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I n t r o d u ct i on t o M i c r o a r r ay D a t a A na l ys i s. O u t l i n e. I n t r o d u c ti o n M i c r o a r rays T e c h n o l o g y T y p e s a n d Us es o f M ic r o a r rays M i c r o a r rays f o r t h e S t u dy o f G e n e E x p r e s si o n F a b r i c a ti o n

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Presentation Transcript
o u t l i n e
Outline
  • Introduction
  • MicroarraysTechnology
  • TypesandUsesofMicroarrays
  • MicroarraysfortheStudyofGeneExpression
  • Fabrication
    • Spottedmicroarrays
  • 2.Oligonucletidemicroarrays
  • ExperimentswithMicroarrays
    • Flowchartofaexperimentwithmicroarrays
  • SoftwareforMicroarrayDataAnalysis
int r o d uc t i o n 1
Introduction(1)

Briefreviewofmolecularbiology...

Mostlifeformsaremadeofcells.Eachindividualhasaverylargeindefinitenumberofcells.

Eachcell

containschromosomes

(e.g.

human

cells

contain23pairs

ofchromosomes).

These

organized

structuresofDNAandinheritedinformation.

proteins

arethecarriersof

AchromosomeisasinglepieceofcoiledDNAcontainingmanygenes,regulatoryelementsandothernucleotidesequences.

int r o d uc t i o n 2
Introduction(2)

Whatelse?

ThegenomeofanorganismisinscribedinDNAorRNAinsomevirus

AgeneisthebasicunitofheredityinalivingorganismandistheportionoftheDNAthatcodesforaproteinoranRNA

Eachprotein-codinggeneisagenetranscribedintoRNAinsomemoleculesandinturnmRNAistranslatedintoatleastoneproteininsomecells

in t r oduc t i o n 3
Introduction(3)

TheCentralDogmaofMolecularBiology

  • Information

flowfromDNAtoRNAto

proteinoccursinfourstages

Replication

TheDNAreplicatesitsinformationinaprocessthatinvolvesmanyenzymes

Transcription

TheDNAcodesfortheproductionofmessengerRNA(mRNA)

Splicing

Ineucaryoticcells,themRNAisprocessedandmigratesfromthenucleustothecytoplasm.

Translation

MessengerRNAcarriescodedinformationtoribosomes.Theribosomesreadthisinformationanduseitforproteinsynthesis.

int r o d uc t i o n 4
Introduction(4)

TechniquesinMolecularBiology

MolecularbiologyhasdevelopedmultipletechniquestomeasurelevelsofRNA,DNA,proteinsormetabolites,suchas

SouthernBlotNorthernBlotDifferentialdisplaySAGE

Post-genomicseraisperformandtoanalyze

characterized

byitscapabilityto

data

sets

from

large-scale

experimentssimultaneously

int r o d uc t i o n 5
Introduction(5)

Theparadigmshift

Withthesameresourcesweobtainapicturewithlowerresolutionbutwithaviewofthewholecontext

vs

Basedon“Theparadigmshift”slidefromJ.Dopazo(CNIO)

int r o d uc t i o n 6
Introduction(6)

Todrawananalogywithpre-genomicsera

Biologyusedto“spy”ongeneseverythingindeepandindividually(i.e.genebygene)

int r o d uc t i o n 7
Introduction(7)

Todrawananalogywithpost-genomicsera

Nowadays,alotofgenescanbe“spied”atthesametime...but...

…Howcanwesplitthewheatfromthechaff?

m ic r oa r rays t e chno l og y 1
MicroarraysTechnology(1)

Broadlyspeaking...

Microarraysareavarietyofplatforms

inwhichhighdensityassaysperformedinparallelonasupport.

aresolid

PublicationsinPubMedwithmicroarraywordinthetitle

10911080

1000

1000986

988

Thistechnologyhaschangedtheway

920

biologistsapproach

problemsandnewchallengesfor

hugeeach

800

introducesstatisticiansquantityofexperiment

numberofpublications

747

becauseofthedatageneratedin

600

544

400

Theyhavebeenusedforallkindsofbiologicalproblems

259

200

171

83

24

5

0

Theliteraturecontainsalmost8000papersusingmicroarraywordinthetitle

1998

2000

2002

2004

2006

2008

2010

year

m ic r oa r rays t e chno l og y 2
MicroarraysTechnology(2)

Thebiologicalprincipleofmicroarraysinvolvedin...

