Parasite-Parasite Interactions in the Wild: How To Detect Them?

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Parasite-Parasite Interactions in the Wild: How To Detect Them?

Eléonore Hellard, David Fouchet, Fabrice Vavre, Dominique Pontier

To cite this version:

Eléonore Hellard, David Fouchet, Fabrice Vavre, Dominique Pontier. Parasite-Parasite Interactions

in the Wild: How To Detect Them?. Trends in Parasitology, Elsevier, 2015. �hal-01227711�

Parasite–Parasite Interactions in the Wild: How To Detect Them?

Eléonore Hellard,

* David Fouchet,

Fabrice Vavre,

and Dominique Pontier

Inter-specific interactions between parasites impact on parasite intra-host dynamics,hosthealth,anddiseasemanagement.Identifyingandunderstand- inginteractionmechanismsinthewildis crucialforwildlifediseasemanage- ment.Itis howevercomplexbecause severalscalesareinterlaced.Parasite–

parasiteinteractionsarelikelytooccurviamechanismsatthewithin-hostlevel, but alsoatupper levels(hostpopulationandcommunity).Furthermore, inter- actionsoccurring atonelevel of organization spread toupperlevels through cascadeeffects.Evenifcascadeeffectsareimportantconfoundingfactors,we arguethatwecanalsobenefitfromthembecauseupperscalesoftenprovidea way to survey a wider range of parasites at lower cost. New protocols and theoreticalstudies(especiallyacrossscales)arenecessarytotakeadvantageof thisopportunity.

Parasite–ParasiteInteractions:FromTheLabtotheField

Parasites (seeGlossary)areubiquitousinthelivingworld. Largelyconsideredas agentsof diseaseanddeath,parasitesarenowrecognizedasintegralpartsofecosystems[1,2]andas majordrivingforcesforbiologicalevolution[3,4].

However,researchintohost–parasiteinteractionsremainsdominatedbythestudyof‘onehost– oneparasite’systems.Suchstudiesignorethreeimportantaspectsthatareemergingquestions [2].First,manypotentiallypathogenicagentssilentlycirculatewithinhostpopulations(e.g.,[5]).

Second, many parasitesinfectseveralhost species(e.g., generalist parasites), with conse- quencesfordiseaseepidemiologyandtheselectivepressuresactingoneachparasiteandhost [6].Third,hostscansimultaneouslycarryseveralagents,withconsequencesforthedynamicsof eachparasite andfor hosthealth(e.g., [7,8]).Inaddition,understanding thespatiotemporal dynamicsofdiseasesandtheevolutionofhostsandparasitesrequiresintegratingprocessesat different scalesof ecological organization,space, and time from the within-hostlevel(e.g., interactionsofparasiteswiththehostimmunesystem,hostresources,andcoinfectingpara- sites)totheecosystemorlandscapelevel(e.g.,influenceofenvironmentalvariablesandofhost communitycompositiononparasitedynamics);itimpliesmovingfrom‘onehost–oneparasite’ systems towardsanecosystemviewofhost–parasiteinteractions,embracingthereal com- plexityofnaturalsystems,oneofthemostexcitingandchallengingtasksfordiseaseecologists today[2,9–11].

Inthispaperwefocusoninteractionsbetweendifferentspeciesofparasitesinwildhosts.Within ahost,theseparasitescaninteractwitheachother,modifyingtheintra-hostand/orinter-host dynamics(spatialand/ortemporal)ofeachother.Suchparasite–parasiteinteractionsincrease

Trends

Hosts are often infected by more thanoneparasitespecies.Numerous experimental andclinicalstudiescar- riedoutatthehostindividuallevelhave revealed that interactions between parasite species impact on parasite dynamics, host health, and disease management.

Fieldstudiesarestillintheirinfancyand robust methods to detect parasite–

parasiteinteractionsincomplexnatural systemsarelacking.

Inthewild,parasite–parasite interac- tionscanoccurnotonlythroughpro- cessesatthehostindividuallevelbut also at population and community levels,stressingtheneedtoinvestigate interactionmechanismsatvariouseco- logicalscales.

Interactionsoccurringatonelevelcan cascadeup,translatingintotheepide- miologicalpatternsobservedathigher levelsoforganization.

Weproposetousecascadeeffectsto detectparasite–parasiteinteractionsin thewild.

