Integrative Functional Genomics

Anil JeggaBiomedical Informatics, CCHMC

Anil.Jegga@cchmc.org

Medical Informatics Bioinformatics & the “omes”

Patient Records

Patient Records

Disease Database

Disease Database→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……

PubMed

Clinical Trials

Clinical Trials

Two Separate Worlds…..

With Some Data Exchange…

Genome

Transcriptome

miRNAome

Interactome

Metabolome

Physiome

Regulome Variome

Pathome Ph

arm

acog

en

om

e

OMIMClinical

Synopsis

Disease

World

>380 “omes” so far………

and there is “UNKNOME” too - genes with no function knownhttp://en.wikipedia.org/wiki/List_of_omics_topics_in_biology

http://omics.org/index.php/Alphabetically_ordered_list_of_omics

Proteome

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.

Motivation

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 all genes involved in brain development in human and mouse that also show iron 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 DelugeTill 2000: 413 papers on microarray!

YearPubMed Articles

2001 834

2002 1557

2003 2421

2004 3508

2005 4400

2006 4824

2007 5108

2008 5884

2009 5207…..

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.

• 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

Information Deluge…..

A researcher would have to scan 130 different journals and read 27 papers per day to follow a single disease, such as breast cancer (Baasiri et al., 1999 Oncogene 18: 7958-7965).

•Accelerin•Antiquitin•Bang Senseless•Bride of Sevenless•Christmas Factor•Cockeye•Crack•Draculin•Dickie’s small eye

Disease names• Mobius Syndrome with

Poland’s Anomaly• Werner’s syndrome• Down’s syndrome• Angelman’s syndrome• Creutzfeld-Jacob

disease

•Draculin•Fidgetin•Gleeful•Knobhead•Lunatic Fringe•Mortalin•Orphanin•Profilactin•Sonic Hedgehog

Data-driven Problems…..

Gene Nomenclature

• 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?

• 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

Some Solutions

1. Generally, the names refer to some feature of the mutant phenotype

2. Dickie’s small eye (Thieler et al., 1978, Anat Embryol (Berl), 155: 81-86) is now Pax6

3. 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!

Rose is a rose is a rose is a rose….. Not Really!

Image Sources: Somewhere from the internet…

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

Foundation Model Explorer

Semantic Groups, Types and Concepts:

• Semantic Group Biology – Semantic Type Cell

• Semantic Groups Object OR Devices – Semantic Types Manufactured Device or Electrical Device or Communication Device

• Semantic Group Organization – Semantic Type Political Group

Database name

No. of Records

Query= p53

Query= TP53

(HGNC)

Query= p53 OR TP53

PubMed 48,679 3360 49,469

PMC 21,193 1529 21,564

Book 782 504 820

Nucleotide 9473 592 9773

Protein 6219 509 6377

Genome 22 1 23

OMIM 403 141 414

SNP 424 337 453

Gene 1642 338 1750

Homologene 63 9 68

GEO Profiles 352,684 15,140 358,999

Cancer Chr 302 161 463

Hepatocellular Carcinoma

CTNNB1

MET

TP53

1. COLORECTAL CANCER [3-BP DEL, SER45DEL]2. COLORECTAL CANCER [SER33TYR]3. PILOMATRICOMA, SOMATIC [SER33TYR]4. HEPATOBLASTOMA, SOMATIC [THR41ALA]5. DESMOID TUMOR, SOMATIC [THR41ALA]6. PILOMATRICOMA, SOMATIC [ASP32GLY]7. OVARIAN CARCINOMA, ENDOMETRIOID TYPE, SOMATIC [SER37CYS]8. HEPATOCELLULAR CARCINOMA SOMATIC [SER45PHE]9. HEPATOCELLULAR CARCINOMA SOMATIC [SER45PRO]10. MEDULLOBLASTOMA, SOMATIC [SER33PHE]