ItisthesameonethatallowsDNAdoublehelicesto

providethebasisforheredity

SequencesofDNAorRNAmoleculescontainingcomplementarybasepairshaveanaturaltendencytobindtogether.

...AAAAAGCTAGTCGATGCTAG...

...TTTTTCGATCAGCTACGATC...

IfweknowthemRNAsequence,wecanbuildaprobeforitusingthecomplementarysequence.

m ic r oa r rays t e chno l og y 3
MicroarraysTechnology(3)

But...Whatisamicroarray?

Itconsistofalargeset(thousandstotenofthousands)ofspecificsequences(knownasprobesorfeatures)attachedinorder(array)tomicroscopicspotsonasolidsupport(nylonorsiliconglass,...).

...

moleculesample1

moleculesample2

moleculesampler

Aprobe(thatcanbeagene,aprotein,ametabolite,...)isusedtohybridizeamoleculeofanucleicacidsample(calledtarget)underhigh-stringencyconditions.

probeprobeprobeprobe

gene1gene2gene3gene4

1

2

3

4

probeprobeprobeprobe5678

spots

probeprobeprobeprobe9101112

Probe-Target

determinetherelative

hybridizationis

usedtoof

abundance

...

nucleicacidsequencesinthetargets(e.g.

todeterminesequences,to

detect

variationsingenesequences,levels,genemapping,...).

expression

probek-3

probeprobeprobek-2k-1k

Microarray

slide13

TypesandUsesofMicroarrays(1)

Typesofmicroarrays

Microarraysspatiallyarrangedonasolidsurfacearemostwidelyused.`

Beadarraysarecreatedby

  • eitherimpregnatingbeadswithdifferentconcentrationsoffluorescentdye,
  • orsometypeofbarcodingtechnology.

Thebeadsareaddressableandusedtobindingeventsthatoccurontheirsurface.

identify

specific

slide14

TypesandUsesofMicroarrays(2)

UsesofMicroarrays(1)

Expressionanalysis

–TheprocessofmeasuringgeneexpressionviaRNA(orcDNAafterreversetranscription)iscalledexpressionanalysisorexpressionprofiling.

Inthisexperimentstheexpressionlevelsofthousandsofgenesaresimultaneouslymonitoredtostudytheeffectsofcertaintreatments,diseases,anddevelopmentalstagesongeneexpression.

ComparativeGenomicHybridization

Comparativegenomichybridization(CGH)orChromosomalMicroarrayAnalysis(CMA)isusedfortheanalysisofcopynumberchanges(increasesordecreases)oftheimportantchromosomalfragmentsharboringgenesinvolvedindiseases.

Mutationanalysis

–AsinglebasedifferencebetweentwosequencesisknownasSingleNucleotidePolymorphism(SNP)anddetectingthemisknownasSNPdetection.

WithgDNAthiskindofarraystrytodetectgenesthatmightdifferfromeachotherbyaslessasasinglenucleotidebase.

slide15

TypesandUsesofMicroarrays(2)

UsesofMicroarrays(2)

ProteinArray

TissueArray

CGHArrays

SNPArrayAffymetrix

CNVArrayIllumina

ExpressionArrays

cDNANylonMembraneArray

GeneChipAffymetrixArray

cDNAAgilentArray

slide16

TypesandUsesofMicroarrays(3)

ApplicationofMicroarrays

Genediscovery

Identificationofnewgenes,knowabouttheirfunctioningandexpressionlevelsunderdifferentconditions.

Molecularclassificationofcomplexdiseases

Toclassifythetypesofcanceronthebasisofthepatternsofgeneactivityinthetumorcells,todevelopmoreeffectivedrugs.

Drugdiscovery

Comparativeanalysisofthegenesfromadiseasedandanormalcellhelptheidentificationofthebiochemicalconstitutionoftheproteinssynthesizedbythediseasedgenes.Thisinformationcanbeusedtosynthesizedrugsthatcombatwiththeseproteinsandreducetheireffect.

