1. http://creativecommons.org/licens es/by-sa/2.0/

2. Large Scale Approaches to the Study of Gene Expression Prof:Rui Alves ralves@cmb.udl.es 973702406 Dept Ciencies Mediques Basiques, 1st Floor, Room 1.08 Website of the Course:http://web.udl.es/usuaris/pg193845/Courses/Bioinformatics_2007/ Course: http://10.100.14.36/Student_Server/

3. Why Studying Gene Expression? Just because the gene is there, is it expressed? If so, under which circumstances ? Are there quantitative aspects to the expression of a gene or genes? Are they expressed differentially (other than in an on-off manner) under different conditions? What TF regulate expression of a given gene?

4. Genome Wide Gene Expression Study of individual gene expression is as old as molecular biology Fully sequenced genomes allow study of how gene expression is regulated in the whole genome simultaneously Create hybridation probes for each mRNA, imprint on slab and use to simultaneously measure how whole genome gene expression changes under different conditions

5. Substrates for High Throughput Arrays Nylon Membrane MicroarrayGeneChip Single label P 33 Single label biotin streptavidin Dual label Cy3, Cy5 Absolute Relative

6. GeneChip ® Probe Arrays 24µm Millions of copies of a specific oligonucleotide probe Image of Hybridized Probe Array Image of Hybridized Probe Array >200,000 different complementary probes Single stranded, labeled RNA target Oligonucleotide probe * * * * *1.28cm GeneChip Probe Array Hybridized Probe Cell

7. GeneChip ® Expression Array Design GeneSequence Probes designed to be Perfect Match Probes designed to be Mismatch Multiple oligo probes 5´3´ 1 nucleotide difference at the center of the sequence Helps quantify noise & non-specific binding

8. Procedures for Target Preparation cDNA Fragment (heat, Mg 2+ ) LLLL Wash & Stain w/ marked avidin Scan Hybridize (16 hours) Labeled transcript Poly (A) + / Total RNA RNA AAAA IVT(Biotin-UTPBiotin-CTP) Labeled fragments L L L L Cells

9. Microarray Technology Used to compare conditions

10. Cells from condition A Cells from condition B mRNA Label Dye 2 Ratio of expression of genes from two sources Label Dye 1 cDNA equaloverunder Mix Total or

11. How to choose fluorophores? Sharp spectra Low overlap between spectra Sustained fluorescence

12. Radioactive Microarrays cDNA Hybridize&Scan Poly (A) + / Total RNA RNA AAAA (Radioactive NTP) Cells

13. GeneSpots on an Array Fluorescence/RadioactivityIntensity ExpressionMeasurement Selection of Cell type DifferentialState/StageSelection RNA Preparation and Labeling (Competitive)Hybridization Microarray Expression Analysis

14. Photons Radioactive emission What is measured? Gene 1 … Experiment 1200… ……… What do we do with the numbers?

15. Normalize the data Background noise exists and must be subtracted from the signal This noise results from contamination and/or non- specific hybridation 1 0.5 Normalization is not simple and no method is the best in all cases In micro arrays normalized ratios are usually given as Log Ratios These give the different between expression in one condition vs expression in another condition or between presence and absence of hybridation

16. Ratio vs. log-ratio A i : Red intensity B i : Green intensity Let Gene1: R 1 = 4, log 2 R 1 = 2 Gene2: R 2 = 1/4, log 2 R 2 = -2 R A*B 4 2 0 Gene2 Gene1 3 1 log 2 (A*B) Advantages of log transformation: Treat up-regulated and down-regulated genes symmetrically! Treat up-regulated and down-regulated genes symmetrically! Transfer multiplication operations to addition operations! Because: Transfer multiplication operations to addition operations! Because: log 2 R 0 -2 Gene2 Gene1 2

17. Expression Vectors As Points in ‘Expression Space’ Experiment 1 Experiment 2 Experiment 3 Similar Expression Exp 1 Exp 2 Exp 3 G1 G2 G3 G4 G5 x yz

18. Biological question Differentially expressed genes Sample class prediction etc. Testing Biological verification and interpretation Microarray experiment Estimation Experimental design Image analysis Normalization Clustering Discrimination

19. Microarray Data Analysis Challenges Few records (samples), usually < 100 Many columns (genes), usually > 1,000 This is very likely to result in false positives, “discoveries” due to random noise Model needs to be explainable to biologists Good methodology is essential for minimizing and controlling false positives

20. Microarray Potential Applications Earlier and more accurate diagnostics New molecular targets for therapy Improved and individualized treatments Fundamental biological discovery (e.g. finding and refining biological pathways) Recent examples –molecular diagnosis of leukemia, breast cancer,... –discovery that genetic signature strongly predicts outcome –a few new drugs, many new promising drug targets

21. Why Studying Gene Expression? Just because the gene is there, is it expressed? If so, under which circumstances ? Are there quantitative aspects to the expression of a gene or genes? Are they expressed differentially (other than in an on-off manner) under different conditions? What TF regulate expression of a given gene?

22. Transcriptional Regulation DNA binding proteins Binding sites (specific sequences) Coding region (transcribed) Non-coding region RNA transcript Gene 1 Gene 2 Gene 3 ActivatorRepressor

23. ChIP-on-Chip Based on –ChIP (Chromatin Immuno-Precipitation) –Microarray In vivo assay Genome-wide location analysis

24. Predicting regulatory modules with CHIP-ChIp experiments cells Crosslink Protein/DNA Break DNA Reverse cross link & Purify DNA Pieces Afinity Purification of Transcription factor Reverse cross link & Purify DNA Pieces bound to TF Compare in Intergenic Microarray Not needed in absolute measurements

25. ChIP-on-Chip Array of intergenic sequences from the whole genome

26. Protein Binding Microarray (PBM) (Bulyk et al.) In vitro assay DNA-binding protein of interest is expressed with an epitope tag, purified and then bound directly to a double-strand DNA microarray Can overcome the shortcomings of ChIP-on-Chip –Poor enrichment –No available antibody –Unknown culture condition or time points

27. Protein Binding Microarray Whole-genome yeast intergenic microarray bound by Rap1

28. What to do with binding sequences? Collect all sequences binding the protein Look for similarity between the sequences Build a motif for the DNA binding site Use motif to further scan DNA looking for other possible candidates

29. Motif Discovery MEME (Expectation Maximization) CONSENSUS (greedy multiple alignment) WINNOWER (Clique finding in graphs) SP-STAR (Sum of pairs scoring) MITRA (Mismatch trees to prune exhaustive search space) BioProspector (Gibbs Sampling Based) MDScan (Differential weight for sequences) Motif Regressor EBMF (Energy Based Motif Finding)

30. ChIP-on-Chip vs PBM Done by Mukherjee et al. Useful when ChIP-on-Chip does not result in enough enrichment * Lee et al., # Lieb et al. Database of transcription factor binding sites

31. Obstacles in TFBS Analysis Variation in binding sequences might be problematic in motif discovery process. –But for differential binding, there is no sequence discrepancy. For eukaryotic systems, lots of transcription factors (TFs) work together with other TFs affecting each other’s binding to DNA

32. To Do Go on line and look to find if there are examples of M. xanthus micro arrays and TF experiments. If you find them, analyze how the KHs and RRs are involved in the regulation under the different conditions.