Systems Biology Approaches to Understanding the Human ...

Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. ThisarticleispartoftheResearchTopic RecentAdvancesinPrecisionVaccineDiscovery&Development Viewall 26 Articles Articles JayEvans UniversityofMontana,UnitedStates GeertLeroux-Roels GhentUniversity,Belgium SUSANAMAGADAN BiomedicalResearchCenter,UniversityofVigo,Spain Theeditorandreviewers'affiliationsarethelatestprovidedontheirLoopresearchprofilesandmaynotreflecttheirsituationatthetimeofreview. Abstract Introduction SystemsToolsforNetwork-BasedAnalysesUsingPPIs MechanisticInsightsIntoHumanImmuneDevelopment MechanisticInsightsIntoHost-PathogenInteractions Mechanism-BasedBiomarkersforDiseaseDiagnosisandPrognosisPrediction DrugDiscoveryandRepurposing DiscussionandtheFuture AuthorContributions Funding ConflictofInterest Acknowledgments Abbreviations References SuggestaResearchTopic> DownloadArticle DownloadPDF ReadCube EPUB XML(NLM) Supplementary Material Exportcitation EndNote ReferenceManager SimpleTEXTfile BibTex totalviews ViewArticleImpact SuggestaResearchTopic> SHAREON OpenSupplementalData PERSPECTIVEarticle Front.Immunol.,30July2020 |https://doi.org/10.3389/fimmu.2020.01683 SystemsBiologyApproachestoUnderstandingtheHumanImmuneSystem BhavjinderK.Dhillon1,MarenSmith1,ArjunBaghela1,AmyH.Y.Lee1,2andRobertE.W.Hancock1* 1CentreforMicrobialDiseasesandImmunityResearch,UniversityofBritishColumbia,Vancouver,BC,Canada 2MolecularBiology&BiochemistryDepartment,SimonFraserUniversity,Burnaby,BC,Canada Systemsbiologyisanapproachtointerrogatecomplexbiologicalsystemsthroughlarge-scalequantificationofnumerousbiomolecules.Theimmunesysteminvolves>1,500genes/proteinsinmanyinterconnectedpathwaysandprocesses,andasystems-levelapproachiscriticalinbroadeningourunderstandingoftheimmuneresponsetovaccination.Changesinmolecularpathwayscanbedetectedusinghigh-throughputomicsdatasets(e.g.,transcriptomics,proteomics,andmetabolomics)byusingmethodssuchaspathwayenrichment,networkanalysis,machinelearning,etc.Importantly,integrationofmultipleomicdatasetsisbecomingkeytorevealingnovelbiologicalinsights.Inthisperspectivearticle,wehighlighttheuseofprotein-proteininteraction(PPI)networksasamulti-omicsintegrationapproachtounravelinformationflowandmechanismsduringcomplexbiologicalevents,withafocusontheimmunesystem.Thisinvolvesacombinationoftools,including:InnateDB,adatabaseofcuratedinteractionsbetweengenesandproteinproductsinvolvedintheinnateimmunity;NetworkAnalyst,avisualizationandanalysisplatformforInnateDBinteractions;andMetaBridge,atooltointegratemetabolitedataintoPPInetworks.Theapplicationofthesesystemstechniquesisdemonstratedforavarietyofbiologicalquestions,including:thedevelopmentaltrajectoryofneonatesduringthefirstweekoflife,mechanismsinhost-pathogeninteraction,diseaseprognosis,biomarkerdiscovery,anddrugdiscoveryandrepurposing.Overall,systemsbiologyanalysesofomicsdatahavebeenappliedtoavarietyofimmunology-relatedquestions,andherewedemonstratethenumerouswaysinwhichPPInetworkanalysiscanbeapowerfultoolincontributingtoourunderstandingoftheimmunesystemandthestudyofvaccines. Introduction Inthefieldofimmunology,asystemsbiologyapproachisnecessarytounderstandingtheimmuneresponsetovaccination,infectionanddiseases,sincetheseinvolvecomplexinteractionsbetweenalargenumberofgenetic,epigenetic,physiologicalandenvironmentalfactors.Systems-levelstrategiescanultimatelybeappliedtobetterunderstandthemolecularchangesinhumansuponexposuretoavaccineoranimmunotherapeutic,tounderstandthemechanismsunderlyingdiseaseorpathogenesis,andtocharacterizetheeffect(s)ofspecificchallengestotheimmunesystem(1–5).Omicstechnologiesoffertheabilitytomeasuresuchaspectsinanunbiasedwaythatishigh-throughputandcost-effective.Severalomicsmethodshavebeenemployedinthecontextofsystemsvaccinology(3),includingbutnotlimitedto,wholegenomesequencing(genomics),RNA-SeqformeasuringmRNAlevels(transcriptomics),high-throughputmassspectrometryformeasuringproteinlevels(proteomics)andmetabolitelevels(metabolomics),CHiP-Seqfordeterminingtranscriptionfactorbindingsites,ATAC-SeqtoidentifyDNAmodificationsites(epigenomics),16SrRNAsequencingformicrobiotaprofiling(microbiomics),andequivalentomicsanalysesperformedatthesingle-celllevel.Recently,therehasalsobeenagrowingefforttoobtainmultipleomicsprofilesinthesameindividuals,sincesharedinsightsacrossomicsdatasetsstrengthenslinksbetweenunderlyingbiologicalmechanismsandresponsesofinterest,andcanprovidemorereliableinterpretationofgenefunction,higher-levelchangesandnovelinsightsnotobservedinsingle-omicsstudies(6–8).Overall,biologicalsamplescanbemanipulatedtogeneratenumerousomicsdatasets,andcanbeappliedtostudyhowourimmunesystemseliciteffective,therapeuticand/orpathologicalresponses. Akeychallengeinsystemsbiologyisbuildingtheappropriatebioinformaticstoolstointegrateomicsdatasets,ultimatelyenablingthecorrelationofglobalchangeswiththeunderlyingbiologicaleventsthatdrovethosechanges.Statisticalandmachinelearningapproacheshavebeenappliedtoomicsdatasets[reviewedpreviously(9–11)]toidentifysetsofmolecularfeaturesthat(i)aredysregulated/correlatedwithobservedphenotypes,(ii)canbeusedasbiomarkerstopredictobservedphenotypes,or(iii)canbetargetedbydrugsforimprovedtherapies.Awidearrayoftoolsareavailabletorunsingle-ormulti-omicsanalysispipelines(12),includingcommercialplatformsandmorerecentlypublished“self-serve”platforms[e.g.,OmicsNet(13),OmicsPlayground(14)].Typically,suchmethodsinterrogateinformationineitherasupervisedorunsupervisedmanner;supervisedmethodsidentifydifferencesbetweenlabeledomicsdatafromdifferentconditions(e.g.,respondersvs.non-respondersortreatedvs.untreated)while,unsupervisedmethodsrevealglobalpatternsofgenedysregulationwithoutanylabels. Downstreamcharacterizationofdysregulatedmoleculescanfurtherourunderstandingofunderlyingbiologicalmechanismsatplay.Thiscanbeachievedbyinterrogatingcuratedfunctionalgenomicsinformationfromdatabasesofgeneontologies(functionaldescriptions),pathways,knowninteractors,transcriptionfactorbindingsites(TFBS)upstreamofdysregulatedgenes,etc.throughvariousenrichmentanalyses.However,alargeproportionofgeneshavenotbeenassignedtocanonicalpathwaysinpathwaysdatabases(suchasKEGGorReactome),sopathwayenrichmentlimitstheabilityofsuchapproachestorevealnovelinsights(15). Theuseofbiologicalnetworksisapowerfulapproachtointegratemulti-omicsdatatoidentifynovelbiologicalinsights(15–18).Tocharacterizetheroleofindividualmolecularfeaturesinlargercellularprocessesandglobalchangesusingnetworksinvolveseitheroverlayingomicsdataonexperimentally-derivedknownnetworks[e.g.,protein-proteininteraction(PPI)networks],orbyinferringnetworksdirectlyfromthedata[e.g.,co-expressedgenes(19)],thestrengths/limitationsofwhichhavebeenreviewedpreviously(15).AfewcommonlyusedbiologicalnetworksalongwithrelatedresourcesandtoolsaresummarizedinTable1.TheapplicationofPPInetworkstointerrelatedysregulatedgenesisaverypowerfulmethodforrevealingthesystems-levelflowofinformationthroughkeyhubs(highlyconnectedproteinnodes)andsubnetworks.BecausePPIsincludedirect,metabolic,andregulatoryinteractionsbetweenproteins,theyessentiallychartpotentiallybiologicallyrelevant,i.e.,functional,interconnections.Thiscanenablethedeterminationofemergentproperties,whichareessentiallynewbiologicalinsightsintotheprocessesdrivingtheobservedtranscriptionaldifferences.TheresultsfromaPPInetworkanalysisarealwaysframedashypothesesratherthanknowledgeperse,andmustbeeventuallytestedusingdownstreamwetlabexperiments(15). TABLE1 Table1.Examplesoffunctionalbiologicalinformationthatcanberepresentedusingnetworks,alongwithcorrespondingdatabases/repositoriesandsupplementarydataanalysistoolsthatcanbeusedtoassessthefunctionaldatainhigh-throughputomicsdatasets. Inthisarticle,weprovideanoverviewofthephilosophiesandmethodologiesthatcanbeemployedintheanalysisofomicsdata,especiallywithregardstointegrationofomicsdatasetsusinganunsupervisednetworkanalysisapproach.Examplesareprovidedofhowsuchanalysesenablenovelhypothesisgenerationfor:(a)immunesystemdevelopment,(b)mechanismsofhost-pathogeninteractions,(c)discoveryofmechanism-basedbiomarkers,and(d)strategiestodefineprospectivenewinterventionsbasedondrugrepurposing.Whilethemethodsaresomewhatbiasedtowardthestudyofinnateimmuneandinflammatoryresponses,itisworthmentioningthat“innateimmunityinstructsadaptiveimmunity”(63)inthat(i)theeffectorsofadaptiveimmunityareofteninnateimmunemechanisms,(ii)manyofthepathwaysinvolvedarethesame,and(iii)vaccineadjuvantsthatimproveadaptiveimmuneresponsesboostinnateimmunity.Therefore,thetoolswedescribehavevalueininvestigatingadaptiveimmunityaswellashumangeneticdiseases/conditionswithanunderlyinginflammatorypathology. SystemsToolsforNetwork-BasedAnalysesUsingPPIs InnateDB(43,44)andotherInternationalMolecularExchange(IMEx)consortiumdatabases(42)providethebasisforunderstandingbiologicalconnectionsincellsaccordingtoknowninteractionsbetweenmolecularelements,suchasproteins.InnateDBisapubliclyavailabledatabase(www.innatedb.com)focusedonelucidatingthegenes,proteins,andmolecular“interactome”oftheinnateimmuneresponse,withanemphasisoncurationofexperimentally-validatedPPIsandsignalingpathwaysinhuman,mouseandbovine.Theinteractomecanbeusedtounderstandtheinterplaybetweenmulti-omicsdatasetsthatmeasuredifferentpartsofalargersystemofphysical,metabolic,andregulatorynetworks.Forexample,humanTRAF6andMyD88areusuallydefinedashavingaroleinthemajorTLR4toNFκBsignalingpathwayofinnateimmunity.However,inInnateDB,theyareexperimentallydocumentedtointeractwith398and129proteins,respectively,inhumans.Thismeansthatthereisamassivepotentialfortheseproteinstobridgeand/orparticipateinmultiplebiologicalpathwayswhenactivatedbyinnateimmunestimuli. InnateDBisanimportanttoolinimmunologyasevidencedbythe>6,000,000hitsfrommorethan55,000visitorsannually.Whileallknownpathways(>3,500)andmolecularinteractions(318,000inhuman)arepresent,theemphasisoninnateimmunityisachievedthroughthecontextualreview,curationandannotationofmolecularinteractionsandpathwaysinvolvedininnateimmunity.Todate,theInnateDBcurationteamhasreviewedmorethan5,200publicationsannotating>27,000molecularinteractionsof>9,400separategenesinrichdetailincludingannotationofthecell,cell-lineandtissuetype;themoleculesinvolved;theinteractiondetectionmethod;etc.Byincludinginteractionandpathwaydatarelevanttoallbiologicalprocesses,amuchbroaderperspectiveofinnateimmunitycanbeachieved,especiallysinceaneffectiveinnateimmuneresponserequiresthecoordinatedeffortsofmanyimportantprocessesincludingtheendocrine,circulatory,andnervoussystems(64).Additionally,itbecomespossibletoinvestigateanybiologicalsignalingprocessofinterestbeyondtheimmunesystem,aswellasinflammationandadaptiveimmunity. InnateDBfacilitatessystems-levelanalysesbyenablingtheintegration,analysisandvisualizationofuser-suppliedquantitativedata,suchasgeneexpressiondata,inthecontextofmolecularinteractionnetworksandpathways.Thisincludesthestatisticallyrobustanalysisofoverrepresentedpathways,interactomes,ontologies,TFBS,andnetworks.Onecan,forexample,refinethenetworktoshowonlymolecularinteractionsbetweenalistofdifferentiallyexpressed(DE)genes(andtheirencodedproducts)orviewallpotentialinteractorsregardlessofwhethertheyareDE.Thiscanaidintheidentificationofimportantnodesthatmaynotberegulatedtranscriptionallyorwhichareexpressedatanearlierorlatertime.NetworksderivedfromInnateDBcanbeinteractivelyvisualizedusingtheCerebralplug-inforCytoscape(65)togeneratebiologicallyintuitive,pathway-likelayoutsofnetworks,orinamorerecentlydevelopedtool,NetworkAnalyst(50–52).NetworkAnalystisanextremelyfastnetworkanalysisandvisualizationtoolfortheanalysisofgeneexpressiondatainthecontextofPPInetworks.Inaddition,MetaBridge(21)isatoolthatcanbeusedfortheintegrationofmetabolite-proteininteractionsintotheseexistingnetworks.Incombination,thesetoolscanbeusedtoperformmulti-omicsintegrationoftranscriptomics,proteomics,andmetabolomicsdatainanunsupervisedmanner. Inadditiontotheseoutlinedmethods,therearebioinformaticstoolsavailableforperformingothertypesofnetworkanalysesspecificallyforstudyingtheimmunesystem.ExamplesincludeimmuneExpresso(66),adataminingtoolbuiltaspartofImmporttocaptureinter-cellinteractions,andOntogenet(67),acomponentoftheImmGendatabaseenablingconstructionofgeneregulatorynetworksbasedonsetsofco-expressedgenes.Suchtoolscanbeusefulinrevealingnovelinter-cellinteractionsorregulatoryfactors,respectively,butultimatelymaybetoolimitedinscopeforasystems-levelanalysis.Thus,wefocushereonhowPPI-basednetworkanalysistoolscanbeappliedtobetterunderstandhumanhealthanddisease. MechanisticInsightsIntoHumanImmuneDevelopment Mostrecently,asapartoftheEPIC-HIPCconsortium,wepublishedastudythatrevealedarobustdevelopmentaltrajectoryofimmuneontogenyduringthefirstweekoflifeinnewbornsusingamulti-omicsintegrationapproach(9).Transcriptomic,proteomic,andmetabolomicdatawerederivedfrom<1mlofbloodcollectedfromWestAfrican(TheGambia)neonatesattwotimepoints:dayoflife(DOL)0andasecondDOL,either1,3,or7. Importantly,throughthisstudy,wewereabletoshowthatmulti-omicsintegrationusingPPInetworks(throughNetworkAnalyst,InnateDB,andMetaBridge)providedsimilarbiologicalinsights,butgreaterdepth,whencomparedtodata-drivensupervisedintegrationapproaches[namely,DIABLO(68)andMultifactorialResponseNetwork(MMRN)(69,70)].Majorobservationsfromthisstudyrevealedthatthefirstweekoflifeishighlydynamic;DOL0andDOL1werequitesimilarwithfewDEgenes,butbyDOL3,1,125DEgenesweredetected,and1,864DEgenesbyDOL7.Theserepresentedseveralkeypathwaysinimmunedevelopment,mainlycenteredaroundinterferonsignaling,thecomplementcascade,andneutrophilactivity.Thesehavepreviouslybeenshowntoplayaroleinthenewbornimmuneresponsetoinfection,butuntilthisstudywerenotidentifiedascentraltoontogenyinthefirstweekoflife.Importantly,thesepathwaysandnearly60%oftranscriptomicchangeswereconfirmedinasecondindependentcohortofneonatesfromPapuaNewGuinea/Australasia,revealingthatneonatalimmunedevelopmentisnotrandom,butfollowsapreciseandpossiblypurposefulage-specificpath. AnunsupervisedPPInetworkwasusedtointegratethetranscriptomic,metabolomics,andproteomicdatatorevealasinglefunctionalnetwork,highlightingthatindividualomicsdatasetsarecomplementary,reportingdifferentfacetsofthesamebiologicalprocesses.Forexample,boththetranscriptomicandproteomicdataconfirmedtheincreaseintypeIinterferon-relatedfunctionsandtheregulationofcomplementcascades.Importantly,thisintegrationalsorevealednovelnodesinthePPInetworkthatwerenotidentifiedbyanysingle-omicsdatasetonitsown,representingnovelbiologicalinsights,includingchangesincellularreplicationmachinery,creatininemetabolism,fibrinclottingcascade,adaptiveimmunitymarkersandphagosomeactivity. Thus,thesesystemsbiologyapproachesallowednovelinsightsintotheimmunedevelopmentaltrajectoryduringthefirstweekoflifeinnewborns.Furtherstudiesarebeingconductedtoprovideinsightsintothemechanisticdifferencesinthesusceptibilityofneonatestoinfection-relateddiseaseordeathduringthiscriticalphaseoflife.Also,inthecontextofvaccinology,anintegrativesystemsbiologyapproachisbeingusedtorevealmechanisticinsightsintothemoleculardeterminantsofvaccinationefficacy,whiletakingintoaccountthisdevelopmentaltrajectory. MechanisticInsightsIntoHost-PathogenInteractions Systemsbiologymethodshavealsobeenleveragedtostudyhost-pathogeninteractions(71).OneexampleisofinfectionbytheobligatehumanintracellularpathogenChlamydiatrachomatis,themajorcauseofbacterialsexually-transmitteddiseases(STDs)andpreventableblindnessworldwide.ThisinvolvedastudythatcoupledtranscriptomicsandproteomicstoassessthemacrophageresponsestoinfectionwithC.trachomatis(72).Macrophageswerederivedfromhumaninducedpluripotentstemcells(iPSdMs),whichshare>95%similarityintermsofgeneexpressionwithprimaryhumanbloodmonocyte-derivedmacrophages,andwereabletosupportthegrowthofC.trachomatisintracellularlytomimicinfectionin-vitro. Pathwayanalysisof2,029DEgenes(fromtranscriptomics)and307DEproteins(fromproteomics)at24hpost-infection,revealedstronginterferonα,β,andγresponses,anddysregulationofvariousToll-likereceptorpathways,theendosomal/vacuolarpathway,energymetabolism,andmetabolismofaminoacidsandnucleotidesandinhibitionoftranslation.MostsignificantlyupregulatedweregenesassociatedwithtypeIinterferonsignaling,includingkeytranscriptionfactorssuchasinterferonregulatoryfactors(IRF)-1,3,and7,whichareknowntocontributetotheregulationoftypeIinterferonsduringChlamydiainfection. Importantly,IRF5andIL-10RA,notpreviouslycharacterizedfortheirroleinChlamydiainfection,wereidentifiedaskeyplayersinlimitinginfectioninmacrophages.Indeed,IRF5−/−andIL-10RA−/−mutantiPSDMcellswerebothshowntohaveincreasedsusceptibilitytoC.trachomatisinfection.Theseresults,alongwithnumerousotherpublishedstudies[e.g.,(73–77)],demonstratethatmulti-omicsintegrationusingPPInetworkscanrevealnovelinsightintothefactorsthatplayasignificantroleinthehostimmuneresponsetoinfections. Mechanism-BasedBiomarkersforDiseaseDiagnosisandPrognosisPrediction