Seq-ing Answers to Questions of Transcriptional Regulation and Epigenetics. Theodore J. Perkins OttBUG Seminar Nov.

Transcription

1 Seq-ing Answers to Questions of Transcriptional Regulation and Epigenetics Theodore J. Perkins OttBUG Seminar Nov. 7, 2016

2 Outline ChIP-seq What s it for? How does it work? Two examples from collaborations Pax3 & Pax7 in myogenesis Tal1 & UTX in leukemia BIDCHIPS What is really present in ChIP-seq signals? Is bias fooling us into wrong conclusions? Can we remove what shouldn t be there? Future work 2 / 36

3 What is ChIP-seq (for)? ChIP-seq = Chromatin Immuno-Precipitation followed by massively paralleling SEQuencing It helps us answer two fundamental questions of molecular biology: 1. Which genes regulate which other genes? 2. How do they do it? 3 / 36

4 ChIP-seq identifies protein-dna interactions genome-wide 4 / 36

5 How does ChIP-seq work? 5 / 36

6 Example: Pax7 from Rudnicki lab 6 / 36

7 Example: Pax7 from Rudnicki lab 7 / 36

8 Example: Pax7 from Rudnicki lab 8 / 36

9 Example: ATF3 in K562 cells from ENCODE 9 / 36

10 Example: ATF3 in K562 cells from ENCODE 10 / 36

11 Soleimani et al. (Dev Cell, 2012) Developmental Cell Article Transcriptional Dominance of Pax7 in Adult Myogenesis Is Due to High-Affinity Recognition of Homeodomain Motifs Vahab D. Soleimani, 1,2,5 Vincent G. Punch, 1,2,5 Yoh-ichi Kawabe, 1,2 Andrew E. Jones, 1,2 Gareth A. Palidwor, 1 Christopher J. Porter, 1 Joe W. Cross, 3 Jaime J. Carvajal, 3 Christel E.M. Kockx, 4 Wilfred F.J. van IJcken, 4 Theodore J. Perkins, 1,2 Peter W.J. Rigby, 3 Frank Grosveld, 4 and Michael A. Rudnicki 1,2, * 11 / 36

12 Pax3 and Pax7 in muscle stem cells DNA Binding Domain Transactivation Domain Pax3 PB HD Pax7 PB HD [Adapted from Pax3 and Pax7 are activating transcription factors with similar structures They have paired- and homeobox-binding domains Both are master regulators of muscle stem cells Pax3 active primarily during development Pax7 active in adult muscle stem cells Pax7 expression can rescue loss of Pax3, but not vice versa 12 / 36

13 ChIP-seq (really ChTAP-seq) reveals binding differences Overexpressed tagged Pax3 or Pax7 in adult mouse muscle stem cells Assayed binding genome-wide with ChTAP-seq Pax7 bound many more sites, and had a stronger preference for homeobox motif (TAATTGATTA) Pax7 strong proliferation and anti-differentiation regulation (based on binding near genes, and microarray differential expression) Pax7 49,302 unique 1,267 unique Pax3 3,381 shared Fraction of peaks containing motif Pax7 Index (100 peaks per bin) Fraction of peaks containing motif Pax3 Pax3 Index (100 peaks per bin) hbox prd hbox/prd hbox/prd 13 / 36

14 Benyoucef et al. (Genes & Dev, 2016) UTX inhibition as selective epigenetic therapy against TAL1-driven T-cell acute lymphoblastic leukemia Aissa Benyoucef, 1,2,3 Carmen G. Palii, 1,3 Chaochen Wang, 4 Christopher J. Porter, 5 Alphonse Chu, 1 Fengtao Dai, 1 Véronique Tremblay, 3,6 Patricia Rakopoulos, 1 Kulwant Singh, 1,3 Suming Huang, 7 Francoise Pflumio, 8,9,10 Josée Hébert, 11,12 Jean-Francois Couture, 3,6 Theodore J. Perkins, 1,5,6 Kai Ge, 4 F. Jeffrey Dilworth, 1,2,3 and Marjorie Brand 1,2,3 14 / 36

15 Aberrant expression of Tal1 can cause leukemia TAL1 Blood vessels Endothelial Cell Endothelial Progenitor TAL1 Hematopoietic Stem Cell ( HSC) CMP CLP MEP GMP TAL1 TAL1 Erythroid Megakaryocytic TAL1 Myeloid Mast cell Lymphoid B Lymphoid T T-Cell T-Cell ALL (Leukemia) Tal1+ ALL (Leukemia) Red Blood Cell Megakaryocyte Eosinophil Neutrophil B lymphocyte T lymphocyte Platelets Monocyte/Macrophage!" 15 / 36