Toxicologicalresearch

Microarraytechnologyprovidesarobustplatformfortheresearchoftheimpactoftoxinsonthecellsandtheirpassingontotheprogeny.

slide17

MicroarraysfortheStudyofGeneExpression(1)

Whatisthegeneexpression?

Thegeneexpressionisthepresenceofthegeneproductsofagene,intheformofmRNA(orprotein),inacell

Toputitstraight:Sincecellscontainthesamegeneticinformation,whatmakesdifferentbraincellsfromheartcellsisthegeneexpression.

slide18

MicroarraysfortheStudyofGeneExpression(2)

FindingDifferentiallyExpressedGenes(DEG)

Tofindgenesthatdisplayalargedifferenceingeneexpressionbetweentwoconditionsandarehomogeneouswithinthem

Typicallystatisticaltests(t-test,Wilcoxontest)areused

Iftherearemorethantwoconditions,orifconditionsarenested,theappropriatestatisticalmethodisANOVA

pvaluesfromthesetestshavetobecorrectedformultipletesting

slide19

MicroarraysfortheStudyofGeneExpression(3)

Exploratorydataanalysis(1)

Tofindgroupsthatarenotdefinedyet(e.g.noveldiseasesubtypes)Methods

fromthisfieldwerethefirsttobeusedformicroarraydata

shouldbeusedonlyifnopriorknowledgeexiststhatcouldbeincorporated

findpatternsinthedata,butanypatterns,whethertheyaremeaningfulornotinclude

Clustering(hierarchicalandpartitioning)Projection(PCA,MDS)

Alizadehetal.Nature403:503–511(2000)

slide20

MicroarraysfortheStudyofGeneExpression(4)

Timeseries,partitioningclusteringandcorrelation

  • Usuallyusedtofindpatternsofco-expressedgenesThemeaningoftimeseriesisdifferentfor
  • Biologists:2-10timepoints
  • statisticians:>200timepoints
  • “Non-optimal”solution:touseclusteringmethodstofindsuchpatterns

Notethattheyarebynomeansexhaustive,andthatnosignificancemeasurecanbeattachedtothem

IncontrasttoEstimationofDistribuitonMethods(EDA),partitioningclustermethodsaremorepopular(e.g.K-meansorSelf-organizingmaps)

Toseekgeneswhoseexpressionprofileissimilartothatofaparadigmaticgene,correlationscanbecalculated,andsortbythem.Thereisnoneedforclustering.

Specialmethodsexistforperiodicchanges(⇒cellcycle),e.g.Fourieranalysis

slide21

MicroarraysfortheStudyofGeneExpression(5)

Classification

Wheninformationaboutgroupingofthesamplesisavailable,itcan(andshould)beusedtogetimprovedresults

Groupingsmaybe:

TreatmentandControlDiseaseandNormalDiseasestage1,2,3MutantandWildTypeGoodandPoorOutcome,Therapysuccessorfailure

...

Onelearnscharacteristicpatternsfromatrainingsetandevaluatebypredictingclassesofatestset

slide22

MicroarraysfortheStudyofGeneExpression(6)

SurvivalAnalysis

Tofindpatternsthatareassociatedwithprolongedpatients’survivaltime

Insteadoftreatingoutcomeasabinaryvariable,canbeused

TheoverallsurvivaltimeorTheeventfreesurvivaltime

ascontinuousvariables,andtrytoestimateitbyregression

Sincetherisktosufferfromrelapseisdecreasingwithtime,linearregressionmodelsarealmostalwaysinappropriatespecializedmodelswouldbebetter

CoxregressionRegressiontrees

slide23

MicroarraysfortheStudyofGeneExpression(7)

Pharmacogenomics

Tofindmolecularpredictorsthattellaboutprobablesuccess(orfailure)ofacertaintherapy.e.g.

estrogenreceptorstatusfortamoxifen(antihormone)therapyHER2/NEUstatusforherceptintherapyinbreastcancer

Onemayregardtreatmentoutcomeasadiscretevariableanduseclassificationmethods

Sometimes,it’sconvenientnottowaitforthefinalendpoint(whichmaybeyearsaway),buttousesurrogatevariables,e.g.

thedropofthebloodlevelofacertainproteinreductionintumorvolume

f abr i ca t i o n
Fabrication

Twomaintechnologies

Therearemanytypesoftechnologies,butprinciplesarethesame

ThemostusedarespottedarraysandInsituarrays

Spottedarrays(akacDNAarraysorStanfordarrays)

