Disease Gene Candidate Prioritization by Integrative Biology

Table of contents:

Background

Networks – deducing functional relationships from PPI data networksProtein interaction networksFunctional modules / network clusters

Phenotype associationGrouping disorders based on their phenotype.Biological implications of phenotype clusters.

Method and examplesIntegrating protein interaction data and phenotype associations in an automated

large scale disease gene finding platform

Background

Background

Finding genes responsible for major genetic disorders can lead to diagnostics, potential drug targets, treatments and large amounts of information about molecular cell biology in general.

BackgroundMethods for disease gene finding post genome era (>2001):

Mircodeletions Translocations

http://www.med.cmu.ac.th/dept/pediatrics/06-interest-cases/ic-39/case39.html

http://www.rscbayarea.com/images/reciprocal_translocation.gif

Linkage analysis

Fagerheim et al 1996.

1q21-1q23.1

chr1:141,600,00-155,900,000

BackgroundAutomated methods for disease gene finding int the post genome era (>2001):

?

(Perez-Iratxeta, Bork et al. 2002) (Freudenberg and Propping 2002)(van Driel, Cuelenaere et al. 2005)(Hristovski, Peterlin et al. 2005)

Grouping:

Tissues, Gene Ontology, Gene Expression, MeSH terms …….

Disease Gene Finding.

Summery

Background

Why do we want to find disease genes, how has it been done until now?

Networks – deducing functional relationships from network theory

Protein interactionnetworksFunctional modules / network clusters

Phenotype association

Grouping disorders based on their phenotype.Biological implications of phenotype clusters.

Method and examples

Combining network theory and phenotype associationsin an automated large scale disease gene finding platformproof of concept.Status of pipeline / infrastructure

Networks and functional modules

Deducing functional relationships from protein interaction networks

daily

weekly

monthly

(de Licthenberg et al.)

Networks

Social Networks, The CBS interactome

daily

weekly

monthly

(de Licthenberg et al.)

Social Networks, The CBS interactome

Networks

Protein interaction networks of physical interactions.

(Barabasi and Oltvai 2004).

Networks

Extracting functional data from protein interaction networks

InWeb

Homo Sapiens

The Ach receptor involved in Myasthenic Syndrome.

Dynamic funcional module:

Eg:

Cell cycle regulation

Metabolism

Trans-organism protein interaction network

Orthologs?

Orthologous genes are direct descendants of a gene in a common ancestor:

(O'Brien K, Remm et al. 2005)

S.Cerevisiae

D. Melanogaster

H.Sapiens

D. Melanogaster Experim.

C. Elegans Experim.

S. Cerevisiae Experim.

H.Sapiens MOSAIC

Trans-organism protein interaction network

Infrastructure status

BIND

IntAct

DIP

MINT

HPRD

Hand-curated

sets

PPI – pred.

GRID

InWeb

Homo Sapiens

Trans-organism ppi

pipeline>122.000 int.

> 22.000 genes

Scoring

A) Topological

B) No publ.

Extraction

perl modules

Direct SQL access

XML or SIF output

Web serverOpis

Command lineInweb.pl

CBS Datawarehouse

Download/reformat db’s

Protein interaction networks scoring the interactions

Number of methods that have shown the same interaction

Number of independent studies that have shown the same interaction

Number of common interaction partners

Cluster issues

Large scale / small scale issues

Disease Gene Finding.

Summery

Background

Why do we want to find disease genes, how has it been done until now?

Networks – deducing functional relationships from network theory

Protein interactionnetworksFunctional modules / network clusters

Phenotype association

Grouping disorders based on their phenotype.Biological implications of phenotype clusters.

Method and examples

Combining network theory and phenotype associationsin an automated large scale disease gene finding platformproof of concept.Status of pipeline / infrastructure

Phenotype association

Phenotype association

Absent liver peroxisomesHepatomegalyIntrahepatic biliary dysgenesisProlonged neonatal jaundicePyloric hypertrophyPatent ductus arteriosusVentricular septal defectsBell-shaped thoraxSmall adrenal glandsAbsent renal peroxisomesClitoromegalyCryptorchidismHydronephrosisHypospadiasRenal cortical microcystsFailure to thriveAbnormal electroretinogramAbnormal helicesAnteverted naresBrushfield spotsCataractsCorneal clouding

Epicanthal foldsFlat faciesFlat occiputGlaucomaHigh arched palateHigh foreheadHypertelorismLarge fontanellesMacrocephalyMicrognathiaNystagmusPale optic diskPigmentary retinopathyPosteriorly rotated earsProtruding tongueRedundant skin folds of neckRound faciesSensorineural deafnessTurribrachycephalyUpward slanting Hyporeflexia or areflexiaHypotonia

PolymicrogyriaSeizuresSevere mental retardationSubependymal cystsPulmonary hypoplasiaCubitus valgusDelayed bone ageMetatarsus adductusRocker-bottom feetStippled epiphyses (especially patellar and acetabular regions)Talipes equinovarusTransverse palmar creaseUlnar deviation of handsWide cranial suturesTransverse palmar creaseHeterotopias/abnormal migrationHypoplastic olfactory lobes

Zelwegger syndrome

palpebral fissuresAutosomal recessiveAlbuminuriaAminoaciduriaDecreased dihydroxyacetone phosphate acyltransferase (DHAP-AT) activityDecreased plasmologenElevated long chain fatty acidsElevated serum iron and iron binding capacityIncreased phytanic acidPipecolic acidemiaBreech presentationDeath usually in first year of lifeGenetic heterogeneityInfants occasionally mistaken as having Down syndromeAgenesis/hypoplasic corpus collosum

