfectingseveralmarinebacteria(Holmfeldtetal.,2013;
Marineviruses 36 Kangetal.,2013;Zhaoetal.,2013),wearebiasedagainstotherrelevantmarinetaxa(Fig.7). Gammaproteobacteria is one of the most abundant bacterial class in the ocean ranged between 5-28%intheMediterraneanSeabasedonCARD-FISHcounts(Ruiz-Gonzálezetal.,2012).Phages that infect the Gammaproteobacteria genera Pseudoalteromonashavebeenpreviouslystudiedin themarineenvironmentandithasbeenreportedtheirecologicalimportance(Moebus,1992a; Wichelsetal.,1998;Wichelsetal.,2002;Thomasetal.,2008).Pseudoalteromonassp.strains mayrepresentsignificant playersonthebacterialproductionbudget.But,todate,onlyfour marine Pseudoalteromonasphageshavebeenisolatedandsequenced(Fig.7).Insummary,a betterknowledgeoftheecologicalandevolutionaryrolesofmarinevirusesandamoreaccurate interpretationoftherapidlyincreasingmetaviromesequenceswouldrequiretheisolationand genomicanalysisofindividualvirusesfromadiversephylogeneticrangeofprokaryotictaxa.
37 Introduction AIMS AND OUTLINE Thisthesisaimedtocontributetotheunderstandingontheecology,biologyandgenomics ofmarineviruses.Virusesarethemostabundantbiologicalentitiesinaquaticecosystems,they constitutethegreatestgeneticdiversityintheoceanandtheyareimportantagentsofmortality playing a main role in marine biogeochemical cycles. Given their importance in the global oceanographicprocesses,hereweaimedtoprovidedataonviraldynamicsinacoastalmarine siteinBlanesBay MicrobialObservatory (BBMO)and investigated theeffectsof theclimate changeonviralabundanceandprocessesintheArcticOcean.Moreover,wealsoencouragedto unravelthecomplexrelationshipsbetweenvirusesandtheirhosts.Westudiedtheinteractions ofaphage-hostsystemundercontrolledconditionsbasedonthehost-rangeanalysesofanarray ofisolatedphagesfromBBMO.Finally,weanalyzedthegenomicfeaturesofdifferentphages genomes belonging to two main viral families and their biogeographical patterns in environmental virusesmetagenomes.Basedonthecurrentknowledgeofthedifferentissuesexplainedinthe introduction,thespecificgoalsandhypothesisofthisthesisaredescribedbellow. OBJECTIVE 1. Explore the dynamics and processes of marine phages in marine ecosystems Absence of seasonality on viral dynamics and salinity as a main driver modulating 1.1. viral abundance in the NW Mediterranean Sea Weinvestigatedthetemporalvariationofviralabundanceandgenomicprofilingduringa periodof5years(from2008to2012).Inordertoexaminewhicharethemechanismsdrivingthe abundanceandgenomicpatternsofmarineviralcommunitiesintheBBMO(BlanesBayMicrobial Observatory),wecomparedtheobservedpatternswithinsituchangesinphysico-chemicaland biological parameters (Chapter 1).Weexpectedtofind:(i)theviralabundancehighlycorrelated withbacterialabundanceorproduction,orcyanobacteriadrivingtheviraldynamicsand(ii) aseasonalpatterninviralcommunitiessinceBBMOisamarinesitewithmarkedseasonality patterns. 1.2. Experimental evaluation of the warming effect on viral, bacterial and protistan communities in two contrasting Arctic systems Wewereespeciallyinterestedintheeffectsofclimatechangeonmicrobialcommunities anditsconnectionwithviralabundanceandproductionandthestrategiesofviralinfection.
Marineviruses 38 Inthiscontext,weexperimentallytestedhowautotrophicandheterotrophicArcticmicrobial communities responded to various increasing temperatures, based on the predicted warming of the sea surface temperature in the Arctic Ocean. In particular, within the heterotrophic microorganisms, we investigated changes in, bacterial and viral abundance and production (lysogenyvs.lysis),andbacteriallossesduetobacterivoryandvirallysis.Finally,wealsoidentified whichtemperaturetriggeredasignificantshiftforeachofthestudiedvariables(Chapter 2).The hypothesesherewere:(i)thatviralabundancefollowedthesametrendthatbacterialabundance; (ii)thelyticviralproductionwoulddominateathightemperaturesandthatthelysogenicstrategy would be predominant at lower temperatures and (iii) bacterial losses due to viruses and protists wouldincreasewiththetemperature. OBJECTIVE 2. To gain insight into marine phage-host interactions at fine scale Thesubjectoftheobjectivewastoincreasethenumberofisolatedmarinephagesto better understand the role of the phages-host interactions in natural communities since the actualnumberofstudiesinthisfieldislowandourknowledgeispoorlyunderstood. 2.1. Marine phage-bacteria interactions at fine-scale within Pseudoalteromonas sp. phages Toimprovetheexistingconceptualmodelsontheroleofphagesinbacterialdiversityand populationdynamics,weanalyzedaphage-hostsystembasedonPseudoalteromonas spp.Several phagesfromBBMOinfectingPseudoalteromonasbacterialstrainswereisolated.Wedetermined themorphologyofthe18isolatedphagesandthehostrangepatternsatfinescaleresolutionby analyzingthewholegenomeprofilesfrombothhostandphages(Chapter 3). OBJECTIVE 3. To improve the knowledge on ecological and genomic features of marine phages genomes Current public dataset on viral genomes are biased towards specific phage genomes restricted to few phylogenetic taxa (mostly cyanophages). Indeed, extra phage genomes are necessarytounderstandtheecologyofvirusesnotonlyoftheabundantphagesbutalsoofthose representingthephagerarebiosphere,andatthesametime,theyarecrucialtogetecological meaningfulinsightsoftheincreasingmetagenomicviraldataset.Forthatreason,wesequenced3 Pseudoalteromonasphagesgenomesbelongingtotwomainfamilyphagesthatrepresentthefirst onesretrievedfromtheMediterrananSea.
39 Introduction 3.1. Life-style and mosaic genome structure of marine Pseudoalteromonas siphovirus B8b isolated from the Northwestern Mediterranean Sea WedeeplycharacterizedandanalyzedoneofthePseudoalteromonas phages in relationship with its host. We performed a detailed analyses on the biological, ecological, phylogenetic, proteomicandgenomicfeaturesofthesiphovirusB8bphage(Chapter 4). 3.2. Comparative genomics and biogeography of Pseudoalteromonas phages Finally,wecarriedoutawholegenomecomparisonof3sequencedPseudoalteromonas phages and we studied the ecological impact and abundance of these phages using the viral metagenomes (metaviromes) available in the public databases (Chapter 5).
Absence of seasonality on viral dynamics and salinity as a main driver modulating viral abundance in the NW Mediterranean Sea 1 ChapterChapter Elena Lara, Elisabet Laia Sà, Clara Ruiz-González, Ramon Massana, Josep M. Gasol, Silvia G. Acinas and Dolors Vaqué
49 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 Figure 1. Principal component analyses (PCA) of (A) the 5 years studied without CARD-FISH data, and (B) the first 2.5 years with the CARD-FISH data. T (temperature); Chla (Chlorophyll a concentration); BP (bacterial production); HF (heterotrophic flagellate abundance); PF (phototrophic flagellate abundance); Bact (bacterial abundance); Viruses (viral abundance); VBR (virus-bacterium ratio); Syn (Synechococcus abundance); Prochl (Prochlorococcus abundance); Ros (Rhodobacterales abundance); Gam (Gammaproteobacteria abundance); SAR11 (SAR11 clade abundance); CF (Bacteroidetes abundance). included specific quantification of bacterial taxa (SAR11, Gammaproteobacteria, Bacteroidetes (or Cytophaga-Flavobacteria, CF) and Rhodobacterales by CARD-FISH data (Fig. 1B). The first ordination axis accounted for 30.51% of the variance, while the second axis accounted for 18.04%. The first axis was mainly related to the temperature and it was included all the bacterial lineages except for the group Rhodobacterales, as well as bacterial abundance and production, Chl a concentration, inorganic nutrients and protist. On the other hand, viruses, salinity,
Chapter 1Viral dynamics and composition in NW Mediterranean Sea 50 Prochlorococcus and the group Rhodobacterales, light penetration (secchi) and rainfall defined the second axis (Fig. 1B). Relationships between viruses and environmental and biological variables Correlations As a result of PCA analysis during the 5 years studied, we detected co-variations between viruses and salinity, light penetration, rainfall and Prochlorococcus abundance (Fig. 1A). The correlation analysis among these variables showed that, in fact, viral abundance and the VBR ratio were significantly negatively correlated with salinity and light penetration (secchi) (Table 1) and rainfall was inversely correlated with salinity. On the other hand, Prochlorococcus abundance was positively correlated with salinity (r = 0.30; p<0.02). Viruses and specific groups of bacterioplankton To provide insights on bacterial community that may potentially affect viral communities dynamics, probes for Gammaproteobacteria, SAR11, Rhodobacterales and Bacteroidetes were selected. Abundances of the bacterial groups and their dynamics with viruses are represented in Fig. 1.SM. Results of hybridization with the probe Non338 (negative control) never exceeded 0.1% of DAPI counts, and were not subtracted of CARD-FISH counts. Gammaproteobacteria and Bacteroidetes represented a 12.5% and 18.5% respectively of the total DAPI counts and they showed a strong seasonality with two marked peaks in spring and late autumn and decreasing their numbers in winter and summer (Fig. 1A.SM, 1B.SM). The SAR11 bacteria did not show any temporal variation during the studied period although peaks were detected in spring-summer (Fig. 1C.SM), they dominated the bacterial community ranging from 20% to 60% of total DAPI counts (average of 35% of cell counts). The Rhodobacterales group was less important within the bacterial community (average of 5% of cell counts) with peaks in May 2008 and in April and May 2009 (Fig. 1D.SM). According with PCA results, correlations between viral abundance or VBR ratio and variables that co-varied were analyzed. The correlations were also determined with all the bacterial groups analyzed by CARD-FISH (Table 1). The SAR11 group showed a high correlation coefficient with VBR and salinity was correlated with viruses (r=-0.48, p=<0.02) and significant correlation was found between the Prochlorococcus sp. and Rhodobacterales abundances (r= -0.40; p<0.05) (Table 1).
51 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 Table 1. Significant correlation coefficients among variables. Variables significantly correlated are labeled in black. Virus (viral abundance); VBR (virus-bacterium ratio); Syn (Synechococcus abundance); Prochl (Prochlorococcus abundance); Ros (Rhodobacterales abundance); Gam (Gammaproteobacteria abundance); SAR11 (SAR11 clade abundance); CF (Bacteroidetes abundance). 2008-2012 (no CARD-FISH data) 2008-April 2010 (with CARD-FISH data) Variables n r PVariables n r P Virus - Salinity 53 -0.53 <0.01 Virus - Salinity 24 -0.48 <0.02 Virus - Secchi 54 -0.30 <0.02 Prochl - Ros 26 -0.40 <0.05 VBR-Salinity 50 -0.30 <0.05 VBR - SAR11 24 -0.55 <0.01 Salinity-Rainfall 56 -0.31 <0.05 Virus - Ros 23 0.32 0.15 SalinitySecchi 67 0.40 <0.01 Virus - Gam 24 0.17 0.42 Salinity-Prochl 64 0.30 <0.02 Virus - CF 23 -0.06 0.78 Prochl-Virus 49 -0.11 0.45 Virus - SAR11 24 -0.25 0.24 Prochl - VBR 48 -0.05 0.70 Virus - Syn 24 -0.25 0.24 Prochl - Secchi 60 -0.02 0.85 Virus - Prochl 17 -0.45 0.07 VBR-Secchi 49 -0.25 0.07 Virus - Secchi 22 -0.17 0.45 Rainfall - Virus 52 0.24 0.08 Virus - Rainfall 17 0.32 0.21 Rainfall - VBR 48 0.10 0.49 VBR - Ros 23 0.23 0.28 Rainfall - Prochl 52 0.10 0.47 VBR - Gam 24 0.09 0.65 Rainfall - Secchi 55 -0.26 0.05 VBR - CF 23 -0.19 0.38 VBR - Syn 24 -0.17 0.41 VBR - Proch 17 -0.42 0.09 VBR - Salinity 24 -0.35 0.09 VBR - Secchi 22 -0.17 0.46 VBR - Rainfall 17 0.46 0.06 Salinity - Ros 34 0.10 0.55 Salinity - Prochl 26 0.25 0.21 Salinity - Secchi 31 0.03 0.86 Salinity - Rainfall 24 0.04 0.83 Secchi - Ros 31 -0.16 0.40 Secchi - Prochl 23 0.07 0.74 Secchi - Rainfall 21 -0.40 0.07 Rainfall - Ros 24 0.21 0.33 Rainfall - Prochl 16 -0.13 0.62
Chapter 1Viral dynamics and composition in NW Mediterranean Sea 52 Viral abundance and composition dynamics during the studied period In surface waters of the Blanes Bay Microbial Observatory (BBMO), total viral abundance did not show any obvious seasonal patterns during the studied period (analysis of variance (ANOVA), p > 0.05), (Fig. 2A). Similar yearly viral abundance values and VBR ratio were found in the first four years (2008-2011), but viral abundance decreased by an order of magnitude during 2012 (Table 2, Fig. 2D.SM). Identical pattern was observed for the VBR ratio, which presented similar values the first four years, and in 2012 significantly decreased (Table 2). Viral abundance ranged about an order of magnitude, and the minimum value was detected in July 2012 (0.73 x 107 cells ml-1) and the maximum in August 2011 (6.12 x 107 cells ml-1) (Table 2). The distinguished subgroups of viruses belonging to low, medium and high subgroups also decreased in 2012 but the very high viruses subgroup showed a significant increased during this year (Table 2). The genomic banding patterns from the viral communities collected in Blanes Bay were compared by cluster analysis, which did not show any seasonal clustering of RAPD-PCR fingerprints (Fig. 3). Band richness varied between 5 (September 2010) and 12 (October 2012) distinct bands (Fig. 3.SM). In autumn 2010 we observed the highest numbers of bands and during the whole 2011 the lowest band richness, while in 2012 increasing RAPD band richness was detected (Fig. 3.SM). However, samples from spring and autumn seasons were clustered together, as well as samples from winter and summer periods (Fig. 3). The maximum similarity among sample banding patterns was 83% between January 2012 and February 2012. The average similarity across all banding patterns was 36%, indicating that most of the samples shared more than a half of the bands but none of the samples displayed identical RAPD pattern. Also, viral assemblages within each year were more similar than between years (Fig. 3).
53 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 A B C D Day of the year Figure 2. Abundances of (A) viral, (B) bacterial, (C) Synechococcus and (D) Prochlorococcus throughout the seasonal study in the BBMO.
