Indiana University Bloomington, IN Junguk Hur School of Informatics & Center for Genomics and Bioinformatics Characterization of transcriptional responses to environmental stress by differential location analysis

Capstone Presentation Junguk Hur OVERVIEW  Background / Motivation  Location Analysis / Differential Binding  TF Response Classification  Comparison with Microarray  Conclusion / Future Work Overview

Capstone Presentation Junguk Hur Central Dogma Overview Background

Capstone Presentation Junguk Hur Transcriptional Regulation Albert et al., Molecular Cell Biology of the Cell Overview Background

Capstone Presentation Junguk Hur Transcriptional Regulation Overview Background Transcription Factors

Capstone Presentation Junguk Hur Response to Environment Overview Background Broach et al. Curr Opin Microbiol 2004

Capstone Presentation Junguk Hur Previous Studies - Expression High-throughput DNA Microarray – mRNA expression Overview Background

Capstone Presentation Junguk Hur Previous Studies - Expression Literature : Gene Expression Microarray Stress Exploring the metabolic and genetic control of gene expression on a genomic scale, DeRisi JL, et al. (1997) Science Genomic expression programs in the response of yeast cells to environmental changes, Gasch AP, et al. (2000) Mol Biol Cell Global and specific translational regulation in the genomic response of Saccharomyces cerevisiae to a rapid transfer from a fermentable to a nonfermentable carbon source, Kuhn KM, et al. (2001) Mol Cell Biol Role of thioredoxin reductase in the Yap1p-dependent response to oxidative stress in Saccharomyces cerevisiae, Carmel-Harel O, et al. (2001) Mol Microbiol Transcriptional Remodeling in Response to Iron Deprivation in Saccharomyces cerevisiae, Shakoury-Elizeh M, et al. (2004). Mol Biol Cell Transcriptional response of steady-state yeast cultures to transient perturbations in carbon source, Ronen M and Botstein D (2005) Proc Natl Acad Sci About 900 publications Overview Background

Capstone Presentation Junguk Hur Previous Studies – ChIP-Chip Which genes are directly regulated by TFs? Harbison et al. Nature 2004 Overview Background (Environment specific use of regulatory code)

Capstone Presentation Junguk Hur Motivation and Goal Limitation of Microarray data for understanding regulatory system upon environmental change Previous qualitative analysis of ChIP-Chip  Integration of heterogeneous data ChIP-Chip (direct regulation) + Microarray (direct/indirect regulation)  Quantitative analysis of TF binding  Better understanding of differential responses of transcriptional regulatory system via differential location analysis Overview Background

Capstone Presentation Junguk Hur Location Analysis Genome-wide Location Analysis : ChIP-on-Chip Exp. In vivo assay based on ChIP (Chromatin Immuno-Precipitation) High-throughput array experiment  Where and how strongly TF binds to Overview Background Location Analysis

Capstone Presentation Junguk Hur Differential Location Analysis Harbison et al. Nature 2004 Saccharomyces cerevisiae (budding yeast) 204 TFs in 14 conditions (352 experiments) Genome-wide location data (11,000 interactions) Overview Background Location Analysis

Capstone Presentation Junguk Hur  ChIP-on-Chip  204 Yeast Transcription Factors (TFs) 1  1 rich medium condition + 13 stress conditions  6540 genes & corresponding promoter sequences.  Microarray Stress ConditionConcentrationTime pointArrayRef. H2O2 – oxidation0.3 mM20 minscDNA2 H2O2 – oxidation0.32 mM20 minscDNA3 H2O2 - oxidation0.4 mM20 minsoligo4 SM (Sulfometuron methyl) – AA starvation 0.2  g/ml 15 minscDNA5 SM (Sulfometuron methyl) – AA starvation 0.2  g/ml 240 minscDNA5 Data Sets - I Overview Background Location Analysis

Capstone Presentation Junguk Hur Data Sets - II  ChIP-on-Chip Data Preprocessing  P-value and ratio data from Harbison et al.  ‘NaN’ point removal  Ratio value below 1  set to 1  Stress conditions  13 stress conditions  147 TF-cond pairs Overview Background Location Analysis