PreviouslysynthesizedcDNAsoroligonucleotidesaredepositedonthechip

Basedon“printing-like”technologies

Insituarrays(akaoligoarraysorAffyarrays)

ProbesaresynthesizeddirectlyonthechipBasedonphotolithographictechniques

Affymetrixarraysarethebest-known...butnottheonlyone!

s p o tte d a r r a y s 1
SpottedArrays(1)

Fromthechipstotheimages

ChipDesignandProduction

SamplePreparation

Hybridization

ScanningandCapturingImages

ImageAnalysis

Quantification

s p o tte d a r r a y s 2
SpottedArrays(2)

Chipdesignandconstruction

  • Productionbeginswiththeselectionofthe"probes"tobeprintedonthearrayIngeneral:chosenfrom
  • GenBank(http://www.ncbi.nlm.nih.gov/)
  • dbEST(http://www.ncbi.nlm.nih.gov/UniGene/index.html)
  • cDNA’sareprintedonthearray
  • Eachspotcancontainuniquesequences
  • Printing”meansadheringsequencestothespots

Amovieoftheprintingprocessisavailablehere

s p o tte d a r r a y s 3
SpottedArrays(3)

Samplepreparation

RNAisextractedfromthesamples

ThisRNAisconvertedtofluorescentlylabeledcDNAbyreversetranscriptioninpresenceoffluorescentlylabelednucleotideprecursors

RNAfromeachsamplesare

labelledfluorescentCy-5)to

withdyes

different(e.g.Cy-3,

allowdirect

comparison

4.Afterlabeling,theyaremixed

andhybridizedsequencesonthe(probes)

witharray

s p o tte d a r r a y s 4
SpottedArrays(4)

Hybridizationwithprobes

Targetslabeledandcombined

Amovieofthehybridizationprocessisavailablehere

s p o tte d a r r a y s 5
SpottedArrays(5)

Scanningandcapturingimage

AfterhybridizationeachDNAspotisilluminatedandfluorescencemeasurestakenforeachdyeseparately

Thesemeasurementswillbeused,aftertheappropriatequalitycontrols,todeterminetherelativeabundance,ofthesequenceofeachspecificgeneinthetwomRNAorDNAsamples

s p o tte d a r r a y s 6
SpottedArrays(6)

Imageanalysis(1)

TIFFimagesareprocessedbyimageanalysisprograms

SPOT,

GenePix

...

toacquireintensityvaluesforeachspot

Thesemeasureswillbeused,aftertheappropriatequalitycontrols,todeterminetherelativeabundance,ofthesequenceofeachspecificgeneinthetwomRNAorDNAsamples

s p o tte d a r r a y s 7
SpottedArrays(7)

Imageanalysis(2)

StepsinImageProcessing

Addressing:Estimatelocationofspotcenters

Segmentation:Classifyeachspota

foreground(signal)background(noise)

3.Informationextraction(quantification)

Foreachspotonthearray,andeachdyeobtain

Signalmeasurements(R,G)

gg

Backgroundmeasurements(bgR,bgG)

gg

Qualityindicators

s p o tte d a r r a y s 8
SpottedArrays(8)

Quantification

Genemeasuredmeasures

expressionis

fromintensityasthe

relative

(corrected)

intensityofonedyevsthe(corrected)relativeintensityoftheother

M=Rg,M

Rg−bgRg

=

Corrected

Gg

Gg−bgGg

Background

correction

maybeaccordingquality

needed,

ornot,array

tothe

s p o tte d a r r a y s 81
SpottedArrays(8)

Overviewoftheprocess

Amovieofthewholeprocessisavailablehere

in si t u c h i ps 1
InsituChips(1)

Fromthechipstotheimages

MainConcepts

SynthesisofOligosontheChip

SamplePreparation

HybridizationProcess

ScanningImages

OutputImages

QuantificationandExpressionMeasures

in si t u c h i ps 11
InsituChips(1)

Mainconcepts(1)

MoreadvanceddesignthanspottedcDNAarrays

TheyareNOTbasedoncompetitivehybridization.Thatis,onechip,onesampleTheyareNOTaddedonthechipafterbeingsynthesizedinvitro

