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 Networks Biological networks Functional 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.
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 Networks Biological networks Functional modules / network clusters Phenotype association - PowerPoint PPT Presentation
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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
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):
Genetically heterogeneous disorders and protein interactions
(Barabasi and Oltvai 2004).
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
Genetically heterogeneous disorders and protein interactions
(Barabasi and Oltvai 2004).
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
Genetically heterogeneous disorders and protein interactions
daily
weekly
monthly
(de Licthenberg et al.)
Social Networks, The CBS interactome
Genetically heterogeneous disorders and protein interactions
daily
weekly
monthly
(de Licthenberg et al.)
Social Networks, The CBS interactome
Genetically heterogeneous disorders and protein interactions
Genetically heterogeneous disorders and protein interactions
Network clustering Functional modules
Genetically heterogeneous disorders and protein interactions
Edge/physical interaction Node/protein
The Ach receptor involved in Myasthenic Syndrome.
Dynamic funcional module:
Eg:
Cell cycle regulation
Metabolism
Network clustering Functional modules
Genetically heterogeneous disorders and protein interactions
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!!
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
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
Phenotype association
(Brunner and van Driel 2004)
Word vectors
Phenotype association
(Brunner and van Driel 2004)
Word vectors
Phenotype association
Word vectors
The Ach receptor involved in Myasthenic Syndrome.
Phenotype association
Word vectors
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
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
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
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