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Functional genomics of the brain: uncovering networks in the CNS using a systems approach

Functional genomics of the brain: uncovering networks in the CNS using a systems approach. 499432230 王凱誼. Introduction.

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Functional genomics of the brain: uncovering networks in the CNS using a systems approach

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  1. Functional genomics of the brain:uncovering networks in the CNSusing a systems approach 499432230王凱誼

  2. Introduction • The brain is composed of a myriad of cell types and each region of the brain has a different complement of cells. (Ramo´n y Cajal S, Azoulay L.., 2010) • Simply defining cells in the brain as three major classes: neurons, astrocytes, and oligodendrocytes, belies the true underlying complexity. • Technical advances in genomic, transcriptomic, and proteomic profiling should allow progress toward capturing the true diversity inherent in the brain.

  3. Introduction • Neuroscience, in general, has been lagging behind other fields in embracing a systems approach in the analysis of large-scale expression datasets. (Geschwind DH, KonopkaG., 2010)

  4. Introduction • Generation and analysis of recent functional genomics studies and how they have provided insight into the anatomy, development and evolution of the brain with implications for disease and therapeutics. • Moving molecular neuroscience beyond single gene approaches and will hopefully assist in integrating this discipline with classic systems neuroscience.

  5. Introduction • Genomic DNA in every cell of an individual should be essentially identical, epigenetic modifications throughout the genome may vary from neuron to neuron.(Dulac C., 2010) • The major divisions of frontal, temporal, parietal, and occipital cortices there may be divergent expression profiles within areas.

  6. Cell-specific gene expression in the brain

  7. Methods for mining the data • Microarray • Serial analysis of gene expression (SAGE) • high-throughput method for quantitating mRNA transcripts • Next generation sequencing(NGS) • RNA-seq(using NGS to examine gene expression) • ChIP–chip (chromatin immunoprecipitationcoupled to microarrays)

  8. Genome-Wide Studies of mRNA Expression in the CNS Using RNA-seq

  9. Many of the patternsidentified in RNA-seq studies need to be assayed andconfirmedin the tissue of interest.

  10. Workflow for analyzing gene expression data change in greatest magnitude or have the smallest P values → further confirmatory or functional studies

  11. Gene ontology and pathway Analysis

  12. A network approach can parse brain data at multiple levels

  13. Conclusion • Generation of gene expression and gene regulation data using microarrays, NGS, ChIP, and network analysis tools is providing an encyclopedia of information about the evolution, development, normal function, and degeneration of the CNS.

  14. Conclusion • Novel biomarkers and potential therapeutic targets have been uncovered using these approaches for a wide scope of CNS diseases such as AD, ALS, autism, glioblastomamultiforme, Parkinson’s disease, and schizophrenia.

  15. A combinatorial approach is needed to achieve a systems level understanding of the CNS

  16. Thank you for your attention

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