Disease Gene Finding. Table of contents:

Disease Gene Finding.

Table of contents:

Background

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

Networks – deducing functional relationships from network theory

NetworksBiological networksFunctional modules / network clusters

Phenotype association

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

Method and examples

Combining network theory and phenotype associations in an automated large scale disease gene finding platform

Proof of concept.

Abstract

Aim

Find new disease genes.

Means

Use protein interaction networks and phenotype association networks for inferring phenotype gneotype relationships.

Proof

Interesting candidates are reported to experimentalcollaborators who perform mutational analysis in patient material.

Background

Background

Aim

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

BackgroundBioinformatic methods for disease gene finding 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 …….


Table of contents:

Background






Method and examples


Proof of concept.

Networks and functional modules

Deducing functional relationships from network theory

Networks and functional modules

Deducing functional relationships from network theory

Network theory is boooooooooring

Networks

Text mining of full text corpora e.g PubMed Central

http://www.biosolveit.de/ToPNet/screenshots/fig1.html

Protein interaction networks of physical interactions.

(Barabasi and Oltvai 2004).

Networks

daily

weekly

monthly

(de Licthenberg et al.)

Networks

Social Networks, The CBS interactome

Genetically heterogeneous disorders and protein interactions


http://www.biosolveit.de/ToPNet/screenshots/fig1.html





Degree (k) :

Number of connections

Protein : Number of interaction partners

Social : Number of collaborators / friends

Degree distribution P(k) :

The probability that a selected node has exactly k links:

Protein : probability of k interaction partners

Social : Probability of k collaborators / friends



Clustering coefficient C(k)

Average clustering coefficient of all nodes with k links.

The average tendency of nodes to form clusters or groups.

Protein : Tendency of interaction partners to interact with each other

Social : Tendency of collaborators / friends to be friends / collaborators of each other.

Hubs, connect distant parts of the network.

Ultra small world


daily

weekly

monthly




daily

weekly

monthly





Network clustering Functional modules


Edge/physical interaction Node/protein

The Ach receptor involved in Myasthenic Syndrome.

Dynamic funcional module:

Eg:

Cell cycle regulation

Metabolism

Network clustering Functional modules


Edge/physical interaction Node/protein

•Grouping of proteins that are functionally undescribed. (30% of proteins in completely sequenced geneomes cannot be appointed to a specific biological function).

•70-80% of interacting proteins share at least one function.

•Grouping of proteins based on function not biochemistry/sequence alignment.

•Correlation between mutation in interacting proteins and phenotype.

•Disease gene finding!!


Table of contents:

Background






Method and examples


Proof of concept.



ConstipationMalrotationPoor suckPyloric stenosisVomitingAtrial septal defectCoarctation of aortaPatent ductus arteriosusVentricular septal defectAmbiguous genitaliaBifid scrotumCryptorchidismCystic kidneysHydronephrosisHypoplastic scrotumHypospadiasMicropenisMicrourethraRenal agenesisSingle kidneyUreteropelvic junction obstruction

Birth weight <2500gmFailure to thriveShort statureAnteverted naresBitemporal narrowingBroad alveolar marginsBroad, flat nasal bridgeCataractsCleft palateDental crowdingEpicanthal foldsHypertelorismHypoplastic tongueLarge central front teethLow-set earsMicrocephalyMicrognathiaPosteriorly rotated earsPtosisStrabismusAutosomal recessiveElevated 7-dehydrocholesterol

Low cholesterolAllelic with Rutledge lethal multiple congenital anomaly syndromeEstimated incidence 1/20,000 - 1/40,000Caused by mutations in the delta-7-dehydrocholesterol reductase geneAbnormal sleep patternAggressive behaviorFrontal lobe hypoplasiaHydrocephalusHypertonia (childhood)Hypotonia (early infancy)Mental retardationPeriventricular gray matter heterotopiasSeizuresSelf injurious behaviorBreech presentationDecreased fetal movement

Hypoplastic lungsIncomplete lobulation of the lungsHip dislocationHip subluxationLimb shorteningMetatarsus adductusOverriding toesPostaxial polydactylyProximally placed thumbsShort thumbsShort, broad toesStippled epiphysesSyndactyly of second and third toesTalipes calcaneovalgusBlonde hairEczemaFacial capillary hemangiomaSevere photosensitivityShrill screaming

Smith-Lemi-Opitz Syndrome


(Brunner and van Driel 2004)

Word vectors


(Brunner and van Driel 2004)

Word vectors


Word vectors

The Ach receptor involved in Myasthenic Syndrome.