Mainidea:Probesaresynthesizedinsitu(onthechip)

Sequencesarebuiltuponthechipsurfacebysequentiallyelongatingagrowingchainwithasinglenucleotideusingphotolithography

Chemicalyieldofthestepwiseelongationislimited

SequencescanNOTgrowtomorethan25merslength(oligo)

Need16-20different25mersequencestouniquelycharacterizeagene

Probe=Individual25mersequence

Probeset=Setof25merscorrespondingtoaparticulargene/EST

in si t u c h i ps 2
InsituChips(2)

Mainconcepts(2)

Affymetrix(http://www.affymetrix.com)istheleadercompanyofthesekindsofchips.TheycallthemGeneChips

Eachgeneisrepresentedbyasetofshortsequences

Someofthesechipscontainwholegenomes,thatis>50.000probesets

Aprobeset(usuallydenotedprobeset)isusedtomeasurethemRNAlevelsofauniquegene

Eachprobesetismadeupofmultipleprobecells

withmillonsofcopiesofoneoligodecopiasdeunoligo(25bp)Organizedinprobepairswith

aPerfectMatch(PM):matchperfectlywithapieceofagene

aMismatch(MM):itisthesametoPMbutwiththecentralnucleotidechangebythecomplementary

in si t u c h i ps 3
InsituChips(3)

Mainconcepts(1)

MoreadvanceddesignthanspottedcDNAarrays

TheyareNOTbasedoncompetitivehybridization.Thatis,onechip,onesampleTheyareNOTaddedonthechipafterbeingsynthesizedinvitro

Mainidea:Probesaresynthesizedinsitu(onthechip)

Sequencesarebuiltuponthechipsurfacebysequentiallyelongatingagrowingchainwithasinglenucleotideusingphotolithography

Chemicalyieldofthestepwiseelongationislimited

SequencescanNOTgrowtomorethan25merslength(oligo)

Need16-20different25mersequencestouniquelycharacterizeagene

Probe=Individual25mersequence

Probeset=Setof25merscorrespondingtoaparticulargene/EST

slide38

InsituChips(4)

GeneChip®expressionarraydesign

in si t u c h i ps 5
InsituChips(5)

Onegene,oneprobeset

Probesareselectedtobespecificoftherepresentedgene

Themusthavegoodpropertiesofhybridization

genesequence

in si t u c h i ps 6
InsituChips(6)

Synthesisofoligosonthechip(1)

GeneChip®probearraysaremanufacturedthroughauniqueandrobustprocess,acombinationofphotolithographyandcombinationalchemistry

ImagecourtesyofAffymetrix

in si t u c h i ps 7
InsituChips(7)

Synthesisofoligosonthechip(2)

mask

mask

mask

mask

mask

mask

mask

C

A

T

C

mask

T

T

T

A

C

GA

TC

AG

A

GeneChip

ImagefromacourseofDanNettleton

in si t u c h i ps 8
InsituChips(8)

Synthesisofoligosonthechip(3)

Severalcopiesofasinglefeaturearedepositedineachcell

ImagecourtesyofAffymetrix

slide43

InsituChips(9)

Samplepreparation

in si t u c h i ps 81
InsituChips(8)

Hybridizationprocess

OncetheoligoshavebeensynthesizedhybridizationisperformedbyaddingmRNAfromthetissuetoanalyzeonthechip

ImagecourtesyofAffymetrix

in si t u c h i ps 9
InsituChips(9)

ScanningImages

Scanningoftaggedandun-taggedprobesonanAffymetrixGeneChip®microarray

ImagecourtesyofAffymetrix

i n s i t u c h ip s 1 0
InsituChips(10)

OutputImage

DatafromanexperimentshowingtheexpressionofthousandsofgenesonasingleGeneChip®probearray

ImagecourtesyofAffymetrix

i n s i t u c h ip s 1 1
InsituChips(11)

Quantification

Intensitiesfromeachelementareextracted

QuantitativeanalysisofthehybridizationresultsisperformedbyanalyzingthehybridizationpatternofthesetofPMandMMprobesofeverygene

Incontrastwithspottedchipsexpressionmeasuresusedhereareabsoluteones.Thatis,eachchipishybridizedwithonlyonetissueatatime

i n s i t u c h ip s 1 2
InsituChips(12)