Capstone Presentation Junguk Hur Differential Binding Ratio Rich mediumStress Condition Increased binding in stress condition Decreased binding in stress condition How different the binding ratio btw different conditions??? Overview Background Location Analysis Differential Binding

Capstone Presentation Junguk Hur Differential Binding Measure A i, B i : binding ratio of TF k to regulated region i under stress and rich medium culture condition respectively -1  P i k  1 Differential binding is represented as P i k by using ChIP-chip binding ratio data between stress condition (A) and rich medium (B). Overview Background Location Analysis Differential Binding Rich mediumStress Condition P i K > 0 UP P i K < 0 DOWN

Capstone Presentation Junguk Hur All ChIP-Chip Binding Data Normal distribution Distribution of P i k - I Ex) FHL1 YPD (Rich Medium) vs SM (Amino acid starvation) High-confidence Binding Data (p  0.001) Skewed to negative direction Overview Background Location Analysis Differential Binding

Capstone Presentation Junguk Hur Distribution of P i k - II Number of UP/DOWN differentially bound regions (genes) for each TF-cond pair High-confidence data point only (p<0.001) 0.5  |P i k |  1 Overview Background Location Analysis Differential Binding -1  P i k  1

Capstone Presentation Junguk Hur Chi-Square Test - I * Chi-Square tests for P i k of 147 TF-condition pairs to check their distributions (Normal or skewed) * R-package & Perl scripts * P<0.05  Skewed distribution Normal Dist. (p=0.97) All ChIP-Chip Binding Data Skewed Dist. (p= ) High-confidence Binding Data (p  0.001) Overview Background Location Analysis Differential Binding

Capstone Presentation Junguk Hur Skewed Distribution Chi-square test : p < TF-Cond pairs (out of 147) : 70% Chi-Square Test - II Overview Background Location Analysis Differential Binding

Capstone Presentation Junguk Hur Classification of TFs - I Depending on patterns of the skewed pattern, 105 TF-condition pairs have been grouped into three categories. UP: More than 2/3 are in ‘+’ direction DOWN: More than 2/3 are in ‘–’ direction BOTH: Similar proportions of ‘+’ and ’-’ Overview Background Location Analysis Differential Binding TF response classification

Capstone Presentation Junguk Hur Classification of TFs - II UP : YAP5_H 2 O 2 Hi BOTH : HPO4_Pi DOWN : SFP1_H 2 O 2 Lo Overview Background Location Analysis Differential Binding TF response classification

Capstone Presentation Junguk Hur Differential Location Analysis - Summary  Comparison of TF binding between rich medium and stress cond.  Represented by P i K  Distribution of P i K  Normal and Skewed  Chi-Square Tests  70% TF-cond pairs  differential distribution  Classification of TF responses  UP, DOWN, and BOTH Overview Background Location Analysis Differential Binding TF response classification

Capstone Presentation Junguk Hur How differential binding? 1. Changes in the TF expression 2. Different TFBS signature for different category 3. Interaction with other TFs expression 4. Modifications in TFs (protein level) 5. Changes in physical structures (epigenetic features) 6. Other unknown reasons Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray 1. Changes in the TF expression

Capstone Presentation Junguk Hur Comparison with Microarray - I ChIP-Chip vs Microarray data expression levels of transcription factors (TFs) H2O2 Low (oxidation) TF-Cond pairs : 28 Skewed : 23 Normal : 5 Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray

Capstone Presentation Junguk Hur Comparison with Microarray - II SM ( Sulfometuron methyl ) Amino acid starvation ChIP-Chip 0.2  g/ml final 2 hours Microarray 0.2  g/ml 15 minutes 5.0  g/ml 4 hours TF-Cond pairs : 34 Skewed : 21 Normal : 13 Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray

Capstone Presentation Junguk Hur Comparison with Microarray - III H2O2 Oxidation stress SM (Sulfometuron methyl) Amino acid starvation Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray

Capstone Presentation Junguk Hur Comparison with Microarray - IV 50~60% of differentially binding TFs have NO significant expression change Changes of gene regulation (differential binding) cannot be fully revealed by Microarray Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray

Capstone Presentation Junguk Hur How differential binding? 1. Changes in the TF expression 2. Different TFBS signature for different category 3. Interaction with other TFs expression 4. Modifications in TFs (protein level) 5. Changes in physical structures (epigenetic features) 6. Other unknown reasons Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray

Capstone Presentation Junguk Hur Conclusion  Categorization of Transcription Factors (TFs) depending on the responses to stress  No significant expression changes in about 50% of the tested TFs in response to environmental changes.  Importance of integrating differential binding of TFs with gene expression to get the bigger picture Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Conclusion / Future Work

Capstone Presentation Junguk Hur Future Work  Investigating the biological mechanism of the differentially binding of TFs 1.Target gene of expression level 2.Post-translational modification 3.Protein-protein interaction  Comprehensive differential analysis Integration Diff. Expression Diff. Location Diff. Protein-Protein Int. (open) Diff. Proteomics (open) Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Diff. Binding Motif Anal. TF-TF Corr. Conclusion / Future Work

Capstone Presentation Junguk Hur References 1.Harbison, C.T., et al., Transcriptional regulatory code of a eukaryotic genome. Nature, (7004): p Shapira, M., E. Segal, and D. Botstein, Disruption of yeast forkhead-associated cell cycle transcription by oxidative stress. Mol Biol Cell, (12): p Gasch, A.P., et al., Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell, (12): p Causton, H.C., et al., Remodeling of yeast genome expression in response to environmental changes. Mol Biol Cell, (2): p Jia, M.H., et al., Global expression profiling of yeast treated with an inhibitor of amino acid biosynthesis, sulfometuron methyl. Physiol Genomics, (2): p Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Diff. Binding Motif Anal. TF-TF Corr. Conclusion / Future Work

Capstone Presentation Junguk Hur Acknowledgements Dr. Haixu Tang Dr. Sun Kim Dr. Mehmet Dalkilic Dr. Predrag Radivojac Seung-hee Bae Sourav Roy Capstone class 2006 School of informatics Center for Genomics and Bioinformatics Dr. Zhixiong Xue at DuPont My Family

Capstone Presentation Junguk Hur

Capstone Presentation Junguk Hur

Capstone Presentation Junguk Hur

Capstone Presentation Junguk Hur How differential binding? 1. Changes in the TF expression 2. Different TFBS signature for different category 3. Interaction with other TFs expression 4. Modifications in TFs (protein level) 5. Changes in physical structures (epigenetic features) 6. Other unknown reasons 2. Different TFBS signature for different category Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Diff. Binding Motif Anal.

Capstone Presentation Junguk Hur Different TFBS signatures? Category “BOTH” has similar numbers of Up-differentially bound and Down-differentially bound regions. Is there any difference between these TFBSs in different groups? Is there any difference between these TFBSs in different groups? Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Diff. Binding Motif Anal.

Capstone Presentation Junguk Hur Different TFBS signatures? Step1. Collect putative TFBS A. Frankel’s motifs (102TFs) B. Gary Stormo’s “Patser” Step2. Create profiles from collected TFBS seqs Step3. Profile comparison A. “MatCompare” program by Michale Zhang BOTH Up seqsDown seqs TFBSs Profile Step3 Step2 Step1 Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Diff. Binding Motif Anal.

Capstone Presentation Junguk Hur Different TFBS signatures? High-Threshold Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Diff. Binding Motif Anal.

Capstone Presentation Junguk Hur Different TFBS signatures? Low-Threshold Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Diff. Binding Motif Anal.

Capstone Presentation Junguk Hur How differential binding? 1. Changes in the TF expression 2. Different TFBS signature for different category 3. Association with other TFs 4. Modifications in TFs (protein level) 5. Changes in physical structures (epigenetic features) 6. Other unknown reasons 3. Association with other TFs Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Diff. Binding Motif Anal. TF-TF Corr.

Capstone Presentation Junguk Hur TF-TF Correlation TF Overview Background Location Analysis Differential Binding TF Response Classification Comp. with Microarray Diff. Binding Motif Anal. TF-TF Corr.