Phenotype association

Word vectors

Phenotype Sim. Score

Adrenoleukodystrophy (202370) 0.781

Hyperpipecolatemia (239400) 0.703

Cerebrohepatorenal Syndr. (214110) 0.682

Refsum Disease (266510) 0.609

Reference : Zelwegger Syndrome (214100)

A relationship between the infantile form of Refsum disease and Zellweger syndrome was suggested by the observations of Poulos et al. (1984) in 2 patients. In the infantile form of Refsum disease, as in Zellweger syndrome, peroxisomes are deficient and peroxisomal functions are impaired (Schram et al., 1986). Clinically, infantile Refsum disease, ZWS, and adreno-leukodystrophy have several overlapping features. (Stokke et al., 1984).(http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id=266510)

214100 202370

Phenotype association

Word vectorsPhenotype association network

Cerebro-Hepato-

renal

Zelwegger

Refsum

Adrenoleuko-dystrophy

Disease Gene Finding.

Summery

Background

Why do we want to find disease genes, how has it been done until now?

Networks – deducing functional relationships from network theory

Protein interactionnetworksFunctional modules / network clusters

Phenotype association

Grouping disorders based on their phenotype.Biological implications of phenotype clusters.

Method and examples

Combining network theory and phenotype associationsin an automated large scale disease gene finding platformproof of concept.

Method –

Proof of concept

Method

InWeb

Homo Sapiens

Word vectors

Phenotype clustering

Results - Benchmark

MIM RANK GENE Probability TRUE

278800 1 ENSG00000032514 0.300326793109544 *278800 2 ENSG00000188611 0.0125655342047565278800 2 ENSG000001382970.0125655342047565278800 2 ENSG000001654060.0125655342047565278800 3 ENSG000001966930.0121357313793756278800 3 ENSG000001855320.0121357313793756278800 4 ENSG000001979100.00680983722337082278800 4 ENSG000001653830.00680983722337082278800 4 ENSG000001725380.00680983722337082. . . .. . . .. . . .. . . .. . . .278800 4 ENSG000001655110.00680983722337082278800 4 ENSG000001823540.00680983722337082278800 4 ENSG000001726610.00680983722337082278800 4 ENSG000001655070.00680983722337082278800 4 ENSG000001784400.00680983722337082278800 4 ENSG000001382990.00680983722337082278800 4 ENSG000001977040.00680983722337082278800 4 ENSG000000127790.00680983722337082278800 4 ENSG000001973540.00680983722337082278800 4 ENSG000001890900.00680983722337082278800 4 ENSG000001075510.00680983722337082278800 4 ENSG000001265420.00680983722337082278800 4 ENSG000001983640.00680983722337082278800 4 ENSG000001858490.00680983722337082278800 4 ENSG000001501650.00680983722337082278800 4 ENSG000001288150.00680983722337082278800 4 ENSG000001786450.00680983722337082278800 4 ENSG000001382930.00680983722337082278800 4 ENSG000001768330.00680983722337082278800 4 ENSG000001792510.00680983722337082278800 4 ENSG000001698260.00680983722337082278800 4 ENSG000001726780.00680983722337082278800 4 ENSG000001977520.00680983722337082278800 5 ENSG000001076430.00412573091718715278800 6 ENSG000001657330.000263885640603109

278800 7 ENSG00000169813 6,63E+07

DE SANCTIS-CACCHIONE SYNDROME

Gene map locus 10q11 >12MB area, 103 ranked genes

CLINICAL FEATURES

De Sanctis and Cacchione (1932) reported a condition, which they called 'xerodermic idiocy,' in which patients had xeroderma pigmentosum, mental deficiency, progressive neurologic deterioration, dwarfism, and gonadal hypoplasia.http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id=278800

Results – Benchmarking

DE SANCTIS-CACCHIONE

SYNDROME Ranked 1

Probability: 0.300326793109544

DNA excision repair

protein ERCC-6

Eukaryotic translation initiation factor 4E (eIF4E)

DNA excision repair protein ERCC-2

Eukaryotic initiation factor 4A-I (eIF4A-I)

*126340 DNA REPAIR DEFECT EM9 OF CHINESE HAMSTER OVARY CELLS, COMPLEMENTATION OF; EM9

#133540 COCKAYNE SYNDROME CKN2

#278730 XERODERMA PIGMENTOSUM, COMPLEMENTATION GROUP D

#278800 DE SANCTIS-CACCHIONE SYNDROME

#601675 TRICHOTHIODYSTROPHY

Results – Benchmarking

DE SANCTIS-CACCHIONE

SYNDROME Ranked 2

Probability 0.0125655342047565

Disease Gene Finding.

Summery

Background

Why do we want to find disease genes, how has it been done until now?

Networks – deducing functional relationships from network theory

Protein interactionnetworksFunctional modules / network clusters

Phenotype association

Grouping disorders based on their phenotype.Biological implications of phenotype clusters.

Method and examples

Combining network theory and phenotype associationsin an automated large scale disease gene finding platformproof of concept.

Acknowledgments

Disease Gene Finding :

Olga RiginaOlof Karlberg

Zenia M. Størling Páll Ísólfur Ólason

Kasper LageAnders GormAnders HinsbyYves Moreau

Niels TommerupSøren Brunak