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Next Generation Sequencers and Progress on Omics Research Harukazu Suzuki PhD. Project Director, RIKEN Omics Science Center, Japan (Yoshihide Hayashizaki,

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Presentation on theme: "Next Generation Sequencers and Progress on Omics Research Harukazu Suzuki PhD. Project Director, RIKEN Omics Science Center, Japan (Yoshihide Hayashizaki,"— Presentation transcript:

1 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) 13 April 2010 BioVisionAlexandria Conference 2010

2 Various types of Next Generation Sequencers SOLiD HeliScope Solexa454 RIKEN OSC as the Japanese sequencing center

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

4 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

5 Promoter mRNAs Tag sequences Transcription Promoter bp CAGE: Cap Analysis of Gene Expression Our original technology. CAGE analyzes 5-end of the capped transcripts by DNA sequencing. 1)Precise transcriptional starting sites (TSSs) are clarified. 2)Expression profile of each promoter (not gene) could be analyzed.

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

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

8 Whats the program in the cell? Genome cytoplasm TF gene RNA TF gene RNA TF1 TF gene RNA TF gene RNA TF3 TF4 TF gene RNA TF0 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. There are programs that maintain equilibrium state of TFs and ncRNAs in the genome(Core Regulation). The resulting attractor basins of the cellular states are analogous to local minima in energy landscapes surrounded by slopes. These homeostatic interactions can be thought of as providing a kind of inertia that regulates "peripheral genes. They determine cellular traits. Core Regulation Peripheral genes ncRNA 2 ncRNA 1 ncRNA 3 E

9 Attracto r Basin 1 Attracto r Basin 2 Cell Differentiation is a transition from basin to basin Attractor Basin 1 Attractor Basin 2 Transition state E Time Our goal 1. Development of a pipeline for systematic analysis of transcriptional regulation 2. Acquisition of new biological insights from the analysis

10 Timecourse data production MonoblastMonocyte PMA stimulation 0 h 1 h2 h4 h6 h12 h48 h 96 h72 h H. Suzuki et al. Nature Genetics, 41:5, (2009)

11 Motif Activity m1 m2m3 m1m4 m1m5 Reaction efficiency Number of possible binding sites Degree of conservation of the motif Chromatin status Effective concentration Number of CAGE tags that mapped on the same site CAGE tag e ps H. Suzuki et al. Nature Genetics, 41:5, (2009)

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

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

14 Promoter analysis

15 Timecourse data production Promoter Analysis Number of promoters per geneNumber of genes Total9026 H. Suzuki et al. Nature Genetics, 41:5, (2009) 24.3M tags: detection level of 1 copy/10 cells at % 29,857 active promoters (with novel promoters) 23,403 promoters linked to 9,026 genes Multiple-promoters in approximately 60% of genes

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

17 Transcription Factor Protein-protein Interactions (TF-PPIs)

18 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. TF modulators Basic TFs TFs (Activators) T. Ravasi, et al, Cell, 140, (2010)

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

20 A TF PPI sub-network critical for cell fate

21 TF network associated with tissue origin Development is not only regulated by TF expression level, but also TF-PPI!!

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

23 Negative regulation (hour) Expression level Time (hour)0 hr96 hrs Differentiation Newly found SMAD3-FLI1 interaction likely negatively regulate the differentiation from monoblast to monocyte. T. Ravasi, et al, Cell, 140, (2010)

24 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.

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


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