Next Generation Sequencers and Progress on Omics Research 13 April 2010 BioVisionAlexandria Conference 2010 Next Generation Sequencers and Progress on Omics Research Harukazu Suzuki PhD. Project Director, RIKEN Omics Science Center, Japan (Yoshihide Hayashizaki, M.D., Ph.D.) (Director, RIKEN Omics Science Center)

Various types of Next Generation Sequencers RIKEN OSC as the Japanese sequencing center 454 Solexa SOLiD HeliScope

Data production per day with DNA sequencers Base/day Sequencing cost per information is drastically decreasing every year. 3

Use of Next Generation Sequencers on Omics research Apply to the CAGE (Cap Analysis of Gene Expression) technology Mammalian Transcriptome Analysis Transcription Regulation Network Promoter analysis Transcription Factor Protein-protein Interactions

CAGE: Cap Analysis of Gene Expression Our original technology. CAGE analyzes 5’-end of the capped transcripts by DNA sequencing. Precise transcriptional starting sites (TSSs) are clarified. Expression profile of each promoter (not gene) could be analyzed. Promoter Transcription 20-27 bp Tag sequences mRNAs Promoter 5 5

DeepCAGE: deep sequencing application of CAGE Precise transcriptional starting sites (TSSs) + Expression profile of each promoter Nat. Genet. 2009 + Sequece-based mapping power Large-scale sequencing AGCTAGCTAGCTAGCTAGCTAG AGCTAGGTAGCTAGCTAGCTAG AGCTAGCTAGCTACCTAGCTAG Nat. Genet. 2009 + small RNA sequencing Mapping on Genome Nat. Genet. 2009

Transcription Regulation Network Analysis Cells are programmed in terms of transcription.

What’s the program in the cell? Core Regulation　 Peripheral genes　 ncRNA 2 ncRNA 1 ncRNA 3 The stable transcriptional states are maintained by multiple factors and state transition requires the concerted actions of multiple transcription factors & microRNAs. The concentration of these factors is kept constant in each stable state. TF gene　 cytoplasm TF0　 RNA Genome TF gene　 There are programs that maintain equilibrium state of TFs and ncRNAs in the genome(Core Regulation). RNA TF1 TF1 The resulting attractor basins of the cellular states are analogous to local minima in energy landscapes surrounded by slopes. RNA RNA TF gene　 TF gene　 TF3 E These homeostatic interactions can be thought of as providing a kind of inertia that regulates "peripheral genes“. They determine cellular traits. RNA TF gene　 TF4 8

Cell Differentiation is a transition from basin to basin Our goal 1. Development of a pipeline for systematic analysis of transcriptional regulation 2. Acquisition of new biological insights from the analysis Attractor Basin 1 Attractor Basin 2 Transition state E Time Attractor Basin 1 Attractor Basin 2 As Yoshihide explained in this morning, there are programs of transcription in cells. The stable state is maintained by multiple TFs, and state transition needs their concerted actions. We call the stable cellular states as attractor basins. Therefore our goal of research is 1. ---------------------- And 2. -----------------------------. 9 9

Timecourse data production H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009) Timecourse data production Monoblast Monocyte PMA stimulation 0 h 1 h 2 h 4 h 6 h 12 h 48 h 96 h 72 h

Effective concentration H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009) Motif Activity eps CAGE tag m1 m1 m1 m2 m3 m1 m4 m1 m5 Number of CAGE tags that mapped on the same site Reaction efficiency 　Number of possible binding sites 　Degree of conservation of the motif 　Chromatin status Effective concentration

Motif Activity vs. mRNA expression profile Motif activity: promoter regulation activity of TFs that bind the motif. PU.1 motif activity Strongly induced during THP-1 cell differentiation PU.1 mRNA expression Slightly up-regulated These changes are caused by protein phosphrylation. Check PU.1 protein level expression　 3000 2000 1000 0 1 4 12 24 96 Band shift in Western blot. Band shift-down was observed by phosphatase treatment. Nuclear translocation in Immunofluorescence The drastic PU.1 motif activity change is considered to occur by both mRNA up-regulation and post-translational modification.