1. COLORECTAL CANCER [3-BP DEL, SER45DEL]2. COLORECTAL CANCER [SER33TYR]3. PILOMATRICOMA, SOMATIC [SER33TYR]4. HEPATOBLASTOMA, SOMATIC [THR41ALA]5. DESMOID TUMOR, SOMATIC [THR41ALA]6. PILOMATRICOMA, SOMATIC [ASP32GLY]7. OVARIAN CARCINOMA, ENDOMETRIOID TYPE, SOMATIC [SER37CYS]8. HEPATOCELLULAR CARCINOMA SOMATIC [SER45PHE]9. HEPATOCELLULAR CARCINOMA SOMATIC [SER45PRO]10. MEDULLOBLASTOMA, SOMATIC [SER33PHE]

1. HEPATOCELLULAR CARCINOMA SOMATIC [ARG249SER]

1. HEPATOCELLULAR CARCINOMA SOMATIC [ARG249SER]

TP53*

aflatoxin B1, a mycotoxin induces a very specific G-to-T mutation at codon 249 in the tumor suppressor gene p53.

Environmental Effects

Many disease states are complex, because of many genes (alleles & ethnicity, gene families, etc.), environmental effects (life style, exposure, etc.) and the interactions.

The REAL Problems

HEPATOCELLULAR CARCINOMALIVER:

•Hepatocellular carcinoma; •Micronodular cirrhosis; •Subacute progressive viral hepatitis

NEOPLASIA: •Primary liver cancer

CTNNB1

MET

TP53

1. ALK in cardiac myocytes 2. Cell to Cell Adhesion Signaling 3. Inactivation of Gsk3 by AKT causes

accumulation of b-catenin in Alveolar Macrophages

4. Multi-step Regulation of Transcription by Pitx2 5. Presenilin action in Notch and Wnt signaling 6. Trefoil Factors Initiate Mucosal Healing 7. WNT Signaling Pathway

1. ALK in cardiac myocytes 2. Cell to Cell Adhesion Signaling 3. Inactivation of Gsk3 by AKT causes

accumulation of b-catenin in Alveolar Macrophages

4. Multi-step Regulation of Transcription by Pitx2 5. Presenilin action in Notch and Wnt signaling 6. Trefoil Factors Initiate Mucosal Healing 7. WNT Signaling Pathway

1. CBL mediated ligand-induced downregulation of EGF receptors

2. Signaling of Hepatocyte Growth Factor Receptor

1. CBL mediated ligand-induced downregulation of EGF receptors

2. Signaling of Hepatocyte Growth Factor Receptor

1. Estrogen-responsive protein Efp controls cell cycle and breast tumors growth

2. ATM Signaling Pathway 3. BTG family proteins and cell cycle

regulation 4. Cell Cycle 5. RB Tumor Suppressor/Checkpoint

Signaling in response to DNA damage

6. Regulation of transcriptional activity by PML

7. Regulation of cell cycle progression by Plk3

8. Hypoxia and p53 in the Cardiovascular system

9. p53 Signaling Pathway 10. Apoptotic Signaling in Response to

DNA Damage 11. Role of BRCA1, BRCA2 and ATR in

Cancer Susceptibility….Many More…..

1. Estrogen-responsive protein Efp controls cell cycle and breast tumors growth

2. ATM Signaling Pathway 3. BTG family proteins and cell cycle

regulation 4. Cell Cycle 5. RB Tumor Suppressor/Checkpoint

Signaling in response to DNA damage

6. Regulation of transcriptional activity by PML

7. Regulation of cell cycle progression by Plk3

8. Hypoxia and p53 in the Cardiovascular system

9. p53 Signaling Pathway 10. Apoptotic Signaling in Response to

DNA Damage 11. Role of BRCA1, BRCA2 and ATR in

Cancer Susceptibility….Many More…..

The REAL Problems

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.'

Hypothesis

Information is not knowledge - Albert Einstein

1. Link driven federations• Explicit links between databanks.

2. Warehousing• Data is downloaded, filtered,

integrated and stored in a warehouse. Answers to queries are taken from the warehouse.

3. Others….. Semantic Web, etc………

Methods for Integration

1. Creates explicit links between databanks

2. query: get interesting results and use web links to reach related data in other databanks

Examples: NCBI-Entrez, SRS

Link-driven Federations

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

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

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

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

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

1.Advantages• complex queries• Fast

2.Disadvantages• require good knowledge• syntax based• terminology problem not

solved

Link-driven Federations

Data is downloaded, filtered, integrated and stored in a warehouse. Answers to queries are taken from the warehouse.