Systemsbiologyanalyseshavealsoledtoinsightsintomechanismsunderlyingdiseaseprognosisandpredictionofdiagnosticbiomarkers.OnesuchstudyoftheentericpathogenSalmonellaentericasv.Typhimurium(78)involvedtheuseoftranscriptomicstocomparegeneexpressioninHIVpatientswithandwithoutsevereinvasivenon-typhoidalSalmonella(iNTS)infections,aswellasHIVpatientswithotheracutebacterialinfections(includingE.coliandStreptococcuspneumoniae).Initially,1,200geneswereupregulatedinHIVpatientswithiNTSandwithotheracutebacterialinfections,comparedtoHIVpatientswithoutabacterialinfection.However,genesupregulatedinpatientswithnon-Salmonellaacuteinfectionsshowedenrichmentforpathwaystypicallyassociatedwithinnateimmune/inflammatoryresponses,whileconverselythegeneexpressionresponseinpatientswithiNTScouldbeexplainedbyupregulationofgenesthatareassociatedwithsuppressionofinflammation(NFKBIB,PI3K,REL,SIGIRR,SOCS4,SOCS7).Thislackofinnateimmuneresponseandviralsignature,whichwassubsequentlyshowntobeconsistentwithincreasedviralload(79),leadingtoinsightsintothepoorprognosisofHIVpatientswithiNTS. Thesetypesofanalyseswerealsousedtoexploreimmunemanipulationusinghostdefense(antimicrobial)peptides.Suchpeptidesselectivelymodulatetheinnateimmuneresponseandprotectagainstinfection,andareproducedbymanyorganismstodefendagainstinfections(80).Furthermore,novelsmallinnatedefenseregulator(IDR)peptideshavebeenshowntobeeffectiveinanimalmodelsagainstantibioticresistantbacteria,tuberculosis,cerebralmalaria,pre-termbirthandinflammation(81,82).TobetterunderstandthecellularcascadethatoccursaftertheseIDRpeptidesenterthecell,transcriptionalchangeswereassessedinhumanmonocytesandperipheralbloodmononuclearcells(83).Thebiologicalrelevanceofthesegeneexpressionchangeswasassessedusingpathwayover-representation,TFBSanalysis,andnetworkanalysiswithNetworkAnalyst,implicating11pathwaysincludingthep38,Erk1/2,andJNKmitogen-activated(MAP)-kinases,NFκB,twoSrcfamilykinases,andmorethan15transcriptionfactors[includingNFκB(mostsubunits),Creb,IRF4,AP-1,AP-2,Are,E2F1,SP1,Gre,andSTAT3].NetworkAnalystshowedthatsomeofthetopconnectedhubproteinswithinnetworksconstructedfromdysregulatedgeneswereinvolvedinthefunctioningofMAPkinasesandinductionofchemokines,anti-inflammatorypathwaysparticularlyTGFβ,andtypeIinterferonresponses.Thesehighlyconnectedhubsrevealmechanisticinsightsandcouldpotentiallyrepresentdiagnosticortreatmentbiomarkers.Ultimately,asimilarapproachcanbeutilizedtoevaluateanyagentperturbingcellularfunction,includingimmunomodulatorsandvaccines,andcandefinebiomarkersdifferentiatingbetweenrespondersandnon-responders. DrugDiscoveryandRepurposing Systemsbiologytechniqueshavebeenappliedtoaidindrugdiscoveryandrepurposingofexistingagentsforthetreatmentofcancers,bacterialandviralinfections,andgeneticdisorders(84).Onesuchstudyaimedatfindingbettertherapeuticsforcysticfibrosis(CF)utilizedtranscriptomicstostudyimmortalizedCFTR−/−(cysticfibrosistransmembraneregulator)epithelialcellsstimulatedforhyperinflammation,astateknowntoleadtodeteriorationoflungfunctioninCFpatients(85).GenesdifferentiallyexpressedbetweenCFTR−/−cellsandcorrectedvariantsweresubmittedtoInnateDBforanalysisandintegrationwithPPInetworks.ThisrevealedtheinterconnectivityoftheCFTRandinnateimmunenetworksthroughthePRKAA1(AMPkinase)/AKT1andHSPB1pathways.GeneswithinthisnetworkwerethensubmittedtoDrugBank(86),allowingfortheidentificationofthediabetesdrugMetforminasanAMPkinaseactivator,whichwasthentestedin-vitroandshowntoreduceinflammationby~50%.DEgenesbetweenCFTR−/−cellsandcorrectedvariantsalsoincluded54genesinvolvedinautophagy.Indiseasestates,autophagyisanadaptiveresponsetostressthatfavorsinfectionsurvivalandresolution(87).FollowupstudiesconfirmedthatCFTRmutantcellsdemonstratedarrestedautophagy.ItwasthendemonstratedthattheantimicrobialpeptideIDR-1018resolvedthisarrestedautophagystateandreducedinflammation.ThesegenesalsorevealedastrongupregulationofERstressandunfoldedproteinresponsepathways,throughactivationoftheIRE-1pathway(88).Followupstudiesshowedthatsalubrinal,aninhibitorofnegativeregulatorGADD34,upregulatedthispathwayandsuppressedinflammation.Thus,throughthesesystemsbiology-basedstudies,novelpharmaceuticals(IDR-1018)and2existingdrugs(Metforminandsalubrinal)wereidentifiedaspotentialtreatmentsforCF-relatedhyperinflammation.Assuch,alongwithnumerousotherstudies[e.g.,(89–92)],ithasbeenshownthatintegratingomicsdatasetsusingresourcessuchasInnateDBandDrugBankcanrevealpotentialdrugtargetsforimprovedtherapies. DiscussionandtheFuture Theanalysesoutlinedinthisarticlemerelyscratchthesurfaceofwhatispossibleusingsystemsbiologyandhigh-throughputomicstechniquestostudytheimmunesystem,e.g.,themajortoolsdescribedhere(43,44,50–52)havebeenusedandcitedmorethan1,500times.Theabove-describedexampleshighlightthatusingunbiasedmulti-omicsexperimentsinconjunctionwithincisivebioinformaticstools,suchasPPInetworkintegration,onecangobeyondthehypothesis-testingscientificmethodtouseunbiasedomicsdatatogeneratefundamentallynewhypothesesanddevelopnewbiologicalinsights.Ultimatelysuchstudiesshouldleadtothedevelopmentofnoveldiagnostics,individualizedtherapiesfordiseasesandvaccines.Furthermore,systemsbiologyapproachescanprovideinvaluableinsightstoinformthestratificationofindividualswiththesamesyndromebutdifferentunderlyingmechanisms,thediagnosisofdiseaseand/orflare-ups,ongoingdevelopmentofnewvaccinesand/oradjuvantsaswellasimmune-basedtherapeuticsprovidinginsightsintotheoptimalstrategiesfordeliveryofinterventions. AuthorContributions BD,MS,andABallcontributedtowritingofthefirstdraftofthisarticle.ALperformeddataanalysisoftheontogenystudyandprovidedvaluablefeedbackthroughthewritingprocess.RHsupervisedallauthors,editedthemanuscriptandprovidedcriticalinsightsandfeedback.Allauthorscontributedtothearticleandapprovedthesubmittedversion. Funding