Chapter 1Viral dynamics and composition in NW Mediterranean Sea 54 2008 2009 2010 2011 2012 Variable Average (min, max) Average (min, max) Average (min, max) Average (min, max) Average (min, max) Temperature (ºC) 17.80 (12.9-24.3) 16.24 (12.16-21.1) 17.05 (11.94-24.43) 17.93 (12.25-23.0) 17.21 (12.24-25.35) Salinity (psu) 38.04 (37.11-38.34) 38.05 (37.6-38.18) 37.8 (37.49-38.17) 37.82 (37.21-38.09) 38.14 (37.91-38.28) Secchi (m) 14.6 (8.0-24.0) 14.78 (8.0-20.0) 12.29 (5.0-22.0) 14.04 (8.5-19.0) 16.12 (8.0-20.0) PO4 -3 concentration (μM) 0.05 (0.02-0.11) 0.07 (0.03-0.12) 0.14 (0.08-0.18) 0.10 (0.07-0.18) 0.08 (0.03-0.22) NO3concentration (μM) 1.11 (0.38-2.66) 0.46 (0.01-1.5) 1.06 (0.08-4.25) 0.78 (0.42-1.54) 0.88 (0.01-3.52) Rainfall (mm) 61.43 (10.1-104.6) 39.32 (3.2-82.1) 72.38 (7.6-141.1) 76.34 (0-275.3) 31.93 (1.5-127.1) Chlorophyll a concentration (µg l-1)0.49 ( 0.18-1.91) 0.55 (0.12-1.16) 0.74 (0.24-1.95) 0.80 (0.2-2.88) 0.50 (0.16-1.21) Synechococcus spp. ab. (104 cells ml-1)2.24 (0.55-6.27) 2.21 (0.38-5.49) 2.10 (0.39-5.73) 1.45 (0.43-3.11) 2.13 (0.35-7.11) Prochlorococcus spp. ab. (103 cells ml-1)8.12 (0-39.4) 7.12 (0-34.5) 3.89 (0.29-16.6) 6.75 (1.17-16.7) 7.07 (0.96-20.9) Bacterial production (µg C l-1 d-1)2.05 (0.07-5.0) 1.09 (0.27-2.02) 0.81 (0.04-4.34) 2.18 (0.29-6.21) 2.53 (0.13-13.56) Bacteria ab. (105 cells ml-1) 7.54 (4.12-11.8) 8.58 (6.77-11.4) 7.74 (4.12-10.8) 8.71 (3.15-13.3) 7.84 (4.37-13.2) Viruses ab. (107 viruses ml-1)4.0 (1.41-5.57) 3.08 (1.79-4.57) 4.62 (2.89-5.72) 4.71 (3.49-6.12) 1.78 (0.73-3.18) VBR 57.20 (29.16-80.03) 38.50 (25.79-66.07) 65.14 (31.74-105.87) 59.13 (32.66-144.68) 26.15 (6.65-47.18) Table 2. Average, minimum and maximum values of the physicochemical and biological parameters during the 5 years studied.
55 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 Figure 3. Multidimensional scaling (MDS) analysis of the (A) monthly samples of viral community composition as revealed by RAPD-PCR (Randomly Amplified Polymorphic DNA-PCR)., and (B) dendogram of the RAPD-PCR banding pattern constructed from the absence/presence matrix grouping the Blanes Bay samples. A B
Chapter 1Viral dynamics and composition in NW Mediterranean Sea 56 Dynamics of environmental and microbial variables driving viral communities Given that PCA and correlation analysis showed that viral abundance was highly correlated with salinity, light penetration, rainfall, cyanobacteria and some of the bacterial groups studied, their dynamics was also analyzed during the 5 years studied. Salinity did not show any seasonal pattern during these 5 years but it was detected two decreasing points in late spring and in winter (Fig. 4B). In contrast, rainfall data showed a recurrent pattern, increasing during the winter months (Fig. 4C). During the 5 years studied, Day of the year A B C Figure 4. Values of (A) temperature, (B), salinity, and (C) rainfall, throughout the seasonal study in the BBMO.
57 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 surface water temperature in Blanes Bay displayed significant seasonal variability (analysis of variance (ANOVA), p < 0.05), (Fig. 4A) with a minimum value of approximately 12ºC in winter and a maximum of 24ºC in summer. Inorganic nutrient concentrations and (PO4 -3, NO3 -) Chl a concentration were characterized by low values (Table 2, Fig.2B.SM and 2C.SM). However, Chl a concentration showed large peaks during the spring seasons (Fig. 2C.SM). In the case of the microbial variables, bacterial abundance did not follow a significant seasonal pattern (ANOVA, p > 0.05) and did not differ significantly among the five years of study (Table 2). However, bacterial abundance showed peaks in spring and autumn (Fig. 2B). Average bacterial production was higher during 2012, probably due to a peak detected in September which was the highest value observed during the 5 studied years. Two peaks of production were also detected in autumn 2010 and 2011 (Fig. 2E.SM). Synecochococcus and Prochlorococcus presented a seasonal pattern (ANOVA, p < 0.05). Synecochococcus showed maximum values in spring and in autumn (Fig. 2B, 2C) while Prochlorococcus had their maxima in autumn (Fig. 2C, 2D). Similar average values of Synechococcus and Prochlorochococcus abundances were observed during the 5 years of study, except in 2010 when the number of Prochlorococcus cells slightly decreased (Table 2). The lowest Synecochococcus abundance was reached in May 2012 (0.35 x 104 cells ml-1, Fig. 2C.SM) and the highest in April 2012 (7.11 x 104 cells ml-1, Fig. 2C.SM). The minimum Prochlorococcus value was detected in April 2008 (0.25 x 103 cells ml-1, Fig. 2C.SM) yet in some samples they were not detected. Their maximum was found in September 2008 (39.4 x 103 cells ml-1, Fig. 2C.SM). Inter-annual values showed that viral abundance, VBR ratio and rainfall values decreased (Fig. 5C, 5D and 5E) during 2012 but salinity and light penetration (secchi disc) values increased (Fig. 5A, 5B, and Table 2). Moreover, Prochlorococcus abundance decreased during 2010 and 2011 while the maximum average values of viral abundance where detected in these two years (Table 2).
Chapter 1Viral dynamics and composition in NW Mediterranean Sea 58 Figure 5. Inter-annual changes over five years of (A) salinity, (B) light penetration, (C) rainfall, (D) viral abundance, and (E) VBR in the BBMO.
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Chapter 1Viral dynamics and composition in NW Mediterranean Sea 70 SUPPLEMENTAL MATERIAL A B C D Figure 1.SM. Seasonal dynamics of viruses and the bacterial groups analyzed by CARD-FISH during the first 2.5 years of the study: (A) Bacteroidetes (Cytophaga and Flavobacteria) and viral abundance, (B) Gammaproteobacteria and viral abundance, (C) SAR11 clade and viral abundance and (D) Roseobacter and viral abundance in the BBMO.
71 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 A B C
Chapter 1Viral dynamics and composition in NW Mediterranean Sea 72 E D Figure 2.SM. Temporal dynamics of (A) temperature and salinity, (B) inorganic nutrient concentration, (C) Chlorophyll a concentration, Synechococcus and Prochlorococcus abundance, (D) bacterial and viral abundance, and (E) bacterial production, over the studied period (2008-2012) in the BBMO.
73 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 Figure 3.SM. RAPD-PCR banding pattern obtained from the monthly samples in BBMO from September 2010 to December 2012. Lane 1, September 2010; Lane 2, October 2010; Lane 3, December 2010; Lane 4, January 2011; Lane 5, February 2011; Lane 6, March 2011; Lane 7 April 2011; Lane 8, May 2011; Lane 9, June 2011; Lane 10, July 2011; Lane 11, August 2011; Lane 12 October 2011; Lane 13, November 2011; Lane 14, December 2011; Lane 15, January 2012; Lane 16, February 2012; Lane 17, March 2012; Lane 18, April 2012; Lane 19, June 2012; Lane 20, July 2012; Lane 21, August 2012; Lane 22, September 2012; Lane 23 October 2012; Lane 24, November 2012 and Lane 25, December 2012. Ladder: EasyLadder I (2000bp - 1000bp - 500bp - 250bp - 100bp, Bioline).
81 Warming effect on Arctic microbial communities Chapter 2 as to reproduce a light environment similar to where the plankton communities were collected, based on measurements from earlier cruises in the same season. For the open Arctic community samples, the temperature was increased gradually over the first 3 d of the experiment from 1.0°C to each final treatment temperature (Fig. 2A). For the Fjord community, the temperature was set immediately to each final treatment temperature because the temperature of the original water was close to the middle of the experimental temperature range (Fig. 2B). Unfortunately, the treatment at 7.0°C had to be discarded after a malfunction in the cooling system that caused a sustained temperature increase to well above the experimental temperature range during the first 3 d of the experiment. Figure 2. Schematic representation of 2 experimental microcosms used to study the effects of Arctic warming on microbial communities. Duplicate carboys were incubated at 7 experimental temperatures, ranging from 1.0° to 10.0°C increasing in 1.5°C steps. (A) Arctic microcosms: the temperature was increased gradually over the first 3 d of the experiment from 1.0°C to each final treatment temperature. (B) Fjord microcosms: the temperature was immediately set to each final treatment temperature.
Chapter 2Warming effect on Arctic microbial communities 82 Chlorophyll a concentration Daily subsamples (50 ml) from each carboy were filtered through Whatmann GF/F glass-fiber filters. After filtration the pigment was extracted in 90% acetone for 24 h and kept refrigerated in the dark. Filters were analyzed according to the fluorometric method of Parsons et al. (1984) and fluorescence was measured spectrophotometrically. Microbial abundances Samples for viral abundance (VA) and bacterial abundance (BA) were collected daily from each microcosm, while pico/ nanoflagellate and ciliate abundances were determined once every 2 d for each experimental temperature over the entire experimental period. Subsamples (2 ml) for VA were fixed with glutaraldehyde (0.5% final concentration), refrigerated, quick frozen in liquid nitrogen and stored at −80°C, as described in Marie et al. (1999). Counts were made using a FACS Calibur flow cytometer (Becton and Dickinson) with a blue laser emitting at 488 nm. Samples were stained with SYBR Green I and run at an optimal event rate (between 100 and 800 events s−1) (Marie et al. 1999), which in our cytometer corresponded to the medium flow speed (Brussaard 2004). Samples (50 ml) were fixed with glutaraldehyde (1% final concentration) for bacteria and pico/nanoflagellate (≤2 to 20 μm) counts. Subsamples of 10 ml for bacteria and 20 ml for pico/nanoflagellate abundances were filtered through 0.2 and 0.6 μm black polycarbonate filters respectively, and stained with DAPI (4,6-diamidino2-phenylindole) (Porter & Feig 1980) to a final concentration of 5 μg ml−1 (Sieracki et al. 1985). The abundances of these microorganisms were determined by epifluorescence microscopy (Olympus BX40-102/E, at 1000 X). Between 200 and 300 bacteria were counted per sample and at least 50 to 300 heterotrophic or phototrophic pico/nanoflagellates were counted per filter from 3 to 4 transects of 5 to 10 mm each. They were grouped into 3 size classes: ≤2 μm, 2−5 μm, >5 μm. Pico and nanoflagellates showing red-orange fluorescence and/or plastidic structures in blue light (B2 filter) were considered phototrophic pico/nanoflagellates (PF), while colorless flagellates showing yellow fluorescence were counted as heterotrophic pico/ nanoflagellates (HF). With this method, we could not distinguish mixotrophic flagellates. The abundances of ciliates and the phagotrophic dinoflagellate Gyrodinium sp. were obtained using the Utermöhl method. 125 ml of sample was fixed with acidic lugol (2% final concentration). Aliquots of the fixed samples (50 to 100 ml) were settled for 24 to 48 h before enumeration. Both the ciliates and the dinoflagellate Gyrodinium sp. were counted in an inverted microscope (Zeiss AXIO - VERT35, at 400 X). Up to 200 ciliates and 100 Gyrodinium sp. were
83 Warming effect on Arctic microbial communities Chapter 2 counted per sample. Ciliates were identified to genus level when possible (Lynn & Small 2000), and were grouped into the subclasses Oligotrichia: oligotrichs (Halteria sp., Strombidium sp. and Laboea sp.); Choreotrichia: naked choreo – trichs (Strobilidium sp.) and loricate choreo trichs (tintinnids); Haptoria: haptorids (Myrionecta sp. and Askenasia sp.); Scuticociliatida (Scuticociliates); and Hypotrichia (Euplotes sp.). Bacterial production Bacterial production (BP) was measured by incorporation of radioactive 3H-leucine following Kirchman et al. (1985) and modified by Smith et al. (1992). Aliquots of 1.5 ml were taken at time zero from in situ and every day from each microcosm and were dispensed into 4 vials (2 ml) plus 2 TCA-killed control vials. Next, 48 μl of a 1 μM solution of 3H-leucine was added to the vials to obtain a final concentration of 40 nM. Incubations were run for 2 to 3 h in the same thermostatic chambers as the experimental microcosms, and stopped with TCA (50% final concentration). Tubes were then centrifuged for 10 min at 12 000 g. Pellets were rinsed with 1.5 ml of 5% TCA, stirred and centrifuged again. Supernatant was removed and 0.5 ml of scintillation cocktail was added. The vials were counted in a Beckman scintillation counter. For each time point, BP was expressed in μmol C l−1 d−1 of 3H-leucine incorporation, applying a conversion factor of 1.5 kg C mol Leu−1 (Kirchman 1992). Viral production and bacterial losses Samples for determining the viral production (VP) and bacterial mortality due to protists (PMM) and viruses (VMM) in the Arctic community were taken three times (at time zero -1ºC-, in the middle and on the eighth day of the experiment) for all experimental temperatures. For the Fjord community, samples were taken twice: at the time zero (5.5 ºC) and on the eighth day for four experimental temperatures (1.0ºC, 5.5ºC, 8.5ºC and 10.0ºC). So that there would be enough water volume to measure the viral production and viral lysis, 0.5 l subsamples from each experimental duplicate were pooled together. Bacterial mortality due to protists was evaluated following the fluorescent-labeled bacteria (FLB) disappearance method (Sherr et al. 1987, Vázquez-Domínguez et al. 1999). For each measurement of the grazing rates, duplicated 1.5 l sterile bottles were filled with 0.5 l aliquots of seawater from each experimental microcosm, and a third bottle was filled with 0.5 l of grazer-free water as a control. Each duplicate and control was inoculated with FLB at 20% of the natural bacterial concentration. The FLB were prepared with a culture of Brevundimonas diminuta (http://cect.org/index2.html) as described in Vazquez-Dominguez et al. (1999). Bottles were
Chapter 2Warming effect on Arctic microbial communities 84 incubated in the tanks at the same experimental temperature as the corresponding microcosms and in the dark for 48 h. Samples for evaluating the pico/nanoflagellate abundances were taken at the initial time of the grazing assay. For assessing the bacterial and FLB abundances, samples were taken at the beginning and at the end of the grazing assay. Abundances of bacteria, FLB and pico/nanoflagellate were assessed by epifluorescence microscopy as explained above. Natural bacteria were identified by their blue fluorescence when excited with UV radiation, while FLB were identified by their yellow-green fluorescence when excited with blue light. Control bottles showed no decrease in FLB at the end of the incubation time. The grazing rates of bacteria were obtained according to the equations of Salat & Marrasé (1994), based on the specific grazing rate (g) and the specific net growth rate (a), and calculated as follows: g = -(1/t) ln (FLBt/FLB0), a = (1/t) ln (BA/BA0), where t is the incubation time, FLBt is the abundance of FLB at the final time, FLB0 is the abundance of FLB at the initial time, and BAt and BA0 are bacterial abundances at the end and beginning of the incubation time respectively. The net bacterial production (BPN, cells ml-1 d-1) in the incubation bottles was obtained with the equation: BPN = BA0 x (eat-1). Then, the grazing rate (G, cells ml-1 d-1) was calculated as: G = (g/a) x BPN. Finally, protist-mediated mortality of bacteria (PMM, % d-1) was calculated as the percentage of the bacterial standing stock (BSS): PMMBSS = (G x 100)/BA0, We used the virus-reduction approach to determine the viral production and bacterial losses due to phages (Wilhelm et al. 2002). Briefly, 1 liter of seawater from each experimental microcosm was pre-filtered through a 0.8 µm pore sized cellulose filter (Whatman) and then concentrated by a spiral-wound cartridge (0.22 µm pore size, VIVAFlow200) to obtain 50 ml of bacterial concentrate. Virus-free water was collected by filtering 0.5 liter of seawater using a cartridge with a 30 kDa molecular mass cutoff (VIVAFlow200). A mixture of virus-free water (150 ml)
85 Warming effect on Arctic microbial communities Chapter 2 and bacterial concentrate (50 ml) was prepared and distributed into four sterile 50 ml Falcon plastic tubes. Two of the tubes were kept as controls, and mitomycin C (Sigma) was added (1 µg ml-1 final concentration) to the other two tubes as the inducing agent of the lytic cycle. All Falcon tubes were incubated in the tanks at the same temperature as the microcosms and in the dark during 12h. Samples for viral and bacterial abundances were collected at time zero and every 4 h of the incubation, fixed with glutaraldehyde (0.5 % final concentration) and stored as described above. Virus and bacterial numbers from the viral production incubations were counted by flow cytometry. The number of viruses released by bacterial cells (burst size, BS) was estimated from viral production measurements, as in Middelboe & Lyck (2002) and Wells & Deming (2006). The increase in viral abundance during short time intervals (4 h) in viral production incubations was divided by the decrease in bacterial abundance in the same time period. We assumed that the bacterial production and viral decay in this time interval were negligible. We estimated burst sizes from 11 to 82 viruses per bacterium. Viral-mediated mortality (VMM) was determined as previously described in Weinbauer et al. (2002) and Winter et al. (2004). Briefly, an increase in viral abundance in the control falcon tubes represents lytic viral production (VPL), and the difference between the viral increase in the mitomycin C treatments and VPL gives the lysogenic production (VPLG). Because part of the bacteria is lost during the bacteria concentration process, the VPL and VPLG were multiplied by the bacterial correction factor to compare the VP values from different incubations. This factor was calculated by dividing the in situ bacterial concentrations by the T0 bacterial abundances in the VP measurements (Winget et al. 2005) and in our case ranged between 0.75 and 2.15. We then calculated the rate of lysed cells (RLC, cells ml-1 d-1) by dividing the lytic viral production (VPL) by the burst size (BS), as described in Guixa-Boixereu (1997). RLC was used to calculate VMM as a percentage of the bacterial standing stock (VMMBSS): VMMBSS = (RLCGR X 100)/BA0, (% d-1), where BA0 is the initial bacterial abundance in the viral production incubation tube. Assuming that the percentage of BSS losses due to viruses is the same in the falcon tubes and grazing bottles, we used VMMBSS to calculate the rate of lysed bacteria in the grazing bottles (RLCGR, cells ml-1 d-1): RLCGR = (VMMBSS x BAGR)/100, where BAGR is the bacterial abundance in the grazing bottles at time 0.