Data Warehousing

Advantages1. Good for very-specific,

task-based queries and studies.

2. Since it is custom-built and usually expert-curated, relatively less error-prone

Disadvantages1. Can become quickly

outdated – needs constant updates.

2. Limited functionality – For e.g., one disease-based or one system-based.

No Integrative Genomics is Complete without Ontologies

• Gene Ontology (GO)

• Unified Medical Language System (UMLS)

Gene World Biomedical World

• 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’ )

The 3 Gene Ontologies

http://www.geneontology.org

Function (what) Process (why)

Drive a nail - into wood Carpentry

Drive stake - into soil Gardening

Smash a bug Pest Control

A performer’s juggling object Entertainment

Example: Gene Product = hammer

http://www.geneontology.org

• 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

GO term associations: Evidence Codes

http://www.geneontology.org

• 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 annotations into own databases

• Provide a link between biological knowledge and

• gene expression profiles

• proteomics data

What can researchers do with GO?

• 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– UMLSKS

http://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 Network

• Complexity reduced by grouping concepts according to the semantic types that have been assigned to them.

• There are currently 15 semantic groups that provide a partition of the UMLS Metathesaurus for 99.5% of the concepts.ACTI|Activities & Behaviors|T053|Behavior

ANAT|Anatomy|T024|Tissue

CHEM|Chemicals & Drugs|T195|Antibiotic

CONC|Concepts & Ideas|T170|Intellectual Product

DEVI|Devices|T074|Medical Device

DISO|Disorders|T047|Disease or Syndrome

GENE|Genes & Molecular Sequences|T085|Molecular Sequence

GEOG|Geographic Areas|T083|Geographic Area

LIVB|Living Beings|T005|Virus

OBJC|Objects|T073|Manufactured Object

OCCU|Occupations|T091|Biomedical Occupation or Discipline

ORGA|Organizations|T093|Health Care Related Organization

PHEN|Phenomena|T038|Biologic Function

PHYS|Physiology|T040|Organism Function

PROC|Procedures|T061|Therapeutic or Preventive Procedure

Semantic Groups (15)

Semantic Types (135) Concepts

(millions)

UMLSKS – Semantic Navigator

Part 2Integrative 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…..

1. Most of the common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors.

2. 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

3. Since multiple genes are associated with same or similar disease phenotypes, it is reasonable to expect the underlying genes to be functionally related.

4. 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 Fanconi anaemia have been shown to be caused by mutations in different interacting proteins.

Background, Problems & Issues

1. Direct protein–protein interactions (PPI) are one of the strongest manifestations of a functional relation between genes.

2. Hypothesis: Interacting proteins lead to same or similar disease phenotypes when mutated.

3. Several genetically heterogeneous hereditary diseases are shown to be caused by mutations in different interacting proteins. For e.g. Hermansky-Pudlak syndrome and Fanconi anaemia. Hence, protein–protein interactions might in principle be used to identify potentially interesting disease gene candidates.

PPI - Predicting Disease Genes

Known Disease Genes

Direct Interactants of Disease Genes

Mining human interactome

HPRDBioGrid

Which of these interactants are potential new candidates?

Indirect Interactants of Disease Genes

7

66

778

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 Schemahttp://

toppgene.cchmc.org

Application Description Input Output

ToppFun Detects functional enrichment of input gene list based on Transcriptome (gene expression), Proteome (protein domains and interactions), Regulome (TFBS and miRNA), Ontologies (GO, Pathway), Phenotype (human disease and mouse phenotype), Pharmacome (Drug-Gene associations), and Bibliome (literature co-citation).

Supported identifiers include NCBI Entrez gene IDs, approved human gene symbols, NCBI Reference Sequence accession numbers;Single gene list.

Html output;Tab-delimited downloadable text file;Graphical charts

ToppGene Prioritize or rank candidate genes based on functional similarity to training gene list.

Same as above but with two gene lists (training and test)

Html output

ToppNet Prioritize or rank candidate genes based on topological features in protein-protein interaction network.