OurbioinformaticsresearchiscurrentlysupportedbyagrantfromtheCanadianInstitutesforHealthResearchFDN-154287,andpreviouslyreceivedfundingfromGenomeCanada,GenomeBC,andtheFoundationfortheNationalInstitutesofHealththroughtheirGrandChallengesinGlobalHealthResearchprogram.RHholdsaCanadaResearchChairandaUBCKillamProfessorship. ConflictofInterest Theauthorsdeclarethattheresearchwasconductedintheabsenceofanycommercialorfinancialrelationshipsthatcouldbeconstruedasapotentialconflictofinterest. Acknowledgments TheauthorswishtoacknowledgecollaboratorsFionaBrinkman,DavidLynn,JeffXia,TamaraMunzner,andpreviouslabmembersErinGill,ChrisFjell,andJenniferGardyaswellastheInnateDBcurationteamfortheirfantasticandcriticalcontributions. Abbreviations CF,cysticfibrosis;CFTR,cysticfibrosistransmembraneregulator;DE,differentiallyexpressed;DOL,dayoflife;IDR,innatedefenseregulator;iNTS,invasivenon-typhoidalSalmonella;MAP,mitogen-activated;PPI,protein-proteininteraction;TFBS,transcriptionfactorbindingsite. References 1.TrautmannL,SekalyR.Solvingvaccinemysteries:asystemsbiologyperspective.NatImmunol.(2011)12:729.doi:10.1038/ni.2078 PubMedAbstract|CrossRefFullText|GoogleScholar 2.MooneyM,McWeeneyS,CanderanG,SékalyR.Asystemsframeworkforvaccinedesign.CurrOpinImmunol.(2013)25:551–5.doi:10.1016/j.coi.2013.09.014 CrossRefFullText|GoogleScholar 3.PulendranB,LiS,NakayaHI.Systemsvaccinology.Immunity.(2010)33:516–29.doi:10.1016/j.immuni.2010.10.006 CrossRefFullText|GoogleScholar 4.ObergAL,KennedyRB,LiP,OvsyannikovaIG,PolandGA.Systemsbiologyapproachestonewvaccinedevelopment.CurrOpinImmunol.(2011)23:436–43.doi:10.1016/j.coi.2011.04.005 PubMedAbstract|CrossRefFullText|GoogleScholar 5.KotliarovY,SparksR,MartinsAJ,MulèMP,LuY,GoswamiM,etal.Broadimmuneactivationunderliessharedsetpointsignaturesforvaccineresponsivenessinhealthyindividualsanddiseaseactivityinpatientswithlupus.NatMed.(2020)26:618–29.doi:10.1038/s41591-020-0769-8 PubMedAbstract|CrossRefFullText|GoogleScholar 6.GeH,WalhoutAJ,VidalM.Integrating‘omic'information:abridgebetweengenomicsandsystemsbiology.TrendsGenet.(2003)19:551–60.doi:10.1016/j.tig.2003.08.009 PubMedAbstract|CrossRefFullText|GoogleScholar 7.LeeAH,ShannonCP,AmenyogbeN,BennikeTB,Diray-ArceJ,IdokoOT,etal.Dynamicmolecularchangesduringthefirstweekofhumanlifefollowarobustdevelopmentaltrajectory.NatCommun.(2019)10:1092.doi:10.1038/s41467-019-08794-x PubMedAbstract|CrossRefFullText|GoogleScholar 8.EbrahimA,BrunkE,TanJ,O'brienEJ,KimD,SzubinR,etal.Multi-omicdataintegrationenablesdiscoveryofhiddenbiologicalregularities.NatCommun.(2016)7:1–9.doi:10.1038/ncomms13091 PubMedAbstract|CrossRefFullText|GoogleScholar 9.ToliosA,DeLasRivasJ,HovigE,TrouillasP,ScorilasA,MohrT.Computationalapproachesincancermultidrugresistanceresearch:Identificationofpotentialbiomarkers,drugtargetsanddrug-targetinteractions.DrugResistUpdates.(2020)48:100662.doi:10.1016/j.drup.2019.100662 PubMedAbstract|CrossRefFullText|GoogleScholar 10.KiddBA,PetersLA,SchadtEE,DudleyJT.Unifyingimmunologywithinformaticsandmultiscalebiology.NatImmunol.(2014)15:118–27.doi:10.1038/ni.2787 PubMedAbstract|CrossRefFullText|GoogleScholar 11.TavassolyI,GoldfarbJ,IyengarR.Systemsbiologyprimer:thebasicmethodsandapproaches.EssaysBiochem.(2018)62:487–500.doi:10.1042/EBC20180003 PubMedAbstract|CrossRefFullText|GoogleScholar 12.BealeDJ,KarpeAV,AhmedW.Beyondmetabolomics:areviewofmulti-omics-basedapproaches.MicrobMetab.(2016)289−312.doi:10.1007/978-3-319-46326-1_10 CrossRefFullText|GoogleScholar 13.ZhouG,XiaJ.OmicsNet:aweb-basedtoolforcreationandvisualanalysisofbiologicalnetworksin3Dspace.NucleicAcidsRes.(2018)46:W514–22.doi:10.1093/nar/gky510 PubMedAbstract|CrossRefFullText|GoogleScholar 14.AkhmedovM,MartinelliA,GeigerR,KweeI.Omicsplayground:acomprehensiveself-serviceplatformforvisualization,analyticsandexplorationofbigomicsdata.NARGenomBioinform.(2020)2:lqz019.doi:10.1093/nargab/lqz019 CrossRefFullText|GoogleScholar 15.CharitouT,BryanK,LynnDJ.Usingbiologicalnetworkstointegrate,visualizeandanalyzegenomicsdata.GenetSelectEvol.(2016)48:27.doi:10.1186/s12711-016-0205-1 PubMedAbstract|CrossRefFullText|GoogleScholar 16.BarabasiA,OltvaiZN.Networkbiology:understandingthecell'sfunctionalorganization.NatRevGenet.(2004)5:101.doi:10.1038/nrg1272 PubMedAbstract|CrossRefFullText|GoogleScholar 17.Saint-AntoineMM,SinghA.Networkinferenceinsystemsbiology:recentdevelopments,challenges,andapplications.CurrOpinBiotechnol.(2020)63:89–98.doi:10.1016/j.copbio.2019.12.002 PubMedAbstract|CrossRefFullText|GoogleScholar 18.MardinogluA,BorenJ,SmithU,UhlenM,NielsenJ.Systemsbiologyinhepatology:approachesandapplications.NatRevGastroenterolHepatol.(2018)15:365–77.doi:10.1038/s41575-018-0007-8 PubMedAbstract|CrossRefFullText|GoogleScholar 19.CostaRL,BoroniM,SoaresMA.Distinctco-expressionnetworksusingmulti-omicdatarevealnovelinterventionaltargetsinHPV-positiveandnegativehead-and-necksquamouscellcancer.SciRep.(2018)8:1–13.doi:10.1038/s41598-018-33498-5 PubMedAbstract|CrossRefFullText|GoogleScholar 20.KanehisaM,GotoS.KEGG:Kyotoencyclopediaofgenesandgenomes.NucleicAcidsRes.(2000)28:27–30.doi:10.1093/nar/28.1.27 PubMedAbstract|CrossRefFullText|GoogleScholar 21.HinshawSJ,LeeAHY,GillEE,HancockREW.MetaBridge:enablingnetwork-basedintegrativeanalysisviadirectproteininteractorsofmetabolites.Bioinformatics.(2018)34:3225–7.doi:10.1093/bioinformatics/bty331 PubMedAbstract|CrossRefFullText|GoogleScholar 22.CroftD,MundoAF,HawR,MilacicM,WeiserJ,WuG.Thereactomepathwayknowledgebase.NucleicAcidsRes.(2014)42:D472–7.doi:10.1093/nar/gkt1102 PubMedAbstract|CrossRefFullText|GoogleScholar 23.MiH,ThomasP.PANTHERpathway:anontology-basedpathwaydatabasecoupledwithdataanalysistools.ProteinNetwPathwAnal.