1LaboratoiredeBiométrieetBiologie Evolutive,UniversitédeLyon, UniversitéLyonI,CentreNationalde laRechercheScientifique(CNRS) UnitéMixtedeRecherche5558, 43Boulevarddu11Novembre1918, 69622,Villeurbanne,France

2PercyFitzPatrickInstitute, DST-NRFCentreofExcellence, UniversityofCapeTown,Private BagX3,Rondebosch7701, SouthAfrica

(synergy)ordecrease(antagonism)thesusceptibilityofthehosttootheragents,theinter-host transmissionrateoftheinteractingparasites,and/ortheseverityofthediseasesymptomsthey induce.Theymayalsogreatlyinfluencetheevolutionoftheparasitesthemselves,inparticular theevolutionoftheirvirulence(reviewedin[12]).Manyexamplesofparasite–parasiteinteractions havebeenidentified,andthereisnowstrongevidenceoftheirimpactonhosthealth,parasite circulation, andpathogen management [8,13–17].Despite this,few studieshave lookedat parasiteinteractionsinwildpopulations(e.g.,[15]).Consideringtheimpacttheycanhaveon wildlifepopulations[18],thatover60%ofhumandiseasesmayhaveazoonoticorigin[19,20], andthatasubstantiallyhigherpercentageoflivestockdiseasesareprobablysharedwithother wild-ranging hosts [21], morework onparasite–parasite interactionsin wild populations is needed.Theirbetterdetectionandunderstandingarecrucialtopreventandmanageinfectious diseases.Theidentificationofsynergiesbetweendifferentparasitespeciesmayhelptoprevent populationdeclinesorextinctions.Bycontrast,whereinteractionsareantagonistic,measures targeting only one parasite species may result in unexpected increases in a second co- circulatingparasitespecies[22,23].Recentstudiessuggestthatantagonistparasitesmayalso helpinfightingproblematicpathogensinnaturalpopulations,asexemplifiedbytheprotective effectofJanthinobacteriumlividumagainstchytridiomycosisinamphibians[24]andofdiverse microbialenemiesofnematodesinduneplants[25].

Currentknowledgeofparasite–parasiteinteractionslargelyresultsfromanimalmodelsandfrom experimentalandclinicalstudieswithanindividual-basedapproach.Thesestudiesarebiased towards human pathogens and suspected interactions (e.g., following the observation of increasedmortalityinapopulation).Theyalsofocusonmechanismsoccurringatthewithin- hostlevel(e.g.,mediatedbythehostimmunesystem,Table1),whereas,aswillbedeveloped further,interactionsbetweenparasitescanalsooccurviamechanismsoccurringatthehost population[26]andprobablyhigherlevelsoforganization.Interactionmechanismsresultingin particularfromhostbehaviorhavebeenlargelyignored,despiteitscrucialroleintransmission processes[27].A mechanisticapproach,aiming atdecipheringtheunderlyingprocessesof parasite–parasite interactions,is necessaryto gobeyondthe simpledescriptionof parasite associationspatterns.Communityecologyalreadyprovedtobeusefultounderstandprocesses shapingwithin-hostparasitecommunities(e.g.,top-downandbottom-upregulationofparasite populationsize,viahostimmunesystemandresources,respectively)[9,11,28],butsofarno frameworkincludeshigherscales.

Studiesarenowbeginningtobeextendedtonaturalpopulations(e.g.,[15]),butdetectingand identifyingparasite–parasiteinteractionsandtheir underlyingmechanismsrepresentameth- odologicalchallengeincomplexfoodwebs[29].Thedifficultyresidesintheexistenceofmultiple confoundingfactors(e.g.,parasitestransmittedbyasimilarvectororasimilarbehavior,and environmentalfactorsexposinghoststoseveralparasitessimultaneously)andpossiblemis- matchesbetweentheleveloforganizationunderstudyandtheleveloforganizationatwhichthe interactionoccurs.Long-termfieldstudiesarerareandcostlyandanincreasingeffortisalsoput on developing methods to deal with more usual empirical epidemiological data such as presence/absence data obtained in cross-sectionalstudies, that is, sampling multiple host individuals,populations,orcommunitiesatonetime[29,30].Otherquestionshavebeenpoorly investigated and represent interestingavenues ofresearch. Are theretraces ofwithin-host interactionsathigherlevelsoforganization(i.e.,doparasite–parasiteinteractionscascadeup?).

Inotherwords,canwedetectparasite–parasiteinteractionsbyexaminingpatternsatlevels higherthantheleveloftheirunderlyingmechanism?Whattypesofpatternscanbeexpected?– or,putdifferently–whataretheconsequencesofwithin-hostinteractionsforthespatiotemporal dynamicsofinteractingparasiteswithinhostpopulations,communities,orattheregionalscale?

Beingabletointerpretepidemiologicalpatternsobtainedinthefieldatdifferentscales,andtolink theobservedpatternstodifferenttypesofparasite–parasiteinteractionsandtheirmechanisms,

3LabExEcofect,Ecoevolutionary DynamicsofInfectiousDiseases, UniversityofLyon,France

*Correspondence:

eleonore.hellard@gmail.com(E.Hellard).

willhelpinidentifyingtheprocessesandtheecologicalandco-evolutionaryconsequencesof parasite–parasiteinteractionsfromthemoleculetotheecosystem.

Currentknowledgedoesnotpermitallthesequestionstobeanswered,andinthefollowingwe reviewwhatisknownsofarontheexistenceandmechanismsofparasite–parasiteinteractions inthewildanddiscusscascadeeffectsofparasite–parasiteinteractionsandtheirpotentialuse todetectparasite–parasiteinteractionsinnaturalpopulations.Wherepossible,concreteexam- pleshighlightingthedifferentmechanismsaregiven,butsomeothermechanisms,theoretical andnotyetdemonstrated,arealsopresented.

