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Detection and analysis of transcriptional control sequences

Detection and analysis of transcriptional control sequences. Wyeth Wasserman October VanBUG Seminar. Centre for Molecular Medicine and Therapeutics Children’s and Women’s Hospital University of British Columbia. Transcription Simplified. URF. Pol-II. URE. TATA.

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Detection and analysis of transcriptional control sequences

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  1. Detection and analysis of transcriptional control sequences Wyeth Wasserman October VanBUG Seminar Centre for Molecular Medicine and Therapeutics Children’s and Women’s Hospital University of British Columbia

  2. Transcription Simplified URF Pol-II URE TATA

  3. Overview of Transcription in Gene Regulation • At the most basic level, transcriptional regulation is defined by binding of TFs to DNA • Complexity is increased by TF interactions, chromatin structure and protein modifications • How can we advance our understanding of regulation by computational analysis?

  4. A short history lesson…

  5. A matrix describing a a set of sites A 14 16 4 0 1 19 20 1 4 13 4 4 13 12 3 C 3 0 0 0 0 0 0 0 7 3 1 0 3 1 12 G 4 3 17 0 0 2 0 0 9 1 3 0 5 2 2 T 0 2 0 21 20 0 1 20 1 4 13 17 0 6 4 Representing Binding Sites for a TF (HNF1) • A single HNF1 site • AAGTTAATGATTAAC • A set of sites represented as a consensus • VDRTWRWWSHDWVWH

  6. TGCTG = 0.9 PFMs to PWMs One would like to add the following features to the model: 1. Correcting for the base frequencies in DNA 2. Weighting for the confidence (depth) in the pattern 3. Convert to log-scale probability for easy arithmetic w matrix f matrix A 5 0 1 0 0 C 0 2 2 4 0 G 0 3 1 0 4 T 0 0 1 1 1 f(b,i)+ A 1.6 -1.7 -0.2 -1.7 -1.7 C -1.7 0.5 0.5 1.3 -1.7 G -1.7 1.0 -0.2 -1.7 1.3 T -1.7 -1.7 -0.2 -0.2 -0.2 Log() p(b)

  7. Performance of Profiles • 95% of predicted sites bound in vitro (Tronche 1997) • MyoD binding sites predicted about once every 600 bp (Fickett 1995) • The Futility Theorem • Nearly 100% of predicted transcription factor binding sites have no function in vivo

  8. A 1 kbp promoter screened with collection of TF profiles

  9. Phylogenetic Footprinting 70,000,000 years of evolution reveals most regulatory regions.

  10. Phylogenetic Footprinting to Identify Functional Segments % Identity 200 bp Window Start Position (human sequence) Actin gene compared between human and mouse with DPB.

  11. Regulatory sites are usually conserved between orthologous genes HUMAN ACGATACGCATCACAGACT.ACAGACTACGGCTAGCA -|-|||||||||-|---|--|||-------|-|---| MOUSE GCAATACGCATCGCGATCAGACATCAGCACG.TGTGA HUMAN ACATCAGCATACACGCAACTACACAGACTACGACTA ---|||||-||||---|-|----||-||-||||--- MOUSE CGTTCAGCTTACAGCTAGCATAGCATACGACGATAC

  12. The 1kbp promoter screen with footprinting

  13. Choosing the ”right” species...(BONUS: What’s the ultimate sin in bioinformatics?) CHICKEN HUMAN MOUSE HUMAN COW HUMAN

  14. ConSite (www.phylofoot.org)

  15. Performance: Human vs. Mouse • Testing set: 40 experimentally defined sites in 15 well studied genes • 85-95% of defined sites detected with conservation filter, while only 11-16%of total predictions retained

  16. de novo Discovery of TF Binding Sites

  17. Unraveling Transcriptional Control Mechanisms Given a set of ”co-regulated” genes, define motifs over-represented in the regulatory regions

  18. Pattern Detection Methods • Exhaustive • e.g. “Moby Dick” (Bussemaker, Li & Siggia) • Identify over-represented oligomers in comparison of “+” and “-” (or complete) promoter collections • Monte Carlo/Gibbs Sampling • e.g. AnnSpec (Workman & Stormo) • Identify strong patterns in “+” promoter collection vs. background model of expected sequence characteristics

  19. Yeast Regulatory Sequence Analysis (YRSA) system

  20. DNA-damage response partially mediating by MCB Classic cell-cycle array data re-clustered by Getz et al PDR3-regulated genes from array study Yeast tests of YRSA System

  21. THE PROBLEM:Pattern Detection in Long Sequences MEF2 SIMILARITY SCORE MEF2 SET RANDOM SET SEQUENCE LENGTH

  22. Four Approaches to Extend Sensitivity • Phylogenetic Footprinting • Human-Mouse eliminates ~75% of sequence • Better background models • e.g. AnnSpec • Better definition of co-regulation • Microarrays occasionally produce noise • Use biochemical knowledge about TFs • TFBS patterns are NOT random

  23. Some characteristics have been explored… • Segmentation: informative positions separated by variable positions (proteins bind as dimers) • Positional Variance: subset of positions contain most of the info • Palindromes are common in the patterns

  24. Our Hypothesis • Point 1: Structurally-related DNA binding domains interact with similar target sequences • Exceptions exist (e.g. Zn-fingers) • Point 2: There are a finite number of binding domains used in human TFs • Approximately 20-25 • Idea: We could use the shared binding properties for each family to focus pattern detection methods • Constrain the range of patterns sought

  25. Frequency Score Comparison of profiles requires alignment and a scoring function • Scoring function based on sum of squared differences • Align frequency matrices with modified Needleman-Wunsch algorithm • Calculate empirical p-values based on simulated set of matrices

  26. Match to bHLH Prediction of TF Class TF Database (JASPAR) COMPARE Jackknife Test 87% correct Independent Test Set 93% correct

  27. FBPs enhance sensitivity of pattern detection

  28. APPLICATION:Cancer Protection Response • Detoxification-related enzymes are induced by compounds present in Broccoli • Arrays, SSH and hard work have defined a set of responsive genes • A known element mediates the response (Antioxidant Responsive Element) • Controversy over the type of mediating leucine zipper TF • NF-E2/Maf or Jun/Fos

  29. Classify New TF Motif Gibbs Sampling Gibbs with FBP Prior Maf (p<0.02) Jun (p<0.98) Application (2) Problem: Given a set of co-regulated genes, determine the common TFBS. Classify the mediating TF. We expect a leucine zipper-type TF.

  30. Regulatory Modules TFs do NOT act in isolation

  31. Layers of Complexity in Metazoan Transcription Chromatin picture used with permission of Zymogenetics.

  32. Liver Differentiation (data mostly from studies of hepatocytes) Early Fetal Mature Stem CEBPa HNF3 HNF1 HNF4

  33. Liver regulatory modules

  34. Models for Liver TFs…(Data that takes 2 months to produce and 10 seconds to present) (Or, what to do with an astrophysicist new to bioinformatics) HNF3 HNF1 HNF4 C/EBP

  35. Training predictive models for modules • Limited by small size of positive training set • We elected to use logistic regression analysis for the first models • Your favorite statistical approach would probably do equally well • data limited

  36. Logistic Regression Analysis * a1 * a2 * a3 * a4 Optimize a vector to maximize the distance between output values for positive and negative training data. Output value is: elogit p(x)= 1 + elogit S “logit”

  37. UDPGT1 (Gilbert’s Syndrome) Wildtype Mutant Liver Module Model Score “Window” Position in Sequence

  38. LRA Models do not account for multiple sites for the same TF* PERFORMANCE • Liver (Genome Research, 2001) • At 1 hit per 35 kbp, identifies 60% of modules • Limited to genes expressed late in liver development • Skeletal Muscle (JMB, 1998) • Set to 1 prediction per 35 000 bp • Identifies 66% of test set correctly * Side-track: Newer Methods

  39. Combining Phylogenetic Footprinting with a Module Model

  40. Genome Scan • Screened the available mouse genomic sequences (~300 MB) for modules and discarded hits for which sequence was not conserved with human (BLAST) • Removed regions for which corresponding human sequence did not score as module • Of ~100 predicted modules • 20 annotated genes: 5 from training, 3 additional modules, 5 liver specific, 3 unknown and 4 not liver

  41. de novo Discovery of Regulatory Modules

  42. Predictive Models 6 0 0 0 7 0 0 2 8 4 7 1 0 2 0 0 4 0 0 8 0 0 0 0 1 0 0 6 Identify and Model Contributing TFs Focus on regulatory modules for pattern detection Cluster Genes by Expression

  43. Finding binding sites in sets of co-regulated human genes • Sequence “space” is too large • Narrow with Phylogenetic Footprinting • Identify patterns in conserved blocks via Gibbs sampling • Assess quality of patterns based on biological knowledge

  44. Phylogenetic Footprinting to Identify Conserved Regions

  45. Skeletal Muscle Genes • One of the most extensively studied tissues for transcriptional regulation • 45 genes partially analyzed • 26 genes with orthologous genomic sequence from human and rodent • Five primary classes of transcription factors • Principal: Myf (myoD), Mef2, SRF • Secondary: Sp1 (G/C rich patches), Tef (subset of skeletal muscle types)

  46. Regulatory regions directing muscle-specific transcription MyoD/Myf SRF Mef2 Tef

  47. de novo Discovery of Skeletal Muscle Transcription Factor Binding Sites Mef2-Like SRF-Like Myf-Like

  48. We will soon be able to define modules for many contexts…

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