MBGP: Introduction: Mouse models

Introduction	Data	Tools	Publications	Further resources	Contact us
			Melbourne Brain Genome Project

Mouse models of Down syndrome

The MBGP is based upon a four-pronged approach to molecular neuroscience: 1) to amass catalogues of genes representing developmental stages, functional regions and stem-cell derived neurospheres of normal and mutant mouse brain;
2) to reveal gene expression profiles characteristic of Down syndrome mouse models;
3) to build and compare databases of expressed genes in mouse models of neurodegenerative diseases;
4) to systematically compare SAGE and microarray data and utilize SAGE data to validate microarray analyses.

1) Gene expression in the developing and mature mouse brain

An understanding of brain development is unlikely to advance without consideration of the multiple gene sets that are activated during complex morphogenetic events including long-distance migration from germinal zones into specific layers and regions; interactions with radial glial scaffold, other migrating neurons and the surrounding extracellular matrix; reciprocal innervation with specific targets, and juggling of the above with progressive lineage restriction and cellular differentiation programs. You can reach our movie review writing service at https://123helpme.org/articles/buy-film-review/ for more information and additional writers' consultations. The goal is to provide a molecular inventory of genes that are expressed in developing and adult brain tissues in defined space and time. Expression profiles derived from different sources of neural tissue will reveal molecular asymmetry, enabling detection of new genes present in one domain but not in another and uncovering different complexities of gene expression in different brain territories. SAGE libraries, containing information on sequence abundance and complexity, can be further analysed for higher order correlations using appropriate algorithms. In this proposal, we plan to extend the analysis of cortical gene expression profiles to earlier (E12) and later (adult) time points and to address the molecular bases of brain regionalization by comparing these libraries with data sets from embryonic (ganglionic eminences) and adult brain regions, including hippocampus, striatum, cerebellum, thalamus and hypothalamus. Comparison of regional gene expression will provide a foundation for higher resolution analysis of particular brain nuclei or cell types and pave the way for system level interpretations. SAGE technology can also be applied to the study of the transcriptional networks activated in response to particular developmental signals. We plan to conduct such studies with two mutant mouse models; p75 and disabled-1. The p75 neurotrophin receptor mediates the death of neural cells, including stem cells, during development and in the damaged and diseased nervous system Coulson 1999, Coulson 2000. Preliminary studies indicate that p75 is likely to be involved in maintaining the crucial balance between neural precursor proliferation and apoptosis however the components that make up the downstream signaling pathway are unknown. The consequences of inactivating the p75 gene on the molecular profile of stem cell neurospheres Rietze 2001 will be assessed. Disabled-1 (Dab1) is part of the Reelin signaling pathway, important for directing neuronal migration and positioning in the developing cortex Rice 1999. Mutations in Dab-1, or in other members of the Reelin pathway, result in inversion of cortical layers however the transcriptional consequences of disrupting the Reelin signaling pathway have not been analyzed. Recent data from the Tan laboratory Hammond 2001 suggests that Dab-1 activation leads to increased neuron-neuron adhesion and we aim to test this hypothesis by comparing the cortical transcriptomes of Dab1 -/- mutants with those of wild-type mice. Similar SAGE strategies have been spectacularly successful, revealing previously unknown components of tumor suppressor signaling pathways Polyak 1997, He 1998.

Systematic comparison of SAGE and microarrays and generation of a control for microarrays

All gene expression technologies have their advantages and disadvantages and thus should be viewed as complementary to each other. The proposed combination of SAGE and microarrays will enhance the utility of both techniques. A systematic comparison of SAGE results to those generated using AGRF microarrays will be performed using the same samples used in the SAGE analyses. Recent comparisons of SAGE with Affymetrix chips Ishii 2000, filter arrays (Nacht 1999; Lyle, Chrast, Antonarakis and Scott, unpublished data) and microarrays (Chrast, Antonarakis and Scott, unpublished; Blackshaw 2000) have shown that the techniques have similar sensitivity, although microarrays tended to underestimate the fold difference in expression determined by SAGE or Northern blot Blackshaw 2000. SAGE continues to have considerable advantages over microarrays despite the higher throughput of microarrays. The advantages include the fact that SAGE does not depend on the prior knowledge of a transcript and that the expression data is absolutely rather than relatively quantitative and in a standard format allowing ready comparison of data sets from different experiments and labs. This section of the proposal aims to make microarray expression data absolutely quantitative and comparable between different experiments by employing SAGE data. Reliable and precise microarray gene expression profiling relies on comparison of hybridization efficiency between an experimental and a reference RNA samples. Differences in hybridization intensity between these RNA targets reflect relative differences in gene expression levels. Using the same reference RNA in different microarray experiments provides a common denominator for accurate and reproducible comparison of gene expression data Eisen 1999, Lash 2000. For comparison of multiple experimental RNA samples (hybridization experiments), a common reference RNA sample provides an essential internal-control and allows comparisons to be made among large numbers of samples (experiments) and can thus dramatically increase the power of a microarray experiment. We will generate large quantities of whole C57BL/6J adult mouse brain RNA as a reference RNA sample for microarray gene expression profiling from pooled samples from routinely sacrificed mice. The mouse reference RNA will be made available as a reference sample through the AGRF allowing inter-laboratory comparisons of microarray data in Australia. Velculescu et al. 14 showed that the number of new unique transcripts identified approached zero at the level of 600,000 tags. We will generate a "saturated" transcriptome of 600,000 tags from the whole C57BL/6J adult mouse brain reference RNA. Microarray elements representing a subset of genes representative of different expression levels in the SAGE transcriptome (600,000 tags) will then act as additional microarray controls. Use of the reference RNA sample should allow conversion of microarray data to absolute expression levels as well as thorough monitoring of sensitivity. With the advent of multiple colour microarray scanners and additional flurophores, it may become possible to include three (or more) RNA samples in a microarray experiment, the reference RNA and the two (or more) samples for which an immediate comparison is desired. Addtionally, the saturated whole mouse brain transcriptome will allow ready identification of transcripts that are specific to the more defined areas of the brain to be studied. 2.