Parasite–ParasiteInteractions:AUbiquitous PhenomenonintheWild?

Macro-parasitesarethemoststudiedinthewild.Interactionsbetweenintestinalhelminthshave beenevidencedindiversemammals(e.g.,[13,31]),birds[32],fish[33],andinvertebrates[34], withemphasisonparasitelocalization(e.g.,[35]),abundance(e.g.,[36]),life-historystrategies, body size, and fecundity ([37] and references therein) within the host. Nonetheless, many observationalapproachesuseduntilrecentlygeneratedinconsistentresultsontheimportance ofsuchinteractionsinstructuringmacro-parasitecommunities[13,38–40].Detectingparasite– parasiteinteractionsfromabundancedataisnoteasy,andsuchinconsistenciesmighthave beenduetotheabsenceofrobustvalidatedmethodsofdetectionuntilrecently[29].Thenature andconsequencesofinteractionsbetweenmacro-parasitesinwildpopulationsthusremainto bebetterestimated.

Glossary

Antigenically-similarparasites:

withsimilarantigens,therefore recognizedbythesamehost antibodiesandimmunecells.

Cascadeeffect:(inthecontextof parasiteinteractions)thespreadofa parasiteinteractiontoupperlevelsof organization,impactingthe spatiotemporaldynamicsofthe interactingparasitesatlevelsatwhich interactiondoesnotinitiallyoccur.For instance,aninteractionoccurring withinhostswillimpactonthewithin- hostdynamicsoftheinteracting parasitesaswellasontheir dynamicsatthehostpopulationand communitylevels.

Falseparasiteinteraction:

statisticalassociationoftwoormore parasitesresultingfromsharedrisk factor(s).Whenthesamehost individualsorpopulationsareatrisk ofinfectionbybothparasites,these parasitesarelikelytobefound togetherinthesamehostsor populationsevenifparasitesdonot interactbiologically(i.e.,evenifthere isnotrueinteraction).Thesefrequent associationsmayprovideanideal contextforcoevolutionbetween parasitestotakeplace.

Horizontallytransmittedparasite:

transmittedthroughdirectorindirect contactwithinfectedindividuals,in contrasttoverticallytransmitted parasitesthatspreadfromone generationtoanother(i.e.,from mothertofetusornewborn).

Next-generationsequencing:high- throughputgenomesequencingthat parallelizesthesequencingprocess, producingthousandsormillionsof sequencesconcurrently.

Parasite:anyorganismlivingatthe expenseofanother,inotherwords anymicro-(e.g.,virus,bacterium, protozoon,fungus)ormacro-(e.g., helminth,arthropod)parasite.

Serotypes:microorganisms belongingtothesamespeciesand sharingthesameantigenicprofile, andarehencerecognizedbythe samespecifichostantibodies.

Susceptibility:relativelikelihoodthat ahostbecomesinfectedandmounts asub-thresholdimmuneresponse whenexposedtoaninfectiousdose atagiventime.Inmostcasesthe capacityofaparasitetoestablishan infectiondependsontheinitialstate oftheimmunesystemofthe exposedhost,whichisdetermined bypreviousandcurrentinfectionsas Table1.MechanismsofDirectParasite–ParasiteInteractionsattheWithin-HostLevel

Function Affected

Interaction Type^a

Mechanism Details Examples Refs

Cellentry Positive Mechanical facilitation

Creationofanentrypoint forotherparasites

Herpessimplexvirus type2–HIVinhumans

[69]

Arguluscoregoni–

Flavobacterium columnareinfish

[70]

Survival Negative Interference Parasiteskillingother parasites

Bacteriaproducing bacteriocins(e.g., Photorhabdus, Xenorhabdus)

[71,72]

Positive Cooperation Productionofpublic goods

Bacteria,yeast,and fungiproducing siderophores(iron-binding molecules)

[73]

Fecundity Positiveor negative

Competition Changeoflife-history strategywhenthere isconflictover transmission

Increasedeggproduction ofCoitocaecumparvum (whosedefinitivehostisafish) beforeMicrophallussp.

(whosedefinitivehostisabird) succeedsinmanipulating theirsharedintermediatehost

[74]

Genome expression

Positiveor negative

Transactivation Thegeneproductsof aparasiteinduce transactivationofthe genesofanotherparasite

Herpessimplexvirus–HIV-1 [69]

Embedded parasites

Integrationofexogenous geneticmaterialfroma parasitemodifiesthe pathogenicityofanother parasite

CTXphiwithinVibriocholerae. [75]

aFromthepointofviewoftheparasite.