16 Tal1 partners with UTX, which can be targeted UTX is a general factor that demethylates H3K27 (among other things) thus de-repressing gene expression ChIP-seq of Tal1 and UTX in leukemic cells suggested Tal1 recruits UTX to activate cancer (And it turns out we can target UTX for knockdown, slowing cancer growth & causing apoptosis.) 16 / 36

17 Ramachandran et al., Epigenetics & Chromatin, 2015 Ramachandran et al. Epigenetics & Chromatin (2015) 8:33 DOI /s RESEARCH Open Access BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates Parameswaran Ramachandran 1,2*, Gareth A. Palidwor 1 and Theodore J. Perkins 1,2* Abstract Background: Unraveling transcriptional regulatory networks is a central problem in molecular biology and, in this quest, chromatin immunoprecipitation and sequencing (ChIP-seq) technology has given us the unprecedented ability to identify sites of protein-dna binding and histone modification genome wide. However, multiple systemic and procedural biases hinder harnessing the full potential of this technology. Previous studies have addressed this problem, but a thorough characterization of different, interacting biases on ChIP-seq signals is still lacking. Results: Here, we present a novel framework where the genome-wide ChIP-seq signal is viewed as being quantifiably influenced by different, measurable sources of bias, which can then be computationally subtracted away. We use a compendium of 123 human ENCODE ChIP-seq datasets to build regression models that tell us how much of a ChIP / 36

18 Most of the ChIP-seq signal is outside of peaks 18 / 36

19 Even some apparent peaks may really be background 19 / 36

20 What else might be reflected in ChIP-seq signals? IgG idna Access. GC Mappab. CTCF chr1 position 20 / 36

21 Data from the ENCODE consortium We downloaded mapped ChIP-seq reads for all TFs and chromatin marks assayed in the three tier-1 cell types (GM12878, H1HESC, K562) 30 transcription factors (e.g., ATF3, CMYC, CEBPB,...) 11 histone marks (e.g., H2AZ, H3K27AC, H3K27ME3,...) Thus a total of 123 ChIP-seq data sets, totalling 2 billion reads We also computed or downloaded five independent (or predictive, or control) genomic signals. Mappability GC bias Chromatin accessibility (DNaseI-seq reads, cell-type specific) Input DNA control (reads, cell-type specific) IgG control (reads, cell-type specific) 21 / 36

22 Staged, multi-scale regression analysis 1. We divided the genome into windows of size 2 k for k = 8,...,20 (256bp up to 1Mbp) For each TF dataset and each window size, we: 2. Compute total ChIP-seq signal in each window 3. Use linear regression to predict the ChIP-seq signal based on the average mappability in the corresponding windows 4. Quantify how much of the TF signal is related to mappability in terms of percentage of variance explained (POV) 5. Then we add GC to the regression, measuring the additional POV 6. Then chromatin accessibility 7. Then idna 8. Then IgG 22 / 36

23 Question 1: What control signals are apparent in ChIP-seq data? We predicted ChIP-seq reads based on the independent variables Predictability under linear regression varies from poor to decent Chromatin accessibility most important factor, also mappability and GC 23 / 36

24 Similar results on H1HESCs and K562s idna more useful predictor in these cell lines Factors have related predictability across cell types 24 / 36

25 Question 2: How does predictability change with scale? Predictability increases with window size (median and quartiles shown) Balance of predictors changes 25 / 36

26 Results for TFs in other cell types Mappability surprisingly unimportant for K562 cells (or is it a surprise?); GC content more important Accessibility important for all cell types 26 / 36

27 Question 3: What s behind literature associations to gene expression? Previous papers had documented strong associations between ChIP-seq signals at transcription starts sites and gene expression We counted ChIP-seq reads in 129bp window centered on TSSes We correlated those to ENCODE gene expression measurements 27 / 36

