Integrative Functional Genomics

Integrative Functional Genomics Anil Jegga Biomedical Informatics, CCHMC Anil.Jegga@cchmc.org

Two Separate Worlds….. Disease World Genome Variome Transcriptome Regulome miRNAome • Name • Synonyms • Related/Similar Diseases • Subtypes • Etiology • Predisposing Causes • Pathogenesis • Molecular Basis • Population Genetics • Clinical findings • System(s) involved • Lesions • Diagnosis • Prognosis • Treatment • Clinical Trials…… Interactome Pharmacogenome Metabolome Physiome Pathome Medical Informatics Bioinformatics & the “omes” PubMed Proteome Disease Database Patient Records OMIM Clinical Synopsis Clinical Trials >380 “omes” so far……… and there is “UNKNOME” too - genes with no function known http://en.wikipedia.org/wiki/List_of_omics_topics_in_biology http://omics.org/index.php/Alphabetically_ordered_list_of_omics With Some Data Exchange…

Motivation To correlate diseases with anatomical parts affected, the genes/proteins involved, and the underlying physiological processes (interactions, pathways, processes). In other words, bringing the disciplines of Medical Informatics (MI) and BioInformatics (BI) together (Biomedical Informatics - BMI) to support personalized or “tailor-made” medicine. How to integrate multiple types of genome-scale data across experiments and phenotypes in order to find genes associated with diseases and drug response

Model Organism Databases: Common Issues • Heterogeneous Data Sets - Data Integration • From Genotype to Phenotype • Experimental and Consensus Views • Incorporation of Large Datasets • Whole genome annotation pipelines • Large scale mutagenesis/variation projects (dbSNP) • Computational vs. Literature-based Data Collection and Evaluation (MedLine) • Data Mining • extraction of new knowledge • testable hypotheses (Hypothesis Generation)

Support Complex Queries • Show me all genes involved in brain development that are expressed in the Central Nervous System. • Show me allgenesinvolved in brain developmentinhumanandmouse that also showiron ion binding activity. • For this set of genes, what aspects of function and/or cellular localization do they share? • For this set of genes, what mutations are reported to cause pathological conditions?

Bioinformatic Data-1978 to present • DNA sequence • Gene expression • Protein expression • Protein Structure • Genome mapping • SNPs & Mutations • Metabolic networks • Regulatory networks • Trait mapping • Gene function analysis • Scientific literature • and others………..

Human Genome Project – Data Deluge No. of Human Gene Records currently in NCBI: ~30K (excluding pseudogenes, mitochondrial genes and obsolete records). Includes ~700 microRNAs NCBI Human Genome Statistics – as on November 4, 2009

The Gene Expression Data Deluge Till 2000: 413 papers on microarray! Problems Deluge! Allison DB, Cui X, Page GP, Sabripour M. 2006. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet. 7(1): 55-65.

Information Deluge….. A researcher would have to scan 130 different journals and read 27 papers per dayto follow a single disease, such as breast cancer (Baasiri et al., 1999 Oncogene 18: 7958-7965). • 3 scientific journals in 1750 • Now - >120,000 scientific journals! • >500,000 medical articles/year • >4,000,000 scientific articles/year • >16 million abstracts in PubMed derived from >32,500 journals