Morerecently,interactionsbetweenmicro-parasites,aswellasbetweenmicro-andmacro- parasites,were also revealedin thewild. We cancite (i) the antagonism between bovine tuberculosis and wormsthat wasshown to accelerate host mortality and have immune- driveneffectsonthesusceptibilityoffree-rangingAfricanbuffalos(Synceruscaffer)[41],and (ii) the modification of field vole (Microtus agrestis) susceptibility due to the interactions between the cowpox virus,Babesia microti, Bartonella spp.,and Anaplasma phagocyto- philum[15].Consequencescanbedramatic.Inhoneybees,themiteVarroadestructorcan destabilize the within-host dynamics of the deformed wing virus, transforming a cryptic virus intoa rapidly-replicatingkiller andparticipatingin thecollapse ofhoneybee colonies (ApismelliferaL.)[18].

Parasite–parasiteinteractionsthusappeartooccurinmanytypesofhostsandbetweenmultiple typesofparasites.However,theirimportanceinstructuringparasiteandhostcommunitiesas wellastheirunderlyingmechanismsinwildpopulationsneedtobebetterinvestigated.

Parasite–ParasiteInteractionsattheEcologicalLevel

Interactions between parasites,as those betweenfree living species,can beviewedas an ecologicalprobleminwhichthedifferentactorsinteractwitheachother.

Withinhostindividuals,thespreadofaparasiteistheresultofinteractionsbetweentheparasite community,thehostimmunesystem,andparasiteresources(Figure1A).Thedifferentparasite species can compete with each other either directly (interference competition, Table 1) or indirectly(apparentorexploitationcompetition,Table2)(reviewedin[7]).

Atthehostpopulationlevel,parasitesneedtospreadtonewhosts.Threestepsarenecessary:

exitfromthehost,at-riskcontact,andsuccessfulinvasionofthenewhost(Figure1B).Ifone parasitealtersanyofthesestepsforanotherparasite,thereisapotentialinteraction.Exitfrom thehostcanbefacilitatedbyinfectionsymptoms(e.g.,rashes,cough),whichcanincreasethe transmission of otherparasites with the same transmission mode.The shedding rateof a parasitecanalsobemodifiedinpresenceofanotherparasite,asforHeligmosomoidespoly- gyrusinpresenceofBordetellabronchiseptica,resultinginsomesuper-shedderhosts[42].At- riskcontactsare,inalargerextent,conditionedbyhostbehavior,which canbeaffectedby parasites. For example, rabies causes aggressiveness andwandering inred foxes(Vulpes vulpes)[43],increasingthespreadofthevirusandofotherparasitestransmittedbybites.A parasite can also affect the spread of other parasites by inducing mass mortality and/or individualconvalescence,hencereducingthepoolofsusceptiblehosts(‘ecologicalinterference’ [26]) and preventing simultaneous outbreaks of other parasites. First evidenced between serotypes of dengue viruses, between strains of echoviruses, and between measles and whoopingcough [26],it also occursbetween the rabbithemorrhagicdiseasevirus (RHDV) andthemyxomavirusinEuropeanrabbits(Oryctolaguscuniculus)[44].AnepidemicofRHDV cankillupto90%oftheinfectedindividuals,compromisingthetransmissionofthemyxoma virusanddelayingitsannualepidemic[44].Duringthethirdstep,theinvasionofanewhost byaparasitecanbeaffectedbythepresenceofotherparasites.Forexample,theimmune memory acquired from a different parasite can reduce the susceptibility of the host to other infections(i.e.,cross-immunity).Thismechanismhasbeenproposedtoexplainwhy European wild rabbits are less susceptible to the helminth Graphidium strigosum after infection by Trichostrongylus retortaeforms [13]. Note that the phenomenon of cross- immunity hasconsequences forbothwithin-(causingless-severeinfection)and between- host (reducinghostsusceptibility) dynamics.

Atthehostmeta-populationorhostcommunitylevel,someparasitesrequirespreadingtoother subpopulationsofthesamehostspeciesand/orofdifferenthostspeciestomaintainorachieve

wellasbyintrinsicfactorssuchas theage,sex,nutritionalstatus,and genotypeofthehost.

Trueparasiteinteraction:biological interactionbetweentwoormore parasites.Thereisaparasite interactionwhenoneparasite increases(synergy)ordecreases (antagonism)theinfectionrisk, diseaseseverity,and/ortransmission rateoftheotheragent(s).Interactions canbeunidirectional(parasiteA influencesparasiteB)orbidirectional (reciprocalinfluencesofAonBand ofBonA),direct(e.g.,interference competition),orindirect(e.g.,viathe hostimmunesystem),andoccur withinhostindividuals,populations, orcommunities.

Type1Thelpercell(Th1):atype ofTlymphocytetriggeredby cytokinesinterleukin(IL)IL-12andIL- 2afterthedetectionofanintracellular antigen(usuallyamicro-parasite)that inducesacellularimmuneresponse (i.e.,viacytotoxicTcellsandnatural killercells).

Type2Thelpercell(Th2):atype ofTlymphocytetriggeredbycytokine IL-4afterthedetectionofan extracellularantigen(usuallyamacro- parasite)thatinducesahumoral immuneresponse(i.e.,viaspecific antibodies).

theirlifecycle.Inthatcase,hostbehaviorssuchasdispersal(forintra-speciestransmissionto othersubpopulations)orescapefrompredators(forbetween-speciestransmission)arefunda- mentalfactorsthatcanbeaffectedbyotherparasites.Forinstance,hostdispersalisprobably severelyreducedbypathogensinducinghighfever.Regardinginter-specifictransmission,one parasitemaymanipulatethebehaviorofitsintermediatehostinawaythatfavorsitstrophic transmission,impactingsimultaneouslyonthetransmissionofotherparasites(reviewedin[45]).

Forexample,thelarvaeofthreespeciesofhelminthwereshowntomanipulatethebehaviorof their common intermediate host,acrab, inaway thatincreases their transmission totheir definitivehost,ashorebird[46].Itisworthnotingthatsuchaninteractionatthehostcommunity levelcanalsocreatea‘false’(i.e.,statistical)interactionatthedefinitivehostlevel.Parasitesthat aretransmittedtogetherwillbepositivelyassociatedwithinhostindividualseveniftheydonot interactintheirdefinitivehost[47].

Toconclude,parasite–parasiteinteractionscanoccuratdifferentlevelsoforganizationthrough different mechanisms.Interactions atthe intra-hostlevel havebeenmuch moreextensively documented than those at the upper levels. However, the latter should not be neglected because(i)theycanbefundamentalfactorstounderstandthespreadandimpactofimportant pathogens,and(ii)theycanproduceparasiteassociationsatupperlevelsthatareimportantto disentanglefromintra-hostinteractionmechanisms.

Immune system Resources Within-host scale

Populaon scale (B)

(A) (C) Upper scales

Transmission to other sub- populaons

Transmission to other

species

Infected host

Suscepble host Transmission to

other hosts

= Exit from the host + Contact with suscepble hosts

+ Successful infecon Parasite community

Figure1.Parasite–ParasiteInteractionsMayOccuratDifferentLevelsofOrganization.(A)Atthewithin-host level,parasitesmayinteracteitherdirectly(byaffectingeachother'ssurvivaland/orreplicationrates)orindirectlythrough resourcecompetitionorthroughthehostimmuneresponse.(B)Atthehostpopulationlevel,parasitetransmissionresults fromthreesuccessivesteps[exitfromthehost,at-riskencounterwithsusceptiblehosts,andparasiteentrywithinnewhost (s)]duringwhichthepresenceofotherparasitesmayplayanimportantrole.(C)Atthehostmeta-populationorcommunity level,thepresenceofmultipleparasitesmayaffecttheprobabilityofsuccessfultransmissionofagivenparasitetodifferent sub-populationsofthesamehostspecies(e.g.,throughbehavioralmanipulationaffectingdispersal)ortodifferenthost species.

UsingCascadeEffectstoDetectParasite–ParasiteInteractionsintheWild?

Beyond the needto further investigate the mechanisms ofparasite–parasite interactionsin naturalsystems,andinparticularatless-investigatedscales,littleisknownonhowinteractions translateacrossotherlevelsoforganizationandimpactonthespatiotemporaldynamicsofthe parasitesatlevelsatwhichtheirinteractiondoesnotinitiallyoccur.Thesecascadeeffectsmight beofparticularinteresttodetectparasite–parasiteinteractionsinthewild,andarediscussed below.

EvidenceofCascadeEffectsintheWild

Intheory,assoonasaparasite–parasiteinteractionaltersthetransmissionrateofoneorboth parasite(s)tootherhostindividuals,species,orsubpopulations(i.e.,toonelevelhigherthanthat at which the parasites interact), it should cascade to the upper levels of organization and translateintospecificepidemiologicalpatternsandspatiotemporaldynamicsforeachparasite.

Thestrengthoftheassociationpatternsbetweentheparasitesmay,however,bedilutedasit proceedsfromonelevelthenext;itsstrengthshoulddependinpartoninitialstrengthofthe interaction. Within-host antagonisms due to phenomena such as cross-immunity and Table2.MechanismsofIndirectParasite–ParasiteInteractionsattheWithin-HostLevel

Interaction Through

Effect^a Mechanism Details Examples Refs

Immune system

Positive Immunosuppression Aparasitereducesthe efficacyoftheimmune system

HIV-1–opportunistic infections,HIV-1–malaria

[8]

Varroadestructor–deformed wingvirusinhoneybees

[18]

Positiveor negative

Alterationofimmune cellactivation

Aparasitealtersthe activationstateof potentialhostcellsor immunecells

HIV–humancytomegalovirus (lymphocyteactivation)

[76]

Th1/Th2trade-off Theinductionbya parasiteofaTh1(orTh2) responseinitshost reduceshostabilityto fightagainstaparasite inducingaTh2(orTh1) response

Helminths(Th2)–

Plasmodium(Th1)

[77]

