metaminer™ cystic fibrosis report

MetaMiner™ Cystic Fibrosis Report

ge ne ge ne

White Paper

n Overview

n MetaMiner CF Phase II •Moregene-diseaseassociationsandbiomarkers •NewCFspecificmapsandnetworks •CFdrugsandcompounds

n Tools - Using MetaMiner CF Content and the MetaDiscovery Platform Search and Data Mining Approaches •UsingtheCFdiseasedetailpage •UsingEZSearch •UsingMetaSearch

Enrichment Analysis

Addressing Data Connectivity and Drawing Pathways •Networkbuildingalgorithmsandinteractomeanalysis •Interactomeanalsyis •Additionaltranslationalapproaches

n How to Access the Cystic Fibrosis Platform

MetaMiner™ Cystic Fibrosis (CF) represents a version of GeneGo’s MetaDiscovery™ suite that isenrichedwith content specific for cystic fibrosis. Now in its second phase of development, thecontent is reflected in several forms of cause-effect relationships including: gene-disease,compounds-disease and compound-protein associations. This information is extracted fromprimary literature,andmanually inputted intoanoracledatabase(1).GeneGo’sMetaDiscoveryplatform further creates analysis tools to enable a user to search or generate hypothesis-drivenoutputs against the background of the database. In addition to the underlying content, theMetaMinerCFplatformextendstheapplicationoftheMetaDiscoveryplatformstoincludepre-builtdisease-specific canonical pathway maps and networks. Therefore, MetaMiner CF is a dataanalysisplatformdesignedtobeusedbyabroadrangeofCFresearchersincludingwetlabbiologists, bioinformaticians,cliniciansandchemists. It isanongoingprojectthatwas initiatedin2007byGeneGoInc.incollaborationwithCysticFibrosisFoundationTherapeutics(CFFT)andacommitteeofexpertscientiststhatalsodictatethecontentofcontinuousquarterlyupdates.ThisreiterativeprocessismediatedandfullyfundedbyCFFTallowingGeneGodeveloperstomaintainandupdateCFcontentundertheguidanceoftheCommittee*.

Theoriginalgoalsofthiscollaborationwereto:

1. Toassembleandmaintainthemostrelevantcysticfibrosisbiologicalandchemicalexperimentaldataavailabletodateinaneasy-to-accessreferenceenvironment

2. Tocreatevisualizationtoolsdepictingkeydisease-causativeanddisease-progressivemechanismsinaseriesofpathwaymapsandnetworkmodels

3. To provide the CF community with a “one-stop-shop” tool for uploading andanalyzingexperimentaldatainadisease-centeredinterface

1

ge ne ge ne

Copyright © 2009 GeneGo, Incorporated 1

White Paper - MetaMiner Cystic Fibrosis (CF)

CONTENT

OVERVIEW

Update of MetaMiner CF Phase IIWe previously reported that the MetaMiner CF content included informationon169genesconnectedby293association linksthatwereextractedfrom220articles (2). The 169geneswere found tohave 26,767additionalphysicalandfunctional interactions with other human proteins, DNA, RNA and compoundspeciesannotatedfrom23,415articles.Also,atotalof89drugsandcompoundswere linked toCFat the time. The firstMetaMinerCF releasealso included25pre-builtcanonicalmapsand10networks, thatvisuallydescribedprocessesofCF origination, development, or perturbation of cellular processes, metabolicandtransportpathways.

A summary of the most current release of MetaMiner CF is listed below. Thisinformation can be easily accessed via the “one-stop shop” Cystic FibrosisDiseasepage(Figure1)that iscompletewith interactive linkstomoredetaileddescriptionsandreferences.

More Gene-Disease Associations and Biomarkers

The current content count for MetaMiner CF has gone from 169 to 206disease-relevant genes accompanied by 42,173 physical and functionalinteractions with other human proteins, DNA, RNA and compounds. Theseinteractionsareannotatedfrom29,807originalarticles.Asetofpublicationsareparticularly noted as key contributions to the current update (3-8). The currentplatformalsoincludesaccesstoseveralgeneexpressionprofiles(GSE6802,GSE9488)(3,4).Atotalof12biomarkers(7metabolitesand5proteins)wereidentifiedandannotatedforCF,and457associationlinks(from240originalarticles),specifictothegenelevel,werecategorizedbythefollowing:

• Promotermethylation 1• Rearrangement 18• Pointmutation 8• Singlenucleotidepolymorphism(SNP) 19• RNAsplicevariant 1• RNAamountchange 72• Alteredinteraction 9 • Proteinamountchange 107

New CF Specific Maps

ThefollowingisacurrentlistofCF-specificGeneGoCanonicalMaps,withthenewmaps listed inboldprint. In total,36mapsand10networkshavebeencreatedsincetheinitiationofthepartnership.ForasamplemappleaserefertoFigure4.

10CFTRtrafficandfoldingrelatedmaps:Foldingandmaturation(normandCF)NormalwtCFTRtraffic/ER-to-GolgiNormalwtCFTRtraffic/SortingendosomeformationDelta508-CFTRtraffic/ER-to-GolgiinCFDelta508-CFTRtraffic/SortingendosomeformationinCFTransport_Clathrin-coated vesicle cycle wtCFTRanddelta508traffic/Clathrincoatedvesiclesformation(normandCF)wtCFTRanddelta508-CFTRtraffic/Genericschema(normandCF)wtCFTRanddeltaF508traffic/LateendosomeandLysosome(normandCF)wtCFTRanddeltaF508traffic/Membraneexpression(normandCF)

1

ge ne ge ne



MetaMiner CFPHASE II

5differentCholesterolandSphingolipidstransport:

Distribution to the intracellular membrane compartments (normal and CF) Generic schema (normal and CF) Influx to the early endosome in lung (normal and CF) Recycling to plasma membrane in lung (normal and CF) Transport from Golgi and ER to the apical membrane (normal and CF)

2differentimmuneresponsemaps:

Antigen presentation by MHC class I Bacterialinfectionsinnormalairways

4differentInhibitoryactionofLipoxinmaps:

LipoxinA4onPDGF,EGFandLTD4signalingLipoxinsandResolvinE1onneutrophilfunctionsOnSuperoxideproductioninneutrophilsOnneutrophilmigration

2mapsontheinfluenceofMucinexpressioninCF:

viaIL-6,IL-17signalingpathwaysviaTLRs,EGFRsignalingpathways

3mapsdescribingCFTRactivation:

Mechanisms of CFTR activation by S-nitrosoglutathione (normal and CF) NO-dependent CFTR activation (normal and CF) Regulation of CFTR activity (norm and CF)

10additionalmaps:

BacterialinfectionsinCFairwaysBeta-2 adrenergic-dependent CFTR expression CFTR translational fidelity (class I mutations) CFTR-dependent regulation of ion channels in Airway Epithelium (norm and CF) CytokineproductionbyTh17cellsinCFCytokineproductionbyTh17cellsinCF(mousemodelversion)IL-1 beta-dependent CFTR expression RegulationofCFTRgating(normalandCF)RegulationofdegradationofdeltaF508CFTRinCFRegulationofdegradationofwt-CFTR

CFspecificnetworksinclude:

CFTRexpressionregulationCysticfibrosis-drugsanddrugstargetsCysticfibrosisandhypoxiaFibrosissignaling:commonfeaturesIntracellularpatternrecognitionreceptorsPulmonaryFibrosisRegulationofClC-2TGFbeta-1incysticfibrosisTLRsignalinginairwayepithelium

CF Drugs and Compounds

In addition, 11 endogenous metabolites were annotated in the diseaseassociationcontext, supportedby11articles. These smallmoleculecompoundsare accompanied by 473 physical and functional interactions (and reactions)withhumanproteins,annotated from506originalarticles. In total,90drugsandcompoundswerelinkedtoCF,including:

• FDA-approveddrugs 15• Drugsinclinicaltrials 63• Discontinueddrugs 9• Pre-clinicaldrugcandidates 2

ge ne ge ne



MetaMiner CFPHASE II

TOOLSGuide to Analysis Approach using MetaMiner CF Content and the GeneGo MetaDiscovery Platform

The CF content is supported by an interface centered on the disease (the CFDiseaseDetailpage:seeFigure1).However,theGeneGoMetaDiscoveryplatformalsoenablesdifferenttypesofanalysesrangingfromageneralsearchtoworkflowsthat assist with “dry lab” research and hypothesis generation. Furthermore, theplatform can be used for complete functional analysis of CF OMICs datasets(gene expression, proteomics, SNPs, metabolomics etc.), browsing of CF mapsandnetworks,orcombinatorialsearchforcellularprocesses,genes,proteinsandcompounds,aswellasforaccessingtherelevantliterature.Withasimple“click”abstractsofarticlesareavailablethroughtheinterfacethatislinkedtoPubMed® fromMetaMinerCF pages or fromanymap, network or search result. HereweprovideanabbreviatedguideshowinghowtousetheMetaDiscoveryplatforminthecontextofMetaMinerCF.

Note: data used for enrichment, network and interactome analysis taken from a proteomics analysis by Balch et al (6).

ge ne ge ne



2

3

1

Figure 1. Accessing the CF Disease Detail page: To access the CF Disease Detail page, from the EZ Start window select “Browse Content” (1) then the “GeneGo Disease-Specific Content (Cystic Fibrosis)” option (2). As shown above, the Disease Detail page lists categories for further searching: SNPs, genes, proteins, compounds, biomarkers, drugs, disease maps and disease pathway network. To access more information select the heading of choice for more options. Shown above, is the option to search more SNPs, genes and proteins (3).

1) Search and Data Mining Approaches The MetaDiscovery interface allows a user to start their analysis or hypothesisgenerationwithasimplequestion.Bysearchingthedatabase,andinthiscaseCFcontentinparticular,aseriesofobservationscanbedrawnandlistscanbegeneratedforfurtheranalysis.Thereareseveralstartingpointstobrowseandsearch:

Using the CF Disease Detail Page MetaMinerCFcontentcanbeaccesseddirectlyfromtheEZStartBrowseContentsectionoftheinterface(Figure1).

Using EZ Search AnotherwaytoaccessCFinformation(orinformationonyourfavoritegene,proteinoranotherdisease)istheEZSearchtool.ThiscanbeaccessedfromtheSearchtaboftheEZStartinterface.ByselectingEZSearchandsearching“cysticfibrosis”,alistofresultsistabulatedaccordingtotypeofannotatedinformationthatexistsintheunderlyingdatabase.For instanceinFigure2,23genesmatchthesearchterms.4proteinsand90drugs (etc.)are tabulated.When selectingacategory in thelefthandpanel,therighthandpaneloftheresultspageprovidesalistofspecificresults,eachina“clickable”formtolinktomoredetails.

ge ne ge ne



TOOLS

Figure 2: Using EZ Search to Search for CF Content: MetaCore’s EZ Search tool enables the user to find all annotated or map or network information about “your favorite gene/protein/disease/miRNA/compound. Shown above is how to access the EZ Search tool from EZ Start (1) and an example of searching for cystic fibrosis (2).

2

1

Using MetaSearch MetaMiner (CF) in conjunction with MetaCore applications also features apowerful Boolean searchengine,whichcanbe launched from the Search tabof theEZStart interfaceunder the search tab (Figure2-1).MetaSearchenablesspecificqueries tocall upon theentiredatabaseof interactionsand functionalontologies,includingCF-specificinformation.QueryresultscanbeeasilyexportedinseveralformsincludingMetaCore-compatiblelistformats,ortoExcel.ThepowerofMetaSearch includes:1)accessing informationfromtheunderlyingdatabaseand2)enablesthegenerationofnewlistsofdataforhypothesisgenerationfromseveralperspectives, includingdrugdiscovery. For instance,aquery for“findallcompoundsforanycategoryofcysticfibrosis”canbedesignedasfollows(Figure3)togeneratealistof11compounds.

ge ne ge ne



TOOLS

Figure 3. Using Boolean-based queries to search for CF content: Shown above is a snapshot of the MetaSearch interface where the question “find all compounds for any category of cystic fibrosis” was proposed. The results are listed in the bottom panel (red box).

ge ne ge ne



TOOLS

1

2

3

4

2) Enrichment Analysis Ifyourstartingpointisalistofgenes,proteins,SNPsormiRNAs,onecommonanalysisapproach is toask “What is themost representative “X”ofmy list?”,where “X”canbeafunctionfromoneoftheGeneGoontologies:GeneGocanonicalMaps,CFdisease-specificmaps(ornetworks),GeneGoprocesses,diseases,metabolicpathways, toxicordrug targetpathwaysorontologies fromthe publicdomainsuchas GeneOntology. TheMetaDiscoveryplatformaddresses this approachbyprovidingenrichmentanalysis toolswherea list isparsedacrossanontologytogeneratehistogramswithaquantitativeassessmentof relevance(p-valueofsignificance).Thehistogramsareinteractiveandfurtherenableausertovisualizepre-builtmapsornetworksrepresentingabiologicalprocesswiththeinputdataoverlaidormarked.ThereforeausercaninputalistfromCFtargetingexperimentsanddeterminekeyperturbedfunctionsacrossoneontologyatatimeoracrossallsimultaneouslyusingaworkflow.TheadditionofCF-specificmapscreatesamorespecificCF-richenvironmentinordertodetermineenrichedCF-specificprocesses.Enrichment analysis can also be used for several different data types or similardatatypesforcomparison.ThestepsrequiredtoconductanenrichmentanalysisworkflowaresummarizedinFigure4.

Figure 4A.

ge ne ge ne



TOOLS

Figure 4. Enrichment Analysis workflow: With a given list of genes/proteins/SNPs/miRNA (etc), enrichment analysis can be used to determine highly representative functions across GeneGo Ontolgies. All enrichment analysis tools are listed in the “Analyze Data” tab (1) from the EZ Start menu. In this example, the enrichment analysis workflow (2) was selected to determine representative functions of 2 data sets: protein expression from HEK293 cells expressing wild-type or DF508 CFTR. After applying a “0” threshold, allowing all data to be used (uploaded experimental expression values can be used to set a cut-off), the top ten results for each ontology were displayed (3). One of the most “enriched maps” from the GeneGo Maps ontology was selected (Regulation of degradation of deltaF508 CFTR in CF) and is shown above (4). The pre-built interactive maps are useful hypothesis-driving tools complete with cause-effect relationships marked with directionality and mechanisms (a), with links to the literature (hexagons), and the ability to overlay the input data (b) represented by red bars. In this example bar #1 = expression in HEK cells expressing wild-type CFTR and #2 = HEK cells expressing the mutant. On disease-specific maps such as this one, disease-specific augmented interactions are marked by magenta-colored arrows.

Figure 4B.

b a

3) Addressing Data Connectivity and Drawing Pathways

Network Building Algorithms and Interactome Analysis Another common approach in determining the collective function of a list ofgenes,proteins,SNPs,miRNA(etc) is toask“Howare these itemsconnected?”,“Whatistheirbiochemicalrelationship?”,“Isthereacommondenominator?”,and“Istheirconnectivityrelevant?”GeneGo’snetwork-buildingalgorithms(Figure5a)providetheflexibilityto“connect-the-dots”betweengenes,etc.ona listwithalevelofcontrolthatcannotbecomparedtoalternativepathway-buildingtools.Forinstance,itispossiblethatasetofgenesfromawild-typeCFTRover-expressingcell linediffers fromtheDelta508-expressingcell line(whichcanbedeterminedwithenrichmentanalysistoo).Asaresult,theinteractionsthatco-existalsodifferatvariouslevels(cellortissueorprocess-dependent).Withthedirectinteractionalgorithm,differencesintheclosest-knitassociationsbetweentheitemsonthelistonlycanbedetermined.However,changesmayoccurseveralstepsawayfromadirectinteractionofagenesetandadifferentalgorithmsuchasauto-expandisrequired.Additionalalgorithmsgivetheabilitytoexpandthebiologicalspaceassociatedwith the input list,determinemostconnectedhubsof signalingandidentify regulatory factors that may impact the list as a whole (i.e. commontranscription factor - Figure5c). Forawhitepaperonnetworkalgorithms,[email protected].

ge ne ge ne



TOOLS

a

b

Select to view network with data

c

Figure 5. Building a network using the Analyze Network-Receptor Algorithms: To assess connectivity between protein expression from HEK293 cells expressing wild-type or DF508 CFTR, with the specific goal of identifying common hubs of regulation by transcription factors, the “analyze network“ algorithm was used (a). In this algorithm, sub-networks generated based on highly-saturated items from the input list, are ranked by a p-value and G-Score and interpreted in terms of Gene Ontology. The user has a choice of sub-network to visualize from a network list (b). In this example, a sub-network generated around CFTR is shown (c). The networks are similar to the maps where the colored arrows represent directionality, effect and mechanism of interaction. Any interactions that are part of a canonical map are highlighted in teal blue.

ge ne ge ne



TOOLS

Interactome Analysis Moreover, the interatome tool assesses connectivity amongst the data versusthedatabase ina tabular formwithina singledataset,orbetweendatasets(Figure6).TheMetaDiscoveryinteractomeoptionsincludetabulatingaccordingtoproteinclassoramountofoverversusunderconnectivity.Belowisasampleoftheinteractomeanalysisbyproteinsfunction.

a

b

Figure 6. Interactome Analysis of CF data: Protein expression from HEK293 cells expressing wild-type or DF508 CFTR were subjected to the interactome by protein class function in MetaCore. Shown is a sample the resulting output including a legend (a) and the list of connected objects (b). A sample observation is marked in red: CFTR is connected to 22 out of 23 items from the data itself and 303 out of 19836 interactions in the database. Similarity, GLUT4 (a glucose transporter) is connected to 7/23 data objects and 666/19836 within the database indicating that CFTR is more connected to the data set than GLUT4, despite GLUT4 having more interactions with the database.