Chapter 2Warming effect on Arctic microbial communities 86 Statistical analysis The Shapiro-Wilk W-test was used to check the normal distribution of the data, and data were logarithmically transformed prior to analyses if necessary. 1-way ANOVA was used to detect a significant shift between 2 consecutive increasing temperatures for each of the variables studied. This means that the comparisons of all data (for each variable) before and after the shift were statistically significant. These statistical analyses were performed using the Kaleidagraph V4.0 and JMP programs. For each experimental temperature, we calculated the average of each variable ±SE for the whole experimental period in the Arctic and Fjord microcosms. RESULTS Physical and biological variables in Arctic and Fjord waters The 2 environments showed clear differences at the time of sampling (Table 1). The water temperature was lower in the open Arctic waters (−1.2°C) than in the Fjord waters (6.2°C). Pigmented microorganism (flagellates and ciliates, e.g. Myrionecta sp.) abundances, as well as chlorophyll a (chl a) concentrations were higher in the open sea Arctic community. In contrast, most heterotrophic variables, such as abundances of bacteria, viruses and Gyrodinium sp., as well as BP and VP and bacterial losses, were higher in the Fjord waters, while phagotrophic ciliate abundances were similar in the 2 environments (Table 1). In both systems phototrophic pico/ nanoflagellates (PF) were dominated by Micromonas sp. (PF ≤2 μm) and free-living forms of Phaeocystis sp. (PF 2−5 μm) (Table 1), while phagotrophic ciliates, such as Strobilidium sp. and tintinnids, were the most abundant groups in the Arctic and Fjord waters respectively. Henceforth, the experiments carried out with the open Arctic waters will be called Arctic microcosms and those with water from Isfjorden will be called Fjord microcosms. The microbial communities in the open Arctic waters will be called the Arctic community, and those found in the Isfjorden waters will be called the Fjord community. Changes in biological variables during the experiments Chlorophyll a concentration and phototrophic pico/nanoflagellate abundance The minimum and maximum values of the chl a concentration for the Arctic and Fjord microcosms over the entire experiment are shown in Table 2. The average chl a concentrations at each temperature for the entire experimental period are shown in Fig. 3A,B. In the Arctic community, the chl a concentration decreased by 50% between 5.5 and 7°C (Fig. 3A, Table 3).
87 Warming effect on Arctic microbial communities Chapter 2 VARIABLES ARCTIC FJORD Temperature(ºC) 1.2 6.2 Chla(µgl 1 ) 0.6 0.1 PF(10 3 cellsml 1 ) 2.1±0.2 0.8±0.3 Micromonassp.(10 3 cellsml 1 ) 0.4±0.09 0.7±0.2 Phaeocysitissp.(10 3 cellsml 1 ) 1.4±0.2 0.02±0.0 BA(10 5 cellsml 1 ) 3.8±0.6 8.0±0.6 VA(10 5 virusml 1 ) 5.4±0.0 8.4±1.5 HF(10 3 cellsml 1 ) 3.0±0.3 0.4±0.03 HF≤2µm(10 3 cellsml 1 ) 1.0±0.1 0.02±0.00 HF25µm(10 3 cellsml 1 ) 1.2±0.2 0.4±0.05 HF>5µm(10 3 cellsml 1 ) 0.7±0.0 0.03±0.02 Gyrodiniumsp.(10 3 cellsl 1 ) 0.2±0.0 2.7±0.5 Phagotrophicciliate(10 3 cellsl 1 ) 1.4±0.3 1.3±0.0 Myrionectasp.(10 3 cellsl 1 ) 3.1±0.9 0.1±0.0 BP(10 2 µmolCl 1 d 1 ) 3.9±0.0 36.4±1.8 VP L (10 5 virusesml 1 d 1 ) 1.7±1.4 2.3±0.2 VP LG (10 5 virusesml 1 d 1 ) Negligible 3.9±0.0 VMM(%BSS) 10.1±6.8 90.6±11.6 PMM(%BSS) 15.2±4.7 33.0±24.6 1 Table 1. In situ values for both Arctic and Fjord waters. Chl a (chlorophyll a concentration); PF (phototrophic pico/nanoflagellate abundance) and the two main identified genera (Micromonas sp. ≤ 2 µm and Phaeocystis sp. 2-5 µm); BA (bacterial abundance); VA (viral abundance); HF (heterotrophic pico/ nanoflagellate abundance); BP (bacterial production); VPL (lytic viral production); VPLG (lysogenic viral production); PMM (protist-mediated mortality as a % of the bacteria standing stock, BSS); VMM (virusmediated mortality as a % of BSS). There was a slight decrease in the Fjord microcosms, but no significant differences were recorded between the chl a concentration at lower and higher temperatures (Fig. 3B, Table 3). In both experimental microcosms, Micromonas sp. (PF ≤2 μm) was the main contributor to the total PF abundance followed by Phaeocystis sp. (PF 2− 5 μm). In both systems, there was a very low abundance of PF >5 μm. Minimum and maximum values during the experiments are shown in Table 2. Average values of Micromonas sp. in the Arctic microcosms were almost constant at increasing temperatures up to 5.5°C, at which they reached a peak then decreased again at
Chapter 2Warming effect on Arctic microbial communities 88 Figure 3. Average values (±SE) over the experimental period for each temperature treatment in Arctic and Fjord microcosms of (A,B) chl a concentration and (C,D) abundance of phototrophic pico/nanoflagellates (PF) of different size classes. The arrow in (A) indicates the temperature at which a shift in chl a abundance occurred in Arctic samples. higher temperatures (Fig. 3C). In the Fjord microcosms, Micromonas sp. values remained high between 2.5°C and 8°C. We did not detect important changes in PF abundances (Fig. 3D). In the 2 microcosm experiments, freeliving PF (2−5 μm), such as Phaeocystis sp., and PF >5 μm showed lower abundances (Fig. 3C,D) than PF ≤2 μm, such as Micromomas sp. (Fig. 3C,D). Temperature ºC 0246810 12 Chlorophyll a (µg l-1) 0 0.2 0.4 0.6 0.8 1.0 A 0246810 12 0 0.5 1.0 1.5 2.0 Chlorophyll a (µg l -1 ) B ARCTIC FJORD Temperature ºC
89 Warming effect on Arctic microbial communities Chapter 2 ARCTIC FJORD VARIABLES Min Tª(ºC) Day Max Tª(ºC) Day Min Tª(ºC) Day Max Tª(ºC) Day Chla(µgl1) 0.1 10.0 10 1.7 2.5 6 0.2 10.0 1 3.0 5.5 7 PF(cellsml1104) 0.06 2.5 4 2.6 5.5 9 0.05 2.5 0 13.1 2.5 7 BA(cellsml1105) 1.3 2.5 4 9.9 5.5 7 7.4 5.5 0 20.4 8.5 3 VA(virusml1105) 1.3 2.5 8 13.8 2.5 8 3.6 10.0 9 17.6 4.0 9 HF(cellsml1103) 0.5 7.0 5 4.6 5.5 3 0.2 1.0 8 3.3 4.0 5 Gyrodiniumsp.(cellsl1103) 0.02 7.0 8 0.7 10.0 2 0.2 8.5 3 3.2 5.5 0 Totalciliates(cellsl1103) 0.04 7.0 7 12.0 10.0 2 0.04 2.5 7 1.5 5.5 0 BP(µmolCl1d1) 0.03 7.0 3 1.3 7.0 9 0.06 10.0 9 0.7 5.5 8 VPL(105virusesml1d1) Negligible 5.5/7.0 8 9.0 4.0 8 0.6 5.5 8 1.9 10.0 8 VPLG(105virusesml1d1) Negligible 1.0/2.5/4.0/5.5/7.0 0/4/8 3.7 5.5 8 Negligible 1.0/8.5 8 3.9 5.5 0 VMM(%BSSd1) Negligible 5.5/7 8 17.1 7.0 4 0.8 1.0 8 96.6 5.5 0 PMM(%BSSd1) 4.1 4.0 4 35.4 8.5 8 3.3 1.0 8 33.0 5.5 0 1 Table 2. Minimum and maximum values for each variable in the two microcosm experiments. Chl a (chlorophyll a concentration); PF (phototrophic pico/nanoflagellate abundance); BA (bacterial abundance); VA (viral abundance); HF (heterotrophic pico/nanoflagellate abundance); BP (bacterial production); VPL (lytic viral production); VPLG (lysogenic viral production); PMM (protist-mediated mortality as a % of the bacterial standing stock, BSS); VMM (virus-mediated mortality of bacteria as a % of BSS). Heterotrophic microbial communities The minimum and maximum values of microbial (bacteria, viruses and protists) abundances when the Arctic and Fjord microcosms were exposed to different temperatures are shown in Table 2. The average BA (bacterial abundance) in the Arctic microcosms increased significantly by around two-fold at temperatures between 4.0 and 5.5°C (Fig. 4A). In the Fjord microcosms the increase in abundances was smaller than for the Arctic microcosms (ca. 1.5 times) and occurred between 2.5 and 4.0°C (Fig. 4B). For both systems, the variations in abundances observed before and after a certain temperature were statistically significant (Table 3). The mean VA (viral abundance) for the Arctic community increased be tween 4.0 and 5.5°C (Table 3, Fig. 3C) and followed a similar trend to that of BA (Fig. 4A,C). In the Fjord community the average VA dropped significantly between 5.5 and 8.5°C (Table 3, Fig. 4D). Changes in the average HF (heterotrophic pico/nanoflagellates) abundances along the temperature gradients for both microcosm experiments did not show clear patterns (Fig. 4E,F). However, when the different HF size classes in the Arctic microcosms were considered it was found that HF ≤2 μm significantly decreased between 4.0 and 5.5°C, while HF >5 μm increased significantly between 7.0 and 8.5°C (Fig. 4E, Table 3). This was not observed in the Fjord microcosms (Fig. 4F, Table 3).