Data-driven Problems….. • How to name or describe proteins, genes, drugs, diseases and conditions consistently and coherently? • How to ascribe and name a function, process or location consistently? • How to describe interactions, partners, reactions and complexes? Some Solutions • Develop/Use controlled or restricted vocabularies (IUPAC-like naming conventions, HGNC, MGI, UMLS, etc.) • Create/Use thesauruses, central repositories or synonym lists (MeSH, UMLS, etc.) • Work towards synoptic reporting and structured abstracting • Generally, the names refer to some feature of the mutant phenotype • Dickie’s small eye (Thieler et al., 1978, Anat Embryol (Berl), 155: 81-86) is now Pax6 • Gleeful: "This gene encodes a C2H2 zinc finger transcription factor with high sequence similarity to vertebrate Gli proteins, so we have named the gene gleeful (Gfl)." (Furlong et al., 2001, Science 293: 1632) What’s in a name! Rose is a rose is a rose is a rose! Gene Nomenclature • Disease names • Mobius Syndrome with Poland’s Anomaly • Werner’s syndrome • Down’s syndrome • Angelman’s syndrome • Creutzfeld-Jacob disease • Accelerin • Antiquitin • Bang Senseless • Bride of Sevenless • Christmas Factor • Cockeye • Crack • Draculin • Dickie’s small eye • Draculin • Fidgetin • Gleeful • Knobhead • Lunatic Fringe • Mortalin • Orphanin • Profilactin • Sonic Hedgehog

Rose is a rose is a rose is a rose….. Not Really! What is a cell? • any small compartment; • (biology) the basic structural and functional unit of all organisms; they may exist as independent units of life (as in monads) or may form colonies or tissues as in higher plants and animals • a device that delivers an electric current as the result of a chemical reaction • a small unit serving as part of or as the nucleus of a larger political movement • cellular telephone: a hand-held mobile radiotelephone for use in an area divided into small sections, each with its own short-range transmitter/receiver • small room is which a monk or nun lives • a room where a prisoner is kept Image Sources: Somewhere from the internet…

Semantic Groups, Types and Concepts: • Semantic Group Biology – Semantic Type Cell • Semantic Groups ObjectORDevices – Semantic Types Manufactured Device or Electrical Device or Communication Device • Semantic Group Organization – Semantic Type Political Group Foundation Model Explorer

HEPATOCELLULAR CARCINOMA SOMATIC [ARG249SER] CTNNB1 TP53* MET Hepatocellular Carcinoma TP53 aflatoxin B1, a mycotoxin induces a very specific G-to-T mutation at codon 249 in the tumor suppressor gene p53. Environmental Effects • COLORECTAL CANCER [3-BP DEL, SER45DEL] • COLORECTAL CANCER [SER33TYR] • PILOMATRICOMA, SOMATIC [SER33TYR] • HEPATOBLASTOMA, SOMATIC [THR41ALA] • DESMOID TUMOR, SOMATIC [THR41ALA] • PILOMATRICOMA, SOMATIC [ASP32GLY] • OVARIAN CARCINOMA, ENDOMETRIOID TYPE, SOMATIC [SER37CYS] • HEPATOCELLULAR CARCINOMA SOMATIC [SER45PHE] • HEPATOCELLULAR CARCINOMA SOMATIC [SER45PRO] • MEDULLOBLASTOMA, SOMATIC [SER33PHE] The REAL Problems Many disease states are complex, because of many genes (alleles & ethnicity, gene families, etc.), environmental effects (life style, exposure, etc.) and the interactions.

ALK in cardiac myocytes • Cell to Cell Adhesion Signaling • Inactivation of Gsk3 by AKT causes accumulation of b-catenin in Alveolar Macrophages • Multi-step Regulation of Transcription by Pitx2 • Presenilin action in Notch and Wnt signaling • Trefoil Factors Initiate Mucosal Healing • WNT Signaling Pathway • HEPATOCELLULAR CARCINOMA • LIVER: • Hepatocellular carcinoma; • Micronodular cirrhosis; • Subacute progressive viral hepatitis • NEOPLASIA: • Primary liver cancer • CBL mediated ligand-induced downregulation of EGF receptors • Signaling of Hepatocyte Growth Factor Receptor CTNNB1 MET • Estrogen-responsive protein Efp controls cell cycle and breast tumors growth • ATM Signaling Pathway • BTG family proteins and cell cycle regulation • Cell Cycle • RB Tumor Suppressor/Checkpoint Signaling in response to DNA damage • Regulation of transcriptional activity by PML • Regulation of cell cycle progression by Plk3 • Hypoxia and p53 in the Cardiovascular system • p53 Signaling Pathway • Apoptotic Signaling in Response to DNA Damage • Role of BRCA1, BRCA2 and ATR in Cancer Susceptibility….Many More….. TP53 The REAL Problems

