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  • OVERALL GOAL OF THE LIPID MAPS PROJECTDiscover all major and minor lipid molecules that play a role in the biology of exemplar mammalian cellsConstruct networks of lipid maps that play a role in cellular metabolism and signalingCreate a comprehensive resource for lipids with functional annotation of the roles they play in cellular metabolism and signaling

    LIPIDMAPS

  • LABORATORY DIRECTORSDennis, PI and Director of Eicosanoid CoreRussel, co-PI and Director of Sterol CoreBrown, co-PI and Director of Phospholipid CoreMerrill, co-PI and Director of Sphingolipid CoreRaetz, co-PI and Director of Novel Lipids CoreGlass, co-PI and Director of Cell Biology and Genomics CoresBoger, co-PI and Director of Synthesis CoreSubramaniam, co-PI and Director of Bioinformatics CoreOther co-PIs

    LIPIDMAPS

  • SPECIFIC GOALS OF LIPIDMAPSDefinition of Our SystemsCells of interestMajor and minor lipid moleculesGene products of interest to lipid molecules

    LIPIDMAPS

  • CHOICE OF MACROPHAGES FOR LMAPS

    -Displays important lipid biology-Likely interactions between lipids and gene products -Mammalian cell -Cell lines and murine and human cells available -Post-mitotic-Measurable responses to lipid molecule input -Complete gene parts list likely available-Requisite mass and homogeneity -Ability to add and subtract genes -Known role of lipids in metabolism and signaling -Quantifiable phenotypes -Of interest to several diseases -Access to diseased and normal counterpart -Available to entire community

    LIPIDMAPS

  • Who are the players?Prepare an initial list of lipid players of interestDetermine which major lipids are present in defined macrophage preparations (mass spectrometry, other)Quantify lipids of interest (??)Identify genes and gene products associated with the defined lipids (genomics, proteomics, bioinformatics, other)

    LIPIDMAPS

  • PlayersMajor and Minor Lipids present under normal, perturbed and pathophysiological conditions. Identification of novel lipids through MS, lipid synthesis and characterization.Creation of the Lipids Database

    LIPIDMAPS

  • LIPIDMAPS

    ACYLGLYCEROLS

    BILE ACIDS (CHOLANOIDS)

    DERIVED LIPIDS

    FATTY ACID

    LONG CHAIN ALCOHOL

    LONG CHAIN ALDEHYDE

    LONG CHAIN BASE and CERAMIDE

    EICOSANOIDS

    ETHER TYPE LIPIDS

    FAT SOLUBLE VITAMINS

    CAROTENOID

    COENZYME Q

    VITAMIN A

    VITAMIN D

    VITAMIN E

    VITAMIN K

    GLYCOLIPIDS

    glycoSPHINGOLIPID

    glycoGLYCEROLIPID and OTHERS

    HOPANOIDS

    ISOPRENOIDS

    LIPID PEROXIDES

    LIPOAMINO ACIDS

    LIPOPOLYSACCHARIDES

    LIPOPROTEINS

    MYCOLIC ACIDS

    PHOSPHOLIPIDS

    GLYCEROPHOSPHOLIPID

    PAF

    SPHINGOPHOSPHOLIPID

    STEROIDS

    WAXES

  • Gene ProductsGenes that code for gene products associated with lipid synthesis. Regulatory elements that are associated with expression of genes identified above.Proteins associated with lipid metabolism and signaling.Modifications of proteins involved in lipid cell biology

    LIPIDMAPS

  • SPECIFIC GOALS OF THE LIPIDMAPSQuantitate responses to stimuliGene expressionMass spec identification of lipidsProtein screensBioinformatics

    LIPIDMAPS

  • Quantitate responses to stimuliBasal levelsSelected stimuli affects (LPS & Others)Changes in gene expression Changes in protein concentrationChanges in protein statesNew and novel lipids in response to stimuli

    LIPIDMAPS

  • Transcript ProfilingOligo arrayscDNA arraysDesign of a mouse macrophage chipPromoter Sequence oligo arraysChIP on a chip

    LIPIDMAPS

  • LIPID MAPS: Bioinformatics Organizational StructureShankar Subramaniam (PI)Eoin Fahy (Coordinator) Lipid Chemistry, MS data analysis

    M Madhusudan LIMS

    Dawn Cotter LIPID MAPS Website

    Babu Guda Lipid Biology (Genes, proteins, pathways)

    Purnima Guda Lipid Biology (Genes, proteins, pathways)

    LIPIDMAPS

  • Molecular Profile ComparisonsRAW-TM-BM-ESChris BennerShankar Subramaniam & Chris GlassUCSD Bioinformatics

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  • Macrophage Cell TypesRAW Macrophage cell lineTM - Thioglycolate elicited macrophagesBM Bone Marrow derived macrophagesES Embryonic Stem Cells (not macrophage)

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  • Abbreviation KeyTM Thioglycolate-induced peritoneal macrophagesBM Bone Marrow derived macrophages