Chapter 2Warming effect on Arctic microbial communities 90 Table 3. Temperature at which a significant shift was detected according to an ANOVA, where N is the sample size; F is the F-test of the variance, and P is the level of significance. Chl a (chlorophyll a concentration); PF (phototrophic pico/nanoflagellate abundance); BA (bacterial abundance); VA (viral abundance); HF (heterotrophic pico/nanoflagellate abundance); BP (bacterial production); VPL (lytic viral production); VPLG (lysogenic viral production); PMM (protist-mediated mortality as a % of the bacterial standing stock, BSS); VMM (virus-mediated mortality of bacteria as a % of BSS). Ciliates and the dinoflagellate Gyrodinium sp. did not show clear responses to in creasing temperature in either system. Thus, in the Arctic microcosms, we detected that the pigmented Myrionecta sp. was the most abundant ciliate and had a tendency to decrease as the temperature increased (Fig. 4G), while we did not observe changes with temperature for the phagotrophic ciliates Strobilidium sp., for the so-called ‘other ciliates’ (comprising Strombidium sp., Euplotes sp., Laboea sp., tintinnids, scuticociliates, Askenasia sp. and Tontonia sp.) or for the dinoflagellate Gyrodinium sp. (Table 3). In the Fjord microcosms, we found that Myrionecta sp., and tintinnids and the dinoflagellate Gyrodinium sp. showed lower and higher average values, respectively, than in the Arctic microcosms (Fig. 4G,H), and they did not show any response to warming (Table 3). However, in the Fjord Strobilidium sp. was not always present, and when averaging the abundance for each temperature together with the other identified ciliates we observed that they decreased significantly at the highest temperatures (Fig. 4H, Table 3). ARCTIC FJORD ANOVA ANOVA VARIABLES NFPTª(ºC) NFPTª(ºC) Chla 169 10.3 0.001 5.57.0 ns PF ns ns BA 112 22.0 <0.0001 4.05.5 109 4711.0 <0.0001 2.54.0 VA 127 5.8 0.01 4.05.5 95 7.2 0.009 5.58.5 HF≤2µm 43 7.6 0.01 4.05.5 ns HF=25µm ns ns HF≥5µm 54 5.6 0.02 7.08.5 ns Totalciliates ns Otherciliates ns 52 22.1 0.001 5.58.5 BP 71 13.8 0.0004 4.05.5 62 3.8 0.05 4.05.5 VPL ns ns VPLG ns ns PMMBSS 41 4.7 0.03 5.57.0 ns VMMBSS ns ns 1
97 Warming effect on Arctic microbial communities Chapter 2 by psychrotolerant bacteria, which grow better at higher temperatures (Morita 1975). Warmer seawater might result in a change in the bacteria community and this could be the reason why we found an increase in BA and BP above 5.5°C (Figs. 4A,B & 5A,B). These results are in agreement with Krause et al. (1993), who observed shifts from psychrophilic to psychrotolerant bacterial communities during the replacement of summer water in the Weddell Sea, as well as a decrease in nutrients and the chl a concentration. Viral lytic and lysogenic production Bacterial and viral abundances in aquatic systems are usually positively correlated, which indicates that they are closely linked, and presumably the environmental parameters that influence bacterial assemblages could also affect the viral community (Wommack & Colwell 2000, Weinbauer 2004, Pradeep Ram & Sime-Ngando 2010, Danovaro et al. 2011). In the Arctic microcosms, VA (viral abundance) followed BA (bacterial abundance), while in the Fjord microcosms VA decreased above the temperature of 5.5°C. This decay could be due to different causes, such as adsorption into host walls or into particles, as well as ingestion by pico/ nanoflagellates (Weinbauer 2004). Also, it would also have to take into account lysogeny. Lysogenic bacteria have prophages (phage nucleic acid) incorporated into their genomes. When the lysogenic host is stressed (e.g. by environmental shifts) the prophage is induced and the lytic cycle activated, producing new viral infective particles. High lysogeny values were found in Antarctic lakes during winter (Lisle & Priscu 2004) and there are a variety of reports for the Arctic that found significant lysogeny during summer (Boras et al. 2010), but in other cases it was not detected at all (Säwström et al. 2007b). Furthermore, at different polar sites, lysogeny showed seasonal variations, with high rates in winter and spring (Laybourn-Parry et al. 2007, Säwström et al. 2007a). We therefore expected to find low VPL (viral lytic production) and high VPLG (lysogenic viral production) at the low experimental temperatures when bacterial abundance was low (Fig. 4A,B). However, our results showed the opposite trend in both systems (Fig. 5C,D). In the Arctic microcosms, when BA increased with temperature (around 5.5°C) and presumably a shift in the bacterial community occurred, VPL decreased significantly, showing similar values as VPLG (Fig. 5C). In the Fjord microcosms, VPL was also important at low experimental temperatures (1.0°C), while VPLG constituted 65% of the total viral production at the in situ temperature (~6.0°C). A plausible explanation is that between 5.5 and 7.0°C, viruses were ‘comfortably installed’ inside the active hosts as prophages, but when the temperature increased (to 8.5 and 10.0°C), the new warming conditions acted as an environmental stress factor, and the lysogenic cycle reverted to the lytic
Chapter 2Warming effect on Arctic microbial communities 98 cycle. Moreover, the stimulation of bacterial growth at higher temperatures could be related to an increase in the nutrient concentrations, and therefore in VP (Pradeep Ram & Sime-Ngando 2008, 2010). In summary, it seems that at higher temperatures than ~7.0°C the lysogenic cycle reverts to a lytic cycle. Bacterial losses In the Arctic microcosms, protistan grazing decreased progressively up to 5.5°C (Fig. 6E) but between 5.0 and 7.0°C there was a shift and the bacterial grazing rates increased, corresponding to high values of BA, BP and lysogeny (Figs. 4A & 5A,C). Fluctuations in bacterivory with temperature were not reflected in changes in the total HF abundances. Nevertheless, we observed differences in the dynamics of the different HF size classes (Fig. 4E). Above 5.5°C, HF ≤2 μm decreased, and around 7.0°C, HF >5 μm increased (Fig. 4E). In the Fjord microcosms, there was a gradual increase in bacterivory as the temperature increased; however, like in the Arctic microcosms, this did not correspond to an increase in the total HF at different temperatures. Although HF are considered to be the main bacterivore microorganisms (Sherr & Sherr 2002), they also ingest prey larger than bacteria to maintain their biomass and growth (Vaqué et al. 2008), and thus trophic cascades could occur (Vaqué et al. 2004). For instance, HF >5 μm could feed on bacteria, on HF ≤5 μm, and on other small prey such as Micromonas sp., which were very abundant in our experiments as in natural Arctic waters (Lovejoy et al. 2007). In addition, phagotrophic ciliates and large flagellates (i.e. Gyrodinium sp.) could prey on pico/nanoflagellates (HF, PF), controlling their abundances and shaping the community (size and composition). In polar systems, bacterivory appears to be an important factor in controlling the bacterial abundance during most of the year (Anderson & Rivkin 2001, Boras et al. 2010). Furthermore, several authors have also found that grazing rates increase with temperature in the Antarctic (Vaqué et al. 2009) and in cold waters (Newfoundland, Choi & Peters 1992). Their results are in agreement with our bacterivory responses to warming in Arctic and Fjord waters. However, in Arctic waters, we found that the effect of temperature on viral lysis was not large enough for it to surpass bacterivory, while in the Fjord microcosms at the in situ temperature; viral-induced mortality was significantly higher than mortality due to protists (Fig. 6B,D). We think that it is necessary to carry out more re - search focused on different sources of bacterial mortality, particularly due to viruses, in different polar areas, at different seasons, as in situ as well as microcosm warming experiments. The results could be used to test and understand the function of viruses in the microbial shunt in these cold marine systems in order to generate predictive models of the effects of future global warming. Indeed, viruses could be
99 Warming effect on Arctic microbial communities Chapter 2 a key biotic component influencing the feedback of climate change in the oceans be cause they supply dissolved nutrients in the euphotic zone, contributing to the recycled primary production and/or to the increase in CO2 due to the respiration of hetero trophic microbes (Danovaro et al. 2011). CONCLUSIONS The results of this experimental study show that heterotrophic and phototrophic microbial communities responded differentially to warming conditions in 2 contrasting Arctic systems. After a gradual increase in temperature we observed a significant increase in the activities and biomasses of heterotrophic microorganisms, and a decrease in biomasses of phytoplankton. Under warmer conditions, bacteria were mainly channeled to higher trophic levels via HF, while viral lysis contributed to increasing the pool of dissolved organic matter in the water column. All the observed changes were larger in the open Arctic waters than in the Fjord waters, with different initial microbial communities and lower and higher temperatures, respectively. In conclusion, warming triggers shifts that would favor heterotrophic communities, which could have a large impact on carbon and nutrient cycling and carbon storage in the Arctic Ocean. ACKNOWLEDGMENTS This study was funded by the Project Arctic Tipping Points (ATP, contract #226248) in the FP7 program of the European Union. E.L. was supported by a grant from the Spanish Ministry of Science and Innovation. We thank R. Gutiérrez and R. Martinez for sampling assistance, the crew of the RV ‘Jan Mayen’ for helping with sampling, E. Halvorsen and M. Daase for logistic support, and The University Centre in Svalbard, UNIS, for hospitality.
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113 Pseudoalteromonas phage-host interactions Chapter 3 used to phage isolation; while only partial 16S rRNA gene was sequenced to the bacterial strains used as hosts at host-range assays. Bacterial DNA was extracted using heat shock; a single bacterial colony was resuspended in 100 μl of miliQ water and warm up to 95ºC during 10 min. The primers for bacterial 16S rRNA gene amplification were 27f (AGAGTTTGATCCTGGCTCAG) and 1492r (GGTTACCTTGTTACGACTT). The thermal program consisted of 1 cycle of 5min at 94ºC, 30 cycles of 1 min at 94ºC, 1min at 55ºC and 2 min at 72ºC, and 1 cycle of 10 min of extension at 72ºC. Completed 16S rRNA genes were sequenced bidirectionally using primers 27f, 358f and 1492r, and 358f-907rM for partial 16S rRNA. Sequences were aligned using the program ClustalW (default parameters) (Larkin et al., 2007). Maximum likelihood trees were built using the Tamura Nei model (Tamura and Nei, 1993) with boostrap analysis (500 replicates) using MEGA version 5.1 (Tamura et al., 2011). All the sequences were submitted to a standard nucleotide-nucleotide BLAST (blastn) search on the NCBI Genbank database for the most closely related organism. Accession numbers of those 16S rRNA sequences are XXX to YYYY. Viral DNA purification and extraction Culture of the different bacterial strains was combined with phages in 20 plaque assays giving confluent lysis. Once plaques appeared, phages were eluted with MSM buffer. The phage lysate was 0.22 μm filtered. Viral DNA was obtained using a Lambda Wizard DNA kit (Promega Corp. Madison, WI) directly on phage lysates (Henn et al., 2010; Sullivan et al., 2010). NaCl 3M was added to 50 ml of phage lysate. The mixture was incubated 1 h at 4°C in the dark followed by centrifugation at 5,000 g, 15 min. The pellet was discarded and polyethylene glycol (PEG 8000 10%) was added to the supernatant. After an incubation of 1 h at 4°C in the dark, it was centrifuged (5,000 g, 15 min). The supernatant was discarded and the pellet was resuspended with phage buffer, MSM. One ml of Purification Resin (Promega, product A7181 Madison WI) was then added and mixed gently by inverting the tube. The mixture was loaded onto a mini-column (Promega, product A7211 Madison WI) through a 5 ml syringe attached to the column, pushing the mixture through with the syringe plunger. The column was then washed with 2 ml 80% isopropanol, the syringe removed and the minicolumn placed into a 1.5 ml Eppendorf tube and centrifuged (10,000 g, 2 min, room temperature) to remove any remaining liquid. Phage DNA was then eluted from the column by adding 100 ml TE buffer heated to 80°C, then placing the column into a 1.5 ml Eppendorf tube and immediately centrifuging (10,000g, 30 sec, room temperature) to recover the DNA. Phage DNA was stored at -20°C for long-term storage.
Chapter 3Pseudoalteromonas phage-host interactions 114 Viral morphologic characterization by Transmission Electron Microscopy (TEM) High-titer phage stocks (lysates) from the selected phages were prepared for TEM (Borsheim et al., 1990; Weinbauer et al., 2002). Five microliters of the viral stock was spotted for 1 min onto fresh glow-discharged Formvard-coated carbon grids. Adsorbed phages in the grid were negatively stained by adding 5 drops of uranyl acetate solution (2%, final conc) for 10 s each time. Excess stain was drawn off with filter paper and the grid air-dried. The grids were observed in a Jeol 1010 (Jeol, Japan) transmission electron microscope operating at 80 kv equipped with a CCD camera camera SIS Megaview III and AnalySIS software. Host-range assays To determine phage host range and bacterial susceptibility to specific phages, a cross infectivity test was done with 89 bacterial strains used as hosts. Plaque assays were performed using strains of Pseudoalteromonas spp.; Alteromonas, spp.; Marinobacterium, spp. Vibrio spp. and Bacteroidetes, Rhodobacterales and Sphingomonadales clades. Two different 10-fold viral dilutions from phages stocks were used in order to distinguish between a clear lysis caused by plaque formation and inhibition of the bacterial lawn. After incubation overnight in the dark, plaque formation was evaluated. A cluster analysis was carried out using PRIMER6 software (Clarke and Gorley, 2001). The binary matrix was transformed into a similarity matrix using Bray-Curtis measure. Dendrograms were generated using the group average method after the SIMPROF test was performed to evaluate the significance of the clusters (p < 0.05). Genomic profiles of isolated viruses and hosts by Randomly Amplified Polymorphic DNA (RAPD) polymerase chain reaction (PCR) In order to distinguish among the isolated viruses and bacterial host at higher resolution and to infer the genetic relatedness, we used the genomic profile approach of RAPD-PCR technique (Winget and Wommack, 2008). First, viral DNA from isolated phages previously purified and extracted was used. The decamer primer CRA-23 (5’ -GCG ATC CCC A3’) was used acting as both, forward and reverse primer. PCR conditions were as follows: 1 cycle of 10 min at 94°C, 30 cycles of 3 min at 35°C, 1min at 72°C and 30 s at 94°C, 1 cycle of 3 min at 35°C and 1 cycle of 10 min of extension at 72°C. RAPD-PCR products were separated by gel electrophoresis on 1% agarose gel in 0.5% TAE run at 90V for 2h and visualized by SYBR SAFE (10.000X, Invitrogen). All isolates of Pseudoalteromonas spp. used as hosts were also analyzed by RAPD-PCR. For bacterial DNA, the primer OPC-11 (5’ –AAAGCTGCGG3’) was used acting as both, forward and reverse primer for RAPD-PCR. PCR conditions were as follows: 1 cycle of 2 min at 94°C, 2 cycles of
115 Pseudoalteromonas phage-host interactions Chapter 3 30 s at 94°C, 30 s at 36°C and 2 min at 72°C, 30 cycles of 20 s at 94°C, 15 s at 36°C, 15 s at 45°C and 1.5 min at 72°C and 1 cycle of 10 min of extension at 72°C. RAPD-PCR products were separated by gel electrophoresis on 1% agarose gel in 0.5% TAE run at 90V for 2h and visualized by SYBR SAFE (10,000X, Invitrogen). For both, viruses and bacterial similarity of resulting genomic banding patterns was assessed by a group-averaged cluster analysis based on a Bray-Curtis dissimilarity matrix. The SIMPROF permutation procedure was used to test the significance of the clusters (p < 0.05). The software tool PRIMER6 (Plymouth Routines in Multivariate Ecological Research) was used to calculate these parameters (Clarke & Warwick, 2001). Networks statistics Host-phage interactions were represented as a bipartite network between all tested phages and all Pseudoalteromonas and Alteromonas bacterial strains. The degree of modularity (whether interactions tend to occur among distinct groups of phages and hosts) and nestedness (whether is there a hierarchy of susceptibility to infection, and a measure of to what extent phage host ranges are subsets one of another) of the network were calculated using the MATLAB package BiMat (Flores et al., 2013). To estimate modularity, the Adaptive BRIM (Bipartite Recursively Induced Modules) algorithm (Barber, 2007) was used, while nestedness was calculated with the Nestedness Temperature Calculator algorithm (NTC; (Atmar and Patterson, 1993)) as implemented in BiMat. Both metrics were statistically tested against a null model of 10000 matrices with the same number of interactions randomly positioned (Bernoulli matrix). RESULTS Bacterial and phage isolation A total of 52 Pseudoalteromonas spp. strains isolated from Blanes Bay Microbial Observatory station (BBMO), a surface coastal site in the NW Mediterranean Sea, during winter of 2001 and 2009 were used as potential phage host (Table 1.SM). Phages were isolated from different Pseudoalteromonas spp. strains that were obtained from the same marine site during winter of 2009. Before proceeding with phage enrichment cultures, simple direct plating of the same marine water sample was performed via plaque assay but phages infecting Pseudoalteromonas spp. bacterial isolates were not sufficiently abundant to isolate them without enrichment. Thus, phages were obtained using liquid enrichment before perform the plaque assays. Nineteen of the 52 Pseudoalteromonas bacterial strains tested were positive in plaque formation; a set of 7 of the
Chapter 3Pseudoalteromonas phage-host interactions 116 0.1 DB16 DB15 DB17 DB18 DB58 DB59 DB62 DB65 DB67 DB21 DB22 ZOCONB9 ALSKE5D ZOCONB10 DB50 DB56 DB53 DB54 DB12 DB79 ZOCONA7 DB89 ZOCONA5 DB32 DB72 DB8 DB9 DB24 DB26 DB1 DB3 ZOCONA8 DB84 DB93 DB94 ZOCONH21 DB92 ALMIC1A DB77 DB7 DB55 DB49 DB71 DB23 DB10 DB88 DB14 DB48 DB30 DB44 DB25 DB28 DB29 DB41 DB42 DB43 DB46 MED306 MED271 MED290 MED107 MED113 MED111 MED169 MED517 MED275 MED292 100 99 82 98 * 2091.6 1816.3 1540.9 1340.6 1891.3 1340.6 689.8 2367.0 2241.8 1866.3 1616.0 1565.9 1415.7 1891.3 1641.0 1265.5 514.6 1140.4 539.6 1472.6 1054.3 1012.5 2309.0 1765.3 1723.5 1221.6 845.2 1514.4 1305.3 1221.6 2183.5 1388.9 1138.9 343.4 Figure 1. 16S rRNA phylogeny of the Pseudoalteromonas and Alteromonas strains used as hosts in this study. In green are labeled the Pseudoalteromonas strains and in purple the Alteromonas strains. Some of the bacterial strains have inserted the banding pattern gel image obtained by RAPD-PCR to compare the profile between strains with nearly identical 16S rRNA gene.