Negative Cross-immunity Hostimmunitytoone parasiteiseffective againstan

antigenically-similar butdifferentparasite

Trichostrongylus retortaeforms–Graphidium strigosuminwildrabbits

[13]

Resources Negative Competition Indirectcompetition betweenparasitesusing thesamelimiting resource

Niche-shiftingof gastrointestinalhelminths inwildrabbits

[13]

Micro-parasitesinfecting redbloodcells–helminths causinganemia

[28]

Reductioninbodysize andeggproductionof gastrointestinalhelminths

[74]

Positiveor negative

Alterationof receptorexpression

Aparasitealtersthe expressionofhost receptorsforanother parasite

Humanherpesvirus6 inducestheexpressionof CD4receptors,thereceptors forHIV-1,onthesurfaceof Tcells

[78]

aOnoneorbothparasite(s).

immunologicaltrade-offswereshowntoleadtoseparategeographicaldistributionsorasyn- chronousepidemicsatthepopulation,meta-population,and/orupperlevels.Jollesetal.[41]

showed, for instance, that immunological trade-offs, together with accelerated mortality of coinfectedAfricanbuffalos,ledtonegativeassociationsoftuberculosisandintestinalworms acrossdifferentscales:withinherds,amongherds,andatthewhole-populationlevel.Cascade effectsofparasite–parasiteinteractionscanalsotranslateintomodificationsofthe temporal dynamicsoftheinteractingparasites.Cross-immunityphenomenabetweentwoguthelminths of the wild rabbit (Oryctolagus cuniculus) were shown to induce a shift in the seasonal abundanceofthetwoparasitesintherabbitpopulation,forcingthemtobeoutofphase[48].

Bycontrast,synergismsmaycreateoverlappingdistributionsand/orsynchronousepidemics [49].Positivecascadingbetweenthewithin-hostandthehostpopulationlevelswilloccuras soon as aparasite–parasite interactionincreases theacquisitionand/ortransmission ofthe interactingparasites.CoinfectionbytherespiratorybacteriaBordetellabronchisepticaandthe gastrointestinal helminth Heligmosomoidespolygyrus turns for instance mice into helminth super-shedders[42].

Inaddition,changesinthespatiotemporaldynamicsofparasitesotherthanthetargetedspecies followingbig campaignsofvaccinationortreatmentcould beindicativeofparasite–parasite interactionsbecausesuchinterventionscanrevealthepresenceofotherpathogensthatwere attenuated before [23,50,51]. This suggests that parasite–parasite interactions might also cascadedown,andthiswoulddeservemoreattention.

OntheNeedforMethodologicalTools

Severalstudies thereforesuggestthat parasite–parasite interactionsdocascade up across levelsoforganization.Thepatternsofassociationobservedateachlevelarelikelytodependon the underlying mechanisms of the parasite–parasite interaction and on its impact on host demography, highlighting theimportance ofadopting a mechanisticapproach. In addition, processesatthewithin-hostlevelmaynotalwaysbevisibleatthepopulationorcommunitylevel, or,conversely,associationpatternsmaynotresultfromparasite–parasiteinteractionsbutmay beduetoother,confounding,factors.Riskfactorssharedbytwoormoreparasites(i.e.,factors increasing the probability of infection by both/all parasites) can, for instance, create false interactions, in other words statistical associations between parasites that do not interact biologically (Box 1). False interactions can occur at the level of the host individual (e.g., confoundingeffectsofhostage,sex),population(e.g.,confoundingeffectsofhostsex-ratio, age-structure, density), or landscape (e.g., confounding effects of geological and climatic factors).Thelattercaninfluencethestructureofparasitecommunities,asshowninthePuumala virus–helminth–bankvole(Myodesglareolus)system[52].Althoughbothparasiteswerepresent intheentirestudyarea,theirassociationinbankvoleswasonlyobservedinthenorthernpartof theregionthatisdistinctfromtheSouthintermsofclimateandprimarysoils.Thesefactors,that affectboththedevelopmentandsurvivalofhelminthtransmissionstagesandPuumalavirus prevalence,createdfalseassociationsbetweentheparasitesatthescaleofthestudysite[52].

Climaticeventscanalsosynchronizesomeepidemicsthatareusuallyoutofphase[53].Using cascade effect to detectparasite–parasite interactions thereforerequires appropriate study designs,measures,andstatisticalmethods(Table3,Figure2)[54].

Newmethodologicaltoolsarebeingdevelopedtodistinguishparasiteassociationpatternsdue toparasite–parasiteinteractionsfromthoseduetoconfoundingfactorsatagivenscale(Box1).

Newmodelingtoolsarealsobeingdevelopedtointerpretpatternsacrossscales,althoughthey arefornowlimitedtosystemsinwhichtheepidemiologicalanddemographicprocessesarewell understood[54,55].Othertoolssuchasstructuralequationmodels[56],whicharelessusedin diseaseecologybutthathaveprovedtobeusefulinthestudyoftrophiccascades(andtheir

effectsondisease[57]),shouldallowmultiplecausalmodelstobecomparedrigorouslyand concurrentlyusingempiricalexperimentalandfielddata.

StudiesatWhatScale(s)?Trade-OffsBetweenEaseofDataCollectionandDataAnalysis Field studies are necessarily carried out on only a limited range of scales. The scales of observation maybechosendeliberatelytoelucidatekeyfeaturesofthenatural system,but areoftenimposedonusbyourperceptuallimitationsorbytechnologicalorlogisticalconstraints.

Eachscalepresentsadvantagesanddisadvantages,andtrade-offsmustbemadebetweenthe ease of data acquisition and the purpose of the study. Studies at lower levels, such as experimental coinfections,give access to molecular mechanisms.Field protocolssurveying hostcoinfectionstatusinformoncoinfectionprobabilitiesandonsymptoms(cross-sectional andlongitudinalstudies),aswellasontheirimpactonsurvivalandreproduction(longitudinal studies).Thesefieldmeasuresarehighlyvaluablebecausetheycanbedrasticallydifferentfrom thosetakeninthelab.Atupperlevels,associationsbetweenparasitescanbesuspected,but decipheringtheunderlyingmechanismsisalmostimpossible.

Box1.IntheField:DealingWithConfoundingFactors

Innaturalpopulations,thestudyofmicro-parasitesisoftencross-sectionalandbasedonindirectsignssuchasspecific antibodies–inotherwords,presence/absencedata.Incontrasttomacro-parasites,whosefollow-upcanbequantitative (fecalorbloodcounts),micro-parasiteinfectionsareoftenshortandsheddingtimestoobrieftomakethesearchforthe micro-parasitesthemselvesefficient.Thiswouldrequirecapturinghostsexactlywhentheyareinfectious.Mostfielddata arethus limitedtoobservedfrequencies ofseronegativeandsingle- ordouble-seropositive individuals,with no informationonthetimeorintensityofinfection.

Inthiscontext,searchingforinteractionsconsistsofdeterminingwhetherparasitesaremoreoftenassociatedthanwould beexpectedbychance.AclassicalmethodtotestforthishypothesisisthePearsonchi-square(x²)test.Itcomparesthe observedfrequenciestothoseexpectedifparasitesaretrulyindependent,underthenullhypothesisthatthejoint distributionofthecellcountsinacontingencytableistheproductoftherowandcolumnmarginals.However,sucha methodignoresconfoundingfactors,andsignificantassociationsdetectedinthismannercanbebiological(true)or statistical(false)interactions.Forinstance,ifmalesaremoreatriskforparasitesAandB,thesemayappearassociated eveniftheydonotinteract(FigureI).

A+

A–

B– 64 16

B+ 16 4

A+

A–

B– 4 16

B+ 16 64

A+

A–

B– 68 32

B+ 32 68

χ² = 25.92 χ² = 0

χ² = 0

Pearson’s χ² per gender (only observed frequencies are shown)

Pearson’s χ² on the whole populaon (only observed frequencies are shown)

FigureI.ParasitesAandBareStatisticallyIndependentWithinGenders(left).Conversely,thereisafalse interactionbetweenthemifonedoesnotdistinguishbetweengenders(right).Alternativemethodsallowtheexpected frequenciestobedeterminedinamodifiedx²analysisthataccountsforconfoundingfactors.Somearebasedonthe estimationof‘pre-interactive’speciesprevalence[79],andrequirepreviousknowledgeofdominancerelationships betweenparasites.Othersuselog–linearmodels(e.g.,[80])orlogisticregressionanalysis(e.g.,[41]).However,these latter methods are based on an asymptotic approximation of the deviance, which might not be relevantfor smallsamples.Whenthesamplesizeissmallrelativetothenumberofmodelparameters,onecanusethecorrected x²[30]whichestimatesexpectedfrequenciesusinglogisticregressionsandwasshowntobemorerobust.This methodenablescorrectionforconfoundingfactorsexistingatanyorganizationlevel(e.g.,attheindividuallevel,atthe landscapelevel).

Table3.DifferentStudyDesignstoAnswerDifferentQuestions

StudyDesign Level(s) DataType Aims Limits Refs

Experimental(lab)

Experimental coinfectionof animalmodels orcells

Molecule, cell,host

Quantitative^a Presence/absence^b Clinical^c

Locationofparasites withinhost

Identifyunderlying mechanisms Determinethe directionofthe interaction

Evaluatetheinfluence oftheorderof infection Searchforco- evolutionarypatterns betweenparasites andbetweenhosts andparasites

Requiressuspicionof interaction

Mainlyappliedto infectionsimpacting onhosthealth Simplifiedunrealistic system,ignoresthe ecologicalcontext

[81]

Case–controlstudy(field) Comparisonof

coinfected hoststoa controlgroup

Host Quantitative^a Presence/absence^b Clinical^c

Locationofparasites withinthehost

Assesstheimpactof simultaneous infectionsonsurvival anddiseaseseverity

Nohistorical information(timeor orderofinfection, previousinfections) Controlgroup (susceptibles;singly infected)hardto generate