Word vectors


Table of contents:

Background






Method and examples


Proof of concept.

Method –

Proof of concept

Method

Method

Input all critical intervals in OMIM (Approx 900)

%125480 MAJOR AFFECTIVE DISORDER 1%132800 MULTIPLE SELF-HEALING SQUAMOUS EPITHELIOMA%137100 IMMUNOGLOBULIN A DEFICIENCY SUSCEPTIBILITY 1%137580 GILLES DE LA TOURETTE SYNDROME%143850 ORTHOSTATIC HYPOTENSIVE DISORDER%156240 MESOTHELIOMA, MALIGNANT%157900 MOEBIUS SYNDROME 1%177900 PSORIASIS SUSCEPTIBILITY 1%209850 AUTISM %252350 MOYAMOYA DISEASE 1%608631 ASPERGER SYNDROME, SUSCEPTIBILITY TO, 2 ;;ASPG2%301845 BAZEX SYNDROME; BZX %608389 BRANCHIOOTIC SYNDROME 3 %600175 SPINAL MUSCULAR ATROPHY%600318 DIABETES MELLITUS, INSULIN-DEPENDENT, 3; IDDM3 ;;INSULIN-DEPENDENT DIABETES MELLITUS 3%601042 CHOREOATHETOSIS/SPASTICITY%601388 DIABETES MELLITUS, INSULIN-DEPENDENT, 12; IDDM12 ;;INSULIN-DEPENDENT DIABETES MELLITUS 12%601493 CARDIOMYOPATHY, DILATED, 1C; CMD1C%603694 DIABETES MELLITUS, NONINSULIN-DEPENDENT, 3 ;;NIDDM3;; NONINSULIN-DEPENDENT DIABETES US 3%604288 CARDIOMYOPATHY, DILATED, 1H; CMD1H

Proof of Concept

%125480 MAJOR AFFECTIVE DISORDER 1%132800 MULTIPLE SELF-HEALING SQUAMOUS EPITHELIOMA%137100 IMMUNOGLOBULIN A DEFICIENCY SUSCEPTIBILITY 1%137580 GILLES DE LA TOURETTE SYNDROME%143850 ORTHOSTATIC HYPOTENSIVE DISORDER%156240 MESOTHELIOMA, MALIGNANT%157900 MOEBIUS SYNDROME 1%177900 PSORIASIS SUSCEPTIBILITY 1%209850 AUTISM %252350 MOYAMOYA DISEASE 1%608631 ASPERGER SYNDROME, SUSCEPTIBILITY TO, 2 ;;ASPG2%301845 BAZEX SYNDROME; BZX %608389 BRANCHIOOTIC SYNDROME 3 14q23.1 SIX1%600175 SPINAL MUSCULAR ATROPHY%600318 DIABETES MELLITUS, INSULIN-DEPENDENT, 3; IDDM3 ;;INSULIN-DEPENDENT DIABETES MELLITUS 3%601042 CHOREOATHETOSIS/SPASTICITY%601388 DIABETES MELLITUS, INSULIN-DEPENDENT, 12; IDDM12 ;;INSULIN-DEPENDENT DIABETES MELLITUS 12%601493 CARDIOMYOPATHY, DILATED, 1C; CMD1C 10q21-q23 VINC_HUMAN %603694 DIABETES MELLITUS, NONINSULIN-DEPENDENT, 3 ;;NIDDM3;; NONINSULIN-DEPENDENT DIABETES US 3%604288 CARDIOMYOPATHY, DILATED, 1H; CMD1H

Input all critical intervals in OMIM (Approx 900)

Proof of Concept

%608389 BRANCHIOOTIC SYNDROME 3 14q23.1 SIX1

Proof of Concept

SIX1 mutations cause branchio-oto-renal syndrome by disruption of EYA1-SIX1-DNA complexes.