117 Pseudoalteromonas phage-host interactions Chapter 3 positive plates was randomly selected for purification and characterization. For the same water sample different plaques morphologies were detected in the positives plates for a single bacterial host, they were purified by three rounds of infection and a total of 25 phages were isolated from 7 Pseudoalteromonas spp. strains. Sequencing of the 16S rRNA gene and inferring genomic patterns of bacterial hosts A total of 89 bacterial strains were used in tis study; 52 belonged to the Pseudoalteromonas genera and 15 to Alteromonas (Table 1.SM), which represented the two genera with positive infection of our isolated phages. The 16S rRNA gene was sequenced completely for the 7 Pseudoalteromonas spp. strains used to phage isolation; while only partial 16S rRNA gene was sequenced of the rest of bacterial strains used as hosts for the host-range test (Table 1.SM, Fig. 1). The complet sequencing of the 16S rRNA gene showed that six of the seven hosts used to isolate the phages belonged to the same Pseudoalteromonas sp. type strain (Pseudoalteromonas sp. RHS-str.402), while the other isolation host strain was Pseudoalteromonas sp. QC44. The Pseudoalteromonas strains showed a 99.9% identity in their 16S rRNA gene, while the Alteromonas strains showed an 80% identity (Fig. 1). Due to the high sequence similarities of 16S rRNA genes between host strains, the genomic profile patterns was also determined using random amplification of polymorphic DNA (RAPD-PCR). This assay yielded reproducible fingerprints from the whole genome that were converted to a similarity dendrogram (Fig. 1A.SM and Fig. 1B.SM). Overall, the analyses of the Pseudoalteromonas spp. genomic banding patterns indicated that all the clusters were no significant at the 95% level when analyzed using the SIMPROF test in PRIMER 6 (data not shown) although discrete clusters could be identified. In fact, we were able to detect differences among strains that according the 16S rRNA gene were identical (Fig. 1). For instance, Pseudoalteromonas strains DB56 and ZOCONA5 were grouped in the same cluster according to the 16S rRNA gene tree while displayed different banding patterns by RAPD-PCR (Fig. 1). Similarly finding were also observed for,the Alteromonas strains DB41 and DB42 (Fig. 1). Interestingly, the only Alteromonas strain that all isolated phages infected (MED111) was clustered in the same group than the strains MED169, MED517, MED275 and MED292 according to 16S rRNA gene although the RAPD-PCR showed a different genomic pattern (Fig. 1). Finally, phages B8b and C5a were isolated from DB23 and DB32 bacterial strains respectively. These phages showed similar behavior in their host range patterns, but the bacterial strains presented also different RAPD-PCR patterns despite being very close according to 16S rRNA gene (Fig. 1).
Chapter 3Pseudoalteromonas phage-host interactions 118 Figure 2. Dendogram showing the cluster analysis of the genomic RAPD-PCR band patterns obtained from the isolated Pseudoalteromonas phages. For each isolated phage is indicated the isolation host strain, viral family according to the TEM morphology, number of infections obtained in the host range analysis and the head and tail dimensions. Clusters labeled in red were not significant according the SIMPROF permutation. Pseudoalteromonas spp. phages characterization by genomic profiling and morphology The genomic profiling patterns of the 25 isolated phages were also examined by RAPDPCR analysis (Fig. 2) to distinguish among the closely related phages and infer their clustering association based on their genomic profiling comparison. This RAPD-PCR approach allows getting a genomic fingerprint of each virus and does not require a priori genetic information. Five decamer primers were tested on viral DNA (OPA-6, OPA-9, OPA-13, CRA-22 and CRA-23) (Winget and Wommack, 2008) but only the primer CRA-23 yielded enough number of bands to
119 Pseudoalteromonas phage-host interactions Chapter 3 BC E F GH I J K LMNO PQR AD Figure 3. Transmission electron micrographs showing negatively stained the 18 Pseudoalteromonas phages isolated. A: phage G3/2, B: phage 306a, C: phage B8b, D: phage G3/4, E: phage 306b, F: phage 271/3, G: phage 271/4, H: phage 271/2, I: phage H1/4, J: phage 306c, K: phage G3/1, L: phage H1/1, M: phage G3/5, N: phage H1/3, O: phage 271/1, P: phage G3/3, Q: phage C5a and R: phage H3/3.
Chapter 3Pseudoalteromonas phage-host interactions 120 discriminate with the maxima accuracy the differences between the isolated phages. The RAPDPCR assay yielded reproducible fingerprints that were converted to a clustering dendrogram (Fig. 2). In general, the genetic profile analyses of the Pseudoalteromonas phages grouped all isolated phages into 7 clusters (Fig. 2). Although we could not distinguish separation according to morphology because almost all the phages belonged to Myoviridae family, it was interesting to find the siphovirus (B8b) grouped in the same cluster than myoviruses (see cluster 1, Fig. 2). Phage C5a and phage H3/3 were clustered in independent single groups despite belonging both to Myoviridae (see clusters 6 and 7; Fig. 2). Some of the phages displayed identical patterns of genomic profiles (phage C5a and C5b, phage H1/2, H1/3, G3/2, G3/6 and H3/1, phage B8a and B8b), and although we are aware that identical RAPD profile are not indicative that identical genome structure phages, we chose only one representative phages of such groups that shared a 100% of identity for further morphological characterization. Hence, the rest of the characterization was done for 18 phages (B8b, C5a, 306a, 306b, 306c, H1/1, H1/3, H1/4, G3/1, G3/2, G3/3, G3/4, G3/5, H3/3, 271/1, 271/2, 271/3 and 271/4). The phenotypic morphology and characterization of the 18 bacteriophages was examined by transmission electron microscopy, TEM (Fig. 3). All of them belonged to the order Caudovirales (Van Regenmortel, 2000). Seventeen of the 18 bacteriophages belonged to the family Myoviridae, having icosahedral heads and long and contractile tails. The head diameters ranged from 58 to 98 x 109 nm and the tail lengths from 106 to 132 nm (Fig. 3). Only one of the isolated bacteriophages belonged to the family Siphoviridae (phage B8b), which contains phages that have icosahedral heads and long flexible tails. There were no phages belonging to the family Podoviridae. All the 17 myoviruses were isolated from the Pseudoaltermonas sp. RHS-str.402 while the only siphovirus (B8b) was isolated from the Pseudoalteromonas sp. QC44 strain (Table 1.SM). Host range analyses from obtained Pseudoalteromonas spp. phages In order to examine the host range of the isolated phages, infectivity was tested on 52 Pseudoalteromonas spp., 15 Alteromonas spp., 3 Marinobacterium, spp., 8 Vibrio spp. strains, 5 Bacteroidetes, 3 Rhodobacterales and 3 Sphingomonadales (Table 1.SM). All the tested bacterial strains were isolated from the same coastal site (BBMO) than the phages. No infections were observed on Vibrio spp., Bacteroidetes and Alphaproteobacterium strains (Table 1.SM). Within Pseudoalteromonas spp. strains, phages showed a large variability in infectivity (Fig. 4). Phage B8b, the only siphovirus isolated, and C5a showed a similar narrow host range, infecting only 3 and 4 respectively Pseudoalteromonas spp. strains besides they were isolated from different bacterial strains. The remaining 16 of the isolated phages showed a broad cross-infectivity pattern (Fig.
121 Pseudoalteromonas phage-host interactions Chapter 3 B8b C5a 271/1 271/2 H1/4 271/4 271/3 306a 306b 306c H1/3 H1/1 H3/3 G3/2 G3/5 G3/1 G3/3 G3/4 DB16 DB15 DB17 DB18 DB58 DB59 DB62 DB65 DB67 DB21 DB22 ZOCONB9 ALSKE5D ZOCONB10 DB50 DB56 DB53 DB54 DB12 DB79 ZOCONA7 DB89 ZOCONA5 DB32 DB72 DB8 DB9 DB24 DB26 DB1 DB3 ZOCONA8 DB84 DB93 DB94 ZOCONH12 DB92 ALMIC1A DB77 DB7 DB55 DB49 DB71 DB23 DB10 DB88 DB14 DB48 DB30 DB44 DB25 DB28 DB29 DB41 DB42 DB43 DB46 M306 M271 M290 M107 M113 M111 M169 M517 M275 M292 infection no infection DB16 DB15 DB17 DB18 DB58 DB59 DB62 DB65 DB67 DB21 DB22 ZOCONB9 ALSKE5D ZOCONB10 DB50 DB56 DB53 DB54 DB12 DB79 ZOCONA7 DB89 ZOCONA5 DB32 DB72 DB8 DB9 DB24 DB26 DB1 DB3 ZOCONA8 DB84 DB93 DB94 ZOCONH12 DB92 ALMIC1A DB77 DB7 DB55 DB49 DB71 DB23 DB10 DB88 DB14 DB48 DB30 DB44 DB25 DB28 DB29 DB41 DB42 DB43 DB46 M306 M271 M290 M107 M113 M111 M169 M517 M275 M292 0.1 Alteromonas strains Alteromonas strains Pseudoalteromonas strains Pseudoalteromonas strains DB23 MED271 MED271 MED306 DB77DB89DB84DB84DB32Isolated from: Pseudoalteromonas sp. RHS-str.402Pseudoalteromonas sp. QC44 82 99 98 100 Figure 4. Heatmap displaying the infection patterns of the isolated phages. In the left of the figure, bacterial hosts are organized according to the 16S rRNA gene phylogeny and at the top phages are distributed according to the cluster obtained by the RAPD-PCR banding profiles. Black rectangles indicated positive infections while the gray rectangles represented no infection. 4). For example, phage 271/2 was able to infect 22 of the 52 Pseudoalteromonas spp. tested and phages 271/3, 271/1 and 271/4, 21 (Fig.4). All the broadest host ranges belonged to Myoviridae family. Phage C5a was the only myovirus that presented a narrow host range. Out of 8 Alteromonas spp. strains tested, only one, M111, was susceptible to infection and the most surprising was that all the phages were able to infect it (Fig. 4). Some of the isolated phages showed an identical host-range pattern, it is the case for the phage G3/3 and G3/4; phage H1/3 and phage H1/1; phage 306a and phage 306b and finally phage 271/4 and 271/3 (Fig. 4). All of them belonged to
Chapter 3Pseudoalteromonas phage-host interactions 122 Myoviridae family and they displayed different RAPD-PCR patterns, and therefore they were not considered identical phages. Pseudoalteromonas spp. phages host-range versus genomic patterns Based on the matrix of the host ranges (lysis/no lysis) and on the banding patterns obtained from the RAPD-PCR analysis (absence/presence of bands) both dendrograms were generated and compared (Fig. 5). The phages genomic fingerprints separated the isolated phages in 7 clusters while the host range tree divided the phages in 4 groups. Both dendrograms showed significantly different clusters, except for phages 271/3, 271/4, 271/1, 271/2 and phage 306a, 306b and 306c because they were the only ones that were grouped together in both dendrograms (Fig. 5). Thus, Pseudoalteromonas phages grouped by host range were not genetically similar. Phage C5a and B8b belonged to different morphology family but they were clustered together by host-range because presented a very similar capacity of infectivity; however their genomic profiling is very different. Hence, comparison between host-range analyses and phage genomic profiles were not coherent for all cases reflecting that many phages that shared identical host range exhibited distinct genomic pattern. Probability of infection To investigate further the correlation between the infection dynamics and the genetic distant of the bacterial strains, the probability of infection was measured for each isolated phage based on the phylogenetic resolution provided by the 16S rRNA gene or by the RAPD-PCR genomic fingerprinting build on the whole bacterial genome (Fig. 6). For both markers and as expected, the probability of infection decreased when the genetic distance increased, however, considerable differences between 16S rRNA gene and RAPD-based methods were detected. Some of the isolated phages showed a significantly negative correlation between the probability of infection and the genetic distance based on 16S rRNA gene (p value ranged from 0.087 and 0.985) but all the phages showed lower p values when the capacity of infection was measured in accordance with the RAPD-PCR patterns. The p values ranged from 2 x 10-4 to 0.04. Our results also showed that the RAPD distance contribute to explain an average of 28% of the probability of infection on the bacterial strains, while the 16S rRNA gene only explained an average of 1% (Fig. 6).
129 Pseudoalteromonas phage-host interactions Chapter 3 Evolutionary context of the phage-host interactions patterns The phage-host infection network taking into account the Pseudoalteromonas and Alteromonas strains fits with previous models on nested pattern to explain the structure of such phage-host interactions (Fig. 7). These results are in agreement with the previous findings of Flores et al. (2011). These authors analyzed 38 studies of phage-host infection networks at narrow taxonomic scale and they found that the majority of the studies were nested matrices. Moreover, the majority of the studies analyzed until now were with closely related bacterial strains. In this study, we determine the infection network also with all the bacterial strains tested in the host range analysis, therefore at larger phylogenetic scale. More complex patterns of infections could increase the compartmentalization and therefore modularity would be the expected pattern in these conditions. In the same work, Flores and co-authors (2011) already suggested that modularity should be expected in studies at larger biogeography or phylogenetic scales. In fact, they recently published the analysis of a data set with largest phage bacteria infection network (215 phages with 286 hosts) and they found a modularity pattern (Flores et al., 2013). However, we found that the infection network was also nested between Pseudoalteromonas and Alteromonas host with no significance for modularity (Fig. 7 and Fig. 3.SM). In an evolutionary context, nestedness can be explained as the result of co-evolutionary processes that lead to specialization (Flores et al., 2011). The most specialist phages infect those hosts that are more susceptible to infection rather than infecting those hosts that are more resistant to infection. This model results would translate into bacteria evolving to increase phage resistance and phages evolving to broader host ranges. Understanding how such co-evolutionary processes uncovered by the phage-interaction network would have an effect on environmental bacterial dynamics is crucial. However, these both models are idealizations and in natural environments might be intermediate mechanisms. For instance, it is possible that phages evolve the ability to infect new hosts and partially lose the ability to infect existing hosts (Agrawal and Lively, 2003). Thus, the most probable in marine natural systems is that phage-host networks do not have a perfectly nested or modular structure (Forde, 2008).
