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Gene Expression

Gene Expression. Chapter 9. What is Gene Expression? . The process of transcribing and translating a gene to yield a protein product Why are we interested in gene expression? Tells us which genes are involved in which functions. Gene Expression.

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Gene Expression

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  1. Gene Expression Chapter 9

  2. What is Gene Expression? • The process of transcribing and translating a gene to yield a protein product • Why are we interested in gene expression? • Tells us which genes are involved in which functions

  3. Gene Expression • Cells are different because of differential gene expression - proteome • About 40% of human genes are expressed at one time. • Gene is expressed by transcribing DNA into single-stranded mRNA - transcriptome • mRNA is later translated into a protein

  4. Molecular Biology Overview Nucleus Cell Chromosome Protein Gene (DNA) Gene (mRNA), single strand cDNA

  5. Gene Expression • Genes control cell behavior by controlling which proteins are made by a cell • House keeping genes vs. cell/tissue specific genes • Regulation: • Transcriptional (promoters and enhancers) • Post Transcriptional (RNA splicing, stability, localization -small non coding RNAs)

  6. Gene Expression • Regulation: • Translational (3’UTR repressors, poly A tail) • Post Transcriptional (RNA splicing, stability, localization -small non coding RNAs) • Post Translational (Protein modification: carbohydrates, lipids, phosphorylation, hydroxylation, methlylation, precursor protein) cDNA

  7. How do you measure Gene Expression?

  8. Traditional Methods • Northern Blotting • Single RNA isolated • Probed with labeled cDNA • Western Blotting • Multiple proteins • Probed with antibodies to a specific protein • RT-PCR • Primers amplify specific cDNA transcripts

  9. How do Microarrays work? • Microarray: • New Technology (first paper: 1995) • Allows study of thousands of genes at same time • Glass slide of DNA molecules • Molecule: string of bases (25 bp – 500 bp) • uniquely identifies gene or unit to be studied

  10. Gene Expression Microarrays The main types of gene expression microarrays: • Short oligonucleotide arrays (Affymetrix) • cDNA or spotted arrays (Brown/Botstein). • Long oligonucleotide arrays (Agilent Inkjet); • Fiber-optic arrays • ...

  11. Fabrications of Microarrays • Size of a microscope slide Images: http://www.affymetrix.com/

  12. Differing Conditions • Ultimate Goal: • Understand expression level of genes under different conditions • Helps to: • Determine genes involved in a disease • Pathways to a disease • Used as a screening tool

  13. Gene Conditions • Cell types (brain vs. liver) • Developmental (fetal vs. adult) • Response to stimulus • Gene activity (wild vs. mutant) • Disease states (healthy vs. diseased)

  14. Expressed Genes • Genes under a given condition • mRNA extracted from cells • mRNA labeled • Labeled mRNA is mRNA present in a given condition • Labeled mRNA will hybridize (base pair) with corresponding sequence on slide

  15. Two Different Types of Microarrays • Custom spotted arrays (up to 20,000 sequences) • cDNA • Oligonucleotide • High-density (up to 100,000 sequences) synthetic oligonucleotide arrays • Affymetrix (25 bases)

  16. Microarray Technology

  17. Microarray Image Analysis • Microarrays detect gene interactions: 4 colors: • Green: high control • Red: High sample • Yellow: Equal • Black: None • Problem is to quantify image signals

  18. Microarray Animations • Davidson University: • http://www.bio.davidson.edu/courses/genomics/chip/chip.html • Imagecyte: • http://www.imagecyte.com/array2.html

  19. Microarray analysis Operation Principle: Samples are tagged with flourescent material to show pattern of sample-probe interaction (hybridization) Microarray may have 60K probe

  20. Microarray Processing sequence

  21. Gene Expression Data Gene expression data on p genes for n samples mRNA samples sample1 sample2 sample3 sample4 sample5 … 1 0.46 0.30 0.80 1.51 0.90 ... 2 -0.10 0.49 0.24 0.06 0.46 ... 3 0.15 0.74 0.04 0.10 0.20 ... 4 -0.45 -1.03 -0.79 -0.56 -0.32 ... 5 -0.06 1.06 1.35 1.09 -1.09 ... Genes Gene expression level of gene i in mRNA sample j Log (Red intensity/Green intensity) = Log(Avg. PM - Avg. MM)

  22. Some possible applications? • Sample from specific organ to show which genes are expressed • Compare samples from healthy and sick host to find gene-disease connection • Probes are sets of human pathogens for disease detection

  23. Huge amount of data from single microarray • If just two color, then amount of data on array with N probes is 2N • Cannot analyze pixel by pixel • Analyze by pattern – cluster analysis

  24. Major Data Mining Techniques • Link Analysis • Associations Discovery • Sequential Pattern Discovery • Similar Time Series Discovery • Predictive Modeling • Classification • Clustering

  25. Some clustering methods and software • Partitioning：K-Means, K-Medoids, PAM, CLARA … • Hierarchical：Cluster, HAC、BIRCH、CURE、ROCK • Density-based： CAST, DBSCAN、OPTICS、CLIQUE… • Grid-based：STING、CLIQUE、WaveCluster… • Model-based：SOM (self-organized map)、COBWEB、CLASSIT、AutoClass… • Two-way Clustering • Block clustering

  26. randomized row column both data clustered Eisen et al. Proc. Natl. Acad. Sci. USA 95 (1998) time

  27. A dendrogram (tree) for clustered genes E.g. p=5 Let p = number of genes. 1. Calculate within class correlation. 2. Perform hierarchical clustering which will produce (2p-1) clusters of genes. 3. Average within clusters of genes. 4 Perform testing on averages of clusters of genes as if they were single genes. Cluster 6=(1,2) Cluster 7=(1,2,3) Cluster 8=(4,5) Cluster 9= (1,2,3,4,5) 1 2 3 4 5

  28. A real case Nature Feb, 2000 Paper by Allzadeh. A et al Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

  29. Discovering sub-groups

  30. Gene Expression is Time-Dependent Time Course Data

  31. Sample of time course of clustered genes time time time

  32. Limitations • Cluster analyses: • Usually outside the normal framework of statistical inference • Less appropriate when only a few genes are likely to change • Needs lots of experiments • Single gene tests: • May be too noisy in general to show much • May not reveal coordinated effects of positively correlated genes. • Hard to relate to pathways

  33. But a few Links • Affymetrixwww.affymetrix.com • Stanford MicroArray Databasehttp://smd.stanford.edu/resources/restech.shtml • Yale Microarray Database http://www.med.yale.edu/microarray/ • NCBI Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/ • University of North Carolina Database https://genome.unc.edu/

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