Transcription regulation network consisting of 30 core motifs H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009) Transcription regulation network consisting of 30 core motifs 55 out of 86 edges were supported by experiments/in the literature. (Novel prediction works well!!) Enriched GO: from cell growth related to cell function related Motif activity Cell cycle Mitosis Microtubele cytoskele Inflammatory response Cell adhesion Immune response Up Monocyte Down Transient Size of nodes： Significance of motifs Edge support Green: siRNA Red: literature Blue: ChIP Monoblast ：enriched GO for regulated genes

Promoter analysis

Timecourse data production H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009) Timecourse data production Promoter Analysis Number of promoters per gene Number of genes 1 3698 2 2087 3 1247 4 752 5 428 6 263 7 169 8 110 9 96 10 52 11 32 12 27 13 14 15 16 17 18 19 20 21 22 23 24 25 26 28 29 Total 9026 24.3M tags: detection level of 1 copy/10 cells at 99.9955% 29,857 active promoters (with novel promoters) 23,403 promoters linked to 9,026 genes Multiple-promoters in approximately 60% of genes

Expression of retrotransposon elements J. Faulkner et al, Nature Genetics, 41:5, 563-571 (2009) Expression of retrotransposon elements Mouse Human Satellite Simple TE Tissues More than 35% show strong tissue specificity (17% for other promoters). Tissues RED: overrepresented Green: underrepresented Tissues Tissues P. Carninci et al.

Transcription Factor Protein-protein Interactions (TF-PPIs)

An atlas of combinatorial transcriptional regulation in mouse and man Typically, Transcription Factors (TFs) do not act independently, but form complexes with other TFs, chromatin modifiers, and co-factor proteins, which together assemble upon the regulatory regions of DNA to affect transcription. A clear and immediate challenge is to infer how larger combinations of TFs can act together to generate emergent behaviours that are not evident when each factor is considered in isolation. TFs (Activators) Basic TFs TF modulators T. Ravasi, et al, Cell, 140, 744-752 (2010)

Human Transcription Factor Interaction Map Natto (fermented beans: Japanese traditional food) Human TF PPIs T. Ravasi, et al, Cell, 140, 744-752 (2010)

A TF PPI sub-network critical for cell fate

TF network associated with tissue origin Development is not only regulated by TF expression level, but also TF-PPI!! Combinatorial interaction among transcription factors is critical for development of tissue (Davidson et al., 2002). To search for TF networks involved in tissue specification, we clustered the TF expression profiles across the 34 human tissues (see above) using two approaches: a basic clustering approach using expression levels only, and a “network-transformed” approach in which we clustered the differences in expression level across each TF-TF interaction, as suggested in a recent study (Taylor et al., 2009). We found that network transformation resulted in a substantially increased separation of tissues into four well-formed clusters (Figure 4a). These corresponded to well-defined tissue classes according to embryonic origin: ectoderm (including Central Nervous System or CNS), mesoderm, endoderm, and cell lines (Figure 4a and Supplementary Figure 1).

TF PPI sub-network between Human and Mouse Spatio-temporal Similarity between human and mouse TF PPI network Human Frontal Cortex Mouse Frontal Cortex A sub-network related to Neural Development

Negative regulation Newly found SMAD3-FLI1 interaction likely negatively regulate the differentiation from monoblast to monocyte. 時間(hour) Expression level Differentiation T. Ravasi, et al, Cell, 140, 744-752 (2010) 0 hr Time (hour) 96 hrs

Summary & Future Perspective Power of the Next Generation Sequencers is rapidly changing a way for the Omics Research. Transcriptome Analysis: deepCAGE Transcriptional Regulation Network Analysis Promoter Analysis TF-PPI analysis Genome: Now $20,000 per person --- $1,000-2,000 within a couple of years. Soon we will know own genome seq. Common events (Cell diffrentiation, Development) to Abnormality (diseases) Large Scale data needs powerful Bioinformatics and collaborations.

Acknowledgement This work has been achieved in the FANTOM4 consortium with support of the Genome Network Project (MEXT). OSC head quarters Yoshihide Hayashizaki Jun Kawai Piero Carninci Carsten Daub