Same as above Html output;Cytoscape compatible input file;Graphical networks

ToppGeNet Identify and prioritize the neighboring genes of the “seeds” in protein-protein interaction network based on functional similarity to the "seed" list (ToppGene) or topological features in protein-protein interaction network (ToppNet).

Single gene list Same as above

ToppGene Suite – Applicationshttp://

toppgene.cchmc.org

Disease Reference Gene ToppGene RankToppNet

Rank

Bipolar Disorder  Le-Niculescu et al. KLF12 2 15

Bipolar Disorder  Le-Niculescu et al. RORB 4 18

Bipolar Disorder  Le-Niculescu et al. RORA 7 13

Bipolar Disorder  Le-Niculescu et al. ALDH1A1 10No interaction data

Bipolar Disorder  Le-Niculescu et al. AK3L1 11No interaction data

Cardiomyopathy Dhandapany et al. MYBPC3 1 2Celiac Disease Hunt et al. SH2B3 1 8Celiac Disease Hunt et al. CCR3 2 3Celiac Disease Hunt et al. IL18R1 3 29Celiac Disease Hunt et al. RGS1 9 26

Celiac Disease Hunt et al. TAGAP 14No interaction data

Celiac Disease Hunt et al. IL12A 14 10Crohns Disease Fisher et al. MST1 1 27Crohns Disease Fisher et al. NKX2-3 1 27

Crohns Disease Fisher et al. IRGM 2No interaction data

Crohns Disease Villani et al. NLRP3 5 1Crohns Disease Fisher et al. IL12B 7 1

Crohns DiseaseBarrett et al.Franke et al. STAT3 11 1

Crohns Disease Franke et al. PTPN2 30 6Obesity Renstrom et al. MC4R 1 1    Mean 6.8 11.75

Results of the genetic disease prioritizations using ToppGene and ToppNet

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.

The gene-disease associations were from recently reported GWAS and include novel disease gene associations.

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

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

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

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

Why is a test set gene ranked higher?

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

Part 3Drug Repositioning

What is Drug Repositioning

1. Drug development: It takes about 15 years and $800 million to bring a drug to market!

2. The number of new drugs approved by the FDA each year remains at just 20–30 compounds. At this rate it will take more than 300 years for the number of approved drugs to double!

3. Instead start from existing (already in the market) or failed drugs (late-stage failures – discontinued in development), and test them to uncover new applications.

4. By-pass early stages of drug development required to assess toxicity - Enter clinical trials comparatively quickly

Discovery of novel disease indications for existing drugs

“The most fruitful basis for the discovery of a new drug is to start with an old drug” - Sir James Black, Nobel Laureate, Physiology and Medicine, 1988

1. Because existing drugs have known pharmacokinetics and safety profiles, and are often approved by regulatory agencies for human use, any newly identified use can be rapidly evaluated in phase II clinical trials, which last ~two years and cost much less (~$17 million).

2. In 2008, of the 31 new medicines that reached their first markets, drug repositioning accounted for one-third.

3. Since this strategy is economically more attractive than the de novo drug discovery and development, pharmaceutical and biotech companies have directed their efforts towards it.

Viagra

Rogaine

Topiramate: From epilepsy to obesity

Integrative Functional Genomics

Approaches

PRADAR (Pharmacoinformatics Radar): Pattern Recognition Algorithms for Drug Analysis and

Repositioning

Adverse Drug Reactions – Mouse Phenotype: New Indications?

From serendipity to “systematic serendipity”

PubMed

Medical Informatics

Patient Records

Patient Records

Disease Database

Disease Database

→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……

Clinical Trials

Clinical Trials

Bioinformatics

Genome

Transcriptome

Proteome

Interactome

Metabolome

Physiome

Regulome Variome

Pathome

Ph

arm

acog

enom

e

Disease

World

OMIM

►Personalized Medicine►Decision Support System►Outcome Predictor►Course Predictor►Diagnostic Test Selector►Clinical Trials Design►Better therapeutics►Hypothesis Generator…..Integrativ

e Genomics

- Biomedic

al Informati

cs

the Ultimate Goal…….