Hypothesis DATA INFORMATION KNOWLEDGE Information is not knowledge - Albert Einstein Integrative Genomics - what is it?Another buzzword or a meaningful concept useful for biomedical research? Acquisition, Integration, Curation, and Analysis of biological data Integrative Genomics: the study of complex interactions between genes, organism and environment, the triple helix of biology. Gene <–> Organism <-> Environment It is definitely beyond the buzzword stage - Universities now have programs named 'Integrated Genomics.'

Methods for Integration • Link driven federations • Explicit links between databanks. • Warehousing • Data is downloaded, filtered, integrated and stored in a warehouse. Answers to queries are taken from the warehouse. • Others….. Semantic Web, etc………

Link-driven Federations • Creates explicit links between databanks • query: get interesting results and use web links to reach related data in other databanks • Examples: NCBI-Entrez, SRS

http://www.ncbi.nlm.nih.gov/Database/datamodel/

Link-driven Federations • Advantages • complex queries • Fast • Disadvantages • require good knowledge • syntax based • terminology problem not solved

Data Warehousing Data is downloaded, filtered, integrated and stored in a warehouse. Answers to queries are taken from the warehouse. • Advantages • Good for very-specific, task-based queries and studies. • Since it is custom-built and usually expert-curated, relatively less error-prone • Disadvantages • Can become quickly outdated – needs constant updates. • Limited functionality – For e.g., one disease-based or one system-based.

Gene World Biomedical World No Integrative Genomics is Complete without Ontologies • Gene Ontology (GO) • Unified Medical Language System (UMLS)

The 3 Gene Ontologies • Molecular Function = elemental activity/task • the tasks performed by individual gene products; examples are carbohydrate binding and ATPase activity • What a product ‘does’, precise activity • Biological Process = biological goal or objective • broad biological goals, such as dna repair or purine metabolism, that are accomplished by ordered assemblies of molecular functions • Biological objective, accomplished via one or more ordered assemblies of functions • Cellular Component= location or complex • subcellular structures, locations, and macromolecular complexes; examples include nucleus, telomere, and RNA polymerase II holoenzyme • ‘is located in’ (‘is a subcomponent of’ ) http://www.geneontology.org

Example: Gene Product = hammer Function (what)Process (why) Drive a nail - into wood Carpentry Drive stake - into soilGardening Smash a bugPest Control A performer’s juggling objectEntertainment http://www.geneontology.org

GO term associations: EvidenceCodes • ISS: Inferred from sequence or structural similarity • IDA: Inferred from direct assay • IPI: Inferred from physical interaction • TAS: Traceable author statement • IMP: Inferred from mutant phenotype • IGI: Inferred from genetic interaction • IEP: Inferred from expression pattern • ND: no data available http://www.geneontology.org

What can researchers do with GO? • Access gene product functional information • Find how much of a proteome is involved in a process/ function/ component in the cell • Map GO terms and incorporate manual annotationsinto own databases • Provide a link between biological knowledge and • gene expression profiles • proteomics data • Getting the GO and GO_Association Files • Data Mining • My Favorite Gene • By GO • By Sequence • Analysis of Data • Clustering by function/process • Other Tools And how?

http://www.geneontology.org/ Gene list enrichment analysis tools (DAVID, FatiGO, ToppGene)

Open biomedical ontologies http://obo.sourceforge.net/

Unified Medical Language System Knowledge Server– UMLSKShttp://umlsks.nlm.nih.gov/kss/ • The UMLS Metathesaurus contains information about biomedical concepts and terms from many controlled vocabularies and classifications used in patient records, administrative health data, bibliographic and full-text databases, and expert systems. • The Semantic Network, through its semantic types, provides a consistent categorization of all concepts represented in the UMLS Metathesaurus. The links between the semantic types provide the structure for the Network and represent important relationships in the biomedical domain. • The SPECIALIST Lexicon is an English language lexicon with many biomedical terms, containing syntactic, morphological, and orthographic information for each term or word.