Ruf RG, Xu PX, Silvius D, Otto EA, Beekmann F, Muerb UT, Kumar S, Neuhaus TJ, Kemper MJ, Raymond RM Jr, Brophy PD, Berkman J, Gattas M, Hyland V, Ruf EM, Schwartz C, Chang EH, Smith RJ, Stratakis

CA, Weil D, Petit C, Hildebrandt F.

Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA.

Urinary tract malformations constitute the most frequent cause of chronic renal failure in the first two decades of life. Branchio-otic (BO) syndrome is an autosomal dominant developmental disorder characterized by hearing

loss. In branchio-oto-renal (BOR) syndrome, malformations of the kidney or urinary tract are associated. Haploinsufficiency for the human gene EYA1, a homologue of the Drosophila gene eyes absent (eya), causes BOR and BO syndromes. We recently mapped a locus for BOR/BO syndrome (BOS3) to human chromosome

14q23.1. Within the 33-megabase critical genetic interval, we located the SIX1, SIX4, and SIX6 genes, which act within a genetic network of EYA and PAX genes to regulate organogenesis. These genes, therefore, represented

excellent candidate genes for BOS3. By direct sequencing of exons, we identified three different SIX1 mutations in four BOR/BO kindreds, thus identifying SIX1 as a gene causing BOR and BO syndromes. To elucidate how these mutations cause disease, we analyzed the functional role of these SIX1 mutations with respect to protein-protein and protein-DNA interactions. We demonstrate that all three mutations are crucial for Eya1-Six1 interaction, and the two mutations within the homeodomain region are essential for specific Six1-DNA binding. Identification of SIX1 mutations as causing BOR/BO offers insights into the molecular basis of otic and renal developmental

diseases in humans.

PMID: 15141091 [PubMed - indexed for MEDLINE]

%604288 CARDIOMYOPATHY, DILATED, 1C; CMD1C 10q21-q23 VINC_HUMAN

Metavinculin mutations alter actin interaction in dilated cardiomyopathy.

Olson TM, Illenberger S, Kishimoto NY, Huttelmaier S, Keating MT, Jockusch BM.

Department of Pediatrics and the Division of Cardiology, University of Utah, Salt Lake City, Utah, USA. [email protected]

BACKGROUND: Vinculin and its isoform metavinculin are protein components of intercalated discs, structures that anchor thin filaments and transmit contractile force between cardiac myocytes. We tested the hypothesis that heritable dysfunction of metavinculin may contribute to the pathogenesis of dilated cardiomyopathy (DCM). METHODS AND RESULTS: We performed mutational analyses of the metavinculin-specific exon of vinculin in 350 unrelated patients with DCM. One missense mutation (Arg975Trp) and one 3-bp deletion (Leu954del) were identified. These mutations involved conserved amino acids, were absent in 500 control individuals, and significantly altered metavinculin-mediated cross-linking of actin filaments in an in vitro assay. Ultrastructural examination was performed in one patient (Arg975Trp), revealing grossly abnormal intercalated discs. A potential risk-conferring polymorphism (Ala934Val), identified in one DCM patient and one control individual, had a less pronounced effect on actin filament cross-linking. CONCLUSIONS: These data provide genetic and functional evidence for vinculin as a DCM gene and suggest that metavinculin plays a critical role in cardiac structure and function. Disruption of force transmission at the thin filament-intercalated disc interface is the likely mechanism by which mutations in metavinculin may lead to DCM.

Proof of Concept


Summery

Background






Method and examples

Combining network theory and phenotype associations in an automated large scale disease gene finding platformProof of concept.

CBS - the Lara Croft of disease gene

finding.

Disease Genes -Treasures in a

genomic jungle.

Hopes & Dreams for the future

Acknowledgements

Disease Gene Finding group at CBS:

Olga Rigina : Database handling, Computer Scientist

Olof Karlberg : Programmer, Pharmacologist

Zenia M. Larsen : Expert in diabetes and related disorders, Engineer

Páll Ísólfur Ólason : Engineer, data flow, text mining.

Kasper Lage : Proteomics, genomics, diseases, Human Biologist

Anders Hinsby : Proteomics, mass spec. expert, Human Biologist

Documents

Disease Gene Finding. Table of contents: