關于真核生物mRNA降解及代謝過程的計算機模擬曹丹亞力桑那大學分子細胞生物學系

DanCaoandRoyParkerHowardHughesMedicalInstitute&DepartmentofMolecularCellularBiologyUniversityofArizonaComputationalmodelingof

mRNAturnoverDNAmRNAProteinTranscription,Splicing,transport…translationdegradationdegradationTheCentralDogmaWhywanttomodelthisprocess?1.IntegrationWhywanttomodelthisprocess(Cont’d)?

Anexplanatory/descriptivetool1)correctionofmisconceptions2)helpinterpretresultcorrectly3)advicesuitableexperimentPredictionfromdiscrepanciesbetweensimulationresultandexperimentalresultAnidealsystemtostartknowledge-drivensimulationPlentyofdataavailabletoestimatetheratesfordistinctsteps,includingsteadystatedistribution,half-lives,effectsofvariousmutants…MajorassumptionsTranscriptionisazerothorderprocess,allotherstepsarefirstorderprocessesAllstepsareirreversibleNofeedbackloopsAsamplescreenshotinputDeterminethefitnessofthemodelwiththeinvivodecaynetwork:MFA2pG&PGK1pGTheyrepresentaunstable(MFA2)andstable(PGK1)mRNAinyeastTheirdegradationhasbeenextensivelycharacterized,withlotsofdataavailabletoestimatetherates.E.g:transcriptionalpulsechasegelcangiveinformationfortheratesofdeadenylationanddecapping.Strongpoly(G)structureatthe3’UTRcantrapthedecayintermediates,giveadditionalinformationaboutinvivoprocess.E.g:ratesofterminaldeadenylationand3’to5’decayComparisonofmodelingresultswithexperimentalobservationsThesimulatedsteadystatepoly(A)distribution,pulsechasegelpattern,previouslycharacterizedmutants(transport,decapping,3’to5’decay…)arealsoconsistentwithexp.observations.ModelingofMFA2pGintranscriptionalpulsechaseObservedComputedModelingofPGK1pGintranscriptionalpulsechase

ObservedComputedThefactthatwecanreproducetheexperimentalresultsbymodelingsuggeststhatourmodelisquiteaccurate,andwehavearelativelyrobustunderstandingoftheinvivoprocess.TheobtainingofagoodmodelforbothMFA2pGandPGK1pGallowsustofurtheranalyzethewholenetwork.Wehaveusedourmodeltoexaminehowtranscriptsrespondtoavarietyofperturbationsbyperformingaseriesofinsilicoexperiments.E.g.:increaseordecreasetherateoftransport,deadenylation,decapping,5’to3’exonucleolyticdecay,3’to5’exonucleolyticdecay…,seehowthetranscriptlevel,half-life,steadystatedistributionareaffected.Whathavewelearned?ComparisonofinsilicomutantsDeadenylationisakeystepincontrollingmRNAturnover.Provideexplanationforwhymanydecayelementsidentifiedaffectingdeadenylation.

3’to5’decayrateforfulllength.Obtainedbymatchingthecalculatedt?withtheobservedt?indcp1mutant.Thecalculated3’to5’decayratesImplication:3’to5’decaybyexosomeshowsmRNAspecificdegradationratesthataredependentonthe5’structureofthemRNA3’to5’decayrateforfragment.Obtainedbymatchingthecalculatedt?offragmentwiththeobservedt?whendecappingisblocked.Half-life.Measuredbytranscriptionalshut-offexperiment.Atthepointwheretranscriptreacheshalfofitsinitiallevel.t?isthoughttorepresenthowlongthemRNAispersistinthecell.Averagelifespan.Calculatedfromthesimulationfortranscriptionalpulsechaseexperiment.MoreaccuraterepresentationoftheaveragetimethemRNAispresentinthecell.Viewonhalf-lifeHalf-lifeAveragelifespan.Thetraditionalwayofmeasuringt1/2mayunderestimatethelifespanofanmRNA.ThedifferenceisduetothedistributionofmRNAatsteadystate.Certain%ofmRNAshasalreadypassedseveraldecaysteps.WhyAveragelifespan

half-life?Decayfromsteadystatet1/2TranscriptionalpulsechaseAverageLifeSpanThemeasurementofmeasureahalf-lifecanpredominantlydifferentstepsinthedecaynetworkDifferentmRNAswillbeaffecteddifferentiallybycertainchangeonspecificstepNeedtobeverycautiouswheninterpretingmRNAspecificeffects.Short-livedmRNAs(MFA2)aremoreresponsivetochangesontransportratethanlong-livedmRNAs(PGK1).PossiblefactorsthatdisruptthecorrelationbetweenmRNAandproteinlevelTheincreaseoftranscriptlevelinthetransportmutantsolelycomesfromtheincreaseinthenuclearpool.Theincreaseoftranscriptlevelinthe5’to3’exomutantssolelycomesfromtheincreaseinthecap-speciesWeareabletosimulateMFA2andPGK1,whichsuggeststhatwehavearelativelyrobustunderstandingaboutyeastmRNAturnover.ThisprogramcanbeadaptabletoothereukaryoticmRNAsthatfollowthesamedegradationscheme.Thisisauseful,explanatorytoolforquantitativeanalysisoftheprocessandregulationofmRNAturnoverineukaryoticcells.SomeInsilicoexperimentsperformedmightbeabletosuggestthebestexp.foraparticularpurpose.E.g:decappingmutants.Discrepanciesbetweeninsilicoresultsandrealresultsmightleadtonewinsightsfortheinvivonetwork.ComputationExperimentationNonsenseMediatedmRNADecay(NMD)AAAAAAA70DNAtranscriptionAUGUAAUAAm7GpppNormaldecay~30’NonsenseDecay~3’

NormalDecayandNMDModelingPredictionExp.testingKnowledgeandhypothesisbasedmodeling.Mighthavemultiplemodels:model1,2,3…n.Allmodelsshouldfitwithcurrentdata.Makepredicationsbyanalyzingthemodelsandperforminginsilicoexperiments.Designandconductcriticalexperimentstotestimportantpredictionsanddistinguishthemodels.Somemodelsmaygetinvalidated.Othersmayneedtoberefinedtofitwellwithnewexperimentaldata.Mayleadtonewhypotheses.ThemostfaithfulmodelofNMDandnewinsightsaboutthisprocess.Illustrationoftheiterativeapproach.Advantage:increasetherateofhypothesisformingandtestingModel1.1Model1.2Multiplemodelsordifferentcombinationsofrateconstantswithinthesamemodelcanbeobtainedtofitwithcurrentobservations.

Model1Model1.1andModel1.2Model1.1TheentryintoPASisinhibitedbymorethan100fold,therateofDIDcontributestothet1/2ofA70NuclearmRNATransport(slow,52folddown,3mint1/2)CytoplasmicmRNA(A70)PASDID(fast)NormaldecayNMDModel1.2TheentryintoPASisnotinhibited,therateoftransport(ormaybeotherunknownstepsupstreamofthebifurcation)issloweddown,whichisusuallyveryfastfornormaltranscript.NuclearmRNATransport(fast)CytoplasmicmRNA(A70)PASDID(slow,3mint1/2)NormaldecayNMDIfentryintoPASisnotinhibited,willseesamedecreaseonsteadystatelevel,butthehalf-lifedoesnotchangemuch.DistinguishModel1.1andModel1.2RelativelevelofnucleartranscriptModel1.1predicts2%nuclearModel1.2predicts90%nuclearAssumption:therate-limitingstepabovethebifurcationisindeedtransportDecappingmutantsdcp1,dcp2…Currentdataisconsistentwithmodel1.2,inwhichtheentryintoPASisnotinhibited,therateofDIDismuchhigherthanPAS,andtheremightbearatelimitingstepupstreamofthebifur