ge ne ge ne



GeneGo, Inc.500RenaissanceDrive,Suite106St.Joseph,Michigan49085USA(888)592-3124,(858)756-7996+447886191274UnitedKingdom

[email protected]

REFERENCES

ACCESS

1) WrightJ.M.,NikolskyY.,SerebryiskayaT,WetmoreD.R.MetaMiner(Cysticfibrosis):adiseaseorientedbioinformaticsanalysisenvironment.MethodsMolBiol.2009;563:353-67

2) WhitePaper(2008)-MetaMinerCysticFibrosis,GeneGoInc.

3) MayerAK,MuehmerM,MagesJ,GueinziusKetal.(2007).DifferentialrecognitionofTLR-dependentmicrobialligandsinhumanbronchialepithelialcells.JImmunol.178(5):3134-42.

4) XuY,TertiltC,QuadriL,CrystalRG,WorgallS.(2009).Influenceofthecysticfibrosistransmembraneconductanceregulatoronexpressionoflipidmetabolism-relatedgenesindendriticcells.RespirRes.10(1):26.

5) Trzcinska-DanelutiAM,LyD,HuynhL,JiangC,FladdC,RotinD.(2009).High-contentfunctionalscreentoidentifyproteinsthatcorrectF508del-CFTRfunction.MolCellProteomics.8(4):780-9.

6) WangX,VenableJ,LaPointeP,HuttDM,KoulovAV,CoppingerJ,GurkanC,KellnerW,MattesonJ,PlutnerH,RiordanJR,KellyJW,YatesJR,andBalchW.(2006).Hsp90CochaperoneAha1 DownregulationRescuesMisfoldingofCFTRinCysticFibrosis.Cell.127:803–815.

7) SinghOV,VijN,MogayzelPJJr,JozwikC,PollardHB,ZeitlinPL.(2006).Pharmacoproteomicsof 4-phenylbutyrate-treatedIB3-1cysticfibrosisbronchialepithelialcells.JProteomeRes.3:562-71.

8) OlleroM,BrouillardF,EdelmanA.(2006).Cysticfibrosisenterstheproteomicsscene:newanswersto oldquestions.Proteomics.14:4084-99.

Additional Translational Approaches MoretranslationalapplicationsofMetaMinerCFinclude:

1)Accesstodrugdetailsfromasearchorfromagene/protein/diseaseofinterest2) Biomarkertargetingworkflows3) Toxicityanalysisworkflows4) Customization of visual outputs using MetaLink to map novel interactions

(unknowntothepublicdomain).5) Customization of visual outputs usingMapEditor to createmapswith custom

objects,text,linksandcellularlocalizations–idealforpresentations Foratrainingsessiononhowtoexecutetheseapproachespleasecontacttraining@genego.com.TheadvancedapplicationsofMetaMinerCFalsoincludeintegrationofGeneGoCFanalyseswiththird-partysoftwaresuitestoenableseamlessexportofcysticfibrosisnetworksoranalysislistsfromMetaMinerCFtoCytoscape,GeneSpring,DecisionSite,andResolver,followingtheadditionoftheappropriateplug-ins.

How to Access the MetaMiner Cystic Fibrosis PlatformAccesstotheCFmaps,networksanddiseasepagesareavailableviaMetaCorethatcanbe licensed fromGeneGo Inc.bycontacting [email protected],Tel: 858-756-7996. AllCF researchers receivediscounts sopleasestate thatyouareaCFresearcher.Wealsohave2-weekfreetrialsavailable.Gotohttp://www.genego.com/trials,andcompletetheonlineform.

*CysticFibrosisCommitteeMembers:

JerryWright,Ph.D. Tzyh-ChangHwang,M.D.,Ph.D.ChristopherKarp,M.D. BenjaminGaston,M.D. PamelaZeitlin,MD,Ph.D. ThomasKelley,Ph.D.AnnHarris,Ph.D.,M.A. DianaWetmore,Ph.D.andElizabethJoseloff,Ph.D.(CFFTSponsors)

*Thecystic fibrosis content is updatedwith regularquarterly updatesof theMetaCore™ database.Therefore,theactualnumbersmaybehigherthanstatedinthereport.

metaminer™ cystic fibrosis report

Documents