Respective contributions of MIAME, GeneOntology and UMLS for transcriptome analysis Fouzia Moussouni, Anita Burgun, Franck Le Duff, Emilie Guérin, Olivier Loréal INSERM U522 and Medical Informatics Laboratory, CHU Pontchaillou Rennes, FRANCE

Transcriptome & DNA microarray study of transcriptionnal response of the cell Normal Pathologic Response to a growth factor Response to genetic disturbances Response to chemics or foods treatment

Pathological situations studied at INSERM U522 IRON overload  DNA mutation(s) Hemochromatosis…  Chronic liver diseases  Fibrosis  Cirrhosis  Hepatocarcinoma Mechanisms

One may deposit thousands of genes 1 gene but multiple facets ! Intensive data generation 1 measure 1 Expression Level 1 Spot intensity Available knowledge on the expressed genes, that need to be capturized and organized. Experimental Raw Data

 Nucleic Sequence components - promoters, introns, exons, transcripts, regulators, …  Chromosomal localization,  Functional proteins and known genes products,  Tissue distribution,  Known gene interactions,  Expression level in physiologic and pathologic conditions,  Known gene variations,  Clinical Implications,  Literature and bibliographic data on a gene. One gene but multiple descriptions

External Sources Analysis Gene Expression warehouse Micro-arrays Substractive banks SAGE Clinical Data ? Need of an integrated gene expression environment (for the liver!) Integration Data cleaning ! experimental data

BIO KNOWLEDGE Gene Expression Warehouse Standardization and controlled specification ONTOLOGY DESIGN Knowledge extraction and data exchange

Standardization ONTOLOGY DESIGN Respective contributions MIAME UMLS GO

MIAME MIAME will provide a standard framework to represent the minimum information that must be reported about microarray experiments : Experience Array Samples Hybridization Measures Normalisation and control Work in progress... Minimum information about a microarray experiment (MIAME) toward standards for microarray data', A. Brazma, at al., Nature Genetics, vol 29 (December 2001), pp

GO is an ontology for molecular biology and Genomics, GeneOntology (GO) But GO is not populated with : GOA è gene sequences è gene products,...

è The Unified Medical Language System (UMLS) is intended to help health professionals and researchers to use biomedical information from different sources. UMLS

è Examples from iron metabolism are studied è How pathologic disease states related to iron metabolism alteration are described in GO and UMLS ?

BIOLOGICAL MODEL FOR IRON METABOLISM Iron metabolism diseases IRON METABOLISM GENES Iron overload aceruloplasminemia Iron deficiency Other diseases hyperferritinemia cataract PATHOLOGIC STATES alteration Other diseases hyperferritinemia cataract

Iron overload due to a gene alteration Iron overload during Aceruloplasminemia mutation Feroxydase activity in plasma Fe2+ Fe3+ Iron binding with plasmatic transferrin Ceruloplasmin Gene THE IRON STAYS INSIDE THE CELL !! NO

BIOLOGICAL MODEL FOR IRON METABOLISM Other diseases hyperferritinemia cataract IRON METABOLISM GENES PATHOLOGIC STATES alteration Iron metabolism diseases Iron overload aceruloplasminemia Iron deficiency

A second scenario related to iron metabolism genes alteration Cataract and hyperferritinemia mutation IRP IRE Translation in excess L_Ferritin gene L_Ferritin mRNA L_Ferritin protein in excess CATARACT and HYPERFERRITINEMIA !

UMLS view Cataract and hyperferritinemia AA, Peptide or Prorein Biologically Active Substance AA, Peptide or Protein Ferritin Iron compound L_FerritinH_Ferritin Metalloprotein RNAbinding Protein Iron Sulfur Prot Cataract Co-occurs In Medline (freq 26) Co-occurs In Medline IRP IRE

GO/ GOAnnotations view Cataract and hyperferritinemia Ferritin Heavy Chain Cell component Ferritin IRP Ferritin Light Chain IRE Ligand binding Prot or carrierFerric iron bindingIron homeostasisIron transport Hydro-lyase Metabolism Cataract Link in GO Annotations DB

Target representation Cataract and hyperferritinemia Cataract IRP Ligand binding Prot or carrier Ferric iron binding Iron homeostasis Iron transport Ferritin Light Chain Ferritin Heavy Chain Ferritin IRE Hyperferritinemia Genes Mutated genes Dynamic links Modeling of biological functions

DNA Chips And more generally … Recapitulative Information on disease states, clinical treatments and followups. Normal vs. pathologic Information on Roles of the genes in Biological and metabolic states ? We need precise and dynamic models to get the whole picture MIAMEMIAME Information on biological samples, experiments and results GOA UMLS

Gene products for Iron metabolism, as they are actually described in GO and UMLS.