Absoluteexpressionmeasures

MeasurestodeterminethequantitativeRNAabundance,i.e.theexpressionlevelbasedontheaverageofthedifferencesPMminusMMforeachprobefamily

Avg.Diff=1¿j∈APM−MM

∣A∣

Manyalternativeshavebeenintroduced

s p o tte d vs in si t u a r ra y s
SpottedvsInsituArrays

PRO\'sandCON\'s

cDNAmicroarraysOligomicroarrays

PRO\'s

PRO\'s

Cheaper

Flexibilitywiththeexperimentaldesign

Highsignalintensity(largesequences)

Quickmanufacture(automated)Highreproducibility

Highspecificity

Alotofprobes/genes

CON\'s

CON\'s

Requiresmorespecializedequipment

ExpensivesLowflexibility

  • Lowreproducibility
  • Cross-hybridization(lowspecificity)
  • Highmanupulation(ssibilityofcontamination)

i n s i t u c h ip s 1 3
InsituChips(13)

Overviewoftheprocess

Amovieofthewholeprocessisavailablehere

ImagecourtesyofAffymetrix

slide51

ExperimentswithMicroarrays

Flowchartofaexperimentwithmicrorrays

s o f t w a r e 1
Software(1)

Whichsoftwarefortheanalysis?

  • Microarrayexperimentsgeneratehugequantitiesofdatawhichhavetobe
  • Stored,managed,visualized,processed...
  • Manyoptionsavailable.However...Notoolsatisfiesalluser’sneedsTrade-off.Atoolmustbe
  • Powerfulbutuserfriendly
  • Completebutwithouttoomanyoption
  • Flexiblebuteasytostartwithandgofurther
  • Available,todate,welldocumentedbutaffordable

s o f t w a r e 2
Software(2)

Someoptionsare...

Partek

CommercialmarketleaderManyusefulfacilitiesSomeinfelicities

  • Bioconductor
  • OpenSourceRsoftwarepackage
  • Vigorousdevelopment,newthingsshowupherefirst
  • GEPAS
  • Freelyavailable
  • BasedonBioconductorandR

s o f t w a r e 3
Software(3)

So,whatyouneedisR?

  • Risanopen-sourcesystemforstatisticalcomputationandgraphics.
  • Itconsistsof
  • Alanguage
  • Arun-timeenvironmentwith
  • Graphics
  • Adebugger
  • AccesstocertainsystemfunctionsItcanbeused
  • either,interactively,throughacommandlanguage
  • orrunningprogramsstoredinscriptfiles

s o f t w a r e 4
Software(4)

RandMicroarrays

Risapopulartoolbetweenstatisticians

  • Oncetheystartedtoworkwithmicroarraystheycontinuedusingit
  • Toperformtheanalysis
  • Toimplementnewtools
  • ThisgaveriseveryfasttolotsoffreeR-basedsoftwaretoanalyzemicroarrays
  • TheBioconductorprojectgroupsmanyofthese(butnotall)developments

s o f t w a r e 5
Software(5)

TheBioconductorproject

http://bioconductor.org

Opensourceandopendevelopmentsoftwareprojectfortheanalysisandcomprehensionofgenomicdata

MostearlydevelopmentsasRpackages

Extensivedocumentationandtrainingmaterialfromshortcourses

Hasreachedverygoodstability...

But,whatisnowastandardmaynotbesoina

future

s o f t w a r e 6
Software(6)

on-lineresources

http://estbioinfo.stat.ub.es/resources/index.html

r e f e r e nc e s
References

Enjoythem!!!

Smyth,G.K.;Yang,Y.H.;Speed,T.(2003)StatisticalIssuesincDNAMicroarrayDataAnalysis.In:MJBrownstein,ABKhodurski(eds.),MethodsinMolecularBiology,HumanaPress2002.

http://www.stat.berkeley.edu/ t̃erry/zarray/TechReport/mareview.pdf

Huber,W.;vonHeydebreck,A.;Vingron,M.(2003)Analysisofmicroarraygeneexpressiondata.In:HandbookofStatisticalGenetics,2nded.,Wiley2003.

http://www.ebi.ac.uk/huber/docs/hvhv.pdf

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