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  • Primary DatasetConcentrate Efforts on Codelink data from 3 Macrophage Cell types (BM, TM, RAW)

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  • CodeLink ArraysAffymetrix ArraysPlus multiple other conditions and KOs

    NOTXLPSLPS+DEXBone Marrow M033-Thio M0661Fetal Liver M0---RAW443Embryonic Stem3--

    NOTXLPSLPS+DEXBone Marrow M064-Thio M0973Fetal Liver M0421RAW---Embryonic Stem---

    LIPIDMAPS

  • Microarray PlatformsAgilent Mouse Oligo Array16,282 Probes12,011 Unique LLCodelink Mouse 10K10,501 Probes9,768 Unique LLhttp://www.amershambiosciences.com

    367283381430

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  • Comparison of Expression ValuesCorrelation between no treatment conditions:Codelink: .94Agilent: .96Between: .53AgilentCodelink

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  • CodelinkAffymetrixAgilentAgilent vs. CodelinkCodelink vs. Affymetrix Agilent vs. Affymetrix

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  • Response to LPSUp-regulated GenesCodelink: 431Agilent: 198Down-regulated GenesCodelink: 400Agilent: 43Reason: Agilent arrays have less range in response.There does not appear to be any significant biological difference in response to LPS

    LIPIDMAPS

  • Fold Change DistributionsCodelinkAgilentAgilent vs. Codelink

    LIPIDMAPS

  • Data Analysis PipelineGene OntologyAlternative Gene OntologiesGenomic PositionTransfac AnalysisAnalysis of Promoter SequencesClustering / Sequence Analysis

    LIPIDMAPS

  • High Throughput Ontology AnalysisGene OntologyBiocarta.orgSuper Array, etc.151601617116948223291042152048223231412142131697170421665209126792119106116302232912985238722052912494168596640319691516016171169482232910421520482232314121421316971704216652091267921191061163022329129852387220529124941685966403196915160161711694822329104215204822323141214213169717042166520912679211910611630223291298523872205291249416859664031969Significant Categories areAssignedInspect ResultsCustom OntologiesTransfac Targets

    LIPIDMAPS

  • Ontology ApproachVery General Layout can make an ontology out of anythingEach group is checked for enrichment with the hypergeometric distribution.Top categories along with the entire tree are printed in HTML using layersColor coding is used so that multiple lists can be viewed at the same timeList of all genes in the group easy links to LocusLink

    LIPIDMAPS

  • Large number of lists have been generated : Genes Induced in RAW cells, Induced in RAW but not TM, High in resting BM cells, etc

    LIPIDMAPS

  • Alternative OntologiesBiocarta, SuperArrayData extracted by a web crawler so that it can stay currentBoth are easier to analyze, especially in the context of cellular signaling

    LIPIDMAPS

  • SuperArray Biocarta

    LIPIDMAPS

  • Genomic PositionHope is to identify regions of transcriptionally active or silenced chromatinGenes have been ordered as they appear on the Chromosomes (link to LocusLink). given to each gene in the reference list| given to genes in the list of interest

    LIPIDMAPS

  • In general it appears there are regions where RNA Pol is running down the strand (up LPS RAW)

    When looking at those that are induced in one cell type but not the other, very few if any regions are continuous (up LPS RAW not TM)

    LIPIDMAPS

  • Log Expression Level along Chromosome (relative position)

    LIPIDMAPS

  • Log Expression along Chromosomes (actual position)

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  • LIPIDMAPS

  • Squared Error in Expression Plots

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  • Gene List GenerationFirst task is to define groups of genes that characterize the differences / similarities between cell typesSAM is the primary tool for identifying differential expression, Clustering sometimes used, Parts List calls, Fold Change, Combinations of the above, etc.Lists are feed through analysis pipelineExamples:Genes highly expressed in RAW in resting state.Genes induced by LPS in TM/BM but not RAWGenes absent in BMEtc

    LIPIDMAPS

  • Expression of receptor components

    LIPIDMAPS

  • Inflammatory Agents

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  • NF-kB, ERK1/2, & JNK

    LIPIDMAPS

  • Interleukins

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  • Transcription Factors

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  • Factor Family Expression

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  • Counter-Example: Stat1Known Target genes do not always follow the expression of their Transcription activator

    LIPIDMAPS

  • Transfac AnalysisTransfac has 2 types of useful information:PSSM for Transcription Factors (more Later)Database of Known binding sitesExtracted known binding sites (mapped all homologues to mouse) and created a transcription network based on biological binding data in Transfac.Transcription factors with enriched target genes can be found using the hypergeometric

    LIPIDMAPS

  • Transcription Factor Enrichment

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  • Visualization of Transcriptional Networks

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  • Differential Response to LPS

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  • AfCS DataInterestingly, targets of each transcription factor almost always become active at the same time point.

    LIPIDMAPS

  • LimitationsTransfacs database of known binding sites is very sparse / incompleteGene O