(2009)563:123–40.doi:10.1007/978-1-60761-175-2_7 PubMedAbstract|CrossRefFullText|GoogleScholar 24.YuG,HeQ.ReactomePA:anR/bioconductorpackageforReactomepathwayanalysisandvisualization.MolBioSyst.(2016)12:477–9.doi:10.1039/C5MB00663E PubMedAbstract|CrossRefFullText|GoogleScholar 25.GeneOntologyConsortium.TheGeneOntology(GO)databaseandinformaticsresource.NucleicAcidsRes.(2004)32(Suppl_1):D258–61.doi:10.1093/nar/gkh036 PubMedAbstract|CrossRefFullText|GoogleScholar 26.ForoushaniAB,BrinkmanFS,LynnDJ.Pathway-GPSandSIGORA:identifyingrelevantpathwaysbasedontheover-representationoftheirgene-pairsignatures.PeerJ.(2013)1:e229.doi:10.7717/peerj.229 PubMedAbstract|CrossRefFullText|GoogleScholar 27.MaH,SorokinA,MazeinA,SelkovA,SelkovE,DeminO,etal.TheEdinburghhumanmetabolicnetworkreconstructionanditsfunctionalanalysis.MolSystBiol.(2007)3:135.doi:10.1038/msb4100177 PubMedAbstract|CrossRefFullText|GoogleScholar 28.ZurH,RuppinE,ShlomiT.iMAT:anintegrativemetabolicanalysistool.Bioinformatics.(2010)26:3140–2.doi:10.1093/bioinformatics/btq602 PubMedAbstract|CrossRefFullText|GoogleScholar 29.BrunkE,SahooS,ZielinskiDC,AltunkayaA,DrägerA,MihN,etal.Recon3Denablesathree-dimensionalviewofgenevariationinhumanmetabolism.NatBiotechnol.(2018)36:272.doi:10.1038/nbt.4072 PubMedAbstract|CrossRefFullText|GoogleScholar 30.AgrenR,BordelS,MardinogluA,PornputtapongN,NookaewI,NielsenJ.Reconstructionofgenome-scaleactivemetabolicnetworksfor69humancelltypesand16cancertypesusingINIT.PLoSCompBiol.(2012)8:e1002518.doi:10.1371/journal.pcbi.1002518 PubMedAbstract|CrossRefFullText|GoogleScholar 31.BlaisEM,RawlsKD,DoughertyBV,LiZI,KollingGL,YeP,etal.Reconciledratandhumanmetabolicnetworksforcomparativetoxicogenomicsandbiomarkerpredictions.NatCommun.(2017)8:1–15.doi:10.1038/ncomms14250 PubMedAbstract|CrossRefFullText|GoogleScholar 32.WangY,EddyJA,PriceND.Reconstructionofgenome-scalemetabolicmodelsfor126humantissuesusingmCADRE.BMCSystBiol.(2012)6:153.doi:10.1186/1752-0509-6-153 PubMedAbstract|CrossRefFullText|GoogleScholar 33.Malik-SheriffRS,GlontM,NguyenTV,TiwariK,RobertsMG,XavierA,etal.BioModels-15yearsofsharingcomputationalmodelsinlifescience.NucleicAcidsRes.(2020)48:D407–15.doi:10.1093/nar/gkz1055 PubMedAbstract|CrossRefFullText|GoogleScholar 34.ENCODEProjectConsortium.TheENCODE(ENCyclopediaofDNAelements)project.Science.(2004)306:636–40.doi:10.1126/science.1105136 CrossRefFullText|GoogleScholar 35.EckerJR,BickmoreWA,BarrosoI,PritchardJK,GiladY,SegalE.ENCODEexplained.Nature.(2012)489:52–4.doi:10.1038/489052a PubMedAbstract|CrossRefFullText|GoogleScholar 36.MathelierA,FornesO,ArenillasDJ,ChenCY,DenayG,LeeJ,etal.JASPAR2016:amajorexpansionandupdateoftheopen-accessdatabaseoftranscriptionfactorbindingprofiles.NucleicAcidsRes.(2016)44:D110–5.doi:10.1093/nar/gkv1176 PubMedAbstract|CrossRefFullText|GoogleScholar 37.MatysV,FrickeE,GeffersR,GößlingE,HaubrockM,HehlR,etal.TRANSFAC®:transcriptionalregulation,frompatternstoprofiles.NucleicAcidsRes.(2003)31:374–8.doi:10.1093/nar/gkg108 PubMedAbstract|CrossRefFullText|GoogleScholar 38.MeyerPE,KontosK,LafitteF,BontempiG.Information-theoreticinferenceoflargetranscriptionalregulatorynetworks.EURASIPJBioinformSystBiol.(2007)2007:1–9.doi:10.1155/2007/79879 PubMedAbstract|CrossRefFullText|GoogleScholar 39.MargolinAA,NemenmanI,BassoK,WigginsC,StolovitzkyG,DallaFaveraR,etal.ARACNE:analgorithmforthereconstructionofgeneregulatorynetworksinamammaliancellularcontext.BMCBioinform.(2006)7:S7.doi:10.1186/1471-2105-7-S1-S7 PubMedAbstract|CrossRefFullText|GoogleScholar 40.VerfaillieA,ImrichováH,VandeSandeB,StandaertL,ChristiaensV,HulselmansG,etal.iRegulon:fromagenelisttoageneregulatorynetworkusinglargemotifandtrackcollections.PLoSCompBiol.(2014)10:e1003731.doi:10.1371/journal.pcbi.1003731 PubMedAbstract|CrossRefFullText|GoogleScholar 41.GeurtsP.dynGENIE3:dynamicalGENIE3fortheinferenceofgenenetworksfromtimeseriesexpressiondata.SciRep.(2018)8:1–12.doi:10.1038/s41598-018-21715-0 PubMedAbstract|CrossRefFullText|GoogleScholar 42.OrchardS,KerrienS,AbbaniS,ArandaB,BhateJ,BidwellS,etal.Proteininteractiondatacuration:theinternationalmolecularexchange(IMEx)consortium.NatMethods.(2012)9:345–50.doi:10.1038/nmeth.1931 PubMedAbstract|CrossRefFullText|GoogleScholar 43.LynnDJ,WinsorGL,ChanC,RichardN,LairdMR,BarskyA,etal.InnateDB:facilitatingsystems-levelanalysesofthemammalianinnateimmuneresponse.MolSystBiol.(2008)4:218.doi:10.1038/msb.2008.55 CrossRefFullText|GoogleScholar 44.BreuerK,ForoushaniAK,LairdMR,ChenC,SribnaiaA,LoR,etal.InnateDB:systemsbiologyofinnateimmunityandbeyond—recentupdatesandcontinuingcuration.NucleicAcidsRes.(2012)41:D1228–33.doi:10.1093/nar/gks1147 PubMedAbstract|CrossRefFullText|GoogleScholar 45.BaderGD,BetelD,HogueCW.BIND:thebiomolecularinteractionnetworkdatabase.NucleicAcidsRes.(2003)31:248–50.doi:10.1093/nar/gkg056 PubMedAbstract|CrossRefFullText|GoogleScholar 46.SalwinskiL,MillerCS,SmithAJ,PettitFK,BowieJU,EisenbergD.Thedatabaseofinteractingproteins:2004update.NucleicAcidsRes.(2004)32(Suppl_1):D449–51.doi:10.1093/nar/gkh086 PubMedAbstract|CrossRefFullText|GoogleScholar 47.LicataL,BrigantiL,PelusoD,PerfettoL,IannuccelliM,GaleotaE,etal.MINT,themolecularinteractiondatabase:2012update.NucleicAcidsRes.(2012)40:D857–61.doi:10.1093/nar/gkr930 PubMedAbstract|CrossRefFullText|GoogleScholar 48.OrchardS,AmmariM,ArandaB,BreuzaL,BrigantiL,Broackes-CarterF,etal.TheMIntActproject-IntActasacommoncurationplatformfor11molecularinteractiondatabases.NucleicAcidsRes.(2014)42:D358–63.doi:10.1093/nar/gkt1115 PubMedAbstract|CrossRefFullText|GoogleScholar 49.OughtredR,StarkC,BreitkreutzB,RustJ,BoucherL,ChangC,etal.TheBioGRIDinteractiondatabase:2019update.NucleicAcidsRes.