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Genetics 2581b Final Review

Genetics 2581b Final Review. Brought to you by Western SOS. Lecture 18. KEY CONCEPTS: Transcriptome - Microarray Interactome – ChIP on Chip, 2 Hy brid Analysis Affinity Capture, Mass Spec 3. Proteome - ICAT 4. Phenome – Gene Knockouts. Transcriptome - Microarrays.

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Genetics 2581b Final Review

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  1. Genetics 2581b Final Review Brought to you by Western SOS

  2. Lecture 18 • KEY CONCEPTS: • Transcriptome - Microarray • Interactome – ChIP on Chip, 2 Hybrid Analysis Affinity Capture, Mass Spec 3. Proteome - ICAT 4. Phenome – Gene Knockouts

  3. Transcriptome - Microarrays • A comparative analysis between two tissues or conditions • A glass slide coated with ORFs • Cy3 and Cy5 stain each sample which is added to slide

  4. Interactome (DNA-Protein)ChIP on Chip • Transcription factors bound covalently to DNA • DNA then sheared • Sample is split in half, antibody to specific protein added to one – other is control • Complexes specific to protein of interest isolated • Red dye incorporated into DNA • Spotted on intergenic array (probes representing cis-control elements)

  5. Interactome – (Protein- Protein)Two Hybrid Assay • Genetically engineered strains of yeast often used • Determine unknown proteins that interact with a protein of interest • Protein of interest 1 is bound to a bait domain • Second protein of interest bound to an activation domain • If two proteins of interest interact bait and activation also interact • Activates gene in yeast which is used to select for interaction

  6. Affinity capture and Mass Spectrometry • Measure mass of proteins by determining migration rates of ionized form through an electric field • Protein cleaved and ionized with laser • Run through vacuum and results analyzed by computer which identifies proteins by comparing with database • Further characterization also possible, run through collision cell and analyze hydrolyzed fragments

  7. Isotope-coded Affinity Tag Approach

  8. Phenome – Gene knockout • Replace wild copy of a gene within yeast genome with selectable marker via homologous recombination • Localizome – genetically engineer GFP onto sequence of interest and view using flourescence microscopy

  9. Lecture 19 • Cancer – disruption in balance between cell proliferation and cell death • DNA replication and repair are not perfect –c mutations in cell cycle occur • Cancers are clonal descendents of a single cell • Is the result of multiple genetic leasions

  10. Cell Cycle – Rao and Johnson Looking for the M-phase promoting factor (MPF) Fused interphase nuclie with M-phase – induces m-phase Fused G1 nuclie with S-phase – induces S-phase S-phase with G2 – doesn’t cause S phase again Indicates cycle has checkpoints for proper order

  11. Cell Cycle Mutants in yeast • A mutations causes block in DNA division – lethal • Mutations are conditional (will not go at restrictive temp) • Cdc2 identified as inducer of mitosis • Associates with cyclin to form MPF

  12. Cyclin dependent kinases • One cdc2 and many cyclins – cdc2 phosphorylates many substrates throughout the cell which are determined by the bound cyclin • Cell cycle controls are highly conserved – human cdc2 rescues mutant yeast cdc2

  13. Lecture 20 • Rb-E2F pathways • A growth factor inhibits p16 or p15 (cyclin dependent kinase inhibitor) allowing cycle to progress • When CKIs not inhibited Rb is not phosphorylated

  14. Oncogenes/Tumor repressor genes • Proto-oncogene: normal, wild-type gene that positively regulates cell proliferation • Oncogene: gene carrying a mutation (gain-of-function) in a positive regulator of cell proliferation, e.g. cell cycle regulators such as • cyclin D (gene amplification), • cdk4 (insensitive to CKI inhibition) • Tumor Suppressor genes: genes which negatively regulate cell proliferation eg. cell cycle regulators such as p16 (CKI) and RB

  15. Cell Cycle Checkpoints

  16. p53 – Guardian of the Genome • Induces genes that negatively regulates cell cycle and positively regulate apoptosis • Found mutated in half of human cancers • DNA damage activates p53

  17. Applications of Expression Profiling Using Microarray Technology • 1) Differentiate between different classes of cancer ie ALL and AML • 2) Determining Resistance of tumors to different drugs • 3) Predicting rate and extent of cancer progression (prognosis)

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