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A new mouse model for the trisomy of the Abcg1-U2af1 region reveals the complexity of the combinatorial genetic code of down syndrome.

Human molecular genetics | Dec 15, 2009

Mental retardation in Down syndrome (DS), the most frequent trisomy in humans, varies from moderate to severe. Several studies both in human and based on mouse models identified some regions of human chromosome 21 (Hsa21) as linked to cognitive deficits. However, other intervals such as the telomeric region of Hsa21 may contribute to the DS phenotype but their role has not yet been investigated in detail. Here we show that the trisomy of the 12 genes, found in the 0.59 Mb (Abcg1-U2af1) Hsa21 sub-telomeric region, in mice (Ts1Yah) produced defects in novel object recognition, open-field and Y-maze tests, similar to other DS models, but induces an improvement of the hippocampal-dependent spatial memory in the Morris water maze along with enhanced and longer lasting long-term potentiation in vivo in the hippocampus. Overall, we demonstrate the contribution of the Abcg1-U2af1 genetic region to cognitive defect in working and short-term recognition memory in DS models. Increase in copy number of the Abcg1-U2af1 interval leads to an unexpected gain of cognitive function in spatial learning. Expression analysis pinpoints several genes, such as Ndufv3, Wdr4, Pknox1 and Cbs, as candidates whose overexpression in the hippocampus might facilitate learning and memory in Ts1Yah mice. Our work unravels the complexity of combinatorial genetic code modulating different aspect of mental retardation in DS patients. It establishes definitely the contribution of the Abcg1-U2af1 orthologous region to the DS etiology and suggests new modulatory pathways for learning and memory.

Pubmed ID: 19783846 RIS Download

Mesh terms: ATP Binding Cassette Transporter, Sub-Family G, Member 1 | ATP-Binding Cassette Transporters | Animals | Anxiety | Disease Models, Animal | Down Syndrome | Electrical Synapses | Exploratory Behavior | Gene Deletion | Gene Dosage | Gene Duplication | Genetic Code | Humans | Learning | Lipoproteins | Memory | Mice | Mice, Mutant Strains | Motor Activity | Nuclear Proteins | Ribonucleoproteins | Splicing Factor U2AF | Trisomy

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Understanding Human Disease Through Mouse Genetics

A portal documenting a project for the development of novel approaches in phenotyping, mutagenesis and informatics to improve the characterization of mouse models for understanding human molecular physiology and pathology. EUMORPHIA has developed a new robust primary screening platform for determining the phenotype of mice: EMPReSS - European Mouse Phenotyping Resource for Standardised Screens. The project is also focused on training new young scientists by funding them to work in a variety of laboratories to gain a broader swathe of techniques. The project has also identified the need for more trained mouse pathologists. To address this, they are setting up training courses in pathology and working at a European level to establish more training.

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UCSC Genome Browser

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European Mouse Mutant Archive

Non-profit repository for the collection, archiving (via cryopreservation) and distribution of relevant mutant strains essential for basic biomedical research. Users may browse by strain, gene, phenotype, or human disease. Its primary objective is to establish and manage a unified repository for maintaining medically relevant mouse mutants and making them available to the scientific community. Therefore, EMMA archives mutant strains and distributes them to requesting researchers. EMMA also hosts courses in cryopreservation, to promote the use and dissemination of frozen embryos and spermatozoa. Dissemination of knowledge is further fostered by a dedicated resource database. Anybody who wants their mutant mouse strains cryopreserved may deposit strains with EMMA. However depositors must be aware that these strains become freely available to other researchers after being deposited.

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Statistical test for model organisms association mapping correcting for the confounding from population structure and genetic relatedness. EMMA takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which substantially increases the computational speed and the reliability of the results. The current implementation of EMMA is available in an R package. The documentation is included in the installation package.

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