[82]

Cross-sectionalstudy(field) Hostsfromone

orseveral population(s) aresampled onceatthe sametime

Host, population, community, ecosystem

Onesampleperhost Quantitative^a Presence/absence^b Clinical^c

Locationofparasites withinhost

Identifyinteractions Estimateprobabilityof coinfection

Forcommonparasites Nohistorical information(timeor orderofinfection, previousinfections)

[30]

Longitudinalstudy(field) Hostsare

repeatedly sampledovera specified periodoftime

Host, population, community, ecosystem

Chronologicaldata Quantitative^a Presence/absence^b Clinical^c

Locationofparasites withinhost

Identifyinteractions Estimateprobabilityof coinfection Determinethe directionofthe interaction

Evaluatetheinfluence theorderofinfection Evaluatetheimpactof coinfectionsonhost fitness

Studycascadeeffects

Hardertocarry Requiressignificant efforts(costly,longin duration)

Possibleethical problemsinnatural populations(frequent capturesorsampling)

[15]

Interventions(includingrandomizedcontrolledtrials)(laborfield) Comparisonof

hostsreceiving ornota medical intervention (random allocation).

Host, population, community, ecosystem

Chronologicalor paireddata Quantitative^a Presence/absence^b Clinical^c

Locationofparasites withinhost

Evaluatetheimpactof anintervention(e.g., treatment)againsta coinfection(e.g.,for thehost;forregional prevalence) Identifyparasite– parasiteinteractions Studycascadeeffects

Possibleethical problems(somehosts arenottreated/

vaccinated)

[22,51]

aParasiteload;immunologicalparameters(e.g.,cellcounts).Quantitativedataarerarerinnaturalpopulations,especiallyfor micro-parasites,butshouldbecomemorefrequentwiththedevelopmentofnewdiagnostictools.

bSpecificantibodies.

cSymptoms(nature,evolution);durationofinfection.

Nevertheless,upperlevelsoforganizationareveryimportantbecauseempiricaldataacquisition tendstobeeasierathigherlevelsinthefield.Thesearchforparasite–parasiteinteractionsinthe labisvery costlyandrequiresknowing whattolookatandwhere,whereas comparingthe geographicdistributionoftwoanimalpathogenscanbeachievedusingexistingepidemiological data.Fieldprotocolsmonitoringhostcoinfectionstatusinvolveintermediatecosts.Theyareless expensivethanlabstudies,butrequirespecificprotocolsandhencecannotrelyonpre-existing data.

Asageneralapproximaterule,fieldstudiescarriedoutatincreasinglevelsoforganizationwillbe associatedwith(i)reducedcosts,(ii)complexdataanalysesowingtomoreconfoundingfactors, and(iii)pooreraccessto theunderlyingmechanisms.Whenpessimistsmightconcludethat thereisnoidealstudylevel,weprefertoviewcooperationbetweenstudiesatdifferentlevelsas anopportunitytoimprovetheefficiencyofstudiesonparasiteinteractions.Becausetheyare lesscostly,upperlevelscanbeusedtoidentifypairsofparasitesthatarelikelytointeractandto motivatestudiesatlower(mechanistic)levels.

Collaborations between several teams and disciplines (e.g., microbiologists, virologists, diseaseecologists,communityecologists,socialscientists)willfacilitatemulti-scalestudies and improveour understandingofthe complexsystems that disease ecology necessarily

Intra-host level (lab)

interacon mechanisms

Host level (lab or field)

morbidity and mortality

Host community (field) Spaotemporal

scales (field) - Parasite replicaon rate

- Gene expression - Cellular receptors - Molecular interacons - …

- Parasite load - Symptoms

- Immunological parameters - Behaviour

- Shedding intensity - Reproducon - …

- Co-incidence - Co-prevalence - Transmission rates - Survival and

reproducon rates - …

In the different species and sub-populaons -

Agent1

Time

Incidence

Key:

Agent2

Disease incidence - Disease prevalence - Infecon and coinfecon

probability - … Disease spaotemporal

dynamics accross habitats and landscapes

Host populaon level (field)

co-circulaon impact on host

fitness

Agent1 Key:

Agent2

Agent1 Key:

Agent2

Time

Incidence

Time

Incidence

Host species

Prevalence

Figure2.DetectionofParasite–ParasiteInteractions:FromtheMoleculartotheEcosystemLevel.Parasite interactionscanbedetectedfromintra-hostlevels(i.e.,frommoleculestoorgans)tothegloballevel.Aninteraction occurringatagivenlevelmayspreadtohigherlevelsoforganization,affectingthe(spatiotemporal)dynamicsofthe interactinginfectiousagentsattheupperlevels.Ateachlevel,differentspecificmeasurescanbe made(italicized).

Interpretingtheobservedpatternsintermsofbiologicalinteractionsisrendereddifficultbytheexistenceofseveral confoundingfactorswhosenumberaccumulatesastheinteractioncascadestotheupperlevelsoforganization.