Unified Medical Language SystemMetathesaurus • about >1 million biomedical concepts • About 5 million concept names from more than 100 controlled vocabularies and classifications (some in multiple languages) used in patient records, administrative health data, bibliographic and full-text databases and expert systems. • The Metathesaurus is organized by concept or meaning. Alternate names for the same concept (synonyms, lexical variants, and translations) are linked together. • Each Metathesaurus concept has attributes that help to define its meaning, e.g., the semantic type(s) or categories to which it belongs, its position in the hierarchical contexts from various source vocabularies, and, for many concepts, a definition. • Customizable: Users can exclude vocabularies that are not relevant for specific purposes or not licensed for use in their institutions. MetamorphoSys, the multi-platform Java install and customization program distributed with the UMLS resources, helps users to generate pre-defined or custom subsets of the Metathesaurus. • Uses: • linking between different clinical or biomedical vocabularies • information retrieval from databases with human assigned subject index terms and from free-text information sources • linking patient records to related information in bibliographic, full-text, or factual databases • natural language processing and automated indexing research

UMLSKS – Semantic Navigator

Part 2 Integrative Functional Genomic Approaches to Identify and Prioritize Disease Genes

Disease Gene Identification and Prioritization Hypothesis: Majority of genes that impact or cause disease share membership in any of several functional relationships OR Functionally similar or related genes cause similar phenotype. • Functional Similarity – Common/shared • Gene Ontology term • Pathway • Phenotype • Chromosomal location • Expression • Cis regulatory elements (Transcription factor binding sites) • miRNA regulators • Interactions • Other features…..

Background, Problems & Issues • Most of the common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors. • High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes.

Background, Problems & Issues Since multiple genes are associated with same or similar disease phenotypes, it is reasonable to expect the underlying genes to be functionally related. Such functional relatedness (common pathway, interaction, biological process, etc.) can be exploited to aid in the finding of novel disease genes. For e.g., genetically heterogeneous hereditary diseases such as Hermansky-Pudlak syndrome and Fanconianaemia have been shown to be caused by mutations in different interacting proteins.

PPI - Predicting Disease Genes Direct protein–protein interactions (PPI) are one of the strongest manifestations of a functional relation between genes. Hypothesis: Interacting proteins lead to same or similar disease phenotypes when mutated. Several genetically heterogeneous hereditary diseases are shown to be caused by mutations in different interacting proteins. For e.g. Hermansky-Pudlak syndrome and Fanconianaemia. Hence, protein–protein interactions might in principle be used to identify potentially interesting disease gene candidates.

Which of these interactants are potential new candidates? 7 Known Disease Genes 66 HPRD BioGrid Mining human interactome 778 Direct Interactants of Disease Genes Indirect Interactants of Disease Genes • Prioritize candidate genes in the interacting partners of the disease-related genes • Training sets: disease related genes • Test sets: interacting partners of the training genes

ToppGene Suite – General Schema http://toppgene.cchmc.org

ToppGene Suite – Applications http://toppgene.cchmc.org

Results of the genetic disease prioritizations using ToppGene and ToppNet The gene-disease associations were from recently reported GWAS and include novel disease gene associations. Training sets: Compiled using “phenotype/disease” annotations in NCBI’s Entrez Gene records and OMIM Test set genes: Artificial linkage interval - Candidate gene + 99 nearest neighboring genes based on their genomic distance on the same chromosome.

ToppGene Suite (http://toppgene.cchmc.org)

ToppGene Suite (http://toppgene.cchmc.org) Why is a test set gene ranked higher?

Integrative Functional Genomics