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Functional Genomics

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  1. Functional Genomics Functional genomic datasets Biological networks Integrating genomic datasets BIO520 Bioinformatics Jim Lund

  2. Functional genomics • Genome scale experiments to understand the function of all the proteins--what they do and how they interact. • Many different experimental designs • Different kinds of information generated. • Each has experimental limitations • Coverage: full genome, limited? • False positives. • False negatives.

  3. The Two-Hybrid Systemfor identifying protein/protein binding pairs • Two hybrid proteins are generated with transcription factor domains • Both fusions are expressed in a yeast cell that carries a reporter gene whose expression is under the control of binding sites for the DNA-binding domain Activation Domain Prey Protein Bait Protein Binding Domain Reporter Gene

  4. The Two-Hybrid System • Interaction of bait and prey proteins localizes the activation domain to the reporter gene, thus activating transcription. • Since the reporter gene typically codes for a survival factor, yeast colonies will grow only when an interaction occurs. Activation Domain Prey Protein Reporter mRNA Bait Protein Reporter mRNA Reporter mRNA Reporter mRNA Binding Domain Reporter mRNA Reporter Gene

  5. Interactions shown as a network

  6. Networks • When methods of detecting functional linkages are applied to all the proteins of an organism, network of interacting, functionally linked proteins can be traced. • As methods improve for detecting protein linkages, it seems likely that most of the proteins will be included in the network.

  7. What do you miss? • Tertiary interactions • Regulated interactions • Subcellular localization dependent • Cofactor dependent (eg. Hormone-regulated) • Low-affinity (Kd>10-6)

  8. YFG GFP Cellular Location • Immunolocalization • FUSION PROTEINS • Prediction • Membrane vs non-membrane • improved by homology • WHICH MEMBRANE • Nuclear vs cytoplasmic

  9. Drosophila Fusion Project (FlyTrap) • Exon GFP vector • Inserts fairly randomly. • Fluorescent sort thousands of embryos. • Find embryos with an insertion that produces GFP expression. • Image • Capture and analyze images • Curate by hand. • Computer image analysis and classification.

  10. Developmental Localization

  11. Mouse genomic gene expression • Allen Brain Atlas (ABA) is an interactive, genome-wide image database of gene expression in the mouse and human brain. 17,000 mouse gene expression patterns, cortex expression for 2,000 human genes.

  12. Allen Brain Atlas

  13. 3D mouse gene expression project Single gene expression database for the mouse research community. Integrated in the Mouse Genome Database (MGD) at the Jackson Laboratory. 10,302 expression entries WT1 expression (red) on a section of the E9 (Theiler Stage 14) embryo from the Edinburgh Mouse Atlas. The gut epithelium is shown in yellow and the neural tube in a blue overlay. WT1 is expressed in the presumptive mesothelium of the coelom and in the intermediate mesoderm (ventral to the somites).

  14. Methods for discovering protein function Gene Knockouts • Automated Binding Assays • High Throughput Enzyme Assays Protein Assay

  15. Genome-wide Knockouts • Yeast Genome • Recombination strategy • Mouse Genome • More in Functional Genomics!!!

  16. Essential vs Non-essential • Transcription similar • >99% essential genes transcribed • Transcript level 70% higher • >90% non-essential transcribed • Genome locations similar • Not clustered • Essential genes rarely near telomeres

  17. Why only 20% essential? • Redundant • 8.5% of non-essential had CLOSE homolog in genome (P<10-150) • Essential in another condition • Marginal Benefit

  18. YEAST Saccharomyces Genome Deletion Project http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html MOUSE Mouse Phenome Database http://phenome.jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home Knockout Mouse Project http://www.knockoutmouse.org/ Resources

  19. Genome-Scale Biochemical Assay • Protein arrays-biochemically active

  20. Databases • Relationships between genes/proteins. • How are different types of experimental data integrated? • Schema • Data quality • Who curates? • Who revises?

  21. Proteome Projects • SwissProt (ExPasy) • http://expasy.org/ch2d/ • Saccharomyces Genome Database (SGD) Gene Function Information • 2-hybrid, functional assignments, pathways. • http://www.yeastgenome.org/SearchContents.shtml • Yale TRIPLES • Database of TRansposon-Insertion Phenotypes, Localization, and Expression in Saccharomyces. • 2-hybrid databases • http://proteome.wayne.edu/YTHwebsites.html

  22. Pathway and interaction databases • KEGG (http://www.genome.jp/kegg/) • Metabolic and signaling pathways • PUMA (http://compbio.mcs.anl.gov/puma2/cgi-bin/index.cgi) • Metabolic and signaling pathways • DIP (http://dip.doe-mbi.ucla.edu/) • Protein-protein interactions • BIND (http://bind.ca/) • Molecular and genetic interactions

  23. KEGG pathway map HISTIDINE METABOLISM Pentose phosphate cycle 5P-D-1-ribulosyl- formimine 3.5.1.- Phosphoribulosyl- Formimino-AICAR-P 2.6.1.- Imidazole- acetole P Phosphoribosyl-AMP L-Hisyidinal 3.6.1.31 3.5.4.19 5.3.1.16 4.2.1.19 3.1.3.15 2.4.2.17 2.4.2.- 2.6.1.9 PRPP Phosphoribosyl- Formimino-AICAR-P Imidazole- Glicerol-3P Phosphoriboxyl-ATP L-Histidinol-P 1.1.1.23 5P Ribosyl-5-amino 4- Imidazole carboxamide (AICAR) 1-Methyl- L-histidine L-Hisyidinal 3.4.13.5 Aneserine 6.3.2.11 2.1.1.- 2.1.1.22 Purine metabolism 6.3.2.11 Carnosine 1.1.1.23 3.4.13.3 3.4.13.20 6.1.1 N-Formyl-L- aspartate Imidazolone acetate Imidazole- 4-acetate Imidazole acetaldehyde Histamine Hercyn 4.1.1.22 1.14135 3.5.3.5 3.5.2.- 1.2.1.3 1.4.3.6 4.1.1.28 L-Histidine

  24. Integrating pathway and expression data The list of genes being activated or inactivated or that are unaffected when comparing two samples becomes more informative if the genes can be mapped onto maps from which functions can be deduced.