28 Significant associations between expression and ChIP-seq, but also controls Cell Type: H1hesc Cell Type: H1hesc ATF3 CMYC CEBPB CHD1 CHD2 CREB1 CTCF EGR1 EZH2 GABP JUND MAFK MAX MXI1 NRF1 NRSF P300 POL2 RAD21 RFX5 SIX5 SP1 SRF TAF1 TBP USF1 USF2 YY1 ZNF1 ZNF2 H2AZ H3K27AC H3K27ME3 H3K36ME3 H3K04ME1 H3K4ME2 H3K4ME3 H3K79ME2 H3K9AC H3K9ME3 H4K20ME1 Mappability GC DNaseI idna IgG Combined Pearson's correlation coefficient (PCC) 28 / 36

29 Due to background signal in ChIP-seq? PCC for predictions using only TFs (Dark green bars in left panel) Gm12878 H1hesc K562 ( = 0.73; pvalue = 4.8e-8) ( = 0.78; pvalue = 1.4e-9) ( = 0.77; pvalue = 3.5e-9) POV explained using DNaseI hypersensitivity in TF and HM ChIP-seq data (from Fig. 2A) 29 / 36

30 No added value for most factors d Cell Type: H1hesc PCC added by individual TF/HM predictors ATF3 CMYC CEBPB CHD1 CHD2 CREB1 CTCF EGR1 EZH2 GABP JUND MAFK MAX MXI1 NRF1 NRSF P300 POL2 RAD21 RFX5 SIX5 SP1 SRF TAF1 TBP USF1 USF2 YY1 ZNF1 ZNF2 H2AZ H3K27AC H3K27ME3 H3K36ME3 H3K04ME1 H3K4ME2 H3K4ME3 H3K79ME2 H3K9AC H3K9ME3 H4K20ME1 30 / 36

31 Question 4: Can we un-bias the ChIP-seq signal? Intuition: If we subtract away the control-like components of ChIP-seq signals, what s left may be more purely binding signal. 1. We found DNA-binding motifs for 17 TFs in Jaspar 2. We obtained peaks for those factors by re-running MACS with default parameters, obtaining MACS scores (M) essentially p-values 3. We estimated true binding signal (B) by taking reads-in-peaks and subtracting predicted-reads-in-peaks based on our genome-wide model 4. We used FIMO to count motif occurrences in the peaks 5. We ranked peaks by one of: raw ChIP-seq read counts (R), MACS score (M) or our binding estimate (B), and correlated to to motif occurrences Peaks with highest true binding ought to have greatest change of harboring a recognizable binding motif 31 / 36

32 Our binding signal is more correlated to motifs For example, for MAFK:!"#$%&'()'*)+,#-.)/&%0)1'%&* L=9<)M)AB!L ; GH")I&(5&(J),.%&1#%,)EIF 67> K#/)",#5)$'H(%)EKF ABCD).$'",)EDF @ 67< : ;7< ;7= ;7> <7; 32 / 36

33 Our binding signal is more correlated to motifs For most TFs we studied:!"#$%&'(&')$*+,&-.,*$/%0 1.2$#*.($34"',$/10 =>?*+3 9:;< 75C7 7D%E% 71D%> HIJK 56G9 56L J1G> J18G 1GL: 8E> 81G I8G> I8G< CC> 33 / 36

34 Future/ongoing work: Better de-debiasing of ChIP-seq data Non-linear / mixture models relating ChIP-seq(s) and Control(s) densities Bayesian methods quantify signal levels and uncertainties Improving peak-calling using those ideas Integration of other data types (Genome Sequence? RNA-seq? Binding of other factors? Histone marks?) ENCODE-DREAM In Vivo Transcription Factor Binding Site Challenge 34 / 36

35 Future/ongoing work: Super-enhancer dynamics in myogenesis Super-enhancers are clusters of binding sites for TF master regulators, with high H3K27ac Enhancer: 10 rpm/bp 14 rpm/bp 21 rpm/bp 3.5 rpm/bp 15 rpm/bp Superenhancer: kb 5kb Oct4 Sox2 Nanog Klf4 Esrrb Normalized ChIP-seq signal H3K4me1 H3K27ac OSN DNaseI Med Ranked enhancers Gck mir [from Whyte et al. (Cell, 2013)] Have been identified as crucial regulatory nodes in defining cell identity We are looking at super-enhancers at multiple time points during renewal and differentiation of muscle stem cells, defined by different master regulators (with Rudnicki lab) 35 / 36

36 Acknowledgements The Perkins lab: Daniel Matt Aseel Basma Sam Julie Gareth Chris Paramesh Funding: NSERC CIHR NIH PSI Foundation ORF CFI OGS CBCF You! 36 / 36