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Gene Regulatory Networks and Neurodegenerative Diseases. Anne Chiaramello, Ph.D Associate Professor George Washington University Medical Center Department of Anatomy and Cell Biology Tel: 202-994-2173 anaaec@gwumc.edu. NIH/NINDS R01NS041391 McCormick Pilot Grant. January 24, 2007.
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Gene Regulatory Networks and Neurodegenerative Diseases Anne Chiaramello, Ph.D Associate Professor George Washington University Medical Center Department of Anatomy and Cell Biology Tel: 202-994-2173 anaaec@gwumc.edu NIH/NINDS R01NS041391 McCormick Pilot Grant January 24, 2007
Long-Term Medical Applications of our Research Programs • Correlation between Altered Gene Expression and Susceptibility for Specific Neurodegenerative Diseases • Genetic Manipulation of Neural Stem/Progenitor Cells to Promote Specific Neuronal Identity and Survival upon Transplantation Overall Strategy • To dissect NeuroD6-Mediated gene regulatory networks responsible for Initiation/ Maintenance of differentiation, and neuronal survival. • To Identify Dysregulation of Neuronal-Specific Genes Associated with Neurodegenerative Disorders. Transcription-Dependent Neuronal Differentiation Undifferentiated Neural Progenitor Cells Differentiated Neurons
Correlation between Altered Gene Expression and Susceptibility for Neurodegenerative Diseases Flowchart to Analyze NeuroD6-Mediated Neuronal Differentiation/Survival GeneChip Affymetrix Microarray Promoter Analysis of NeuroD6 Target Genes Functional Analysis of NeuroD6 Target Genes Constructing NeuroD6-mediated Transcriptional Regulatory Network Ab Initio and Experimental Approaches Silencing of Target Genes(siRNA) Identification of Regulatory Elements and Associated SNPs Flow Cytometry/Cell Death Assays
PC12 PC12-ND6 PC12-ND6 Serum removal Normal growth conditions Identification of NeuroD6-Regulated Target Genes During Neuronal Survival by GeneChip Affymetrix Microarray
Core Promoter (-250/+150 bp) TSS Enhancers Proximal Promoter (5’UTR) Inr DPE TATA +1 -35-25 bp +28-32 bp -2000 bp Computational Approaches to Identify the Underlying Transcriptional Network of Gene Expression from Microarray Analysis. 1-Microarray analysis does not directly reveal the regulatory networks that underlie the observed transcriptional module mediated by NeuroD6. Combining promoter analysis with microarray results can shed light on NeuroD6-regulated networks 2-A promoter is defined as a functional region immediately upstream and downstream of a Transcriptional start site (TSS) that is ultimately involved in the regulation of transcription. 3-The putative TSS for 17,702 transcripts corresponding to 13,300 genes have been annotated. Given a correct estimate of ~25,000 human genes, promoters for a majority of genes in the human genome remain to be fully defined. 4-Furthermore, transcriptional regulation of most genes originates from at least two distinct promoters,located in different non-coding exons, with the upstream promoter most of the time unknown.
Ab Initio Methods to Predict Promoter Structure • Database of Transcription Start site (db TSS) • Cold Spring Harbor Laboratories Mammalian promoter Database • Genome Browser: UCSC, ENSEMBL, NCBI • Cap Analysis of Gene Expression (Riken, CAGE Data) • Promoter Predictions Algorithms • GRAIL Exp v3.3 (Gene Recognition and Analysis Internet work) • Promo H Algorithm • Promoter Inspector • Dragon Promoter Finder • De novo FIRST EF ( First Exon-Finding) • CpG Island (NCBI Map Viewer) • Phylogenic Footprinting Analysis: multi-species sequenced conservation • (ClustalW and Genome Browsers UCSC, ENSEMBL, NCBI) Experimental Approaches for Promoter Identification • 5’RACE • Primer Extension • Luciferase Reporter-Promoter Assay
Phylogenetic Analysis of the NeuroD6 Promoter P2 P1 SNPs UCSC Genome Browser
-750 -1453 • TRANSFAC • JASPAR • rVista +1 AP1 MEF2 HoxD NFkB -1800 • Huge numbers of false positive Sp1 Ets1 E7 Hes1 E5 E2 Ets1 E3 Ets2 Hes1 E1 Hes1 E4 Cdx1 Cdx1 C/EBP Ets2 E6 MEF2 6 Sp1 sites * * * * * * * * * * * Prediction of Transcription Factor Binding Sites (TFBS) • To reduce false positives, focus on: • binding sites conserved among conserved species identified by several algorithms. • Position Weight Matrix comparison • Module Searcher Experimental Verification of TFBS • DNaseI Footprinting Analysis/EMSA • ChIp • Site-direct mutagenesis/reporter-promoter assay.