(2019)47:D529–41.doi:10.1093/nar/gky1079 PubMedAbstract|CrossRefFullText|GoogleScholar 50.XiaJ,BennerMJ,HancockREW.NetworkAnalyst-integrativeapproachesforprotein–proteininteractionnetworkanalysisandvisualexploration.NucleicAcidsRes.(2014)42:W167–74.doi:10.1093/nar/gku443 PubMedAbstract|CrossRefFullText|GoogleScholar 51.XiaJ,GillEE,HancockREW.NetworkAnalystforstatistical,visualandnetwork-basedmeta-analysisofgeneexpressiondata.NatProtoc.(2015)10:823.doi:10.1038/nprot.2015.052 PubMedAbstract|CrossRefFullText|GoogleScholar 52.ZhouG,SoufanO,EwaldJ,HancockREW,BasuN,XiaJ.NetworkAnalyst3.0:avisualanalyticsplatformforcomprehensivegeneexpressionprofilingandmeta-analysis.NucleicAcidsRes.(2019)47:W234–41.doi:10.1093/nar/gkz240 PubMedAbstract|CrossRefFullText|GoogleScholar 53.LiuX,ChangC,HanM,YinR,ZhanY,LiC,etal.PPIExp:aweb-basedplatformforintegrationandvisualizationofProtein-Proteininteractiondataandspatiotemporalproteomicsdata.JProteomeRes.(2018)18:633–41.doi:10.1021/acs.jproteome.8b00713 PubMedAbstract|CrossRefFullText|GoogleScholar 54.GoughNR.Science'ssignaltransductionknowledgeenvironment:theconnectionsmapsdatabase.AnnNYAcadSci.(2002)971:585–7.doi:10.1111/j.1749-6632.2002.tb04532.x PubMedAbstract|CrossRefFullText|GoogleScholar 55.KrullM,PistorS,VossN,KelA,ReuterI,KronenbergD,etal.TRANSPATH®:aninformationresourceforstoringandvisualizingsignalingpathwaysandtheirpathologicalaberrations.NucleicAcidsRes.(2006)34(Suppl_1):D546–51.doi:10.1093/nar/gkj107 PubMedAbstract|CrossRefFullText|GoogleScholar 56.LawV,KnoxC,DjoumbouY,JewisonT,GuoAC,LiuY,etal.DrugBank4.0:sheddingnewlightondrugmetabolism.NucleicAcidsRes.(2014)42:D1091–7.doi:10.1093/nar/gkt1068 PubMedAbstract|CrossRefFullText|GoogleScholar 57.QinC,ZhangC,ZhuF,XuF,ChenSY,ZhangP,etal.Therapeutictargetdatabaseupdate2014:aresourcefortargetedtherapeutics.NucleicAcidsRes.(2014)42:D1118–23.doi:10.1093/nar/gkt1129 PubMedAbstract|CrossRefFullText|GoogleScholar 58.HeckerN,AhmedJ,vonEichbornJ,DunkelM,MachaK,EckertA,etal.SuperTargetgoesquantitative:updateondrug-targetinteractions.NucleicAcidsRes.(2012)40:D1113–7.doi:10.1093/nar/gkr912 PubMedAbstract|CrossRefFullText|GoogleScholar 59.KuhnM,SzklarczykD,Pletscher-FrankildS,BlicherTH,VonMeringC,JensenLJ,etal.STITCH4:integrationofprotein-chemicalinteractionswithuserdata.NucleicAcidsRes.(2014)42:D401–7.doi:10.1093/nar/gkt1207 PubMedAbstract|CrossRefFullText|GoogleScholar 60.GaultonA,BellisLJ,BentoAP,ChambersJ,DaviesM,HerseyA,etal.ChEMBL:alarge-scalebioactivitydatabasefordrugdiscovery.NucleicAcidsRes.(2012)40:D1100–7.doi:10.1093/nar/gkr777 PubMedAbstract|CrossRefFullText|GoogleScholar 61.LiuT,LinY,WenX,JorissenRN,GilsonMK.BindingDB:aweb-accessibledatabaseofexperimentallydeterminedprotein-ligandbindingaffinities.NucleicAcidsRes.(2007)35(Suppl_1):D198–201.doi:10.1093/nar/gkl999 PubMedAbstract|CrossRefFullText|GoogleScholar 62.YamanishiY,KoteraM,MoriyaY,SawadaR,KanehisaM,GotoS.DINIES:drug-targetinteractionnetworkinferenceenginebasedonsupervisedanalysis.NucleicAcidsRes.(2014)42:W39–45.doi:10.1093/nar/gku337 PubMedAbstract|CrossRefFullText|GoogleScholar 63.JainA,PasareC.Innatecontrolofadaptiveimmunity:beyondthethree-signalparadigm.JImmunol.(2017)198:3791–800.doi:10.4049/jimmunol.1602000 PubMedAbstract|CrossRefFullText|GoogleScholar 64.GardyJL.Enablingasystemsbiologyapproachtoimmunology:focusoninnateimmunity.TrendsImmunol.(2009)30:249–62.doi:10.1016/j.it.2009.03.009 PubMedAbstract|CrossRefFullText|GoogleScholar 65.BarskyA,GardyJL,HancockREW,MunznerT.Cerebral:acytoscapepluginforlayoutofandinteractionwithbiologicalnetworksusingsubcellularlocalizationannotation.Bioinformatics.(2007)23:1040–2.doi:10.1093/bioinformatics/btm057 PubMedAbstract|CrossRefFullText|GoogleScholar 66.BhattacharyaS,DunnP,ThomasCG,SmithB,SchaeferH,ChenJ,etal.ImmPort,towardrepurposingofopenaccessimmunologicalassaydatafortranslationalandclinicalresearch.SciData.(2018)5:180015.doi:10.1038/sdata.2018.15 PubMedAbstract|CrossRefFullText|GoogleScholar 67.ShayT,KangJ.Immunologicalgenomeprojectandsystemsimmunology.TrendsImmunol.(2013)34:602–9.doi:10.1016/j.it.2013.03.004 PubMedAbstract|CrossRefFullText|GoogleScholar 68.SinghA,ShannonCP,GautierB,RohartF,VacherM,TebbuttSJ,etal.DIABLO:anintegrativeapproachforidentifyingkeymoleculardriversfrommulti-omicsassays.Bioinformatics.(2019)35:3055–62.doi:10.1093/bioinformatics/bty1054 PubMedAbstract|CrossRefFullText|GoogleScholar 69.ChaussabelD,BaldwinN.Democratizingsystemsimmunologywithmodulartranscriptionalrepertoireanalyses.NatRevImmunol.(2014)14:271–80.doi:10.1038/nri3642 PubMedAbstract|CrossRefFullText|GoogleScholar 70.LiS,SullivanNL,RouphaelN,YuT,BantonS,MaddurMS,etal.Metabolicphenotypesofresponsetovaccinationinhumans.Cell.(2017)169:862–77.e17.doi:10.1016/j.cell.2017.04.026 PubMedAbstract|CrossRefFullText|GoogleScholar 71.YeungA,HaleC,ClareS,PalmerS,ScottJB,BakerS,etal.Usingasystemsbiologyapproachstudyhost-pathogeninteractions.BacteriaIntracell.(2019)18:337–47.doi:10.1128/microbiolspec.BAI-0021-2019 PubMedAbstract|CrossRefFullText|GoogleScholar 72.YeungAT,HaleC,LeeAH,GillEE,BushellW,Parry-SmithD,etal.Exploitinginducedpluripotentstemcell-derivedmacrophagestounravelhostfactorsinfluencingchlamydiatrachomatispathogenesis.NatCommun.(2017)8:1–12.doi:10.1038/ncomms15013 PubMedAbstract|CrossRefFullText|GoogleScholar 73.ElmassryMM,MudaliarNS,Colmer-HamoodJA,etal.NewmarkersforsepsiscausedbyPseudomonasaeruginosaduringburninfection.Metabolomics.(2020)16:1–16.doi:10.1007/s11306-020-01658-2 PubMedAbstract|CrossRefFullText|GoogleScholar 74.BaschalEE,LarsonED,BootpetchRobertsTC,etal.Identificationofnovelgenesandbiologicalpathwaysthatoverlapininfectiousandnonallergicdiseasesoftheupperandlowerairwaysusingnetworkanalyses.FrontGenet.