FLOW-ON BENEFITS TO THE RESEARCH COMMUNITY

Access of SAGE data and technology to other investigators The proposal outlined here represents an internationally competitive program in medical genomics with a neuroscience convergence based upon our research interests, skills and track records. Compared to other fields, the neuroscience research community in Australia, although one of the largest groups funded by the NHMRC, is relatively under accessed in the area of large scale genomics. If funded, this venture will represent a rare opportunity to redress the imbalance and provide increased opportunities and resources for neuroscientists eager to access gene profiling technologies. Sydney Brenner recently estimated that it will take approximately 40 years of work for each mammalian gene to determine function. The dissemination of the SAGE data generated under this proposal to Australian researchers will provide stimulus for hypothesis-driven research on genes of neurological importance, thus enhancing Australias long-term position in neurosciences and biotechnology. A major strength of the SAGE technique is that information about absolute transcript abundance in particular tissues or cell types is cumulative and can be stored. This feature allows cross-referencing and mining of data from other SAGE projects via the unique tag identifier number assigned to each tag. Because the tag identifier number is calculated from the sequence of the tag, consistency of experimental design in selection of the anchoring enzyme (specifying position) and tagging enzyme (determining tag length) is crucial. In keeping with the vast majority of SAGE libraries constructed to date (SAGE 2000, Baltimore, MD, USA, Sept.18-20), we will continue to use the anchoring enzyme NlaIII and the tagging enzyme BsmF1 during SAGE library construction. In this way, the value of newly generated SAGE data is enhanced by access to other large data sets for mouse such as those generated in the laboratories of Dr. Connie Cepko at Harvard University (over 600,000 tags from retina and hypothalamus libraries) or Dr Scott during his time in Geneva (250,000 tags including over 150,000 tags from P30 mouse brain libraries). In addition to generating quantitative expression data for known genes, the ability to identify unknown genes via their SAGE tag provides a ready resource for Australian neuroscientists to mine the data in ways relevant to their own particular research and capitalize on burgeoning genome sequences. Data generated under this proposal has been made freely available to the Australian research community via this website It allows online comparisons of the data produced. It will enable researchers to query the databases with a tag to identify a gene (if known), with a gene to a tag to determine expression levels in the libraries, browse the libraries, or submit their own SAGE data. Statistics of the SAGE libraries generated and analysed will be available to allow monitoring of the progress of the project. Additionally, all data will be available as SAGE libraries and Microsoft access files. We will also try to develop additional tools to allow interspecies comparisons (mouse vs human), integration to microarray data and use of genomic sequence for tag identification. A factor limiting the more widespread use of SAGE has been the degree of technical difficulty. The participating laboratories will aid other interested investigators in the production of SAGE libraries by supplying proven reagents and expertise. Dissemination of the SAGE technology in Australia will feed into AGRF contract sequencing.

Validation of the Mouse as a Model for Global Differential Gene Expression Studies.

How valid is the mouse as an experimental model for human? Differences in gene expression between developmentally regulated mouse and human orthologues have been demonstrated Ross 2000, however preliminary comparisons of the mouse and human brain transcriptomes show that there is good correlation for highly expressed genes in both transcript identity and abundance Fougerousse 2000. With the completion of both the human and mouse genomes, it will be possible to capitalise enormously on SAGE data, both pre-existing and that to be generated under this proposal. Detailed comparisons will be possible and the absolutely quantitative nature of SAGE data will be important in determining the validity of mouse global gene expression studies for extrapolation to human. A longer term goal is that the genes identified may be possible targets for therapeutic intervention. Translation and comparison of our mouse studies to human neurological diseases will bring our analyses closer to commercial application.