Chapter 3Pseudoalteromonas phage-host interactions 130 ACKNOWLEDGMENTS We thank the members of the department of transmission electron microscopy in the scientific and technological center at the University of Barcelona. We also are grateful to Matt Sullivan and Karin Holmfeldt for their advices in this work. This work has been supported by the Spanish projects MICROVIS (CTM2007-62140/MAR), PANGENOMICS (CGL2011-26848/BOS) and FLAME (CGL2010-16304). Financial support was provided by a Ph.D. fellowship from the Spanish government to E. Lara.
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135 Pseudoalteromonas phage-host interactions Chapter 3 SUPPLEMENTAL MATERIAL A B Figure 1.SM. RAPD-PCR banding pattern obtained from (A) the Pseudoalteromonas hosts and (B) Alteromonas hosts. In figure A: Line 1, ZOCONB10; Line 2, MED290; Line 3, DB3; Line 4, DB17; Line 5, DB65; Line 6, DB67; Line 7, DB26; Line 8, DB92; Line 9, ZOCONA5; Line 10, ZOCONB9; Line 11, DB93; Line 12, DB94; Line 13, MED107; Line 14, MED271; Line 15, DB22; Line 16, DB48; Line 17, ZOCONA8; Line 18, ZOCONA7; Line 19, ALMIC1A; Line 20, ALSKE5D; Line 21, DB15; Line 22, DB16; Line 23, DB1; Line 24, DB21; Line 25, DB84; Line 26, DB77; Line 27, DB72; Line 28, DB58; Line 29, DB59; Line 30, DB54; Line 31, DB50; Line 32, DB62; Line 33, DB23; Line 34, DB12; Line 35, DB18; Line 36, DB7; Line 37, DB14; Line 38, DB79; Line 39, DB55; Line 40, DB49; Line 41, DB8; Line 42, DB24; Line 43, DB89; Line 44, DB88; Line 45, DB53; Line 46, DB56; Line 47, DB71; Line 48, DB10; Line 49, DB9; Line 50, MED306; Line 51 DB32 and Line 52, ZOCONH12. In figure B: Line 1, DB25; Line 2, MED292; Line 3, MED517; Line 4, MED169; Line 5, MED275; Line 6, MED111; Line 7; MED113; Line 8, MED517; Line 9, DB29; Line 10, DB30; Line 11, DB41; Line 12, DB42; Line 13, DB43; Line 14, DB44 and Line 15, DB46. Ladder: EasyLadder I (2000bp – 1000 bp – 500 bp – 250bp – 100bp, Bioline).
Chapter 3Pseudoalteromonas phage-host interactions 136 N= 0.886 Z N = 14.13 Figure 2. SM. Significant nested matrix between isolated phages and all the hosts used in the host range analysis. Red line represents the isocline.
137 Pseudoalteromonas phage-host interactions Chapter 3 Taxon Strain Isolation year Isolation location % Similaruty 16s rRNA gene (GenBAnk accession no.) Pseudoalteromonas sp. RHS-str.402 MED306 2001 BBMO 100.0 ( HE586873) Pseudoalteromonas sp. RHS-str.402 DB89 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB32 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB77 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 MED271 2001 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB84 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 M107 2001 BBMO 100.0 (FR821205) Pseudoalteromonas sp. RHS-str.402 DB21 2009 BBMO 100.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB16 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB53 2009 BBMO 100.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB79 2009 BBMO 100.0 (HE586873) Pseudoalteromonas sp. QC44 DB23 2009 BBMO 99.0 (JN384138) Pseudoalteromonas sp. QC44 ZOCONA7 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB62 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 ZOCONB9 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 ZOCONA8 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB50 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB54 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB56 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB67 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB59 2009 BBMO 99.0 (JN384138) Pseudoalteromonas sp. QC44 DB58 2009 BBMO 99.0 (JN384138) Pseudoalteromonas sp. QC44 DB65 2009 BBMO 99.0 (JN384138) Pseudoalteromonas sp. HK-3 DB1 2009 BBMO 100.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB14 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB18 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB22 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB24 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB72 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB8 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB9 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB93 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB94 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. SXBYC5n DB55 2009 BBMO 100.0 (EU343664) Pseudoalteromonas sp. SXBYC5n DB49 2009 BBMO 99.0 (EU343664) Pseudoalteromonas sp. SXBYC5n DB10 2009 BBMO 99.0 (EU343664) Pseudoalteromonas sp. SXBYC5n DB7 2009 BBMO 99.0 (EU343664) Pseudoalteromonas sp. SXBYC5n DB71 2009 BBMO 99.0 (EU343664) Pseudoalteromonas sp. SXBYC5n DB88 2009 BBMO 99.0 (EU343664) Pseudoalteromonas sp. AB333f DB15 2009 BBMO 99.0 (FR821205) Pseudoalteromonas sp. AB333f MED290 2001 BBMO 99.0 (FR821205) Pseudoalteromonas sp. DIT 46 ALSKE5D 2009 BBMO 100.0 (HQ199603) Pseudoalteromonas sp. DIT 46 ZOCONA5 2009 BBMO 100.0 (HQ199603) Pseudoalteromonas atlantica DB92 2009 BBMO 89.0 (AJ874344) Pseudoalteromonas sp. 114Z-7 DB17 2009 BBMO 99.0 (JX310123) Pseudoalteromonas sp. 19(2006) DB48 2009 BBMO 99.0 (DQ642825) Pseudoalteromonas sp. AB474f ALMIC1A 2009 BBMO 99.0 (FR821209) Pseudoalteromonas sp. BSi20316 DB26 2009 BBMO 99.0 (DQ492738) Pseudoalteromonas sp. CI4 DB3 2009 BBMO 99.0 (EU935585) Pseudoalteromonas sp. D32 ZOCONB10 2009 BBMO 100.0 (AY576005) Pseudoalteromonas sp. MB103 ZOCONH12 2009 BBMO 100.0 (AB519012) Pseudoalteromonas sp. NBRC 102015 DB12 2009 BBMO 99.0 (AB681662) Gammaproteobacteria/Alteromonadales/Pseudoalteromonadaceae/Pseudoalteromonas Table 1.SM. Bacterial strains used to isolate phages and to carry out the host range analysis. Bacterial strains used to isolate phages are labeled in black.
Chapter 3Pseudoalteromonas phage-host interactions 138 Alteromonas genovensis strain: LMG 24078 DB25 2009 BBMO 99.0 (NR042667) Alteromonas genovensis strain: LMG 24078 DB29 2009 BBMO 99.0 (NR042667) Alteromonas genovensis strain: LMG 24078 DB41 2009 BBMO 99.0 (NR042667) Alteromonas genovensis strain: LMG 24078 DB42 2009 BBMO 99.0 (NR042667) Alteromonas genovensis strain: LMG 24078 DB46 2009 BBMO 99.0 (NR042667) Alteromonas sp. MED111 2001 BBMO 100.0 (DQ681132) Alteromonas sp. MED113 2001 BBMO 100.0 (DQ681133) Alteromonas sp. MED169 2001 BBMO 100.0 (DQ681147) Alteromonas sp. MED275 2001 BBMO 100.0 (DQ681166) Alteromonas sp. MED517 2001 BBMO 100.0 (DQ681179) Alteromonas genoviensis strain I96 DB28 2009 BBMO 99.0 (FJ040187) Alteromonas genoviensis strain I96 DB43 2009 BBMO 99.0 (FJ040187) Alteromonas sp. BCw006 DB30 2009 BBMO 100.0 (FJ889589) Alteromonas sp. BCw006 DB44 2009 BBMO 99.0 (FJ889589) Alteromonas macleodii M292 2001 BBMO 99.0 (CP003917) Marinobacterium georgiense CECT7200 2006 Augusta (Georgia) AB681881 Marinobacterium jannaschii CECT7201 2006 United States AB680864 Marinobacterium stanieri CECT7202 2006 Oahu (Hawaii) AB021367 Vibrio gigantis MED227 2001 BBMO 97.8 (AJ582807) Vibrio gigantis MED241 2001 BBMO 100.0 (AJ582807) Vibrio pectenicida MED535 2001 BBMO 99.7 (Y13830) Vibrio sp. MED222 2001 BBMO 100.0 (AF242274) Vibrio sp. MED126 2001 BBMO 99.8 (AJ316207) Vibrio sp. MED140 2001 BBMO 99.6 (DQ219366) Vibrio splendidus MED511 2001 BBMO 100.0 (AJ874364) Vibrio tasmaniensis MED181 2001 BBMO 99.1 (AJ514912) Dokdonia donghaensis MED134 2001 BBMO 99.4 (DQ003277) Polaribacter dokdonensis MED152 2001 BBMO 99.5 (DQ004686) Leeuwenhoekiella accommodimaris MED217 2001 BBMO 97.0 (AJ780980) Salegentibacter mishustinae MED220 2001 BBMO 94.1 (AY576653) Salegentibacter slinus MED532 2001 BBMO 99.0 (EF486353) Nereida sp. ZOCOND2 2009 BBMO 99.8 (AY612764) Nereida sp. ZOCONH4 2009 BBMO 99.8 (AY612764) Nereida sp. MED365 2001 BBMO 100.0 (DQ681170) Erythrobacter litoralis MED155 2001 BBMO 99.0 (AF465836) Erythrobacter citreus MED456 2001 BBMO 100.0 (AF118020) Erythrobacter citreus M539 2001 BBMO 100.0 (AF118020) BBMO: Blanes Bay Microbial Observatory. NW Mediterranean Sea. Alphaproteobacteria/ Sphingomonadales/ Erythrobacteraceae/ Erythrobacter Gammaproteobacteria/ Alteromonadales/ Alteromonadaceae/ Marinobacterium Gammaproteobacteria/ Vibrionales/Vibrionaceae/ Vibrio Gammaproteobacteria/Alteromonadales/Alteromonadaceae/ Alteromonas Bacteroidetes/ Bacteroidales/ Bacteroidaceae Alphaproteobacteria/ Rhodobacterales/ Rhodobacteraceae/ Nereida
145 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 Electron microscopy of Pseudoalteromonas phage B8b Transmission electron microscopy grids were prepared by placing 10 µl of CsCl-purified lysate (see above) onto 200 mesh formvar-coated copper grids (Ted Pella) for 5 min. The solution was subsequently removed with filter paper and grids were negatively stained with 2% uranyl acetate solution by rinsing the grids with 2 drops of the solution and staining for 45 s with a third drop. The grids were examined using a Philips CM12 microscope with an accelerating voltage of 80 kV. Pulse-field gel electrophoresis Phage genome size was determined by pulse field gel electrophoresis (PFGE) (Steward, 2001). Phage lysate was concentrated by Amicon Ultra-15 centrifugal filter units (Millipore) from 5 ml to a final volume of 400 µl. Of this, equal amounts were mixed with melted 1.6% low-melting-point agarose (Pronadisa), transferred to plugs molds, left to solidify at room temperature for a few minutes and then kept at 15 minutes at 4°C. Plugs were incubated overnight at 50°C in ESP (0,5 M EDTA, pH 9, 0.1% N-laurylsarcosine and 1 mg ml-1 proteinase K) and stored at 4°C until further analysis. PFGE was performed on a CHEF-DR III system (Bio-Rad) using 1% agarose gel (LE agarose SeaKem n.50005 BERLABO S.A.). The gel was run for 22h in 0.5X TBE buffer (1X TBE is 89 M Tris, 2 mM EDTA, and 89 mM boric acid, pH 8.3) at a 5.0-15.0 seconds switch time, 6V cm-1 and an included angle of 120°. After electrophoresis, the gel was stained with SYBR Gold (Molecular probes, 10,000X) diluted to 10-4 in 150 ml of TBE for 15 min and washed with MQ water for 15 min. Lambda Low Range (New England Biolabs) was used as molecular size marker. One-step growth experiments The burst sizes and one-step growth curves were determined as described by (Weiss et al., 1994), with minor modifications. One milliliter of Pseudoalteromonas sp. QC44 overnight culture was transferred to 10 ml of fresh 20% Zobell media and incubated with shaking (120 RPM) for about 20 min, until the A600 was ~ 0.02, which was equivalent to a viable cell count of around 108 cells ml-1. The concentration of bacterial cells at A600 ~ 0.02 was verified by flow cytometry (Gasol and Del Giorgio, 2000). One milliliter of the bacterial culture was then transferred to an eppendorf tube and mixed with phage at a multiplicity of infection of 0.1. The mixture was incubated at room temperature for 15 min to allow phage adsorption. After this adsorption, the mixture was diluted to 10-2 in 20 ml of 20% Zobell media. Samples were removed to enumerate total and free phage concentration. In order to detect the free phages, samples were 0.22 µm filtrated before
Chapter 4Life-style and genome structure of Pseudoalteromonas phage B8b 146 plating. The number of phages in both cases was determined, in duplicate, using the double-agarlayer method. Finally, burst size was calculated as the ratio of the final count of liberated phage particles to the initial count of infected bacterial cells during the latent period. Phage specificity To determine phage host range and bacterial susceptibility, a cross infectivity test was done where plaque assays were performed with phage B8b on 52 strains of Pseudoalteromonas sp. as well as 37 strains of Alteromonas spp., Marinobacter spp., Vibrio spp., Bacteroidetes, Nereida spp., and Erytrobacter spp. (see Table 1.SM) using 100 μl of two different phage stock dilutions (10-5 and 10-8), in order to distinguish between a clear lysis caused by plaque formation or inhibition of the bacterial lawn. Lysis was evaluated after overnight incubation in the dark. One the bacterial strains that showed phage susceptibility in the first test, a more thorough analysis was performed to determine the efficiency of infection on each strain. Here, plaque assays were performed with a range of 10X diluted phage stock and plaques were enumerated after 1 and 2 days incubation. Viral DNA purification and genome sequencing Viral DNA was obtained using the Lambda Wizard DNA kit (Promega Corp. Madison, WI) (Henn et al., 2010; Sullivan et al., 2010). Phage lysate from ~15 fully lysed plates were concentrated using polyethylene glycol as described earlier (CsCl purification section). One ml of Purification Resin (Promega, product A7181 Madison WI) was added to 1.5 ml of phages (the PEG pellet resuspended with MSM) and mixed gently by inverting the tube. The mixture was loaded onto a mini-column (Promega, product A7211 Madison WI) through a 5 ml syringe attached to the column, pushing the mixture through with the syringe plunger. The column was then washed with 2 ml 80% isopropanol, the syringe was removed and the mini-column placed into a 1.5 ml eppendorf tube and centrifuged (10,000 g, 2 min, room temperature) to remove any remaining liquid. Phage DNA was eluted from the column by adding 100 ml TE buffer (80°C), and the DNA was recovered in a 1.5 ml eppendorf tube through centrifugation (10,000 g, 30 s, room temperature). Phage DNA was stored at -20°C. The genome was sequenced by the Lifesequencing company (Valencia, Spain) using the standard shotgun sequencing reagents and a 454 GS FLX Titanium Sequencing System (Roche), according to the manufacturer’s instructions. Genome assembly and annotation B8b phage genome sequences were assembled into 4 contigs using Newbler (Roche). In
147 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 the absence of complete genome coverage, attempts were made to close the gaps using PCR and by direct Sanger sequencing. Forward and reverse primers were designed for every contig using PRIMER3 VERSION 0.4.0 (Clarke, 1993), producing a 300-400 bp overlap among the different contigs. The PCR was carried out in a 25 µl volume containing 12.5 µl of GoTaq Green Master Mix (Promega), 0.5 µl (10 µM) of each primer pair and 1 µl of phage stock adjusting the volume with sterile water. PCR profiles consisted of an initial denaturation step of 5 min at 94°C, followed by 30 cycles at 94°C 30 s, 55°C 30 s, and 72°C 2.5 min with a final extension at 72°C for 5 min. The amplification products were separated by gel electrophoresis on 1% agarose gel in 0.5% TAE run at 90V for 30 min and visualized by SYBR SAFE (10,000X, Invitrogen). Unfortunately, we failed to close the genome since we could not get readable sequences from PCR amplicons derived from any contig combination and we did not obtain any good enough sequence from direct sequencing. ORFs were predicted using a pseudo-automated pipeline where ORFs first were assigned by GeneMark Heuristic (Besemer and Borodovsky, 1999) followed by refinement through synteny and maximizing ORF size where alternative start sites were present. Gene identification and annotation was done using the BLASTP program against the NCBI non-redundant (nr) database (e-value cut off <0.001, August 2013). Proteome analysis Phages were harvested with MSM from fully lysed plates and CsCl purified as described above. The purified phage particles were prepared prior to 2d-LC-MS/MS analyses using an optimization of the FASP kit (Protein Discovery, Knoxville, TN) (Wisniewski et al., 2009). All reagents were provided for in the kit. Briefly purified phage were re-suspended in 8M Urea/10mM DTT, denatured and passed over the 30kDa filter, then washed with 8M Urea and treated with iodoacetamide (IAA) to label cysteine residues. IAA was washed away with 8M Urea and then 50mM Ammonium Biocarbonate. Sequencing grade trypsin was then added and digestion processed overnight. The next day peptides were eluted from the 30kDa filter via Ammonium Biocarbonate buffer, NaCl buffer and water/0.1% Formic acid. Three aliquots were prepared per sample and frozen at -80°C until 2d-LC-MS/MS analyses. The FASP prepared peptides (>500 ng) were loaded onto the back column of a split phase 2D column (~3-5cmSCX and 3-5cm C-18) (all packing materials purchased from Phenomenex, Torrance, CA). The column was loaded to the HPLC and washed with 100% aqueous solution for 5 min, followed by a ramp from 100% aqueous to 100% organic solution for 10 min. The column was connected to a front column (RP C-18, 15cm) with a nanospray source on LTQVelos and run for 5 – 12 h two dimensional
Chapter 4Life-style and genome structure of Pseudoalteromonas phage B8b 148 separation of increasing salt pulses (ammonium acetate) followed by water to organic gradients (see (Verberkmoes et al., 2009). All instrument were run in a data-dependent manner as previous described (Erickson et al.; Verberkmoes et al., 2009). To recruit peptides to the phage genomes, the resultant MS/MS spectra were searched against a database consisting of annotated phage proteins, all phage ORFs > 30 aa (to identify ORFs possibly missed through the annotation), and proteins from sequenced Pseudoalteromonas bacteria (Pseudoalteromonas haloplanktis TAC125, Pseudoalteromonas sp. TW-7, Pseudoalteromonas atlantica T6c, Pseudoalteromonas tunicata D2) and eukaryotic organisms (human and mouse) to use as indicator for false positives. Data analyses were performed using SEQUEST and filtered with DTA Select with conservative filters (Verberkmoes et al., 2009). For proteomics, databases, peptide and protein results, MS/MS spectra and supplementary tables are archived and available at https://compbio.ornl.gov/Cellulophaga_ phages_proteome, while MS .raw files or other extracted formats are available upon request. Phylogenetic analysis DNA polymerase, phage portal protein, and phage large terminase amino acid sequences of known bacteriophages were used to investigate the phage B8b phylogeny. Multiple sequence alignment has been automatically performed using the program ClustalW (default parameters) (Larkin, 2007). Maximum likelihood trees were built using the JTT model (Jones et al., 1992) with bootstrap analysis (1000 replicates) using MEGA version 5.1 (Tamura et al., 2011). Fragment recruitment analysis of B8b phage on POV (Pacific Ocean Viral metagenomics). Phage B8b genome fragment recruitment analyses (FRA) were performed to get a sense of the relative abundance of this phage in 32 marine viral metagenomes from the Pacific Ocean Virome1 (Hurwitz and Sullivan, 2013) (available at CAMERA (http://camera.calit2.net) under the following project accessions: CAM_P_0000914 and CAM_P_0000915). We used the Reciprocal Best Blast approach (RBB) (Raes et al., 2007) applying the same rationale to that employed elsewhere (Zhao et al., 2013). Briefly, individual metagenomics samples are made into a BLAST database, and then the predicted ORFS of B8b are searched against it using TBLASTn. After this initial blast, hits to the POV database are extracted and become the query for a second BLAST search (BLASTx) against a internal protein genome reference database with a total size of 8.512.217 ORFs that included: (i) protein viral genomes (Refseq Release 60; 4.958 genomes and 163.830 ORFs), (ii) bacterial genomes (RefSeq Release 60; 197.527 contigs and 8.348.231 ORFs) and (iii) the Pseudoalteromonas phage B8b (4 Contigs, 55 ORFs). Only metagenomic sequences that returned as a best hit a sequence from the genome of the Pseudoalteromonas phage B8b
149 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 are extracted from the database and count as hits for subsequent step. Finally, to calculate the relative abundance of B8b phage and two other phages used as reference genomes (the abundant Pelagiphage HTV0C10P (KC465898) and the non marine Enterobacteria phage T4 (NC_000866) in the POV dataset, we normalized the number of hits to: 1) protein length, 2) sequencing depth and 3) mean abundances across the 32 POV metagenomes. This is: the number of hits H was divided to the total number of sequences N and 2) to the amino acid length of the hit protein L. Finally, to avoid larger number of significant figures, the abundances were rescaled to the mean abundances across all samples. Phage genome accession numbers: Accession number of the B8b phage genes was deposited into NCBI under the following accession number XXXX. RESULTS AND DISCUSSION Morphology and genome structure of the siphovirus B8 genome Phage B8b was isolated from Blanes Bay Microbial Observatory (BBMO), an oligotrophic surface coastal site in the NW Mediterranean Sea, and it formed clear, round plaques when grown on its host of isolation, Pseudoalteromonas sp. QC-44. Morphological examination showed that phage B8b belonged to the Siphoviridae family based on ICTV rules of nomenclature (Van Regenmortel et al., 2000) and had a isometric capsid of 46 nm in diameter connected to a long and flexible tail of 235 nm in length (Fig. 1). While the PFGE analyses predicted that Pseudoalteromonas phage B8b had a genome size of 46 kb (Fig. 1.SM), the combined length of the 4 sequenced contigs were only 42.700 bp. These represented two major contigs (20.209 and 19.353 bp) and two short contigs (2.155 and 1.012 bp) and as these contigs could not be closed, the complete genome is likely larger although we sequenced about 90% of the phage. Moreover, the PFGE results showed that our phage had a concatemeric genome, which are produced by rolling circle replication and/or recombination and is a common phenomenon for virus genomes (Rao and Feiss, 2008). The obtained banding pattern in the PFGE gel suggested a multiple copies of the original DNA linked in a continuous series of different sizes (Fig 1.SM). The genome had a GC content of 50% and 58 ORFs were predicted proteins (Table 1). Thirty of these ORFs had significant sequence similarity to proteins in GenBank, but only 12 could be annotated to a function (Table 1), which is similar to other 𝐴𝐴!"! = 𝐻𝐻∗𝑁𝑁!!∗𝐿𝐿!! 𝐻𝐻∗𝑁𝑁!!∗𝐿𝐿!!
Chapter 4Life-style and genome structure of Pseudoalteromonas phage B8b 150 Figure 1. Transmission electron micrograph showing negatively stained Pseudoalteromonas phage B8b. previously sequenced marine Pseudoalteromonas phages (Männistö et al., 1999; Duhaime et al., 2011; Hardies et al., 2013). Among the genes with detected similarity, 40% were most similar to viruses, 26.66% to prophages and 33.33% showed similarity to genes detected in bacterial genomes (Table 1). Pseudoalteromonas phage B8b displayed two distinctive supermodules: contig 1 had several genes involved in DNA replication and nucleotide metabolism such as DNA primase (Contig1_ORF10), helicase (Contig1_ORF21) and DNA polymerase (Contig1_ORF23). Furthermore, the majority of ORFs with highest similar to phages (9 of 12) were detected in contig 1 and 7 of these were most similar to siphoviruses. A packaging/structural module was observed in contig 2 and contained proteins including phage terminases (Contig2_ORF2 and ORF4), phage portal protein (Contig2_ORF6), prohead peptidase (Contig2_ORF14), and tail tape measure protein (Contig2_ORF22). No genes involved in lysogenic function (integrase, excisionase, repressor and antirepressor) or encoding for transcription regulatory functions were detected. Distinctive genes in Pseudoalteromonas phage B8b The B8b genome encoded a RecT protein (Contig1_ORF15), which is involved in homologous recombination of importance to a variety of cellular processes, including the maintenance of genomic integrity (Kogoma, 1996). It provide means for repair of DNA doublestranded breaks, which can arise during DNA replication as well as after damage by external factors such as irradiation (Haber, 1999) and as a ssDNA-binding protein, RecT promotes ssDNA annealing, strand transfer, and strand invasion in vitro (Hall and Kolodner, 1994). In Escherichia coli, homologous recombination is mediated by bacteriophage RecT protein that permits efficient DNA engineering in various E. coli hosts (Zhang et al., 1998). Thus, RecT might facilitate integration of the phage B8b genome into the bacterial hosts genome, opening up for the potential of phage B8b to act as a prophage.
151 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 The presence of chaperone GroES (also called chaperonin 10; Contig2_ORF19) in B8b is unique as it is the first time GroES been reported in a siphovirus, while it previously been detected in myoviruses and podoviruses (Holmfeldt et al., 2013). Chaperonins are known to promote the correct folding of newly synthesized polypeptides and to prevent aggregation of proteins denatured under stress (Kurochkina et al., 2012). In Escherichia coli, the genes that encode for GroES/GroEL chaperonin system were first identified as host factors required for bacteriophage morphogenesis and subsequent work established that the GroES and GroEL proteins were essential for the correct assembly of λ proheads and T5 tails (Georgopoulos et al., 1973; Keppel et al., 2002). The presence of this gene in phage B8b might point out that possibly could have a more complex viral capsid or tail structure than other siphoviruses, which requires that it provide its own chaperonin. Proteomic analysis Given that only 5 structural proteins were identified by sequence similarity, we performed virion structural proteomic analyses to experimentally determine the remaining structural proteins. The portal protein, prohead peptidase, tail tape measure protein as well as 8 ORFs of unknown function in contig 2 were detected as part of the phage particle (Table 1). Further, the 2 ORFs of unknown function in contig 3 are part of the phage structural particle, as well as two proteins of unknown function in contig 1. Three spectra also matched against the DNA polymerase gene, however, they were considered false positives as the total peptides detected covered <4% of the gene. Phylogenetic relationships In order to get insights of the phylogenetic relatedness of phage B8b compared to other phages, three relevant key genes were investigated: the B8b DNA polymerase, the phage large terminase, and phage portal protein (Figs. 2A.SM, 2B.SM and 2C.SM). DNA polymerase genes are crucial in genomic replication and mutagenic repair and it has been used in new isolated phages to know the phylogenetic relationships (Chen and Suttle, 1996; Angly et al., 2009; Baudoux et al., 2012). Surprisingly, the B8b DNA polymerase clustered together with several myoviruses (Fig. 2A.SM; Table 1A.SM). Two of them were isolated from marine bacteria (Edwardsiella phage MSW-3 and Klebsiella phage JDOO1) (Cui et al., 2012; Yasuike et al., 2013) and most of them were lytic phages, except for Vibrio phage CP-T1 that is known to be capable of temperate behavior (Comeau et al., 2012). Although DNA polymerases have been suggested to be good phylogenetic marker for investigating viral phylogeny, since they offer the greatest
Chapter 4Life-style and genome structure of Pseudoalteromonas phage B8b 152 Contig_ORF Nucleotide start position Nucleotide end position Strand Product lenght (aa) % aa ID Predicted identity or function of product Strain with closest hit (Evalue) Accesion number Taxonomy Sequence count Spectral count Sequence coverage (%) Contig1_ORF1 7 630 + 624 Hypothetical phage protein Non-significant Contig1_ORF2 676 1221 + 546 42.0 dUTPase Lactococcus phage Q33 (8.0E-13) AFV51054.1 Siphoviridae, Caudovirales Contig1_ORF3 1218 1625 + 135 Hypothetical phage protein Non-significant Contig1_ORF4 1622 2128 + 168 Hypothetical phage protein Non-significant Contig1_ORF5 2121 2456 + 1 1 1 Hypothetical phage protein Non-significant Contig1_ORF6 2536 3297 + 762 37.0 DNA binding protein Salmonella phage E1 (4.0E-45) WP_003849806.1 Siphoviridae, Caudovirales Contig1_ORF7 3282 3500 + 72 Hypothetical phage protein Non-significant Contig1_ORF8 3503 3760 + 85 Hypothetical phage protein Non-significant Contig1_ORF9 3753 4376 + 201 Hypothetical phage protein Non-significant Contig1_ORF10 6914 4560 - 2355 35.0 DNA primase Salmonella phage FSL SP-062 (1.0E-72) AGF89287.1 Siphoviridae, Caudovirales Contig1_ORF11 7155 6919 - 236 Hypothetical phage protein Non-significant Contig1_ORF12 7580 7152 - 429 47.0 Conserved hyphotetical phage protein Edwardsiella phage MSW-3 (6.0E-101) YP_007348969 Myoviridae, Caudovirales Contig1_ORF13 8746 7580 - 1167 Hypothetical phage protein Non-significant Contig1_ORF14 9873 8749 - 1125 33.0 Conserved hyphotetical phage protein Salmonella phage FSL SP-062 (2.0E-33) AGF89282.1 Siphoviridae, Caudovirales 3 8 10.9 Contig1_ORF15 10783 9878 - 906 42.0 RecT protein Marichromatium purpuratum 984 (1.0E-4) WP_005220619 Gammaproteobacteria, Chromatiales Contig1_ORF16 11037 10813 - 225 51.0 Conserved hyphotetical phage protein Acinetobacter phage Ac42 (2.0E-20) YP_004009376 Myoviridae, Caudovirales Contig1_ORF17 1 1 1 1 7 11281 + 165 Hypothetical phage protein Non-significant Contig1_ORF18 11286 11549 + 264 Hypothetical phage protein Non-significant Contig1_ORF19 11546 11743 + 198 Hypothetical phage protein Non-significant Contig1_ORF20 11709 11891 + 171 Hypothetical phage protein Non-significant Contig1_ORF21 11958 13628 + 1671 35.0 Helicase Salmonella phage FSL SP-062 (3.0E-92) AGF89284.1 Siphoviridae, Caudovirales Contig1_ORF22 13621 13857 + 237 Hypothetical phage protein Non-significant Contig1_ORF23 13847 16057 + 2211 39.0 DNA polymerase Salmonella phage FSL SP-062 (9.0E-145) AGF89344.1 Siphoviridae, Caudovirales 2 3 3.9 Contig1_ORF24 16103 16705 + 603 Hypothetical phage protein Non-significant Contig1_ORF25 16705 16938 + 234 Hypothetical phage protein Non-significant Contig1_ORF26 17471 17001 - 471 40.0 Conserved hyphotetical phage protein Klebsiella phage phiKO2 (2.0E-17) YP_006634.1 Siphoviridae, Caudovirales Contig1_ORF27 17695 17468 - 228 41.0 Conserved Hypothetical phage protein Alishewanella jeotgali KCTC 22429 (7.0E-11) WP_008951684 Gammaproteobacteria, Alteromonadales Contig1_ORF28 19877 17688 - 2190 30.0 Conserved Hypothetical phage protein Marinobacterium stanieri S30 (5.0E-28) WP_010325175 Gammaproteobacteria, Alteromonadales (Prophage) 4 4 7.8 Contig1_ORF29 20170 19874 - 282 33.0 Conserved Hypothetical phage protein Pseudoalteromonas sp. BSi20652 (7.0E-5) WP_008172253 Gammaproteobacteria, Alteromonadales Contig2_ORF1 256 486 + 76 Hypothetical phage protein Non-significant Contig2_ORF2 410 1006 + 198 25.0 Small terminase subunit Escherichia phage vB_EcoM_ECO1230-10 (7.12E-7) ADE87936.1 Myoviridae, Caudovirales Contig2_ORF3 990 1502 + 170 28.0 Conserved hyphotetical phage protein Shewanella frigidimarina NCIMB 400 (8.32E-4) YP_750332 Gammaproteobacteria, Alteromonadales Contig2_ORF4 1519 3510 + 663 59.0 Phage large terminase subunit Marinobacterium stanieri S30 (3.00E-125) WP_010322164 Gammaproteobacteria, Alteromonadales (Prophage) Contig2_ORF5 3514 3729 + 71 Hypothetical phage protein Non-significant 5 10 76.40 Contig2_ORF6 3719 5218 + 499 40.0 Phage portal protein Marinobacterium stanieri S30 (5.0E-126) WP_010322159 Gammaproteobacteria, Alteromonadales (Prophage) 18 46 42.20 Contig2_ORF7 5232 5660 + 142 Hypothetical phage protein Non-significant Genomic data Proteomic data Table 1. Genomic annotation and structural proteomics results
153 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 Contig2_ORF8 5662 6108 + 148 Hypothetical phage protein Non-significant Contig2_ORF9 6053 6283 + 76 Hypothetical phage protein Non-significant Contig2_ORF10 6264 6524 + 86 Hypothetical phage protein Non-significant Contig2_ORF11 6821 6525 - 98 31.0 Conserved hyphotetical phage protein Pseudoalteromonas phage RIO-1 (2.0E-6) YP_008051111.1 Podoviridae, Caudovirales Contig2_ORF12 7423 6803 - 206 40.0 Conserved Hypothetical phage protein Pseudoalteromonas phage RIO-1 (2.0E-6) YP_008051111.1 Podoviridae, Caudovirales 6 10 37.30 Contig2_ORF13 7800 7423 - 125 Hypothetical phage protein Non-significant 6 22 57.90 Contig2_ORF14 7912 9966 + 684 44.0 Peptidase U35 phage prohead HK97 Marinobacterium stanieri S30 (3.0E-168) WP_010322158 Gammaproteobacteria, Alteromonadales (Prophage) 28 937 42.70 Contig2_ORF15 10022 10360 + 1 1 2 48.0 Conserved hyphotetical phage protein Marinobacterium stanieri S30 (1.0E-17) WP_010322157 Gammaproteobacteria, Alteromonadales (Prophage) 7 67 43.00 Contig2_ORF16 10341 10682 + 1 1 3 Hypothetical phage protein Non-significant 3 3 33.60 Contig2_ORF17 10675 11298 + 207 41.0 Conserved hyphotetical phage protein Vibrio crassostreae (3.0E-44) WP_017059000 Gammaproteobacteria, Vibrionales Contig2_ORF18 11295 11771 + 158 30.0 Conserved Hypothetical phage protein Marinobacterium stanieri S30 (4.0E-4) WP_010322154 Gammaproteobacteria, Alteromonadales (Prophage) 3 3 24.40 Contig2_ORF19 11774 12148 + 124 35.0 Chaperone GroES Pseudoalteromonas tunicata (1.0E-4) WP_009840504 Gammaproteobacteria, Alteromonadales Contig2_ORF20 12148 12318 + 56 Hypothetical phage protein Non-significant Contig2_ORF21 12318 13079 + 253 40.0 Conserved hyphotetical phage protein Marinobacterium stanieri S30 (8.01E-39) WP_010322152 Gammaproteobacteria, Alteromonadales (Prophage) 9 290 54.90 Contig2_ORF22 13148 17356 + 1402 32.0 Phage tail tape measure protein TP901, core region Marinobacterium stanieri S30 (1.29E-161) WP_010322151 Gammaproteobacteria, Alteromonadales (Prophage) 46 74 43.50 Contig2_ORF23 17359 17769 + 136 39.0 Conserved hyphotetical phage protein Pseudoaltermonas sp. S9 (8.0E-17) WP_010490777 Gammaproteobacteria, Alteromonadales Contig2_ORF24 17769 19343 + 523 35.0 Conserved Hypothetical phage protein Pseudomonas aeruginosa (7.0 E-73) WP_019396974.1 Gammaproteobacteria, Pseudomonadales 12 21 36.19 Contig3_ORF1 9 1694 + 1644 38.0 Conserved hyphotetical phage protein Pseudomonas aeruginosa (5.0 E-82) WP_019396974.1 Gammaproteobacteria, Pseudomonadales 12 38 27.63 Contig3_ORF2 1694 2154 + 154 Hypothetical phage protein Non-significant 3 3 30.10 Contig4_ORF1 12 350 + 339 29.0 Conserved hyphotetical phage protein Klebsiella oxytoca (9.0E-5) WP_004131755 Gammaproteobacteria, Enterobacteriales Contig4_ORF2 343 609 + 267 Hypothetical phage protein Non-significant Contig4_ORF3 824 699 - 126 Hypothetical phage protein Non-significant
Chapter 4Life-style and genome structure of Pseudoalteromonas phage B8b 154 number of viral homologs (Fileé et al., 2002), our results showed that genomic sequences were not compatible with relationships based on phage morphology and that morphological features available would be not sufficient to reveal the relationships among the members of what may possibly be a very large group (Lawrence et al., 2002). The situation may be complicated further if one considers the potential opportunity for dynamic genetic exchange in marine environments. Notably, while such rampant mosacism may be the case in siphoviruses, other groups (e.g., T4like myoviruses) appear to have clear signals of vertical descent, particularly in their core gene sets (Ignacio-Espinoza and Sullivan, 2012; Ignacio-Espinoza et al., 2013). The phage large terminase and phage portal protein are commonly highly conserved among phage genes, possibly due to their specific enzymatic functions (Casjens, 2005) and phage phylogeny has been investigated using these genes in several other studies (Serwer et al., 2004; Sullivan et al., 2009; Comeau et al., 2012; Huang et al., 2012). The phage terminases are DNA packaging enzymes that contain the ATPase activity that powers DNA translocation and most terminases also contain an endonuclease that during DNA packaging cuts concatemeric DNA into genome lengths. Terminases must also recognize viral DNA in a pool that may also include host DNA (Catalano et al., 1995; Rao and Feiss, 2008). Phage portal proteins one the other hand, are structurally associated with the phage capsid and facilitate DNA packaging during head assembly (Rao and Feiss, 2008). Phylogenetically, both B8b terminase and portal protein were closest related to Stenotrophomonas phage S1 (Figs. 2B and 2C.SM; Table 1B and 1C.SM), a temperate siphovirus isolated from sewage (Garcia et al., 2008). They also clustered together with the putative temperate siphoviruses Synechococcus S-CBS1 (terminase and portal) and S-CBS3 (terminase) (Huang et al., 2012) as well as several temperate myoviruses, like Acidithiobacillus phage AcaML (terminase) (Tapia et al., 2012), Halomonas phage phiHAP-1 (portal) (Mobberley et al., 2008), and Vibrio phage VP882 (Lan et al., 2009). Biology characterization of Pseudoalteromonas phage B8b The one-step growth curve of phage B8b showed a latent period of 70 min and approximately 172 new viral particles were released from each infected Pseudoalteromonas sp. QC-44 cell (Fig. 2). These values differed from the marine Pseudoalteromonas phage PM2, which produced 300 viral particles per infected cell about 70-90 min after infection (Kivelä et al., 1999), as well as other marine siphoviruses, e.g. Vibrio phage SIO-2, which had a latent period of 45-60 min and an average burst size of 60 (Baudoux et al., 2012) or the cyanosiphovirus S-BBS1, which had a 540 min (9 h) of latent period and approximately 250 progeny viruses were produced per infected host cell (Suttle and Chan, 1993). However, this is not surprising as burst size and latent
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273 vida, per tot això i per les bones estones que sempre passem juntes o con nuestros perrones, mil gràcies!!!. Espero que aquests 10 anys només siguin els primers que hem de compartir. A tod@s mis amig@s de Barcelona, Altafulla, en especial a Esther C. (Xupiiii), Esther P., Laia, Marta, Annabel y a los demás!! A todos mis compañeros de campañas; en la del Ártico: Raquel, Iñigo, Regino, Johnna, Jordi F., Txetxu y Lolo. Y en la de Malaspina: Raquel, Víctor, Irene, Cris (aiiii Crisss, como se te echa de menos!!!), Paqui (mi Paaaaquiiii, que grandes momentos compartidos en el Hespérides), Ángel, Zuqui, Oscar, Rafel (vas fer que els participants del leg 6 fóssim l’enveja del altres per tenir-te de jefe de campanya, inoblidables les nits amb la teva guitarra repassant els moments del dia, gràcies!) y a Ricardo, por hacer de esta campaña un viaje inolvidable. Raquel y Iñigo se merecen una mención especial…Raquelilla…gran amiga y gran compañera, las campañas no hubieran sido lo mismo sino las hubiera compartido contigo. Gracias por tu apoyo, por nuestros ratos en cubierta, por los ratos de risas durante experimentos y muestreos interminables. Espero que nuestros caminos se unan en el futuro. Iñiguin!! Y a ti que te voy a decir que ya no sepas…eres una de las mejores cosas que me ha pasado durante estos 5 años. Contigo he pasado momentos inolvidables y ratos en los que más me he reído en mi vida. Te adoro!!! Pero eso ya lo sabes jodio…solo espero que sigamos en contacto como hasta ahora porque eres uno de los amigos que quiero cerca!!! A Carlos Duarte y Paul Wassman, por darme la oportunidad de embarcarme en estas dos campañas y a toda la tripulación del Jan Mayen y del Hespérides por todos esos momentos que hacen que uno nunca quiera bajarse del barco. I will always be thankful to Matt Sullivan and all the people at his lab (especially thanks to Cristina Howard, Natalie Solonenko, Karin Holmfeldt and Boonie Poulos). Matt, thanks a lot for your hospitality and for you support and help during and after my stay in Tucson. Half of this thesis would have not been possible without all that I have learned from you and from your people. THANKS SO MUCH. Pero sobretodo esta tesis no hubiera sido posible sin el apoyo de mi familia, en especial de mis padres. Papa!!!! Si es que esto para ti no empezó aquí. La cosa viene de más lejos…cuando ya estaba yo empeñada en estudiar biología y me fui a Girona. Ahí empezaron los viajes con
274 el Nissan pariiba y pabajo…después te salí con la increíble idea de dejar un trabajo fijo en una multinacional para irme a hacer el doctorado en virus marinos…santa paciencia que has tenido que tener! Pero a pesar de lo descabelladas que te hayan parecido mis inquietudes SIEMPRE me has apoyado y ayudado en todo. Mama, siempre has luchado porque estudiara, para que llegara lejos…mira donde he llegado! Y no lo hubiera hecho sin vosotros! Gracias por tus infinitos ánimos y paciencia, por tu cariño y por motivarme a seguir adelante siempre con energía positiva! Nunca podré agradeceros suficiente todo lo que habéis hecho por mi. Además también he podido llegar hasta aquí gracias a todo lo que siempre me habéis inculcado y enseñado: trabajar por lo que crees y lo que te gusta y dedicarle tiempo y esfuerzo. GRACIAS POR TODO. Vuestro apoyo y cariño ha sido y será imprescindible en cada nuevo paso que dé. A mi hermano, ejemplo de lucha constante y del que he aprendido que reírse de uno mismo y de tus miedos es el mejor remedio para seguir adelante. Pero mi familia no solo consta de 4 miembros, sino que para mi siempre ha sido de 8. A mis tios (Malina y Tiet) y mis primos (Ainhoa y David). Hemos crecido juntos, y para mi sois mis segundos padres y mis hermanos. Tengo que agradeceros vuestro apoyo y cariño. En especial a mi Malina, que siempre ha estado ahí para darme buenos consejos, para escucharme, para sacarme las fuerzas cuando ya no me quedan y porque siempre he admirado tu energía y tu fortaleza. También mención especial a mi primo David. Ya sabes cuanto te quiero, siempre alegrándome la vida! Que grande eres couso!!! A Bea, que no es solo familia sino buena amiga. Gracias a los dos por los buenos ratos en el chiringuito, los paseos perrunos playeros, las cenas juntos, las excursiones. Grandes recuerdos de Castefa!! Y a Pol!!!! El nuevo miembro de la familia…la cosa más bonita que hay en el mundo! A mi prima Ainhoa también tengo que agradecerle el diseño gráfico de esta tesis. Mil gracias Nhoica, ha quedao preciosííísssimaaaa! No me quiero olvidar del resto de la familia, tios, tias, primos, etc y en especial a mis abuelicas… estaríais tan orgullosas de vuestra nieta si estuvierais aquí! Por último...no podía faltar (sino no sería yo...) que nombre a mi compañero de 4 patas más fiel...mi acompañanante en todas mis aventuras durante los últimos 8 años...MI TITO LACK!! Ese perrón al que adoro y que forma parte de mi en todo lo que hago y allí a donde voy...así que estas líneas son para él!! Cada uno de vosotros ha sido importante para poder realizar y finalizar esta tesis, así que… VA POR USTEDES!!!