(2020)10:1352.doi:10.3389/fgene.2019.01352 PubMedAbstract|CrossRefFullText|GoogleScholar 75.SunX,HuaS,GaoC,BlackmerJE,OuyangZ,ArdK,etal.Immune-profilingofZIKV-infectedpatientsidentifiesadistinctfunctionofplasmacytoiddendriticcellsforimmunecross-regulation.NatCommun.(2020)11:1–13.doi:10.1038/s41467-020-16217-5 PubMedAbstract|CrossRefFullText|GoogleScholar 76.SmithJ.ImmunologicalmolecularresponsesofhumanretinalpigmentepithelialcellstoinfectionwithToxoplasmagondii.FrontImmunol.(2019)10:708.doi:10.3389/fimmu.2019.00708 PubMedAbstract|CrossRefFullText|GoogleScholar 77.MulindwaJ,MatovuE,EnyaruJ,ClaytonC.BloodsignaturesforsecondstagehumanAfricantrypanosomiasis:atranscriptomicapproach.BMCMedGenom.(2020)13:1–12.doi:10.1186/s12920-020-0666-5 PubMedAbstract|CrossRefFullText|GoogleScholar 78.SchreiberF,LynnDJ,HoustonA,PetersJ,MwafulirwaG,FinlayBB,etal.ThehumantranscriptomeduringnontyphoidSalmonellaandHIVcoinfectionrevealsattenuatedNFκB-mediatedinflammationandpersistentcellcycledisruption.JInfectDis.(2011)204:1237–45.doi:10.1093/infdis/jir512 PubMedAbstract|CrossRefFullText|GoogleScholar 79.PreziosiMJ,KandelSM,GuineyDG,BrowneSH.MicrobiologicalanalysisofnontyphoidalSalmonellastrainscausingdistinctsyndromesofbacteremiaorenteritisinHIV/AIDSpatientsinSanDiego,California.JClinMicrobiol.(2012)50:3598–603.doi:10.1128/JCM.00795-12 PubMedAbstract|CrossRefFullText|GoogleScholar 80.ScottMG,DullaghanE,MookherjeeN,GlavasN,WaldbrookM,ThompsonA,etal.Ananti-infectivepeptidethatselectivelymodulatestheinnateimmuneresponse.NatBiotechnol.(2007)25:465–72.doi:10.1038/nbt1288 PubMedAbstract|CrossRefFullText|GoogleScholar 81.MansourSC,delaFuente-NúñezC,HancockREW.PeptideIDR-1018:modulatingtheimmunesystemandtargetingbacterialbiofilmstotreatantibiotic-resistantbacterialinfections.JPeptideSci.(2015)21:323–9.doi:10.1002/psc.2708 PubMedAbstract|CrossRefFullText|GoogleScholar 82.WuBC,LeeAHY,HancockREW.Mechanismsoftheinnatedefenseregulatorpeptide-1002anti-inflammatoryactivityinasterileinflammationmousemodel.JImmunol.(2017)199:3592–603.doi:10.4049/jimmunol.1700985 PubMedAbstract|CrossRefFullText|GoogleScholar 83.MookherjeeN,HamillP,GardyJ,BlimkieD,FalsafiR,ChikatamarlaA,etal.SystemsbiologyevaluationofimmuneresponsesinducedbyhumanhostdefencepeptideLL-37inmononuclearcells.MolBioSyst.(2009)5:483–96.doi:10.1039/b813787k PubMedAbstract|CrossRefFullText|GoogleScholar 84.HopkinsAL.Networkpharmacology:thenextparadigmindrugdiscovery.NatChemBiol.(2008)4:682.doi:10.1038/nchembio.118 PubMedAbstract|CrossRefFullText|GoogleScholar 85.MayerML,BlohmkeCJ,FalsafiR,FjellCD,MaderaL,TurveySE,etal.RescueofdysfunctionalautophagyattenuateshyperinflammatoryresponsesfromCysticFibrosiscells.JImmunol.(2013)190:1227–38.doi:10.4049/jimmunol.1201404 PubMedAbstract|CrossRefFullText|GoogleScholar 86.KnoxC,LawV,JewisonT,LiuP,LyS,FrolkisA,etal.DrugBank3.0:acomprehensiveresourcefor‘omics'researchondrugs.NucleicAcidsRes.(2010)39(Suppl_1):D1035–41.doi:10.1093/nar/gkq1126 PubMedAbstract|CrossRefFullText|GoogleScholar 87.SahaS,PanigrahiDP,PatilS,BhutiaSK.Autophagyinhealthanddisease:acomprehensivereview.BiomedPharmacother.(2018)104:485–95.doi:10.1016/j.biopha.2018.05.007 PubMedAbstract|CrossRefFullText|GoogleScholar 88.BlohmkeCJ,MayerML,TangAC,HirschfeldAF,FjellCD,SzeMA,etal.Atypicalactivationoftheunfoldedproteinresponseincysticfibrosisairwaycellscontributestop38MAPK-mediatedinnateimmuneresponses.JImmunol.(2012)189:5467–75.doi:10.4049/jimmunol.1103661 PubMedAbstract|CrossRefFullText|GoogleScholar 89.ChengF,DesaiRJ,HandyDE,WangR,SchneeweissS,BarabásiAL,etal.Network-basedapproachtopredictionandpopulation-basedvalidationofinsilicodrugrepurposing.NatCommun.(2018)9:1–12.doi:10.1038/s41467-018-05116-5 PubMedAbstract|CrossRefFullText|GoogleScholar 90.ChengF,MurrayJL,ZhaoJ,ShengJ,ZhaoZ,RubinDH.Systemsbiology-basedinvestigationofcellularantiviraldrugtargetsidentifiedbygene-trapinsertionalmutagenesis.PLoSCompBiol.(2016)12:e1005074.doi:10.1371/journal.pcbi.1005074 PubMedAbstract|CrossRefFullText|GoogleScholar 91.HanZ,XueW,TaoL,ZhuF.Identificationofnovelimmune-relevantdrugtargetgenesforAlzheimer'sdiseasebycombiningontologyinferencewithnetworkanalysis.CNSNeurosciTher.(2018)24:1253–63.doi:10.1111/cns.13051 PubMedAbstract|CrossRefFullText|GoogleScholar 92.AbhyankarV,BlandP,FernandesG.Theroleofsystemsbiologicapproachincellsignalinganddrugdevelopmentresponses-aminireview.MedSci.(2018)6:43.doi:10.3390/medsci6020043 PubMedAbstract|CrossRefFullText|GoogleScholar Keywords:systemsbiology,multi-omicintegration,transcriptomics,innateimmunity,immuneontogeny,host-pathogeninteraction,drugdiscoveryandrepurposing,systemsvaccinology Citation:DhillonBK,SmithM,BaghelaA,LeeAHYandHancockREW(2020)SystemsBiologyApproachestoUnderstandingtheHumanImmuneSystem.Front.Immunol.11:1683.doi:10.3389/fimmu.2020.01683 Received:11March2020;Accepted:24June2020;Published:30July2020. Editedby:JayEvans,UniversityofMontana,UnitedStates Reviewedby:SusanaMagadan,UniversityofVigo,SpainGeertLeroux-Roels,GhentUniversity,Belgium Copyright©2020Dhillon,Smith,Baghela,LeeandHancock.Thisisanopen-accessarticledistributedunderthetermsoftheCreativeCommonsAttributionLicense(CCBY).Theuse,distributionorreproductioninotherforumsispermitted,providedtheoriginalauthor(s)andthecopyrightowner(s)arecreditedandthattheoriginalpublicationinthisjournaliscited,inaccordancewithacceptedacademicpractice.Nouse,distributionorreproductionispermittedwhichdoesnotcomplywiththeseterms. *Correspondence:RobertE.W.Hancock,[email protected] COMMENTARY ORIGINALARTICLE Peoplealsolookedat SuggestaResearchTopic>